Question: An overview of artificial intelligence applications in cognitive radio systems.

Cognitive Radio: A Paradigm Shift in Wireless Communications Empowered by Intelligence

Cognitive radio technology represents a groundbreaking approach to enhancing the utilization and efficiency of a vital natural resource: the radio electromagnetic spectrum. At its core, cognitive radio is an intelligent wireless communication system, fundamentally anchored in software-defined radio technology. It is characterized by its environmental awareness and its capacity to employ a methodology akin to understanding-by-building, enabling it to learn from its surroundings and dynamically adapt to statistical variations in input stimuli. This dynamic adaptation is guided by two primary objectives: ensuring highly reliable communication at any time and location, and optimizing the utilization of the radio spectrum.

The paper delves into the concept of interference temperature, introducing it as a novel metric for the quantification and management of interference within wireless communications. It then proceeds to explore three pivotal cognitive tasks that define the operational capabilities of cognitive radio:

1. **Radio-Scene Analysis**: This involves the cognitive radio's ability to analyze its radio environment, identifying and interpreting the various signals and patterns present. This analysis is crucial for the system to make informed decisions regarding spectrum usage and to mitigate potential interference.

2. **Channel-State Estimation and Predictive Modeling**: Cognitive radio systems must continuously estimate the state of the wireless channels they operate on. This includes not only the current conditions but also predictive modeling to anticipate future channel states. This capability is essential for proactive spectrum management and for maintaining high-quality communication links.

3. **Transmit-Power Control and Dynamic Spectrum Management**: Transmit-power control is a critical function for cognitive radio systems, allowing them to adjust their power output to achieve optimal communication while minimizing interference. Dynamic spectrum management involves the real-time allocation and re-allocation of spectrum resources based on the current demands and conditions of the wireless environment.

The discussion further touches upon the emergent behavior of cognitive radio systems, highlighting how these systems can evolve and adapt over time, learning from past experiences to improve their performance and efficiency in managing wireless communications. This paradigm shift in wireless communications, driven by intelligence and adaptability, promises to revolutionize the way we utilize and manage the radio spectrum, paving the way for more efficient and reliable wireless networks.

This article explores network densification as the key mechanism for wireless evolution over the next decade. Network densification includes densification over space (e.g, dense deployment of small cells) and frequency (utilizing larger portions of radio spectrum in diverse bands). Large-scale cost-effective spatial densification is facilitated by self-organizing networks and intercell interference management. Full benefits of network densification can be realized only if it is complemented by backhaul densification, and advanced receivers capable of interference cancellation.

It is undeniable that artificial intelligence (AI) and blockchain concepts are spreading at a phenomenal rate. Both technologies have distinct degree of technological complexity and multi-dimensional business implications. However, a common misunderstanding about blockchain concept, in particular, is that “a blockchain is decentralized and therefore no one controls it”. But the underlying development of a blockchain system is still attributed to a cluster of core developers. Take smart contract as an example, it is essentially a collection of codes (or functions) and data (or states) that are programmed and deployed on a blockchain (say, Ethereum) by different human programmers. It is thus, unfortunately, less likely to be free of loopholes and flaws. In this article, through a brief overview about how artificial intelligence could be used to deliver bug-free smart contract so as to achieve the goal of blockchain 2.0, we to emphasize that the blockchain implementation can be assisted or enhanced via various AI techniques. The alliance of AI and blockchain is expected to create numerous possibilities.

The paper presents an overview of the state of the art in collision avoidance related with transportation systems like the automatic identification system (AIS) for maritime transportation, traffic alert and collision avoidance system/automatic dependent surveillance-broadcast (TCAS/ADS-B) for aircraft, and the car-2-car communication system (C2C) for road transportation. The examined systems rely on position detection and direct communication among vehicles. Alike a collision avoidance system for railway transportation "RCAS" is introduced. Focussing on the communication aspects, possible applicability of the examined state of the art systems to RCAS is studied. The analysis are performed at different communication system layers, namely application (APP) layer, media access control (MAC) layer and physical layer (PHY), which are the most relevant for a single hop network broadcast system as favorized in RCAS. Since multihop and addressed communication are not foreseen in a first RCAS approach, the network layer is not taken into account.

This paper is an overview of technologies that fall under the term augmented reality. Augmented reality refers to a system in which the physical surroundings of a person are mixed with real-time computer generated information creating an enhanced perception of surrounding environment. Being partly virtual and real, augmented reality applications have quite extreme requirements to be practical to use. It also has very much potential in numerous different application areas. These issues make augmented reality both an interesting and challenging subject from scientific and business perspectives.

In this paper, we discuss the application of machine learning techniques for performance prediction problems in wireless networks. These problems often involve using existing measurement data to predict network performance where direct measurements are not available. We explore the performance of existing machine learning algorithms for these problems and propose a simple taxonomy of main problem categories. As an example, we use an extensive real-world drive test data set to show that classical machine learning methods such as Gaussian process regression, exponential smoothing of time series, and random forests can yield excellent prediction results. Applying these methods to the management of wireless mobile networks has the potential to significantly reduce operational costs while simultaneously improving user experience. We also discuss key challenges for future work, especially with the focus on practical deployment of machine learning techniques for performance prediction in mobile wireless networks.

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas such as smart homes, smart cities, health care, transportation, etc. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to the battlefield specific challenges such as the absence of communication infrastructure, and the susceptibility of devices to cyber and physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and the information dissemination in the presence of adversaries. This work aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to study the communication of mission-critical data among different types of network devices and consequently design the network in a cost effective manner.

Autonomous vehicles have potential applications in many fields, such as replacing humans in hazardous environments, conducting military missions, and performing routine tasks for industry. Driving ground vehicles is an area where human performance has proven to be reliable. Drivers typically respond quickly to sudden changes in their environment. While other control techniques may be used to control a vehicle, fuzzy logic has certain advantages in this area; one of them is its ability to incorporate human knowledge and experience, via language, into relationships among the given quantities. Fuzzy logic controllers for autonomous vehicles have been successfully applied to address various (and sometimes simultaneous) navigational issues,including: • reaching a static target (Baturone, et al., 2004, Boada, et al., 2005, El Hajjaji & Bentalba, 2003, Maeda et al., 1991, Chen & Ozguner, 2005), • tracking moving targets (Ollero, et al., 2001), • maintaining stable vehicular velocity (Holzmann et al., 1998, Nobe & Wang, 2001), • following a lane or a wall (Rosa & Garcia-Alegre, 1990, Hessburg & Tomizuka, 1994, Peng & Tomizuka, 1993) and, • avoiding collision with static and dynamic obstacles (Baturone, et al., 2004, Murphy, 2001, Godjevac, et al., 2001, Seraji, 2005, Lee & Wang, 1994, Wheeler & Shoureshi, 1994, Ye & Wang, 2001). Several researchers combined fuzzy logic controllers with various learning techniques, such as: • supervised learning method (Godjevac, 2001), • evolutionary method (Hoffman, 2001, Kim, et al., 2001), • neural network (Pasquier, et al., 2001, Cang, et al., 2003), • reinforcement learning (Dai, et al., 2005) and, • optimization methods (Hong, 1997, Sanchez, et al., 1999).

This paper presents an overview of the cloud radio access network (C-RAN), which is a key enabler for future mobile networks in order to meet the explosive capacity demand of mobile traffic, and reduce the capital and operating expenditure burden faced by operators. We start by reviewing the requirements of future mobile networks, called 5G, followed by a discussion on emerging network concepts for 5G network architecture. Then, an overview of C-RAN and related works are presented. As a significant scenario of a 5G system, the ultra dense network deployment based on C-RAN is discussed with focuses on flexible backhauling, automated network organization, and advanced mobility management. Another import feature of a 5G system is the long-term coexistence of multiple radio access technologies (multi-RATs). Therefore, we present some directions and preliminary thoughts for future C-RAN-supporting Multi-RATs, including joint resource allocation, mobility management, as well as traffic steering and service mapping.

The concept of using big data (BD) for wireless communication network optimization is no longer new. However, previous work has primarily focused on long-term policies in the network, such as network planning and management. Apart from this, the source of the data collected for analysis/model training is mostly limited to the core network (CN). In this article, we introduce a novel data-driven intelligent radio access network (RAN) architecture that is hierarchical and distributed and operates in real time. We also identify the required data and respective workflows that facilitate intelligent network optimizations. It is our strong belief that the wireless BD (WBD) and machine-learning/artificial-intelligence (AI)-based methodology applies to all layers of the communication system. To demonstrate the superior performance gains of our proposed methodology, two use cases are analyzed with system-level simulations; one is the neural-network-aided optimization for Transmission Control Protocol (TCP), and the other is prediction-based proactive mobility management.

The idea of Smart Grid has started to evolve more rapidly with the enhancement in Communication Technologies. Two way communication is a key aspect in realizing Smart Grids and is easily possible with the help of modern day advancements in both wired and wireless communication technologies. This paper discusses some of the major communication technologies which include IEEE specified ZigBee, WiMAX and Wireless LAN (Wi-Fi) technologies, GSM 3G/4G Cellular, DASH 7 and PLC (Power Line Communications), with special focus on their applications in Smart Grids. The Smart Grid environments and domains such as Home Area Automation, Substation Automation, Automated Metering Infrastructure, Vehicle-to-Grid Communications, etc. are considered as priority areas for developing smarter grids. The advancements, challenges and the opportunities present in these priority areas are discussed in this paper. & 2012 Elsevier Ltd. All rights reserved.


Question: An overview of artificial intelligence applications in cognitive radio systems.

"Neural Networks: A Comprehensive Foundation" by Professor Haykin is a notable work, now in its second edition, a rarity in the vast sea of contemporary publications. This book is frequently cited in current literature; however, it does not align with its title's suggestion. It is not a conventional textbook on the theories and techniques associated with neural networks, also known as connectionist systems or neurocomputational systems, nor does it provide the foundational or theoretical underpinnings of neural networks. Instead, the book focuses on four key chapters that offer an exceptional presentation of the major models in this field of research, including perceptrons and Radial-Basis Function Networks (RBFNs). Despite not being a comprehensive treatise on neural network theory, Haykin's work stands out for its in-depth exploration of these fundamental models.

**Question**: A Study on the Impact of Big Data on Business Intelligence

**Documents**: Big Data, Hadoop, and the Future of Analytics
The explosion of data, known as Big Data, has fundamentally changed the way we approach information analysis and business intelligence. This phenomenon, driven by the proliferation of digital devices and the internet, has resulted in an unprecedented volume, velocity, and variety of data. The traditional data management and analysis tools are no longer sufficient to handle the challenges posed by Big Data. This has led to the emergence of new technologies and frameworks, such as Hadoop, designed to efficiently process and analyze large datasets. Hadoop, with its distributed file system and MapReduce programming model, allows for the scalable processing of data across multiple servers. This capability is essential for deriving insights from Big Data, which is critical for enhancing business intelligence and decision-making processes. The future of analytics is inextricably linked with Big Data and Hadoop, as they provide the necessary tools and infrastructure to harness the power of Big Data for business advantage.

**Similar Documents**: The advent of Big Data, characterized by an exponential increase in data volume, velocity, and variety, has necessitated a transformative shift in data analysis and business intelligence strategies. This data surge, fueled by the proliferation of digital devices and internet usage, has outpaced the capabilities of conventional data management and analysis tools. In response, innovative technologies and frameworks like Hadoop have emerged, specifically designed to address the complexities of Big Data. Hadoop's distributed file system and MapReduce model enable the scalable processing of vast datasets across multiple servers, a critical capability for extracting meaningful insights from Big Data. This, in turn, enhances business intelligence and decision-making processes.

Against the backdrop of increasing progresses in AI research paired with a rise of AI applications in decision-making processes, security-critical domains as well as in ethically relevant frames, a largescale debate on possible safety measures encompassing corresponding long-term and short-term issues has emerged across different disciplines. One pertinent topic in this context which has been addressed by various AI Safety researchers is e.g. the AI alignment problem for which no final consensus has been achieved yet. In this paper, we present a multidisciplinary toolkit of AI Safety strategies combining considerations from AI and Systems Engineering as well as from Cognitive Science with a security mindset as often relevant in Cybersecurity. We elaborate on how AGI “Self-awareness” could complement different AI Safety measures in a framework extended by a jointly performed Human Enhancement procedure. Our analysis suggests that this hybrid framework could contribute to undertake the AI alignment problem from a new holistic perspective through security-building synergetic effects emerging thereof and could help to increase the odds of a possible safe future transition towards superintelligent systems.

Analogous to the way humans use the Internet, devices will be the main users in the Internet of Things (IoT) ecosystem. Therefore, device-to-device (D2D) communication is expected to be an intrinsic part of the IoT. Devices will communicate with each other autonomously without any centralized control and collaborate to gather, share, and forward information in a multihop manner. The ability to gather relevant information in real time is key to leveraging the value of the IoT as such information will be transformed into intelligence, which will facilitate the creation of an intelligent environment. Ultimately, the quality of the information gathered depends on how smart the devices are. In addition, these communicating devices will operate with different networking standards, may experience intermittent connectivity with each other, and many of them will be resource constrained. These characteristics open up several networking challenges that traditional routing protocols cannot solve. Consequently, devices will require intelligent routing protocols in order to achieve intelligent D2D communication. We present an overview of how intelligent D2D communication can be achieved in the IoT ecosystem. In particular, we focus on how state-of-the-art routing algorithms can achieve intelligent D2D communication in the IoT.

In wireless environments, transmission and reception costs dominate system power consumption, motivating research effort on new technologies capable of reducing the footprint of the radio, paving the way for the Internet of Things. The most important challenge is to reduce power consumption when receivers are idle, the so called idle-listening cost. One approach proposes switching off the main receiver, then introduces new wake-up circuitry capable of detecting an incoming transmission, optionally discriminating the packet destination using addressing, then switching on the main radio only when required. This wake-up receiver technology represents the ultimate frontier in low power radio communication. In this paper, we present a comprehensive literature review of the research progress in wake-up radio (WuR) hardware and relevant networking software. First, we present an overview of the WuR system architecture, including challenges to hardware design and a comparison of solutions presented throughout the last decade. Next, we present various medium access control and routing protocols as well as diverse ways to exploit WuRs, both as an extension of pre-existing systems and as a new concept to manage low-power networking.

Since wireless network virtualization enables abstraction and sharing of infrastructure and radio spectrum resources, the overall expenses of wireless network deployment and operation can be reduced significantly. Moreover, wireless network virtualization can provide easier migration to newer products or technologies by isolating part of the network. Despite the potential vision of wireless network virtualization, several significant research challenges remain to be addressed before widespread deployment of wireless network virtualization, including isolation, control signaling, resource discovery and allocation, mobility management, network management and operation, and security as well as non-technical issues such as governance regulations, etc. In this paper, we provide a brief survey on some of the works that have already been done to achieve wireless network virtualization, and discuss some research issues and challenges. We identify several important aspects of wireless network virtualization: overview, motivations, framework, performance metrics, enabling technologies, and challenges. Finally, we explore some broader perspectives in realizing wireless network virtualization.

The systems based on intelligent sensors are currently expanding, due to theirs functions and theirs performances of intelligence: transmitting and receiving data in real-time, computation and processing algorithms, metrology remote, diagnostics, automation and storage measurements...The radio frequency wireless communication with its multitude offers a better solution for data traffic in this kind of systems. The mains objectives of this paper is to present a solution of the problem related to the selection criteria of a better wireless communication technology face up to the constraints imposed by the intended application and the evaluation of its key features. The comparison between the different wireless technologies (WiFi, Wi-Max, UWB, Bluetooth, ZigBee, ZigBeeIP, GSM/GPRS) focuses on their performance which depends on the areas of utilization. Furthermore, it shows the limits of their characteristics. Study findings can be used by the developers/ engineers to deduce the optimal mode to integrate and to operate a system that guarantees quality of communication, minimizing energy consumption, reducing the implementation cost and avoiding time constraints. Keywords—Wireless communications; Performances; Energy; Protocols; Intelligent sensors; Applications

This work explores the scope of Fuzzy C-Means (FCM) clustering on energy detection based cooperative spectrum sensing (CSS) in single primary user (PU) cognitive radio network (CRN). PU signal energy sensed at secondary users (SUs) are forwarded to the fusion center (FC). Two different combining schemes, namely selection combining (SC) and optimal gain combining are performed at FC to address the sensing reliability problem on two different optimization frameworks. In the first work, optimal cluster center points are searched for using differential evolution (DE) algorithm to maximize the probability of detection under the constraint of meeting the probability of false alarm below a predefined threshold. Simulation results highlight the improved sensing reliability compared to the existing works. In the second one, the problem is extended to the energy efficient design of CRN. The SUs act here as amplifyand-forward (AF) relays and PU energy content is measured at the FC over the combined signal from all the SUs. The objective is to minimize the average energy consumption of all SUs while maintaining the predefined sensing ∗. Santi P. Maity . Email addresses: santipmaity@it.iiests.ac.in (Santi P. Maity ), subhankar.ece@gmail.com (Subhankar Chatterjee), t_acharya@telecom.iiests.ac.in (Tamaghna Acharya) Preprint submitted to Elsevier October 28, 2015 constraints. Optimal FCM clustering using DE determines the optimal SU amplifying gain and the optimal number of PU samples. Simulation results shed a light on the performance gain of the proposed approach compared to the existing energy efficient CSS schemes.

Commercial automotive radars used today are based on frequency modulated continuous wave signals due to the simple and robust detection method and good accuracy. However, the increase in both the number of radars deployed per vehicle and the number of such vehicles leads to mutual interference, cutting short future plans for autonomous driving and active safety functionality. We propose and analyze a radar communication (RadCom) approach to reduce this mutual interference while simultaneously offering communication functionality. We achieve this by frequency division multiplexing radar and communication, where communication is built on a decentralized carrier sense multiple access protocol and is used to adjust the timing of radar transmissions. Our simulation results indicate that radar interference can be significantly reduced, at no cost in radar accuracy.

Latest advances in hardware technology and state of the art of computer vision and arti,cial intelligence research can be employed to develop autonomous and distributed monitoring systems. The paper proposes a multi-agent architecture for the understanding of scene dynamics merging the information streamed by multiple cameras. A typical application would be the monitoring of a secure site, or any visual surveillance application deploying a network of cameras. Modular software (the agents) within such architecture controls the di0erent components of the system and incrementally builds a model of the scene by merging the information gathered over extended periods of time. The role of distributed arti,cial intelligence composed of separate and autonomous modules is justi,ed by the need for scalable designs capable of co-operating to infer an optimal interpretation of the scene. Decentralizing intelligence means creating more robust and reliable sources of interpretation, but also allows easy maintenance and updating of the system. Results are presented to support the choice of a distributed architecture, and to prove that scene interpretation can be incrementally and e4ciently built by modular software. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

—constructing intelligent machines, whether or not these operate in the same way as people do; —formalizing knowledge and mechanizing reasoning, both commonsense and refined expertise, in all areas of human endeavor; —using computational models to understand the psychology and behavior of people, animals, and artificial agents; and —making working with computers as easy and as helpful as working with skilled, cooperative, and possibly expert people.

Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner. In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.


Question: Offering context-aware security in IoT environments by utilizing the context sharing feature.

This comprehensive survey explores the evolution and application of context-aware computing in the realm of the Internet of Things (IoT), considering the burgeoning proliferation of sensors worldwide. The paper opens by outlining the IoT paradigm and foundational concepts of context-awareness, setting the stage for an in-depth examination of the context life cycle. A meticulous analysis of a curated selection of 50 projects, spanning both academic research and commercial innovations from 2001 to 2011, is undertaken using a proprietary taxonomy. This analysis uncovers valuable insights and lessons from the past, while also pointing towards promising avenues for future exploration. The survey encompasses a wide spectrum of methodologies, models, functionalities, systems, applications, and middleware solutions pertinent to context awareness within the IoT. Its aim extends beyond mere comparison and consolidation of historical research; it also highlights the relevance and adaptability of these findings in the context of the IoT's ever-expanding landscape. Specifically, the paper underscores the significance of context sharing as a pivotal feature in enhancing security measures for IoT environments, illustrating how context-aware systems can dynamically adapt security protocols based on environmental conditions and user behavior. This perspective not only enriches our understanding of context-aware computing's role in IoT security but also suggests innovative strategies for addressing the security challenges inherent in interconnected devices.

The Internet of Things (IoT) refers to the interconnection of billions of constrained devices, denoted as “smart objects” (SO), in an Internet-like structure. SOs typically feature limited capabilities in terms of computation and memory and operate in constrained environments, such low-power lossy networks. As IP has been foreseen as the standard for smart-object communication, an e↵ort to bring IP connectivity to SOs and define suitable communication protocols (i.e. CoAP) is being carried out within standardization organisms, such as IETF. In this paper, we propose a constrained version of the Session Initiation Protocol (SIP), named “CoSIP”, whose intent is to allow constrained devices to instantiate communication sessions in a lightweight and standard fashion. Session instantiation can include a negotiation phase of some parameters which will be used for all subsequent communication. CoSIP can be adopted in several application scenarios, such as service discovery and publish/subscribe applications, which are detailed. An evaluation of the proposed protocol is also presented, based on a Java implementation of CoSIP, to show the benefits that its adoption can bring about, in terms of compression rate with the existing SIP protocol and message overhead compared with the use of CoAP.

This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.

Cloud computing is becoming increasingly important for provision of services and storage of data in the Internet. However there are several significant challenges in securing cloud infrastructures from different types of attacks. The focus of this paper is on the security services that a cloud provider can offer as part of its infrastructure to its customers (tenants) to counteract these attacks. Our main contribution is a security architecture that provides a flexible security as a service model that a cloud provider can offer to its tenants and customers of its tenants. Our security as a service model while offering a baseline security to the provider to protect its own cloud infrastructure also provides flexibility to tenants to have additional security functionalities that suit their security requirements. The paper describes the design of the security architecture and discusses how different types of attacks are counteracted by the proposed architecture. We have implemented the security architecture and the paper discusses analysis and performance evaluation results.

Digital twins open up new possibilities in terms of monitoring, simulating, optimizing and predicting the state of cyber-physical systems (CPSs). Furthermore, we argue that a fully functional, virtual replica of a CPS can also play an important role in securing the system. In this work, we present a framework that allows users to create and execute digital twins, closely matching their physical counterparts. We focus on a novel approach to automatically generate the virtual environment from specification, taking advantage of engineering data exchange formats. From a security perspective, an identical (in terms of the system's specification), simulated environment can be freely explored and tested by security professionals, without risking negative impacts on live systems. Going a step further, security modules on top of the framework support security analysts in monitoring the current state of CPSs. We demonstrate the viability of the framework in a proof of concept, including the automated generation of digital twins and the monitoring of security and safety rules.

A blockchain is a distributed and decentralized ledger that contains connected blocks of transactions. Unlike other ledger approaches, blockchain guarantees tamper proof storage of approved transactions. Due to its distributed and decentralized organization, blockchain is beeing used within IoT e.g. to manage device configuration, store sensor data and enable micro-payments. This paper presents the idea of using blockchain as a service for IoT and evaluates the performance of a cloud and edge hosted blockchain implementation.

Almost all businesses are aware of the potential gains that the Internet of Things (IoT) has to offer, they are unsure of how to approach it. This article proposes a business model that builds on Holler et al., (2014) [1]. The model consists of three dimensions: “Who, Where, and Why”. “Who” describes collaborating partners, which builds the “Value Network”. “Where” describes sources of value co-creation rooted in the layer model of digitized objects, and “Why” describes how partners benefit from collaborating within the value network. With the intention of addressing “How”, the proposed framework has integrated the IoT strategy category, tactics, and value chain elements. The framework is then validated through the case studies of some successful players who are either the Awardees of the IoT Award 2014 or the ICT Award 2015 of Hong Kong.

Smart world is envisioned as an era in which objects (e.g., watches, mobile phones, computers, cars, buses, and trains) can automatically and intelligently serve people in a collaborative manner. Paving the way for smart world, Internet of Things (IoT) connects everything in the smart world. Motivated by achieving a sustainable smart world, this paper discusses various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT. Particularly, an overview regarding IoT and green IoT is performed first. Then, the hot green information and communications technologies (ICTs) (e.g., green radio-frequency identification, green wireless sensor network, green cloud computing, green machine to machine, and green data center) enabling green IoT are studied, and general green ICT principles are summarized. Furthermore, the latest developments and future vision about sensor cloud, which is a novel paradigm in green IoT, are reviewed and introduced, respectively. Finally, future research directions and open problems about green IoT are presented. Our work targets to be an enlightening and latest guidance for research with respect to green IoT and smart world.

The Internet of healthcare things is essentially a new model that changes the way of the delivery and management of healthcare services. It utilizes digital sensors and cloud computing to present a quality healthcare service outside of the classical hospital environment. This resulted in the emergence of a new class of online web 4.0 services, which are termed “cloud healthcare services”. Cloud healthcare services offer a straightforward opportunity for patients to communicate with healthcare professionals and utilize their personal IoHT devices to obtain timely and accurate medical guidance and decisions. The personal IoHT devices integrate sensed health data at a central cloud healthcare service to extract useful health insights for wellness and preventive care strategies. However, the present practices for cloud healthcare services rely on a centralized approach, where patients’ health data are collected and stored on servers, located at remote locations, which might be functioning under data privacy laws somewhat different from the ones applied where the service is running. Promoting a privacy respecting cloud services encourages patients to actively participate in these healthcare services and to routinely provide an accurate and precious health data about themselves. With the emergence of fog computing paradigm, privacy protection can now be enforced at the edge of the patient’s network regardless of the location of service providers. In this paper, a framework for cloud healthcare recommender service is presented. We depicted the personal gateways at the patients’ side act as intermediate nodes (called fog nodes) between IoHT devices and cloud healthcare services. A fog-based middleware will be hosted on these fog nodes for an efficient aggregation of patients generated health data while maintaining the privacy and the confidentiality of their health profiles. The proposed middleware executes a two-stage concealment process that utilizes the hierarchical nature of IoHT devices. This will unburden the constrained IoHT devices from performing intensive privacy preserving processes. At that, the patients will be empowered with a tool to control the privacy of their health data by enabling them to release their health data in a concealed form. The further processing at the cloud healthcare service continues over the concealed data by applying the proposed protocols. The proposed solution was integrated into a scenario related to preserving the privacy of the patients’ health data when utilized by a cloud healthcare recommender service to generate health insights. Our approach induces a straightforward solution with accurate results, which are beneficial to both patients and service providers.

Power utilities globally are increasingly upgrading to Smart Grids that use bi-directional communication with the consumer to enable an information-driven approach to distributed energy management. Clouds offer features well suited for Smart Grid software platforms and applications, such as elastic resources and shared services. However, the security and privacy concerns inherent in an information-rich Smart Grid environment are further exacerbated by their deployment on Clouds. Here, we present an analysis of security and privacy issues in a Smart Grids software architecture operating on different Cloud environments, in the form of a taxonomy. We use the Los Angeles Smart Grid Project that is underway in the largest U.S. municipal utility to drive this analysis that will benefit both Cloud practitioners targeting Smart Grid applications, and Cloud researchers investigating security and privacy.

As the Internet has changed the way people communicate with each other in everyday life, the number of home Wi-Fi access routers has grown up significantly over the past few years. However, the security of routers used in every household is still in the low level. The common vulnerabilities in routers can be easily exploited by an attacker in order to obtain user’s sensitive information or even compromise the devices to be a part of the botnet network. Therefore, we developed one-stop service firmware analysis tool that can perform both static and dynamic analysis for the router firmware called “Firmaster”. textbfThe program is operated under graphical user interface (GUI) of Qt creator running on the Ubuntu Linux machine. textbfVulnerabilities of firmware analyzed by Firmaster program are based on OWASP’s Top 10 IoT Vulnerabilities 2014. Firmaster contains seven main functions: password cracking, SSL scanning, web static analysis, firmware update analysis, web dynamic analysis, port scanning and the summary report.


Question: Enabling context-aware security in IoT environments via the context sharing feature.

Innovative Context-Aware Security Framework for IoT Environments: The Cerberus Approach

In the era of ubiquitous computing, the vision of smart spaces, which are information-dense environments that seamlessly integrate physical spaces, devices, sensors, and machinery, has gained significant traction. These smart spaces are designed to perceive their surroundings, interpret context, and dynamically adjust their behavior in response to changes. However, the realization of such environments also introduces new security challenges. Without robust security measures, ubiquitous computing landscapes could become hotbeds for vulnerabilities and threats. To address this, a novel security paradigm is required—one that is not only context-aware but also capable of intelligent decision-making.

Cerberus, a context-aware security scheme, emerges as a promising solution for securing smart spaces. This mechanism goes beyond traditional security frameworks by integrating automated reasoning capabilities with context-awareness. The system is designed to perform authentication and access control in a non-intrusive manner, intelligently adapting to the dynamic contexts of ubiquitous computing environments. By incorporating situational information, Cerberus can detect changes in context and adjust security measures accordingly, ensuring that security services remain effective and relevant in the face of rapidly evolving conditions.

The integration of context-awareness with automated reasoning in Cerberus allows for a more nuanced and adaptive approach to security in smart spaces. This approach not only enhances the security posture of ubiquitous computing environments but also ensures that security services are seamlessly integrated into the user experience, striking a balance between security and usability. Through its innovative design, Cerberus sets a new standard for securing IoT environments, paving the way for safer and more resilient smart spaces.

Smart Cities gained importance as a means of making ICT enabled services and applications available to the citizens, companies and authorities that are part of a city's system. It aims at increasing citizens' quality of life, and improving the efficiency and quality of the services provided by governing entities and businesses. This perspective requires an integrated vision of a city and of its infrastructures, in all its components. A Smart City can be taken according to six characteristics: All these domains raise new challenges in security and privacy, since users implicitly expect systems to be secure and privacy-preserving. One of the critical elements is which role(s) the city will take up as an actor within an increasingly complex value network. New players enter the market, actors shift their business strategies, roles change, different types of platforms emerge and vie for market dominance, technological developments create new threats and opportunities, etc. An element related to the trend of platformisation is cloud computing, which is increasingly helping the private sector to reduce cost, increase efficiency, and work smarter. One particular challenge relates to open data business models. Activities necessary for Public Sector Information provision can be identified. The development of efficient and effective e-government is a prerequisite. Transnational authentication systems for citizens and businesses, agreed frameworks for data privacy, and the sharing and collection of individual and business data, are key. Smart Cities need to be able to integrate themselves into national, regional and international infrastructures. Although the implementation aspects depend strongly on the authorities of these infrastructures, European wide recommendations and directives will definitely contribute to accelerate the deployment of Smart Cities. Health, inclusion and assisted living will play an essential role, since the demand for related services is rising, because ageing is changing disease composition. Requirements address a number of technologies, beyond the ones related to mobile and fixed networks. An integrated perspective on healthcare solutions for the near-to long-term can be foreseen, bridging a direct gap in between the health area and the technological development of communications (radio and network components). The needs for mobility in urban areas result into a number of problems, such as traffic congestion and energy consumption, which can be alleviated by exploiting Intelligent Transportation Systems and further adoption of vehicle-to-vehicle and vehicle-to-infrastructure communication networks. The information being managed in this area can be relevant to other domains, which increases its potential. An effective deployment …

The Third Generation Partnership Project (3GPP) has been working on developing specifications on Machine Type Communications (MTC), also known as Machine to Machine Communications (M2M), which is all part of Internet of Things (IoT), a technology that enables machines and devices to be inter-connected via the internet. This paper presents recent M2M/IoT feature enhancements in 3GPP. These features include architectural enhancements for inclusion of a service exposure framework, group based IoT management, efficient monitoring mechanisms for IoT devices, optimization of IoT device power consumption, and high latency communications. These M2M/IoT related functionalities and enhancements are a continuing effort within 3GPP to make the mobile network fulfil the ever changing M2M/IoT requirements, and in so doing allow operators/service providers to offer unique services.

Many Internet of Things (IoT) technologies have been used in applications for money flow, logistics flow, people flow, interactive art design, and so on. To manage these increasing disparate devices and connectivity options, ETSI has specified end-to-end machine-to-machine (M2M) system architecture for IoT applications. Based on this architecture, we develop an IoT EasyConnect system to manage IoT devices. In our approach, an IoT device is characterized by its “features” (e.g., temperature, vibration, and display) that are manipulated by the network applications. If a network application handles the individual device features independently, then we can write a software module for each device feature, and the network application can be simply constructed by including these brick-like device feature modules. Based on the concept of device feature, brick-like software modules can provide simple and efficient mechanism to develop IoT device applications and interactions.

In this paper, we consider the design of a system in which Internet-connected mobile users contribute sensor data as training samples, and collaborate on building a model for classification tasks such as activity or context recognition. Constructing the model can naturally be performed by a service running in the cloud, but users may be more inclined to contribute training samples if the privacy of these data could be ensured. Thus, in this paper, we focus on privacy-preserving collaborative learning for the mobile setting, which addresses several competing challenges not previously considered in the literature: supporting complex classification methods like support vector machines, respecting mobile computing and communication constraints, and enabling user-determined privacy levels. Our approach, Pickle, ensures classification accuracy even in the presence of significantly perturbed training samples, is robust to methods that attempt to infer the original data or poison the model, and imposes minimal costs. We validate these claims using a user study, many real-world datasets and two different implementations of Pickle.

The Internet of Things (IoT) is the result of many different enabling technologies such as embedded systems, wireless sensor networks, cloud computing, big-data, etc., which are used to gather, process, infer, and transmit data. Combining all these technologies requires a research effort to address all the challenges of these technologies, especially for sensing and delivering information from the physical world to cloud-hosted services. In this article we outline the most important issues related to standardization efforts, mobility of objects, networking and gateway access, and QoS support. In particular, we describe a novel IoT network architecture that integrates software defined networking (SDN) and data distribution service (DDS) middleware. The proposed architecture will improve service delivery of IoT systems and will bring flexibility to the network.

During the last several years the advancements in technology made it possible for small sensor nodes to communicate wirelessly with the rest of the Internet. With this achievement the question of securing such IP-enabled Wireless Sensor Networks (IP-WSNs) emerged and has been an important research topic since. In this thesis we discuss our implementation of TLS and DTLS protocols using a pre-shared key cipher suite (TLS PSK WITH AES 128 CCM 8) for the Contiki operating system. Apart from simply adding a new protocol to the set of protocols supported by the Contiki OS, this project allows us to evaluate how suitable the transport-layer security and pre-shared key management schemes are for IP-WSNs.

Once attackers have injected code into a victim program's address space, or found a memory disclosure vulnerability, all sensitive data and code inside that address space are subject to thefts or manipulation. Unfortunately, this broad type of attack is hard to prevent, even if software developers wish to cooperate, mostly because the conventional memory protection only works at process level and previously proposed in-process memory isolation methods are not practical for wide adoption. We propose shreds, a set of OS-backed programming primitives that addresses developers' currently unmet needs for fine-grained, convenient, and efficient protection of sensitive memory content against in-process adversaries. A shred can be viewed as a flexibly defined segment of a thread execution (hence the name). Each shred is associated with a protected memory pool, which is accessible only to code running in the shred. Unlike previous works, shreds offer in-process private memory without relying on separate page tables, nested paging, or even modified hardware. Plus, shreds provide the essential data flow and control flow guarantees for running sensitive code. We have built the compiler toolchain and the OS module that together enable shreds on Linux. We demonstrated the usage of shreds and evaluated their performance using 5 non-trivial open source software, including OpenSSH and Lighttpd. The results show that shreds are fairly easy to use and incur low runtime overhead (4.67%).

We propose a trust management system (TMS) for the social Internet of things (SIOT) called TMCoI-SIOT. The proposed system integrates numerous factors such as direct and indirect trust; transaction factors and social modeling of trust. The novelty of our approach can be summed up in three aspects. The first aspect concerns the network architecture which is seen as a set of nodes groups called community of interest (CoI). Each community nodes shares the same interests and is headed by an administrator (admin. The second aspect is the trust management system that prevents On-Off attacks. The third aspect concerns the trust prediction method that uses the Kalman filter technique in order to estimate nodes behaviors and prevent possible attacks. We prove the effectiveness of the proposed system by simulation against TMS attacks.

The Internet of Things has increased the number of connected devices by an order of magnitude. This increase comes with a corresponding increase in complexity, which has led to centralized architectures that do not respect the privacy of the users and incur high development and deployment costs. We propose the Open Thing Platform as a new distributed architecture which is built around ThingTalk, a new high level declarative programming language that can express the interaction between multiple physical devices and online services. The architecture abstracts away the details of the communication without sacrificing privacy. We experiment with ThingTalk in multiple domains and we show that several applications can be written in a few lines of code and with no need for any server infrastructure.

What can you glean from using inexpensive, off-the-shelf parts to create Internet of Things (IoT) use cases? As it turns out, a lot. The fast productization of IoT technologies is leaving users vulnerable to security and privacy risks.


Question: Facilitating context-aware security in IoT environments by leveraging the context sharing feature.

In the realm of pervasive computing, the Internet of Things (IoT) has brought about a transformation where ordinary objects are endowed with the ability to sense and communicate information from their surroundings, transforming them into intelligent entities. However, these smart objects are often situated in environments that are subject to continuous and unpredictable changes. These dynamic conditions can be harnessed by the objects to adjust their operational parameters or behaviors. Inspired by the European Union's Seventh Framework Programme (FP7) project SocIoTal, this paper explores the integration of context information into the decision-making process of smart objects for security purposes. By treating context as a fundamental component, this work highlights the potential for implementing context-aware security measures in IoT environments, enabling objects to make security decisions that are adaptive and responsive to their changing surroundings.

The majority of business activity of our integrated and connected world takes place in networks based on cloud computing infrastructure that cross national, geographic and jurisdictional boundaries. Such an efficient entity interconnection is made possible through an emerging networking paradigm, Software Defined Networking (SDN) that intends to vastly simplify policy enforcement and network reconfiguration in a dynamic manner. However, despite the obvious advantages this novel networking paradigm introduces, its increased attack surface compared to traditional networking deployments proved to be a thorny issue that creates skepticism when safety-critical applications are considered. Especially when SDN is used to support Internet-of-Things (IoT)-related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. The overall number of connected nodes makes the efficient monitoring of all entities a real challenge, that must be tackled to prevent system degradation and service outage. This position paper provides an overview of common security issues of SDN when linked to IoT clouds, describes the design principals of the recently introduced Blockchain paradigm and advocates the reasons that render Blockchain as a significant security factor for solutions where SDN and IoT are involved.

The form of communication that we see now is either human-human or human-device, but the Internet of Things (IoT) promises a great future for the internet where the type of communication is machine-machine (M2M). It aims to unify everything in our world under a common infrastructure, giving us not only control of things around us, but also keeping us informed of the state of the things. This paper aims to provide a comprehensive overview of the IoT scenario and reviews its enabling technologies and the sensor networks. Also, it describes a six-layered architecture of IoT and points out the related key challenges. However, this manuscript will give good comprehension for the new researchers, who want to do research in this field of Internet of Things and facilitate knowledge accumulation in efficiently.

This paper puts the Internet of Things in a wider context: How it relates to the Future Internet overall, and where the business value lies so that it will become interesting for enterprises to invest in it. Real-World Awareness and Business Process Decomposition are the two major paradigms regarding future business value. The major application domains where the Internet of Things will play an important role and where there are concrete business opportunities are highlighted. But there are also many technical challenges that need to be addressed. These are listed and it is shown how they are tackled by existing research projects with industrial participation.

The IoT Internet of Things being a promising technology of the future. It is expected to connect billions of devices. The increased communication number is expected to generate data mountain and the data security can be a threat. The devices in the architecture are fundamentally smaller in size and low powered. In general, classical encryption algorithms are computationally expensive and this due to their complexity and needs numerous rounds for encrypting, basically wasting the constrained energy of the gadgets. Less complex algorithm, though, may compromise the desired integrity. In this paper we apply a lightweight encryption algorithm named as Secure IoT (SIT) to a quantized speech image for Secure IoT. It is a 64-bit block cipher and requires 64-bit key to encrypt the data. This quantized speech image is constructed by first quantizing a speech signal and then splitting the quantized signal into frames. Then each of these frames is transposed for obtaining the different columns of this quantized speech image. Simulations result shows the algorithm provides substantial security in just five encryption rounds. Keywords—IoT, Security, Encryption, quantized speech image, SNR, PESQ, Histogram, Entropy, Correlation.

The Internet of Things (IoT) is the result of many different enabling technologies such as embedded systems, wireless sensor networks, cloud computing, big-data, etc., which are used to gather, process, infer, and transmit data. Combining all these technologies requires a research effort to address all the challenges of these technologies, especially for sensing and delivering information from the physical world to cloud-hosted services. In this article we outline the most important issues related to standardization efforts, mobility of objects, networking and gateway access, and QoS support. In particular, we describe a novel IoT network architecture that integrates software defined networking (SDN) and data distribution service (DDS) middleware. The proposed architecture will improve service delivery of IoT systems and will bring flexibility to the network.

A major challenge in computer security is about establishing the trustworthiness of remote platforms. Remote attestation is the most common approach to this challenge. It allows a remote platform to measure and report its system state in a secure way to a third party. Unfortunately, existing attestation solutions either provide low security, as they rely on unrealistic assumptions, or are not applicable to commodity low-cost and resource-constrained devices, as they require custom secure hardware extensions that are difficult to adopt across IoT vendors. In this work, we propose a novel remote attestation scheme, named Boot Attestation, that is particularly optimized for low-cost and resource-constrained embedded devices. In Boot Attestation, software integrity measurements are immediately committed to during boot, thus relaxing the traditional requirement for secure storage and reporting. Our scheme is very light on cryptographic requirements and storage, allowing efficient implementations, even on the most low-end IoT platforms available today. We also describe extensions for more flexible management of ownership and third party (public-key) attestation that may be desired in fully Internet-enabled devices. Our scheme is supported by many existing off-the-shelf devices. To this end, we review the hardware protection capabilities for a number of popular device types and present implementation results for two such commercially available platforms.

The proliferation of consumer Internet of Things (IoT) devices offers as many convenient benefits as it poses significant vulnerabilities. Patching or otherwise mitigating these vulnerabilities will be difficult for the existing home security ecosystem. This paper proposes a central security manager that is built on top of the smarthome's hub or gateway router and positioned to intercept all traffic to and from devices. Aware of the status of all devices in the home and of reported vulnerabilities, the security manager could intervene as needed to deter or alleviate many types of security risks. Modules built atop this manager could offer convenient installation of software updates, filter traffic that might otherwise exploit devices, and strengthen authentication for both legacy and future devices. We believe that this design offers the potential to increase security for smarthome IoT devices, and we encourage other researchers and regulators to explore and extend our ideas.

Context is a key issue in interaction between human and computer, describing the surrounding facts that add meaning. In mobile computing research published the parameter location is most often used to approximate context and to implement context-aware applications. We propose that ultra-mobile computing, characterized by devices that are operational and operated while on the move (e.g. PDAs, mobile phones, wearable computers), can significantly benefit from a wider notion of context. To structure the field we introduce a working model for context, discuss mechanisms to acquire context beyond location, and application of context-awareness in ultra-mobile computing. We investigate the utility of sensors for context-awareness and present two prototypical implementations-a light sensitive display and an orientation aware PDA interface. The concept is then extended to a model for sensor fusion to enable more sophisticated context recognition. Based on an implementation of the model an experiment is described and the feasibility of the approach is demonstrated. Further we explore fusion of sensors for acquisition of information on more sophisticated contexts. 1 Introduction Context is " that which surrounds, and gives meaning to something else " =. Various areas of computer science have been investigating this concept over the last 40 years, to relate information processing and communication to aspects of the situations in which such processing occurs. Most notably, context is a key concept in Natural Language Processing and more generally in Human-Computer Interaction. For instance, state of the art graphical user interfaces use context to adapt menus to contexts such as user preference and dialogue status. A new domain, in which context currently receives growing attention, is mobile computing. While a first wave of mobile computing was based on portable general-purpose computers and primarily focussed on location transparency, a second wave is now based on ultra-mobile devices and an interest in relating these to their surrounding situation of usage. Ultra-mobile devices are a new class of small mobile computer, defined as computing devices that are operational and operated while on the move, and characterized by a shift from general-purpose computing to task-specific support. Ultra-mobile devices comprise for instance Personal Digital Assistants (PDAs), mobile phones, and wearable computers. A primary concern of context-awareness in mobile computing is awareness of the physical environment surrounding a user and their ultra-mobile device. In recent work, this concern has been addressed by implementation of location-awareness, for instance based on global positioning, or the use of beacons. Location …

Smart Cities gained importance as a means of making ICT enabled services and applications available to the citizens, companies and authorities that are part of a city's system. It aims at increasing citizens' quality of life, and improving the efficiency and quality of the services provided by governing entities and businesses. This perspective requires an integrated vision of a city and of its infrastructures, in all its components. A Smart City can be taken according to six characteristics: All these domains raise new challenges in security and privacy, since users implicitly expect systems to be secure and privacy-preserving. One of the critical elements is which role(s) the city will take up as an actor within an increasingly complex value network. New players enter the market, actors shift their business strategies, roles change, different types of platforms emerge and vie for market dominance, technological developments create new threats and opportunities, etc. An element related to the trend of platformisation is cloud computing, which is increasingly helping the private sector to reduce cost, increase efficiency, and work smarter. One particular challenge relates to open data business models. Activities necessary for Public Sector Information provision can be identified. The development of efficient and effective e-government is a prerequisite. Transnational authentication systems for citizens and businesses, agreed frameworks for data privacy, and the sharing and collection of individual and business data, are key. Smart Cities need to be able to integrate themselves into national, regional and international infrastructures. Although the implementation aspects depend strongly on the authorities of these infrastructures, European wide recommendations and directives will definitely contribute to accelerate the deployment of Smart Cities. Health, inclusion and assisted living will play an essential role, since the demand for related services is rising, because ageing is changing disease composition. Requirements address a number of technologies, beyond the ones related to mobile and fixed networks. An integrated perspective on healthcare solutions for the near-to long-term can be foreseen, bridging a direct gap in between the health area and the technological development of communications (radio and network components). The needs for mobility in urban areas result into a number of problems, such as traffic congestion and energy consumption, which can be alleviated by exploiting Intelligent Transportation Systems and further adoption of vehicle-to-vehicle and vehicle-to-infrastructure communication networks. The information being managed in this area can be relevant to other domains, which increases its potential. An effective deployment …

For the Internet of Things to finally become a reality, obstacles on different levels need to be overcome. This is especially true for the upcoming challenge of leaving the domain of technical experts and scientists. Devices need to connect to the Internet and be able to offer services. They have to announce and describe these services in machine-understandable ways so that user-facing systems are able to find and utilize them. They have to learn about their physical surroundings, so that they can serve sensing or acting purposes without explicit configuration or programming. Finally, it must be possible to include IoT devices in complex systems that combine local and remote data, from different sources, in novel and surprising ways. We show how all of that is possible today. Our solution uses open standards and state-of-the art protocols to achieve this. It is based on 6LowPAN and CoAP for the communications part, semantic web technologies for meaningful data exchange, autonomous sensor correlation to learn about the environment, and software built around the Linked Data principles to be open for novel and unforeseen applications.


Question: Offering context-aware security for IoT environments by utilizing a context sharing feature.

This paper outlines a context-aware security architecture designed for the evolving landscape of modern applications, particularly those in IoT environments. The central focus is on developing security services that seamlessly integrate "context" to offer dynamic access control and enforce policies effectively. Building upon a previously introduced generalized access control model that leverages contextual data for policy formulation, this document delves into a practical system-level service architecture, complemented by initial implementation insights. The proposed architecture aims to enhance authentication mechanisms, provide adaptable access control, and enable a security subsystem that can adjust its behavior in response to the prevailing environmental conditions. Through the lens of context-aware security services, the architecture not only bolsters authentication and access control but also ensures that security measures are responsive and context-sensitive. The paper elaborates on the architectural design and implementation details, illustrating its application in securing a range of pilot applications, thereby showcasing the potential of context-aware security in IoT environments.

Internet of Things is an up-and-coming information and technology industry, and in such an environment of the concept and the content and the extension of Internet of Things are not very distinctive, the project of Internet of Things which is a piece of small area with small-scale and self-system obtain gratifying achievement and bright future, it can promote the development of Internet of Things in some extent. But there are some serious hidden danger and potential crisis problems. The paper focuses on the application of Internet of Things in the nation and even in the global in the future, analysing the existed security risks of the Internet of Things 's network points, transmission, finally we propose some suggestive solutions due to these problems.

The Internet of Things (IoT) has the strong potential to support a human society interacting more symbiotically with its physical environment. Indeed, the emergence of tiny devices that sense environmental cues and trigger actuators after consulting logic and human preferences promises a more environmentally aware and less wasteful society. However, the IoT inherently challenges software development processes, particularly techniques for ensuring software reliability. Researchers have developed debugging tools for wireless sensor networks (WSNs), which can be viewed as the enablers of perception in the IoT. These tools gather run-time information on individual sensor node executions and node interactions and then compress that information.

The Internet of Things (IoT) continues to grow as uniquely identifiable objects are added to the internet. The addition of these devices, and their remote connectivity, has brought a new level of efficiency into our lives. However, the security of these devices has come into question. While many may be secure, the sheer number creates an environment where even a small percentage of insecure devices may create significant vulnerabilities. This paper evaluates some of the emerging vulnerabilities that exist and puts some figures to the scale of the threat.

This paper proposes a distributed authorization architecture for Internet of Things adopting a hyperledger fabric framework. This paper also applies Genetic Algorithm to enhance the consensus component, traditionally used for Kafka, in order to determine the best configuration over different parameters, such as input transactions and their success rates. The experimental results confirm our superior performance in that the transaction transfer rate of our optimization, called GA Kafka, outperforms the traditional Kafka by 69.43%, and 89.97% for transaction transfer success rate.

Securing the rapidly expanding Internet of Things (IoT) is critical. Many of these “things” are vulnerable baremetal embedded systems where the application executes directly on hardware without an operating system. Unfortunately, the integrity of current systems may be compromised by a single vulnerability, as recently shown by Google’s P0 team against Broadcom’s WiFi SoC. We present ACES (Automatic Compartments for Embedded Systems), an LLVM-based compiler that automatically infers and enforces inter-component isolation on bare-metal systems, thus applying the principle of least privileges. ACES takes a developer-specified compartmentalization policy and then automatically creates an instrumented binary that isolates compartments at runtime, while handling the hardware limitations of baremetal embedded devices. We demonstrate ACES’ ability to implement arbitrary compartmentalization policies by implementing three policies and comparing the compartment isolation, runtime overhead, and memory overhead. Our results show that ACES’ compartments can have low runtime overheads (13% on our largest test application), while using 59% less Flash, and 84% less RAM than the Mbed μVisor—the current state-of-theart compartmentalization technique for bare-metal systems. ACES ‘ compartments protect the integrity of privileged data, provide control-flow integrity between compartments, and reduce exposure to ROP attacks by 94.3% compared to μVisor.

In this report, we describe the Unified Cyber Security ontology (UCO) to support situational awareness in cyber security systems. The ontology is an effort to incorporate and integrate heterogeneous information available from different cyber security systems and most commonly used cyber security standards for information sharing and exchange. The ontology has also been mapped to a number of existing cyber security ontologies as well as concepts in the Linked Open Data cloud. Similar to DBpedia which serves as the core for Linked Open Data cloud, we envision UCO to serve as the core for the specialized cyber security Linked Open Data cloud which would evolve and grow with the passage of time with additional cybersecurity data sets as they become available. We also present a prototype system and concrete use-cases supported by the UCO ontology. To the best of our knowledge, this is the first cyber security ontology that has been mapped to general world ontologies to support broader and diverse security use-cases. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe potential future work directions.

Interconnected everyday objects, either via public or private networks, are gradually becoming reality in modern life — often referred to as the Internet of Things (IoT) or Cyber-Physical Systems (CPS). One stand-out example are those systems based on Unmanned Aerial Vehicles (UAVs). Fleets of such vehicles (drones) are prophesied to assume multiple roles from mundane to high-sensitive applications, such as prompt pizza or shopping deliveries to the home, or to deployment on battlefields for battlefield and combat missions. Drones, which we refer to as UAVs in this paper, can operate either individually (solo missions) or as part of a fleet (group missions), with and without constant connection with a base station. The base station acts as the command centre to manage the drones' activities; however, an independent, localised and effective fleet control is necessary, potentially based on swarm intelligence, for several reasons: 1) an increase in the number of drone fleets; 2) fleet size might reach tens of UAVs; 3) making time-critical decisions by such fleets in the wild; 4) potential communication congestion and latency; and 5) in some cases, working in challenging terrains that hinders or mandates limited communication with a control centre, e.g. operations spanning long period of times or military usage of fleets in enemy territory. This self-aware, mission-focused and independent fleet of drones may utilise swarm intelligence for a), air-traffic or flight control management, b) obstacle avoidance, c) self-preservation (while maintaining the mission criteria), d) autonomous collaboration with other fleets in the wild, and e) assuring the security, privacy and safety of physical (drones itself) and virtual (data, software) assets. In this paper, we investigate the challenges faced by fleet of drones and propose a potential course of action on how to overcome them.

Traditional power grids are being transformed into Smart Grids (SGs) to solve the problems of uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers’ premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide the first comprehensive survey on IoTaided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems.

Technology integration is becoming an impetus to everyday lives. This new interconnected world can be found from our most private spaces to the public ones. Smart homes, which is the use of Internet of Things (IoT) within a home, has become the utmost concern in the security and privacy domain. In this paper, we review the current literature from both research and practice and offer five research trends for security and privacy in smart homes: potential for remote security breaches, risks for smart home devices, privacy violations, infrastructure vulnerabilities, and digital forensics challenges. We integrated these five trends in a conceptual model showcasing their roles in the rapidly changing IoT landscape. Combining the trends and smart forensics, we elucidate the bimodal lenses in security and privacy: preventive and investigative. These discussions offer research directions and practitioners’ implications for further development of a safe and secured smart home.

In mobile cloud computing, mobile devices can rely on cloud computing and information storage resource to perform computationally intensive operations such as searching, data mining, and multimedia processing. In addition to providing traditional computation services, mobile cloud also enhances the operation of traditional ad hoc network by treating mobile devices as service nodes, e.g., sensing services. The sensed information, such as location coordinates, health related information, should be processed and stored in a secure fashion to protect user's privacy in the cloud. To this end, we present a new mobile cloud data processing framework through trust management and private data isolation. Finally, an implementation pilot for improving teenagers' driving safety, which is called FocusDrive, is presented to demonstrate the solution.


Question: Offering context-aware security in IoT environments by utilizing a context sharing feature.

This paper delves into the Internet of Things (IoT), a transformative paradigm that hinges on the integration of various technologies and communication solutions. At its core, the IoT amalgamates identification and tracking systems, wired and wireless sensor and actuator networks, advanced communication protocols shared with the Next Generation Internet, and distributed intelligence for smart objects. Given the interdisciplinary nature of this field, progress in the IoT necessitates collaborative efforts across domains such as telecommunications, informatics, electronics, and social science. Recognizing the complexity of this discipline, this survey serves as a guide for newcomers and contributors alike, aiming to foster its evolution. The paper consolidates diverse perspectives on the IoT paradigm and provides an overview of the enabling technologies that underpin it. However, it also highlights that significant challenges remain unaddressed by the research community, which are meticulously examined within. (2010 Elsevier B.V. All rights reserved.)

The survey underscores the Internet of Things (IoT) as a groundbreaking concept, characterized by the convergence of multiple technologies and communication strategies. Integral to the IoT are identification and tracking mechanisms, along with wired and wireless sensor and actuator networks. Additionally, it involves the adoption of enhanced communication protocols, often shared with the Next Generation Internet, and the deployment of distributed intelligence for smart devices. Given the multi-faceted nature of the IoT, its development requires a harmonious blend of expertise from various sectors, including telecommunications, informatics, electronics, and social science. This paper serves as an introductory resource for those wishing to navigate the intricacies of the IoT and contribute to its advancement. It compiles various interpretations of the IoT paradigm and offers a comprehensive review of the foundational technologies. Yet, the paper also points out that critical issues are yet to be tackled by the research community, which are scrutinized in detail. (2010 Elsevier B.V. All rights reserved.)

The Internet of Things (IoT) is the result of many different enabling technologies such as embedded systems, wireless sensor networks, cloud computing, big-data, etc., which are used to gather, process, infer, and transmit data. Combining all these technologies requires a research effort to address all the challenges of these technologies, especially for sensing and delivering information from the physical world to cloud-hosted services. In this article we outline the most important issues related to standardization efforts, mobility of objects, networking and gateway access, and QoS support. In particular, we describe a novel IoT network architecture that integrates software defined networking (SDN) and data distribution service (DDS) middleware. The proposed architecture will improve service delivery of IoT systems and will bring flexibility to the network.

This paper puts the Internet of Things in a wider context: How it relates to the Future Internet overall, and where the business value lies so that it will become interesting for enterprises to invest in it. Real-World Awareness and Business Process Decomposition are the two major paradigms regarding future business value. The major application domains where the Internet of Things will play an important role and where there are concrete business opportunities are highlighted. But there are also many technical challenges that need to be addressed. These are listed and it is shown how they are tackled by existing research projects with industrial participation.

Securing the rapidly expanding Internet of Things (IoT) is critical. Many of these “things” are vulnerable baremetal embedded systems where the application executes directly on hardware without an operating system. Unfortunately, the integrity of current systems may be compromised by a single vulnerability, as recently shown by Google’s P0 team against Broadcom’s WiFi SoC. We present ACES (Automatic Compartments for Embedded Systems), an LLVM-based compiler that automatically infers and enforces inter-component isolation on bare-metal systems, thus applying the principle of least privileges. ACES takes a developer-specified compartmentalization policy and then automatically creates an instrumented binary that isolates compartments at runtime, while handling the hardware limitations of baremetal embedded devices. We demonstrate ACES’ ability to implement arbitrary compartmentalization policies by implementing three policies and comparing the compartment isolation, runtime overhead, and memory overhead. Our results show that ACES’ compartments can have low runtime overheads (13% on our largest test application), while using 59% less Flash, and 84% less RAM than the Mbed μVisor—the current state-of-theart compartmentalization technique for bare-metal systems. ACES ‘ compartments protect the integrity of privileged data, provide control-flow integrity between compartments, and reduce exposure to ROP attacks by 94.3% compared to μVisor.

To realize the vision of Internet of Things, there must be mechanisms to discover resources and their capabilities. Thus resource discovery becomes a fundamental requirement of any IoT platform. This paper provides a comprehensive categorization of the current technology landscape for discovery while pointing out their advantages and limitations. Then, a novel search engine based resource discovery framework is proposed. The framework comprises of a proxy layer which includes drivers for various communication technologies and protocols. There is a central registry to store the configurations of the resources and index them based on the configuration parameters. A “lifetime” attribute is introduced which denotes the period through which a resource is discoverable. The search engine ranks the resulting resources of a discovery request and returns an URI to directly access each resource. The functionalities of the proposed framework are exposed using RESTful web services. The framework allows discovery of both smart and legacy devices. We have performed gap analysis of current IoT standards for discovery mechanisms and provide suggestions to improve them to maintain interoperability. The outcomes are communicated to Standard Development Organizations like W3C (in Web of Things Interest Group) and oneM2M.

Benefits of Internet of Things and cloud–fog-edge computing are associated with the risks of confidentiality, integrity, and availability related with the loss of information, denial of access for a long time, information leakage, conspiracy and technical failures. In this article, we propose a configurable, reliable, and confidential distributed data storage scheme with the ability to process encrypted data and control results of computations. Our systemutilizes Redundant ResidueNumber System (RRNS)with newmethod of error correction codes and secret sharing schemes. We introduce the concept of an approximate value of a rank of a number (AR), which allows us to reduce the computational complexity of the decoding from RNS to binary representation, and size of the coefficients. Based on the properties of the approximate value and arithmetic properties of RNS,we introduce AR-RRNSmethod for error detection, correction, and controlling computational results. We provide a theoretical basis to configure probability of information loss, data redundancy, speed of encoding and decoding to cope with different objective preferences, workloads, and storage properties. Theoretical analysis shows that by appropriate selection of RRNS parameters, the proposed scheme allows not only increasing safety, reliability, and reducing an overhead of data storage, but also processing of encrypted data. © 2017 Elsevier B.V. All rights reserved.

Power utilities globally are increasingly upgrading to Smart Grids that use bi-directional communication with the consumer to enable an information-driven approach to distributed energy management. Clouds offer features well suited for Smart Grid software platforms and applications, such as elastic resources and shared services. However, the security and privacy concerns inherent in an information-rich Smart Grid environment are further exacerbated by their deployment on Clouds. Here, we present an analysis of security and privacy issues in a Smart Grids software architecture operating on different Cloud environments, in the form of a taxonomy. We use the Los Angeles Smart Grid Project that is underway in the largest U.S. municipal utility to drive this analysis that will benefit both Cloud practitioners targeting Smart Grid applications, and Cloud researchers investigating security and privacy.

The Internet of Things (IoT), often referred as the future Internet; is a collection of interconnected devices integrated into the world-wide network that covers almost everything and could be available anywhere. IoT is an emerging technology and aims to play an important role in saving money, conserving energy, eliminating gap and better monitoring for intensive management on a routine basis. On the other hand, it is also facing certain design constraints such as technical challenges, social challenges, compromising privacy and performance tradeoffs. This paper surveys major technical limitations that are hindering the successful deployment of the IoT such as standardization, interoperability, networking issues, addressing and sensing issues, power and storage restrictions, privacy and security, etc. This paper categorizes the existing research on the technical constraints that have been published in the recent years. With this categorization, we aim to provide an easy and concise view of the technical aspects of the IoT. Furthermore, we forecast the changes influenced by the IoT. This paper predicts the future and provides an estimation of the world in year 2020 and beyond. Keywords—Internet of Things; Future Internet; Next generation network issues; World-wide network; 2020

The synergy between the cloud and the IoT has emerged largely due to the cloud having attributes which directly benefit the IoT and enable its continued growth. IoT adopting Cloud services has brought new security challenges. In this book chapter, we pursue two main goals: 1) to analyse the different components of Cloud computing and the IoT and 2) to present security and privacy problems that these systems face. We thoroughly investigate current security and privacy preservation solutions that exist in this area, with an eye on the Industrial Internet of Things, discuss open issues and propose future directions.

The Internet of Energy (IoE) provides an effective networking technology for distributed green energy, which allows the connection of energy anywhere at any time. As an important part of the IoE, electric vehicles (EVs), and charging pile management are of great significance to the development of the IoE industry. Previous work has mainly focused on network performance optimization for its management, and few studies have considered the security of the management between EVs and charging piles. Therefore, this paper proposes a decentralized security model based on the lightning network and smart contract in the blockchain ecosystem; this proposed model is called the lightning network and smart contract (LNSC). The overall model involves registration, scheduling, authentication, and charging phases. The new proposed security model can be easily integrated with current scheduling mechanisms to enhance the security of trading between EVs and charging piles. Experimental results according to a realistic infrastructure are presented in this paper. These experimental results demonstrate that our scheme can effectively enhance vehicle security. Different performances of LNSC-based scheduling strategies are also presented.

Data collection is required to be safe and efficient considering both data privacy and system performance. In this paper, we study a new problem: distributed data sharing with privacy-preserving requirements. Given a data demander requesting data from multiple distributed data providers, the objective is to enable the data demander to access the distributed data without knowing the privacy of any individual provider. The problem is challenged by two questions: how to transmit the data safely and accurately; and how to efficiently handle data streams? As the first study, we propose a practical method, Shadow Coding, to preserve the privacy in data transmission and ensure the recovery in data collection, which achieves privacy preserving computation in a data-recoverable, efficient, and scalable way. We also provide practical techniques to make Shadow Coding efficient and safe in data streams. Extensive experimental study on a large-scale real-life dataset offers insight into the performance of our schema. The proposed schema is also implemented as a pilot system in a city to collect distributed mobile phone data.