Question: Using deep residual networks for foreground segmentation in surveillance videos for anomaly detection.

In this update, we unveil the CDnet 2014 dataset, an expanded and enriched benchmark for change detection algorithms, a critical component in the field of video analytics. Building upon our previous contribution, the change-detection.net (CDnet) benchmark, this new release significantly broadens the scope by incorporating 22 more videos, bringing the total annotated frames to 70,000. These additions are strategically classified into 5 novel categories, each meticulously designed to mirror the complexities and challenges prevalent in real-world surveillance scenarios. We delve into the specifics of these categories, offering a comprehensive analysis. Additionally, we compile and evaluate the outcomes of over a dozen methodologies that were showcased at the IEEE Change Detection Workshop 2014. Our assessment underscores the distinctive advantages and limitations of these approaches, simultaneously pinpointing the lingering issues within the realm of change detection. This comprehensive update to the CDnet dataset is poised to serve as a robust testing ground for the advancement and refinement of change and motion detection techniques.

The increasing availability of video data, through existing traffic cameras or dedicated field data collection, paves the way for the collection of massive datasets about the microscopic behaviour of road users using computer vision techniques. Analysis of such datasets helps to understand the normal road user behaviour, and it can be used for realistic prediction of future motion and computing surrogate safety indicators. A multi-level motion pattern learning framework is developed that enables automated scene interpretation, anomalous behaviour detection and surrogate safety analysis. Firstly, Points of interest (POI) are learnt based on Gaussian Mixture Model and the Expectation Maximization algorithm and then used to form activity paths (APs). Secondly, motion patterns, represented by trajectory prototypes, are learnt from road users’ trajectories in each AP using a two-stage trajectory clustering method based on spatial then temporal (speed) information. Finally, motion prediction relies on matching at each instant partial trajectories to the learnt prototypes to evaluate the potential for collision by computing indicators. An intersection case study demonstrates the framework ability in many ways: it helps to reduce the computation cost up to 90 %, clean trajectories dataset from tracking outliers, use actual trajectories as prototypes without any preand post-processing and predict future motion realistically to compute surrogate safety indicators.

Title of dissertation: Anomaly Detection in Noisy Images Xavier Gibert Serra, Ph.D. Examination, Fall 2015 Dissertation directed by: Professor Rama Chellappa Department of Electrical and Computer Engineering Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work. Anomaly Detection in Noisy Images by Xavier Gibert Serra Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2015 Advisory Committee: Professor Rama Chellappa, Chair/Advisor Professor Piya Pal Professor Shuvra Bhattacharyya Professor Vishal M. Patel Professor Amitabh Varshney, Dean’s Representative

In recent years, deep learning algorithm has been one of the most used method in machine learning. Success rate of the most popular machine learning problems has been increased by using it. In this work, we develop an eye detection method by using a deep neural network. The designed network, which is accepted as an input by Caffe, has 3 convolution layers and 3 max pooling layers. This model has been trained with 16K positive and 52K negative image patches. The last layer of the network is the classification layer which operates a softmax algorithm. The trained model has been tested with images, which were provided on FDDB and CACD datasets, and also compared with Haar eye detection algorithm. Recall value of the method is higher than the Haar algorithm on the two datasets. However, according to the precision rate, the Haar algorithm is successful on FDDB dataset.

The advanced sensing and imaging capability of today's sensor networks enables real time monitoring in a large area. In order to provide continuous monitoring and prompt situational awareness, an abstract-level autonomous information processing framework is developed that is able to detect various categories of abnormal traffic events with unsupervised learning. The framework is based on cogent confabulation model, which performs statistical inference in a manner inspired by human neocortex system. It enables detection and recognition of abnormal target vehicles within the context of surrounding traffic activities and previous events using likelihood-ratio test. A neuromorphic architecture is proposed which accelerates the computation for real-time detection by leveraging memristor crossbar arrays.

In this paper we propose a novel approach for detecting and tracking objects in videos with variable background i.e. videos captured by moving cameras without any additional sensor. The performance of tracking in videos with variable background depends on the successful detection of an object in variable background. The most attractive feature of detecting an object in variable background is that it does not depend on any a priori information of the scene. In a video captured by a moving camera, both the background and foreground are changing in each frame of the image sequence. So for these videos, modeling a single background with traditional background modeling methods is infeasible and thus the detection of actual moving object in a variable background is a challenging task. To detect actual moving object in this work, spatio-temporal blobs have been generated in each frame by spatio-temporal analysis of the image sequence using a three-dimensional Gabor filter. Then individual blobs, which are parts of one object are merged using Minimum Spanning Tree to form the moving object in the variable background. The height, width and four-bin grayvalue histogram of the object are calculated as its features and an object is tracked in each frame using these features to generate the trajectories of the object through the video sequence. In this work, problem of data association during tracking is solved by Linear Assignment Problem and occlusion is handled by the application of kalman filter. The major advantage of our method over most of the existing tracking algorithms is that, the proposed method does not require initialization in the first frame or training on sample data to perform. Performance of the algorithm has been tested on benchmark videos and very satisfactory result has been achieved. The performance of the algorithm is also comparable and superior with respect to some benchmark algorithms.

Deep Neural Networks (DNNs) have recently shown outstanding performance on image classification tasks [14]. In this paper we go one step further and address the problem of object detection using DNNs, that is not only classifying but also precisely localizing objects of various classes. We present a simple and yet powerful formulation of object detection as a regression problem to object bounding box masks. We define a multi-scale inference procedure which is able to produce high-resolution object detections at a low cost by a few network applications. State-of-the-art performance of the approach is shown on Pascal VOC.

We tackle the problem of one-shot segmentation: finding and segmenting a previously unseen object in a cluttered scene based on a single instruction example. We propose a novel dataset, which we call cluttered Omniglot. Using a baseline architecture combining a Siamese embedding for detection with a U-net for segmentation we show that increasing levels of clutter make the task progressively harder. Using oracle models with access to various amounts of ground-truth information, we evaluate different aspects of the problem and show that in this kind of visual search task, detection and segmentation are two intertwined problems, the solution to each of which helps solving the other. We therefore introduce MaskNet, an improved model that attends to multiple candidate locations, generates segmentation proposals to mask out background clutter and selects among the segmented objects. Our findings suggest that such image recognition models based on an iterative refinement of object detection and foreground segmentation may provide a way to deal with highly cluttered scenes.

As an alternative to the tracking-based approaches that heavily depend on accurate detection of moving objects, which often fail for crowded scenarios, we present a pixelwise method that employs dual foregrounds to extract temporally static image regions. Depending on the application, these regions indicate objects that do not constitute the original background but were brought into the scene at a subsequent time, such as abandoned and removed items, illegally parked vehicles. We construct separate longand short-term backgrounds that are implemented as pixelwise multivariate Gaussian models. Background parameters are adapted online using a Bayesian update mechanism imposed at different learning rates. By comparing each frame with these models, we estimate two foregrounds. We infer an evidence score at each pixel by applying a set of hypotheses on the foreground responses, and then aggregate the evidence in time to provide temporal consistency. Unlike optical flow-based approaches that smear boundaries, our method can accurately segment out objects even if they are fully occluded. It does not require on-site training to compensate for particular imaging conditions. While having a low-computational load, it readily lends itself to parallelization if further speed improvement is necessary.

A novel approach is proposed in this paper for automatic forest fire detection from video. Based on 3D point cloud of the collected sample fire pixels, Gaussian mixture model is built and helps segment some possible flame regions in single image. Then the new specific flame pattern is defined for forest, and three types of fire colors are labeled accordingly. With 11 static features including color distributions, texture parameters and shape roundness, the static SVM classifier is trained and filters the segmented results. Using defined overlapping degree and varying degree, the remained candidate regions are matched among consecutive frames. Subsequently the variations of color, texture, roundness, area, contour are computed, then the average and the mean square deviation of them are obtained. Together with the flickering frequency from temporal wavelet based Fourier descriptors analysis of flame contour, 27 dynamic features are used to train the dynamic SVM classifier, which is applied for final decision. Our approach has been tested with dozens of video clips, and it can detect forest fire while recognize the fire like objects, such as red house, bright light and flying flag. Except for the acceptable accuracy, our detection algorithm performs in real time, which proves its value for computer vision based forest fire surveillance.

We present a mobile vision system for multi-person tracking in busy environments. Specifically, the system integrates continuous visual odometry computation with tracking-by-detection in order to track pedestrians in spite of frequent occlusions and egomotion of the camera rig. To achieve reliable performance under real-world conditions, it has long been advocated to extract and combine as much visual information as possible. We propose a way to closely integrate the vision modules for visual odometry, pedestrian detection, depth estimation, and tracking. The integration naturally leads to several cognitive feedback loops between the modules. Among others, we propose a novel feedback connection from the object detector to visual odometry which utilizes the semantic knowledge of detection to stabilize localization. Feedback loops always carry the danger that erroneous feedback from one module is amplified and causes the entire system to become instable. We therefore incorporate automatic failure detection and recovery, allowing the system to continue when a module becomes unreliable. The approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver stable tracking performance in scenes of previously infeasible complexity.


Question: Using deep residual networks for foreground segmentation in surveillance videos to detect anomalies.

Introducing the Microsoft COCO: A Comprehensive Dataset for Contextual Object Recognition
In this paper, we unveil a new dataset designed to push the boundaries of object recognition by integrating it into the wider context of scene understanding. Our objective is to capture common objects in their natural, everyday settings, thereby enriching the context in which they are recognized. To achieve precise localization of objects, we have annotated images with per-instance segmentations. The dataset is composed of 91 object categories, all easily identifiable by a four-year-old child, reflecting their ubiquity in everyday life. With over 2.5 million labeled instances spread across 328,000 images, the creation of this dataset has been a massive undertaking, involving extensive participation from crowd workers facilitated by innovative user interfaces for category detection, instance spotting, and segmentation. We offer a thorough statistical analysis, comparing our dataset to PASCAL, ImageNet, and SUN datasets, highlighting its distinct contributions. Furthermore, we present baseline performance metrics for both bounding box and segmentation detection tasks using a Deformable Parts Model, showcasing the potential of our dataset for advancing object recognition research.

**Question**: A Comparative Study of Machine Learning Techniques for Predicting Stock Market Trends

**Documents**: Predicting Stock Market Indices Using Support Vector Regression
The stock market prediction is a very difficult task, full of challenges due to the volatility and complexity of the capital markets. In this paper, we investigate the application of support vector regression (SVR) models for predicting stock market indices. The SVR models are applied to two different datasets of Dow Jones Industrial Average (DJIA) and NASDAQ indices. The models are trained and tested on historical data of the indices using daily, weekly, and monthly data. The results are compared with other prediction models, such as autoregressive integrated moving average (ARIMA), exponential smoothing (ES), and multilayer perceptron (MLP) neural network. The empirical results show that SVR models have better performance in terms of mean absolute error (MAE) and root mean square error (RMSE) for predicting stock market indices than other models.

**Similar Documents**: An Empirical Evaluation of Support Vector Regression in Stock Market Index Forecasting
Forecasting stock market trends is a notoriously complex endeavor, given the inherent volatility and intricacies of financial markets. This study explores the efficacy of support vector regression (SVR) models in predicting stock market indices, focusing on two prominent benchmarks: the Dow Jones Industrial Average (DJIA) and the NASDAQ. SV

This paper proposes a robust motion detection method for illumination variations which uses histogram of oriented gradients. The detection process is divided into two phases: coarse detection and refinement. In the coarse detection phase, first, a texture-based background model is built which implements a group of adaptive histograms of oriented gradients; then, by comparing the histogram of oriented gradients of each pixel between current frame and background model, a foreground is segmented based on texture feature which is not susceptible to illumination variations, whereas some missing foreground regions exist; finally, the result based on texture is optimized by combining the pixel-wise detection result produced by Gaussian Mixture Model (GMM) algorithm, which greatly improves the detection performance by incorporating efficient morphological operations. In the refinement phase, the above detection result is refined based on the distinction in color feature to eliminate errors like shadows, noises, redundant contour, etc. Experimental results show the effectiveness and robustness of our approach in detecting moving objects in varying illumination conditions.

An unsupervised video object segmentation algorithm, which discovers a primary object in a video sequence automatically, is proposed in this work. We introduce three energies in terms of foreground and background probability distributions: Markov, spatiotemporal, and antagonistic energies. Then, we minimize a hybrid of the three energies to separate a primary object from its background. However, the hybrid energy is nonconvex. Therefore, we develop the alternate convex optimization (ACO) scheme, which decomposes the nonconvex optimization into two quadratic programs. Moreover, we propose the forward-backward strategy, which performs the segmentation sequentially from the first to the last frames and then vice versa, to exploit temporal correlations. Experimental results on extensive datasets demonstrate that the proposed ACO algorithm outperforms the state-of-the-art techniques significantly.

Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed.

In recent years, deep learning algorithm has been one of the most used method in machine learning. Success rate of the most popular machine learning problems has been increased by using it. In this work, we develop an eye detection method by using a deep neural network. The designed network, which is accepted as an input by Caffe, has 3 convolution layers and 3 max pooling layers. This model has been trained with 16K positive and 52K negative image patches. The last layer of the network is the classification layer which operates a softmax algorithm. The trained model has been tested with images, which were provided on FDDB and CACD datasets, and also compared with Haar eye detection algorithm. Recall value of the method is higher than the Haar algorithm on the two datasets. However, according to the precision rate, the Haar algorithm is successful on FDDB dataset.

The condition of railway tracks needs to be periodically monitored to ensure passenger safety. Cameras mounted on a moving vehicle such as a hi-rail vehicle or a geometry inspection car can generate large volumes of high resolution images. Extracting accurate information from those images has been challenging due to background clutter in railroad environments. In this paper, we describe a novel approach to visual track inspection using material classification and semantic segmentation with Deep Convolutional Neural Networks (DCNN). We show that DCNNs trained end-to-end for material classification are more accurate than shallow learning machines with hand-engineered features and are more robust to noise. Our approach results in a material classification accuracy of 93.35% using 10 classes of materials. This allows for the detection of crumbling and chipped tie conditions at detection rates of 86.06% and 92.11%, respectively, at a false positive rate of 10 FP/mile on the 85-mile Northeast Corridor (NEC) 2012-2013 concrete tie dataset.

Tracking by detection is a common approach to solving the Multiple Object Tracking problem. In this paper we show how deep metric learning can be used to improve three aspects of tracking by detection. We train a convolutional neural network to learn an embedding function in a Siamese configuration on a large person re-identification dataset offline. It is then used to improve the online performance of tracking while retaining a high frame rate. We use this learned appearance metric to robustly build estimates of pedestrian’s trajectories in the MOT16 dataset. In breaking with the tracking by detection model, we use our appearance metric to propose detections using the predicted state of a tracklet as a prior in the case where the detector fails. This method achieves competitive results in evaluation, especially among online, realtime approaches. We present an ablative study showing the impact of each of the three uses of our deep appearance metric.

This paper presents a content-adaptively sparse reconstruction method for abnormal events detection by exploiting the low-rank property of video sequences. In dictionary learning phase, the bases which describe more important characteristics of the normal behavior patterns are assigned with lower reconstruction costs. Based on the low-rank property of the bases captured by the low-rank approximation, a weighted sparse reconstruction method is proposed to measure the abnormality of testing samples. Multiscale 3-D gradient features, which encode the spatiotemporal information, are adopted as the low level descriptors. The benefits of the proposed method are threefold: first, the low-rank property is utilized to learn the underlying normal dictionaries, which can represent groups of similar normal features effectively; second, the sparsity-based algorithm can adaptively determine the number of dictionary bases, which makes it a preferable choice for representing the dynamic scene semantics; and third, based on the weighted sparse reconstruction method, the proposed method is more efficient for detecting the abnormal events. Experimental results on the public datasets have shown that the proposed method yields competitive performance comparing with the state-of-the-art methods.

In recent years, image forensics has attracted more and more attention, and many forensic methods have been proposed for identifying image processing operations. Up to now, most existing methods are based on hand crafted features, and just one specific operation is considered in their methods. In many forensic scenarios, however, multiple classification for various image processing operations is more practical. Besides, it is difficult to obtain effective features by hand for some image processing operations. In this paper, therefore, we propose a new convolutional neural network (CNN) based method to adaptively learn discriminative features for identifying typical image processing operations. We carefully design the high pass filter bank to get the image residuals of the input image, the channel expansion layer to mix up the resulting residuals, the pooling layers, and the activation functions employed in our method. The extensive results show that the proposed method can outperform the currently best method based on hand crafted features and three related methods based on CNN for image steganalysis and/or forensics, achieving the state-of-the-art results. Furthermore, we provide more supplementary results to show the rationality and robustness of the proposed model.

Robust scene understanding of outdoor environments using passive optical sensors is a onerous and essential task for autonomous navigation. The problem is heavily characterized by changing environmental conditions throughout the day and across seasons. Robots should be equipped with models that are impervious to these factors in order to be operable and more importantly to ensure safety in the real-world. In this paper, we propose a novel semantic segmentation architecture and the convoluted mixture of deep experts (CMoDE) fusion technique that enables a multi-stream deep neural network to learn features from complementary modalities and spectra, each of which are specialized in a subset of the input space. Our model adaptively weighs class-specific features of expert networks based on the scene condition and further learns fused representations to yield robust segmentation. We present results from experimentation on three publicly available datasets that contain diverse conditions including rain, summer, winter, dusk, fall, night and sunset, and show that our approach exceeds the state-of-the-art. In addition, we evaluate the performance of autonomously traversing several kilometres of a forested environment using only the segmentation for perception.

Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs). We show how to improve semantic segmentation through the use of contextual information, specifically, we explore 'patch-patch' context between image regions, and 'patch-background' context. For learning from the patch-patch context, we formulate Conditional Random Fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image input and sliding pyramid pooling is effective for improving performance. Our experimental results set new state-of-the-art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL-Context, and SIFT-flow. In particular, we achieve an intersection-overunion score of 78:0 on the challenging PASCAL VOC 2012 dataset.


Question: An IoT-driven self-aware irrigation system utilizing Raspberry Pi.

An IoT-Driven Autonomous Sensory Irrigation System Utilizing Raspberry Pi

In an effort to revolutionize agricultural practices, particularly in India, an innovative, Internet of Things (IoT) based irrigation system has been conceptualized, harnessing the capabilities of Raspberry Pi. This system aims to significantly enhance the productivity and quality of agricultural produce by automating the irrigation process. Key features include real-time monitoring and notification to farmers via an on-board Liquid Crystal Display (LCD) and SMS alerts to their mobile numbers, ensuring timely and informed decision-making. This design is particularly advantageous for farmers grappling with irregular power supply, as it maintains a consistent water supply, even under challenging conditions. The integration of a SIM900 Module enables farmers to stay updated with system activities, thus fostering a high level of system transparency. This device holds the potential to be a game-changer for Indian agriculture, offering an affordable solution to farmers. Moreover, it significantly reduces human labor, presenting a low-cost, high-impact system with significant social implications. The proposed design not only supports sustainable farming practices but also addresses the pressing need for efficient water management in agriculture.

Water is nature's most precious resource and growing demand is pushing fresh water supplies to the brink of non-renewability. New technological and social initiatives that enhance conservation and reduce waste are needed. Providing consumers with fine-grained real-time information has yielded benefits in conservation of power and gasoline. Extending this philosophy to water conservation, we introduce a novel water monitoring system, NAWMS, that similarly empowers users.
 The goal of our work is to furnish users with an easy-to-install self-calibrating system that provides information on when, where, and how much water they are using. The system uses wireless vibration sensors attached to pipes and, thus, neither plumbing nor special expertise is necessary for its installation. By implementing a non-intrusive, autonomous, and adaptive system using commodity hardware, we believe it is cost-effective and widely deployable.
 NAWMS makes use of the existing household water flow meter, which is considered accurate, but lacks spatial granularity, and adds vibration sensors on individual water pipes to estimate the water flow to each individual outlet. Compensating for manufacturing, installation, and material variabilities requires calibration of these low cost sensors to achieve a reasonable level of accuracy. We have devised an adaptive auto-calibration procedure, which attempts to solve a two phase linear programming and mixed linear geometric programming problem.
 We show through experiments on a three pipe testbed that such a system is indeed feasible and adapts well to minimize error in the water usage estimate. We report an accuracy, over likely domestic flow-rate scenarios, with long-term stability and a mean absolute error of 7%.

Along with acceleration of various countries' digitization and informationization, according to the 12th 5-year development plan, the internet of things (IOT) will become one of Chinese emerging strategic industries. Based on the analysis of IOT's basic concept and key technology, this paper studies IOT's application in "Digital Agriculture" construction. The main contents include: IOT's application in agricultural production, IOT's application in logistics and distribution of agricultural products, and IOT's application in agricultural products safety traceability.

The Internet of Things (IoT) is an evolving global trend in Web-based information architecture aiding in the exchange of services and goods over a network without necessitating human-to-human or human-to-computer interaction. It has the potential to revolutionize physical world interaction of individuals and the organizations. The application of IoT can be recognized significantly in many areas such as in healthcare, resource management, learning, knowledge processing, and many more. The practical realization of IoT is met with a plethora of security and privacy challenges that need to be tackled for IoT’s successful deployment on a commercially viable large scale. This paper analyzes the security issues related to IoT networks through an analysis of the existing empirical researches to get an insight on the security requirements of the IoT networks. The findings of the study revealed that security threats are one of the biggest and ever-growing challenges for IoT, and it is essential to substantially mitigate them for the success of this platform.

The Internet of Things (IoT) is revolutionizing the Internet of the future and the way smart devices, people, and processes interact with each other. Challenges in endpoint devices (EDs) communication are due not only to the security and privacy-related requirements but also due to the ever-growing amount of data transferred over the network that needs to be handled, which can considerably reduce the system availability to perform other tasks. This paper presents an IPv6 over low power wireless personal area networks (6LoWPAN) accelerator for the IP-based IoT networks, which is able to process and filter IPv6 packets received by an IEEE 802.15.4 radio transceiver without interrupting the microcontroller. It offers nearly 13.24% of performance overhead reduction for the packet processing and the address filtering tasks, while guaranteeing full system availability when unwanted packets are received and discarded. The contribution is meant to be deployed on heterogeneous EDs at the network edge, which include on the same architecture, field-programmable gate array (FPGA) technology beside a microcontroller and an IEEE 802.15.4-compliant radio transceiver. Performed evaluations include the low FPGA hardware resources utilization and the energy consumption analysis when the processing and accepting/rejection of a received packet is performed by the 6LoWPAN accelerator.

This article presents a methodology based on design thinking and user experience design methods for creating what we call `people-aware' IoT applications, where user needs, not technological opportunities, drive the development. This methodology is divided into 7 steps: discovery, capturing, research, design, prototype, evaluate and refine. The tools used include conventional user experience procedures such as problem identification, group brainstorming, surveys, or interviews, mixed with more IoT-specific design specificities. The results of the methodology include well-described and user-oriented scenarios meeting user's needs and also a complete toolbox to assist the implementation and the testing of abovementioned scenarios in an IoT perspective. The article describes the methodology in detail with the help of a use case conducted in a business environment available for the project that leads to the identification and partial design of concrete people-aware IoT applications in the context of a smart meeting room.

The widespread use of Internet of Things (IoT), especially smart wearables, will play an important role in improving the quality of medical care, bringing convenience for patients and improving the management level of hospitals. However, due to the limitation of communication protocols, there exists non unified architecture that can connect all intelligent things in smart hospitals, which is made possible by the emergence of the Narrowband IoT (NB-IoT). In light of this, we propose an architecture to connect intelligent things in smart hospitals based on NB-IoT, and introduce edge computing to deal with the requirement of latency in medical process. As a case study, we develop an infusion monitoring system to monitor the real-time drop rate and the volume of remaining drug during the intravenous infusion. Finally, we discuss the challenges and future directions for building a smart hospital by connecting intelligent things.

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.

With the emergence of Internet-of-Things (IoT), there is now growing interest to simplify wireless network controls. This is a very challenging task, comprising information acquisition, information analysis, decision-making, and action implementation on large scale IoT networks. Resulting in research to explore the integration of software-defined networking (SDN) and IoT for a simpler, easier, and strain less network control. SDN is a promising novel paradigm shift which has the capability to enable a simplified and robust programmable wireless network serving an array of physical objects and applications. This paper starts with the emergence of SDN and then highlights recent significant developments in the wireless and optical domains with the aim of integrating SDN and IoT. Challenges in SDN and IoT integration are also discussed from both security and scalability perspectives.

The Internet of Things (IoT) envisions pervasive, connected, and smart nodes interacting autonomously while offering all sorts of services. Wide distribution, openness and relatively high processing power of IoT objects made them an ideal target for cyber attacks. Moreover, as many of IoT nodes are collecting and processing private information, they are becoming a goldmine of data for malicious actors. Therefore, security and specifically the ability to detect compromised nodes, together with collecting and preserving evidences of an attack or malicious activities emerge as a priority in successful deployment of IoT networks. In this paper, we first introduce existing major security and forensics challenges within IoT domain and then briefly discuss about papers published in this special issue targeting identified challenges.

Falls can cause serious traumas such as brain injuries and bone fractures, especially among elderly people. Fear of falling might reduce physical activities resulting in declining social interactions and eventually causing depression. To lessen the effects of a fall, timely delivery of medical treatment can play a vital role. In a similar scenario, an IoT-based wearable system can pave the most promising way to mitigate serious consequences of a fall while providing the convenience of usage. However, to deliver sufficient degree of monitoring and reliability, wearable devices working at the core of fall detection systems are required to work for a prolonged period of time. In this work, we focus on energy efficiency of a wearable sensor node in an Internet-of-Things (IoT) based fall detection system. We propose the design of a tiny, lightweight, flexible and energy efficient wearable device. We investigate different parameters (e.g. sampling rate, communication bus interface, transmission protocol, and transmission rate) impacting on energy consumption of the wearable device. In addition, we provide a comprehensive analysis of energy consumption of the wearable in different configurations and operating conditions. Furthermore, we provide hints (hardware and software) for system designers implementing the optimal wearable device for IoT-based fall detection systems in terms of energy efficiency and high quality of service. The results clearly indicate that the proposed sensor node is novel and energy efficient. In a critical condition, the wearable device can be used continuously for 76 h with a 1000 mAh li-ion battery.


Question: An IoT-driven self-aware irrigation system utilizing Raspberry Pi.

This paper introduces an advanced, IoT-based percipient irrigation system, designed for autonomous operation using a Raspberry Pi as the central control unit. The system integrates fuzzy logic and mobile technology to achieve optimized water and fertilizer application for agricultural crops. A network of wireless sensors, including temperature and soil moisture sensors, is deployed to gather real-time environmental data. This data is transmitted wirelessly to a central hub, where it is processed using fuzzy logic algorithms to determine the precise duration for which irrigation valves should remain open. The Raspberry Pi, powered by solar photovoltaic cells, not only processes the sensor data but also facilitates remote monitoring and control via a cellular communication link. Users can send text messages to the system to schedule and adjust irrigation times, enabling precise control over water usage. The system's ability to accurately calculate the water demand for crops provides a scientific foundation for water-saving irrigation practices and ensures optimal fertilizer application, thereby enhancing agricultural efficiency and sustainability.

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.

A Wireless sensor network is designed and implemented to automatically and remotely monitor and control the water quality parameters such as temperature, turbidity, pH and dissolved oxygen required for pisiculture. The measurements of the system which is at base station are communicated to the remote farmer through built-in Wi-Fi of NI-myRIO and displayed on user’s computer screen from where he can monitor and change the settings of the system. An audio output is also produced from speakers of the computer. SMS alert is sent to farmer’s mobile for variations in the monitored parameters of the system at the base station via GSM modem. The programming of the entire system is done using LabVIEW software and NI myRIO which has inbuilt processor, FPGA, Wi-Fi and web servicing capabilities. Web publishing tool of LabVIEW allows internet access to the front panel of the system to any remote user.

Internet of things (IoT) is nowadays one of the fastest growing technologies for both private and business purposes. Due to a big number of IoT devices and their rapid introduction to the market, security of things and their services is often not at the expected level. Recently, machine learning algorithms, techniques, and methods are used in research papers to enhance IoT security. In this paper, we systematically review the state-of-the art to classify the research on machine learning for the IoT security. We analysed the primary studies, identify types of studies and publication fora. Next, we have extracted all machine learning algorithms and techniques described in primary studies, and identified the most used ones to tackle IoT security issues. We classify the research into three main categories (intrusion detection, authentication and other) and describe the primary studies in detail to analyse existing relevant works and propose topics for future research.

Over the last few years, the convincing forward steps in the development of Internet of Things (IoT)-enabling solutions are spurring the advent of novel and fascinating applications. Among others, mainly radio frequency identification (RFID), wireless sensor network (WSN), and smart mobile technologies are leading this evolutionary trend. In the wake of this tendency, this paper proposes a novel, IoT-aware, smart architecture for automatic monitoring and tracking of patients, personnel, and biomedical devices within hospitals and nursing institutes. Staying true to the IoT vision, we propose a smart hospital system (SHS), which relies on different, yet complementary, technologies, specifically RFID, WSN, and smart mobile, interoperating with each other through a Constrained Application Protocol (CoAP)/IPv6 over low-power wireless personal area network (6LoWPAN)/representational state transfer (REST) network infrastructure. The SHS is able to collect, in real time, both environmental conditions and patients' physiological parameters via an ultra-low-power hybrid sensing network (HSN) composed of 6LoWPAN nodes integrating UHF RFID functionalities. Sensed data are delivered to a control center where an advanced monitoring application (MA) makes them easily accessible by both local and remote users via a REST web service. The simple proof of concept implemented to validate the proposed SHS has highlighted a number of key capabilities and aspects of novelty, which represent a significant step forward compared to the actual state of the art.

In the past couple of decades, there is rapid growth in terms of technology in the field of agriculture. Different monitoring and controlled systems are installed in order to increase the yield. The yield rate may deceases due to numerous factors. Disease is one of the key factors that cause the degradation of yield. So the developed monitoring system mainly focuses on predicting the start of germination of the disease. Sensor module is used to detect different environmental condition across the farm and the sensed data is displayed on Liquid crystal display using microcontroller. Microcontroller wirelessly transmits different environment conditions across the farm to central unit where data is stored, and analysed. Central unit checks the present data with disease condition and if matches then it commands microcontroller to operate relay. Sensor module is tested for different temperature range and it is found that there are little variations in recorded values. Wireless data transfer is tested with the introduction of various obstacles like wall, metal body, magnet, etc. and it is found that same data is transferred to central unit but with some amount of delay in it. The developed system nearly predicts the start of germination of disease.

The Internet of Things (IoT) has inspired solutions that are already available for addressing problems in various application scenarios, such as healthcare, security, emergency support and tourism. However, there is no clear approach to modeling these systems and envisioning their capabilities at the design time. Therefore, the process of designing these systems is ad hoc and its real impact is evaluated once the solution is already implemented, which is risky and expensive. This paper proposes a modeling approach that uses human-centric wireless sensor networks to specify and evaluate models of IoT-based systems at the time of design, avoiding the need to spend time and effort on early implementations of immature designs. It allows designers to focus on the system design, leaving the implementation decisions for a next phase. The article illustrates the usefulness of this proposal through a running example, showing the design of an IoT-based solution to support the first responses during medium-sized or large urban incidents. The case study used in the proposal evaluation is based on a real train crash. The proposed modeling approach can be used to design IoT-based systems for other application scenarios, e.g., to support security operatives or monitor chronic patients in their homes.

Parking the car is one of the difficult task that we are facing in our day to day life. The main issue is providing the sufficient parking system. Now a days it is very hard to find the availability of parking slots. The various places(public) that is shopping mall, cinema hall etc finds it difficult to search the available parking area. This calls for the situations of an Smart car parking system which is based on IoT and commanded by Android application which are equipped with IR sensors and microcontroller (arduinouno).In this paper a small prototype of smart car parking system which is based on IoT is implemented. The paper proposed a system that the user will automatically find the parking space throught an android application via server. In addition to this we can say that the its a new way of communication between humans and the things with the help of new technology based on IoT.

Internet of Things is the integration of a variety of technologies. The Internet of Things incorporates transparently and impeccably large number of assorted end systems, providing open access to selected data for digital services. Internet of things is a promising research in commerce, industry, and education applications. The abundance of sensors and actuators motivates sensing and actuate devices in communication scenarios thus enabling sharing of information in Internet of Things. Advances in sensor data collection technology and Radio Frequency Identification technology has led large number of smart devices connected to the Internet, continuously transmitting data over time.In the context of security, due to different communication overloads and standards conventional security services are not applicable on Internet of Things as a result of which the technological loopholes leads to the generation of malicious data, devices are compromised and so on. Hence a flexible mechanism can deal with the security threats in the dynamic environment of Internet of Things and continuous researches and new ideas needs to be regulated periodically for various upcoming challenges. This paper basically tries to cover up the security issues and challenges of Internet of Things along with a

The Internet of Things (IoT) has connected an incredible diversity of devices in novel ways, which has enabled exciting new services and opportunities. Unfortunately, IoT systems also present several important challenges to developers. This paper proposes a vision for how we may build IoT systems in the future by reconceiving IoT's fundamental unit of construction not as a "thing", but rather as a widely and finely distributed "microservice" already familiar to web service engineering circles. Since IoT systems are quite different from more established uses of microservice architectures, success of the approach depends on adaptations that enable them to met the key challenges that IoT systems present. We argue that a microservice approach to building IoT systems can combine in a mutually enforcing way with patterns for microservices, API gateways, distribution of services, uniform service discovery, containers, and access control. The approach is illustrated using two case studies of IoT systems in personal health management and connected autonomous vehicles. Our hope is that the vision of a microservices approach will help focus research that can fill in current gaps preventing more effective, interoperable, and secure IoT services and solutions in a wide variety of contexts.

The need for real-time/reliable/low-maintenance distributed monitoring systems, e.g., wireless sensor networks, has been becoming more and more evident in many applications in the environmental, agro-alimentary, medical, and industrial fields. The growing interest in technologies related to sensors is an important indicator of these new needs. The design and the realization of complex and/or distributed monitoring systems is often difficult due to the multitude of different electronic interfaces presented by the sensors available on the market. To address these issues the authors propose the concept of a Universal Intelligent Sensor Interface (UISI), a new low-cost system based on a single commercial chip able to convert a generic transducer into an intelligent sensor with multiple standardized interfaces. The device presented offers a flexible analog and/or digital front-end, able to interface different transducer typologies (such as conditioned, unconditioned, resistive, current output, capacitive and digital transducers). The device also provides enhanced processing and storage capabilities, as well as a configurable multi-standard output interface (including plug-and-play interface based on IEEE 1451.3). In this work the general concept of UISI and the design of reconfigurable hardware are presented, together with experimental test results validating the proposed device.


Question: An IoT-driven self-aware irrigation system utilizing Raspberry Pi.

This document outlines the development and implementation of a cutting-edge, Internet of Things (IoT)-based percipient irrigation system, utilizing a Raspberry Pi as the central control unit. The system is designed to optimize water usage in agricultural settings, particularly in regions characterized by water scarcity. By employing a distributed wireless sensor network, the system achieves site-specific irrigation management, enabling farmers to enhance crop productivity while conserving water resources.

The architecture of the system involves the strategic placement of multiple sensor stations across the field, determined by a soil property map. These stations continuously monitor field conditions, including moisture levels, temperature, and other relevant parameters. The collected data is wirelessly transmitted to a base station, where it is processed and analyzed to determine the optimal irrigation schedule and water distribution.

To facilitate real-time control and monitoring, the irrigation system is equipped with a programmable logic controller (PLC) that receives georeferenced location data from a differential Global Positioning System (GPS). This information is crucial for adjusting the irrigation rates according to the specific needs of different areas within the field. The Raspberry Pi, acting as the system's brain, communicates with the PLC and the wireless sensor network, ensuring seamless operation and data exchange.

The system's software interface, developed using graphic user interface (GUI) technology, provides farmers with an intuitive platform to access real-time field conditions and control the irrigation system remotely. This capability significantly enhances the system's flexibility and responsiveness, allowing for immediate adjustments in response to changing environmental conditions.

Overall, the IoT-based percipient irrigation system using a Raspberry Pi represents a significant advancement in precision agriculture. By integrating wireless sensor networks, real-time data processing, and remote control capabilities, the system offers a scalable and sustainable solution for optimizing water usage and enhancing crop yields in arid and semiarid regions.

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.

-Today we are living in 21 century where automation is playing important role in human life. Home automation allows us to control household appliances like light, door, fan, AC etc. It also provides home security and emergency system to be activated. Home automation not only refers to reduce human efforts but also energy saving and time efficiency. The main objective of home automation and security is to help handicapped and old aged people who will enable them to control home appliances and alert them in critical situations. This paper put forwards the design of home automation and security system using ARM7 LPC2148 board. The design is based on a standalone embedded system board ARM7 LPC2148 at home. Home appliances are connected to the ARM7 and communication is established between the ARM7 and ARM9 with Bluetooth device. The home appliances are connected to the input / output ports of the embedded system board and their status is passed to the ARM7. We would develop an authentication to the system for authorized person to access home appliances. The device with low cost and scalable to less modification to the core is much important. It presents the design and implementation of automation system that can monitor and control home appliances via ARM9 S3C2440A board.

Internet of Things (IoT) has recently received a great attention due to its potential and capacity to be integrated into any complex system. As a result of rapid development of sensing technologies such as radio-frequency identification, sensors and the convergence of information technologies such as wireless communication and Internet, IoT is emerging as an important technology for monitoring systems. This paper reviews and introduces a framework for structural health monitoring (SHM) using IoT technologies on intelligent and reliable monitoring. Specifically, technologies involved in IoT and SHM system implementation as well as data routing strategy in IoT environment are presented. As the amount of data generated by sensing devices are voluminous and faster than ever, big data solutions are introduced to deal with the complex and large amount of data collected from sensors installed on structures.

To promote smart campus, National Chiao Tung University is deploying several location-based IoT applications based on wireless communications technologies including LoRa and NB-IoT. The applications are indoor/outdoor environment conditions monitoring (such as PM2.5, temperature, and humidity), emergency buttons, and so on. This paper uses PM2.5 measurement application as an example to present the performance of LoRa when it is used on campus. We investigated the LoRa transmission performance from LoRa end-devices to the LoRa gateway. We show how packet losses were affected by the distance between the end-device and the gateway, the transmit power, the payload length, the antenna angle, the time of day, and the weather conditions. The pattern of LoRa packet losses were also measured and we found that in our long-term PM2.5 monitoring application, more than 99\% of LoRa packet losses occurred with three or less consecutive packet losses.

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.

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 growing interest in the Internet of Things (IoT) has resulted in a number of wide-area deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multi-hop ad-hoc and MANET routing protocols, they all must be effectively integrated to create a seamless communication platform. Managing these open, geographically distributed, and heterogeneous networking infrastructures, especially in dynamic environments, is a key technical challenge. In order to take full advantage of the many opportunities they provide, techniques to concurrently provision the different classes of IoT traffic across a common set of sensors and networking resources must be designed. In this paper, we will design a software-defined approach for the IoT environment to dynamically achieve differentiated quality levels to different IoT tasks in very heterogeneous wireless networking scenarios. For this, we extend the Multinetwork INformation Architecture (MINA), a reflective (self-observing and adapting via an embodied Observe-Analyze-Adapt loop) middleware with a layered IoT SDN controller. The developed IoT SDN controller originally i) incorporates and supports commands to differentiate flow scheduling over task-level, multi-hop, and heterogeneous ad-hoc paths and ii) exploits Network Calculus and Genetic Algorithms to optimize the usage of currently available IoT network opportunities. We have applied the extended MINA SDN prototype in the challenging IoT scenario of wide-scale integration of electric vehicles, electric charging sites, smart grid infrastructures, and a wide set of pilot users, as targeted by the Artemis Internet of Energy and Arrowhead projects. Preliminary simulation performance results indicate that our approach and the extended MINA system can support efficient exploitation of the IoT multinetwork capabilities.

The promising potential of the emerging Internet of Things (IoT) technologies for interconnected medical devices and sensors has played an important role in the next-generation healthcare industry for quality patient care. Because of the increasing number of elderly and disabled people, there is an urgent need for a real-time health monitoring infrastructure for analyzing patients’ healthcare data to avoid preventable deaths. Healthcare Industrial IoT (HealthIIoT) has significant potential for the realization of such monitoring. HealthIIoT is a combination of communication technologies, interconnected apps, Things (devices and sensors), and people that would function together as one smart system to monitor, track, and store patients’ healthcare information for ongoing care. This paper presents a HealthIIoT-enabled monitoring framework, where ECG and other healthcare data are collected by mobile devices and sensors and securely sent to the cloud for seamless access by healthcare professionals. Signal enhancement, watermarking, and other related analytics will be used to avoid identity theft or clinical error by healthcare professionals. The suitability of this approach has been validated through both experimental evaluation, and simulation by deploying an IoT-driven ECG-based health monitoring service in the cloud. © 2016 Elsevier B.V. All rights reserved.

The Internet of Things (IoT) is able to connect billions of devices and services at anytime in any place, with various applications. Recently, the IoT became an emerging technology. One of the most significant current research discussion topics on the IoT is about the smart car parking. A modern urban city has over a million of cars on its roads but it does not have enough parking space. Moreover, most of the contemporary researchers propose management of the data on cloud. However, this method may be considered as an issue since the raw data is sent promptly from distributed sensors to the parking area via cloud and then received back after it is processed. This is considered as an expensive technique in terms of the data transmission as well as the energy cost and consumption. While the majority of proposed solutions address the problem of finding unoccupied parking space and ignore some other critical issues such as information about the nearest car parking and the roads traffic congestion, this paper goes beyond and proposes the alternative method. The paper proposes a smart car parking system that will assist users to solve the issue of finding a parking space and to minimise the time spent in searching for the nearest available car park. In addition, it provides users with roads traffic congestion status. Moreover, the proposed system collects the raw data locally and extracts features by applying data filtering and fusion techniques to reduce the transmitted data over the network. After that, the transformed data is sent to the cloud for processing and evaluating by using machine learning algorithms.

The rapid growth in population in India causes more crowding at public bus stops. People long wait for arriving of buses and suddenly gather near bus when it arrives and travel in overcrowded buses on footboards which leads to accidents. Theft is another risk of overcrowding. All this happens due to lack of proper information of arriving of buses at many of the bus stops. To solve this problem in a cost effective way, we have developed an IoT based bus tracking system to show the current location of the bus and seat availability in the arriving buses. We have used RFID technology for tracking the bus and Thingspeak web server for displaying location of the bus and seat availability in android application in a smart phone. This system enables the commuters to know the exact location of the bus, and occupancy level in the bus, which enable the commuters to take decision, whether to board this bus or next. This reduces the waiting time and overcrowding at bus stops. It also reduces the risk of accidents and thefts.


Question: An IoT-driven self-aware irrigation system utilizing Raspberry Pi.

An IoT-Driven Autonomous Percipient Irrigation System Utilizing Raspberry Pi

This paper presents the conception and realization of an automated irrigation system, powered by a thermoelectric generator (TEG) functioning as a soil moisture sensor. The system incorporates a TEG integrated into dual heat exchangers, which measures the temperature difference between the atmosphere and the soil to gauge soil moisture content. The Raspberry Pi microcontroller, based on the TEG's output, governs the irrigation process autonomously. This system's intelligence lies in its ability to adjust irrigation according to the soil's moisture requirements, thus promoting water conservation and efficiency over traditional manual systems. Moreover, it enhances plant growth by ensuring optimal watering at the appropriate times. The system's design underscores the potential of IoT in precision agriculture, offering a sustainable solution to irrigation management.

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to “improve” their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.

Solid waste management is one of the most important challenges in urban areas throughout the world and it is becoming a critical issue in developing countries where a rapid increase in population has been observed. Waste collection is a complex process that requires the use of large amount of money and an elaborate management of logistics. In this paper an approch to smart waste collection is proposed able to improve and optimize the handling of solid urban waste. Context of smart waste management requires interconnection among heterogeneous devices and data sharing involving a large amount of people. Smart-M3 platform solves these problems offering a high degree of decoupling and scalability.Waste collection is made by real-time monitoring the level of bin's fullness through sensors placed inside the containers. This method enables to exempt from collecting semi-empty bins. Furthermore, incoming data can be provided to decisional algorithms in order to determine the optimal number of waste vehicles or bins to distribute in the territory. The presented solution gives important advantages for both service providers and consumers. The formers could obtain a sensible cost reduction. On the other hand, users may benefit from a higher level of service quality. In order to make users feel closer to their community, they can interact with the system to be aware about the fulness state of the nearest bins. Finally, a mechanism for collecting “green points” was introduced for encouraging citizens to recycle.

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.

Healthcare is one of the most rapidly expanding application areas of the Internet of Things (IoT) technology. IoT devices can be used to enable remote health monitoring of patients with chronic diseases such as cardiovascular diseases (CVD). In this paper we develop an algorithm for ECG analysis and classification for heartbeat diagnosis, and implement it on an IoT-based embedded platform. This algorithm is our proposal for a wearable ECG diagnosis device, suitable for 24-hour continuous monitoring of the patient. We use Discrete Wavelet Transform (DWT) for the ECG analysis, and a Support Vector Machine (SVM) classifier. The best classification accuracy achieved is 98.9%, for a feature vector of size 18, and 2493 support vectors. Different implementations of the algorithm on the Galileo board, help demonstrate that the computational cost is such, that the ECG analysis and classification can be performed in real-time.

Next generation Smart Cities have the potential to enhance the citizens quality of life and to reduce the overall energy expenditure. In particular, emerging smart metering technologies promise to greatly increase energy efficiency in the residential and industrial areas. In this context, new power metering methods such as Non-Intrusive Load Monitoring (NILM) can provide important real-time information about the contribution of any single appliance. In this paper, we present a complete hardware-software design that concentrates locally an efficient event-based supervised NILM algorithm for load disaggregation. This new kind of power analysis, which usually requires high computing capability, is executed real-time on a low-cost and easy-to-install smart meter ready for the Internet of Things (IoT).

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.

Internet of Things (IoT) has recently received a great attention due to its potential and capacity to be integrated into any complex system. As a result of rapid development of sensing technologies such as radio-frequency identification, sensors and the convergence of information technologies such as wireless communication and Internet, IoT is emerging as an important technology for monitoring systems. This paper reviews and introduces a framework for structural health monitoring (SHM) using IoT technologies on intelligent and reliable monitoring. Specifically, technologies involved in IoT and SHM system implementation as well as data routing strategy in IoT environment are presented. As the amount of data generated by sensing devices are voluminous and faster than ever, big data solutions are introduced to deal with the complex and large amount of data collected from sensors installed on structures.

Nowadays, IoT services are typically delivered as physically isolated vertical solutions, in which all system components ranging from sensory devices to applications are customized and tightly coupled for the requirements of each specific project. The efficiency and scalability of such service delivery model are intrinsically limited, posing significant challenges to IoT solution providers. Therefore, we propose a novel PaaS framework that provides essential platform services for IoT solution providers to efficiently deliver and continuously extend their services. This paper first introduces the IoT PaaS architecture, on which IoT solutions can be delivered as virtual verticals by leveraging computing resources and middleware services on cloud. Then we present the detailed mechanism and implementation of domain mediation, which helps solution providers to efficiently provide domain-specific control applications. The proposed approaches are demonstrated through the implementation of a domain mediator for building management and two use cases using the mediator.

Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Interfacing an every object together through internet looks very difficult, but within a frame of time Internet of Things will drastically change our life. The enormous data captured by the Internet of Things (IoT) are considered of high business as well as social values and extracting hidden information from raw data, various data mining algorithm can be applied to IoT data. In this paper, We survey systematic review of various data mining models as well as its application in Internet of Thing (IoT) field along with its merits and demerits. At last, we discussed challenges in IoT.

Nowadays, researchers are paying increasing attention to embedding systems. Cost reduction has lead to an increase in the number of platforms supporting the operating system Linux, jointly with the Raspberry Pi motherboard. Thus, embedding devices on Raspberry-Linux systems is a goal in order to make competitive commercial products. This paper presents a low-cost fingerprint recognition system embedded into a Raspberry Pi with Linux.


Question: An IoT-driven self-aware irrigation system utilizing Raspberry Pi.

This paper presents an innovative wireless design for a low-cost irrigation system, specifically tailored for Jew's-ear cultivation in Lishui, Zhejiang, China, employing ZigBee technology. With a focus on labor and water conservation, the wireless approach ensures a straightforward installation and maintenance process, unlike traditional wired systems. The paper meticulously details the hardware configuration and software algorithms of the wireless sensor/actuator node and the portable controller, which respectively serve as the end device and coordinator within the ZigBee wireless sensor network. A comprehensive assessment of the system's performance is provided, showcasing its reliability and practicality through prolonged and efficient operation in the field. This study serves as a pioneering application of wireless sensor networks in irrigation management, offering a viable strategy for the development of extensive, remotely managed intelligent irrigation systems.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: An efficient hybrid whale optimization algorithm and pattern search technique for economic dispatch problem
In this paper, a hybrid algorithm combining whale optimization algorithm (WOA) and pattern search (PS) technique is proposed to solve economic dispatch problem. The proposed algorithm is used to solve the economic dispatch problem with valve-point effect, transmission loss, and prohibited operating zones. The hybrid algorithm is tested on three standard test systems and the obtained results are compared with other algorithms from the literature. The comparison shows that the proposed hybrid algorithm outperforms other algorithms in terms of solution quality and robustness.

**Similar Documents**: This paper introduces an innovative hybrid algorithm that merges the Whale Optimization Algorithm (WOA) with the Pattern Search (PS) technique to address the economic dispatch problem. The proposed hybrid approach is applied to tackle economic dispatch challenges, including valve-point effects, transmission losses, and prohibited operating zones. To evaluate its effectiveness, the hybrid algorithm is applied to three benchmark test systems, and the outcomes are contrasted with results from alternative algorithms in the literature. The comparative analysis reveals that the proposed hybrid algorithm surpasses competing methods in terms of solution quality and stability, demonstrating its superiority in solving complex economic dispatch problems.

**Question**: A novel feature selection method for high-dimensional data classification using swarm intelligence

**Documents**: A novel feature selection method based on the whale optimization algorithm for high-dimensional data classification
Feature selection is an important process in machine learning and data mining, especially for high-dimensional data classification. In this paper, a novel feature selection method based on the whale optimization algorithm (WOA) is proposed. The proposed method is used to select the most relevant features for high

Nowadays deep neural networks are widely used to accurately classify input data. An interesting application area is the Internet of Things (IoT), where a massive amount of sensor data has to be classified. The processing power of the cloud is attractive, however the variable latency imposes a major drawback for neural networks. In order to exploit the apparent trade-off between utilizing the available though limited embedded computing power of the IoT devices at high speed/stable latency and the seemingly unlimited computing power of Cloud computing at the cost of higher and variable latency, we propose a Big-Little architecture for deep neural networks. A small neural network trained to a subset of prioritized output classes can be used to calculate an output on the embedded devices, while a more specific classification can be calculated in the Cloud only when required. We show the applicability of this concept in the IoT domain by evaluating our approach for state of the art neural network classification problems on popular embedded devices such as the Raspberry Pi and Intel Edison.

Nowadays, IoT services are typically delivered as physically isolated vertical solutions, in which all system components ranging from sensory devices to applications are customized and tightly coupled for the requirements of each specific project. The efficiency and scalability of such service delivery model are intrinsically limited, posing significant challenges to IoT solution providers. Therefore, we propose a novel PaaS framework that provides essential platform services for IoT solution providers to efficiently deliver and continuously extend their services. This paper first introduces the IoT PaaS architecture, on which IoT solutions can be delivered as virtual verticals by leveraging computing resources and middleware services on cloud. Then we present the detailed mechanism and implementation of domain mediation, which helps solution providers to efficiently provide domain-specific control applications. The proposed approaches are demonstrated through the implementation of a domain mediator for building management and two use cases using the mediator.

In this paper we have developed a system for web based environment monitoring using the WSN Technology. WSN sensor nodes transmit data to the cloud-based database via Web API request. Measured data can be monitored by the user anywhere from internet by using the Web Application which one is also compatible for mobile phones. If the data measured by sensor node exceeds the configured value range in Web Application, Web Application sends a warning e-mail to users for improving environmental conditions.

Machine intelligence is a developing technology which has made its way to various fields of engineering and technology. Robots are slowly being implemented in the field of agriculture, very soon AgriBots are to take over the agricultural fields and be used for various difficult and tiresome tasks involving agriculture. They have become the inevitable future of agriculture. This paper proposes an idea that will help in effective cultivation of vast areas of land left uncultivated or barren. Numerous farmers are dying during hill farming mainly due to falling from heights, which can be reduced by this technological effort. The proposed work will help in cultivation in remote areas and increase green cover as well as help farmers in harsh environments. The Agricultural Aid to Seed Cultivation (AASC) robot will be an unmanned aerial vehicle equipped with a camera, a digital image processing unit and a seed cultivation unit. A quadcopter is chosen as an aerial vehicle is independent of the form and shape of the ground and is not deterred by these factors while providing high mobility and reliability. The research aims about the new technology which can be suitable for any kind of remote farming.

The Internet of Things (IoT) is an emerging classical model, envisioned as a system of billions of small interconnected devices for posing the state-of-the-art findings to real-world glitches. Over the last decade, there has been an increasing research concentration in the IoT as an essential design of the constant convergence between human behaviors and their images on Information Technology. With the development of technologies, the IoT drives the deployment of across-the-board and self-organizing wireless networks. The IoT model is progressing toward the notion of a cyber-physical world, where things can be originated, driven, intermixed, and modernized to facilitate the emergence of any feasible association. This paper provides a summary of the existing IoT research that underlines enabling technologies, such as fog computing, wireless sensor networks, data mining, context awareness, real-time analytics, virtual reality, and cellular communications. Also, we present the lessons learned after acquiring a thorough representation of the subject. Thus, by identifying numerous open research challenges, it is presumed to drag more consideration into this novel paradigm.

Over the last few years, the convincing forward steps in the development of Internet of Things (IoT)-enabling solutions are spurring the advent of novel and fascinating applications. Among others, mainly radio frequency identification (RFID), wireless sensor network (WSN), and smart mobile technologies are leading this evolutionary trend. In the wake of this tendency, this paper proposes a novel, IoT-aware, smart architecture for automatic monitoring and tracking of patients, personnel, and biomedical devices within hospitals and nursing institutes. Staying true to the IoT vision, we propose a smart hospital system (SHS), which relies on different, yet complementary, technologies, specifically RFID, WSN, and smart mobile, interoperating with each other through a Constrained Application Protocol (CoAP)/IPv6 over low-power wireless personal area network (6LoWPAN)/representational state transfer (REST) network infrastructure. The SHS is able to collect, in real time, both environmental conditions and patients' physiological parameters via an ultra-low-power hybrid sensing network (HSN) composed of 6LoWPAN nodes integrating UHF RFID functionalities. Sensed data are delivered to a control center where an advanced monitoring application (MA) makes them easily accessible by both local and remote users via a REST web service. The simple proof of concept implemented to validate the proposed SHS has highlighted a number of key capabilities and aspects of novelty, which represent a significant step forward compared to the actual state of the art.

Internet, a revolutionary invention, is always transforming into some new kind of hardware and software making it unavoidable for anyone. 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). 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.

This paper presents our field experience in data collection from remote sensors. By letting tractors, farmers, and sensors have short-range radio communication devices with delay-disruption tolerant networking (DTN), we can collect data from those sensors to our central database. Although, several implementations have been made with cellular phones or mesh networks in the past, DTN-based systems for such applications are still under explored. The main objective of this paper is to present our practical implementation and experiences in DTN-based data collection from remote sensors. The software, which we have developed for this research, has about 50 kbyte footprint, which is much smaller than any other DTN implementation. We carried out an experiment with 39 DTN nodes at the University of Tokyo assuming an agricultural scenario. They achieved 99.8% success rate for data gathering with moderate latency, showing sufficient usefulness in data granularity.

Along with acceleration of various countries' digitization and informationization, according to the 12th 5-year development plan, the internet of things (IOT) will become one of Chinese emerging strategic industries. Based on the analysis of IOT's basic concept and key technology, this paper studies IOT's application in "Digital Agriculture" construction. The main contents include: IOT's application in agricultural production, IOT's application in logistics and distribution of agricultural products, and IOT's application in agricultural products safety traceability.

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.