On the Development of Distributed Estimation Techniques for Wireless Sensor Networks

Wireless sensor networks (WSNs) have lately witnessed tremendous demand, as evidenced by the increasing number of day-to-day applications. The sensor nodes aim at estimating the parameters of their corresponding adaptive filters to achieve the desired response for the event of interest. Some of the burning issues related to linear parameter estimation in WSNs have been addressed in this thesis mainly focusing on reduction of communication overhead and latency, and robustness to noise. The first issue deals with the high communication overhead and latency in distributed parameter estimation techniques such as diffusion least mean squares (DLMS) and incremental least mean squares (ILMS) algorithms. Subsequently the poor performance demonstrated by these distributed techniques in presence of impulsive noise has been dealt separately. The issue of source localization i.e. estimation of source bearing in WSNs, where the existing decentralized algorithms fail to perform satisfactorily, has been resolved in this thesis. Further the same issue has been dealt separately independent of nodal connectivity in WSNs. This thesis proposes two algorithms namely the block diffusion least mean squares (BDLMS) and block incremental least mean squares (BILMS) algorithms for reducing the communication overhead in WSNs. The theoretical and simulation studies demonstrate that BDLMS and BILMS algorithms provide the same performances as that of DLMS and ILMS, but with significant reduction in communication overheads per node. The latency also reduces by a factor as high as the block-size used in the proposed algorithms. With an aim to develop robustness towards impulsive noise, this thesis proposes three robust distributed algorithms i.e. saturation nonlinearity incremental LMS (SNILMS), saturation nonlinearity diffusion LMS (SNDLMS) and Wilcoxon norm diffusion LMS (WNDLMS) algorithms. The steady-state analysis of SNILMS algorithm is carried out based on spatial-temporal energy conservation principle. The theoretical and simulation results show that these algorithms are robust to impulsive noise. The SNDLMS algorithm is found to provide better performance than SNILMS and WNDLMS algorithms. In order to develop a distributed source localization technique, a novel diffusion maximum likelihood (ML) bearing estimation algorithm is proposed in this thesis which needs less communication overhead than the centralized algorithms. After forming a random array with its neighbours, each sensor node estimates the source bearing by optimizing the ML function locally using a diffusion particle swarm optimization algorithm. The simulation results show that the proposed algorithm performs better than the centralized multiple signal classification (MUSIC) algorithm in terms of probability of resolution and root mean square error. Further, in order to make the proposed algorithm independent of nodal connectivity, a distributed in-cluster bearing estimation technique is proposed. Each cluster of sensors estimates the source bearing by optimizing the ML function locally in cooperation with other clusters. The simulation results demonstrate improved performance of the proposed method in comparison to the centralized and decentralized MUSIC algorithms, and the distributed in-network algorith

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Biologically inspired, self organizing communication networks.

PhDThe problem of energy-efficient, reliable, accurate and self-organized target tracking in Wireless Sensor Networks (WSNs) is considered for sensor nodes with limited physical resources and abrupt manoeuvring mobile targets. A biologically inspired, adaptive multi-sensor scheme is proposed for collaborative Single Target Tracking (STT) and Multi-Target Tracking (MTT). Behavioural data obtained while tracking the targets including the targets’ previous locations is recorded as metadata to compute the target sampling interval, target importance and local monitoring interval so that tracking continuity and energy-efficiency are improved. The subsequent sensor groups that track the targets are selected proactively according to the information associated with the predicted target location probability such that the overall tracking performance is optimized or nearly-optimized. One sensor node from each of the selected groups is elected as a main node for management operations so that energy efficiency and load balancing are improved. A decision algorithm is proposed to allow the “conflict” nodes that are located in the sensing areas of more than one target at the same time to decide their preferred target according to the target importance and the distance to the target. A tracking recovery mechanism is developed to provide the tracking reliability in the event of target loss. The problem of task mapping and scheduling in WSNs is also considered. A Biological Independent Task Allocation (BITA) algorithm and a Biological Task Mapping and Scheduling (BTMS) algorithm are developed to execute an application using a group of sensor nodes. BITA, BTMS and the functional specialization of the sensor groups in target tracking are all inspired from biological behaviours of differentiation in zygote formation. Simulation results show that compared with other well-known schemes, the proposed tracking, task mapping and scheduling schemes can provide a significant improvement in energy-efficiency and computational time, whilst maintaining acceptable accuracy and seamless tracking, even with abrupt manoeuvring targets.Queen Mary university of London full Scholarshi

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Design and optimization of wireless sensor networks for localization and tracking

Knowledge of the position of nodes in a WSN is crucial in most wireless sensor network (WSN) applications. The gathered information needs to be associated with a particular location in a specific time instant in order to appropiately control de surveillance area. Moreover, WSNs may be used for tracking certain objects in monitoring applications, which also requires the incorporation of location information of the sensor nodes into the tracking algorithms. These requisites make localizacion and tracking two of the most important tasks of WSN. Despite of the large research efforts that have been made in this field, considerable technical challenges continue existing in subjects areas like data processing or communications. This thesis is mainly concerned with some of these technical problems. Specifically, we study three different challenges: sensor deployment, model independent localization and sensor selection. The first part of the work is focused on the task of sensor deployement. This is considered critical since it affects cost, detection, and localization accuracy of a WSN. There have been significant research efforts on deploying sensors from different points of view, e.g. connectivity or target detection. However, in the context of target localization, we believe it is more convenient to deploy the sensors in views of obtaining the best estimation possible on the target positioning. Therefore, in this work we suggest an analysis of the deployment from the standpoint of the error in the position estimation. To this end, we suggest the application of the modified Cram´er-Rao bound (MCRB) in a sensor network to perform a prior analysis of the system operation in the localization task. This analysis provides knowledge about the system behavior without a complete deployment. It also provides essential information to select fundamental parameters properly, like the number of sensors. To do so, a complete formulation of the modified information matrix (MFIM) and MCRB is developed for the most common measurement models, such as received signal strength (RSS), time-of-arrival (ToA) and angle-of-arrival (AoA). In addition, this formulation is extended for heterogeneous models that combine different measurement models. Simulation results demonstrate the utility of the proposed analysis and point out the similarity between MCRB and CRB. Secondly, we address the problem of target localization which encompasses many of the challenging issues which commonly arise in WSN. Consequently, many localization algorithms have been proposed in the literature each one oriented towards solving these issues. Nevertheless, it have seen tahta the localization performance of above methods usually relies heavily on the availability of accurate knowledge regarding the observation model. When errors in the measurement model are present, their target localization accuracy is degraded significantly. To overcome this problem, we proposed a novel localization algorithm to be used in applications where the measurement model is not accurate or incomplete. The independence of the algorithm from the model provides robustness and versatility. In order to do so, we apply radial basis functions (RBFs) interpolation to evaluate the measurement function in the entire surveillance area, and estimate the target position. In addition, we also propose the application of LASSO regression to compute the weigths of the RBFs and improve the generalization of the interpolated function. Simulation results have demonstrated the good performance of the proposed algorithm in the localization of single or multiples targets. Finally, we study the sensor selection problem. In order to prolong the network lifetime, sensors alternate their state between active and idle. The decision of which sensor should be activated is based on a variety of factors depending on the algorithm or the sensor application. Therefore, here we investigate the centralized selection of sensors in target-tracking applications over huge networks where a large number of randomly placed sensors are available for taking measurements. Specifically, we focus on the application of optimization algorithms for the selection of sensors using a variant of the CRB, the Posterior CRB (PCRB), as the performance-based optimization criteria. This bound provides the performance limit on the mean square error (MSE) for any unbiased estimator of a random parameter, and is iteratively computed by a particle filter (in our case, by a Rao-Blackwellized Particle Filter). In this work we analyze, and compare, three optimization algorithms: a genetic algorithm (GA), the particle swarm optimization (PSO), and a new discrete-variant of the cuckoo search (CS) algorithm. In addition, we propose a local-search versions of the previous optimization algorithms that provide a significant reduction of the computation time. Lastly, simulation results demonstrate the utility of these optmization algorithm to solve a sensor selection problem and point out the reduction of the computation time when local search is applied. ---------------------------------------------------Las redes de sensores se presentan como una tecnología muy interesante que ha atraído considerable interés por parte de los investigadores en la actualidad [1, 109]. Recientes avances en electrónica y en comunicaciones inalámbricas han permitido de desarrollo de sensores de bajo coste, baja potencia y multiples funciones, de reducido tamaño y con capacidades de comunicación a cortas distancias. Estos sensores, desplegados en gran número y unidos a través de comunicaciones inalámbricas, proporcionan grandes oportunidades en aplicaciones como la monitorización y el control de casas, ciudades o el medio ambiente. Un nodo sensor es un dispositivo de baja potencia capaz de interactuar con el medio a través de sus sensores, procesar información localmente y comunicar dicha información a tus vecinos más próximos. En el mercado existe una gran variedad de sensores (magnéticos, acústicos, térmicos, etc), lo que permite monitorizar muy diversas condiciones ambientales (temperatura, humedad, etc.) [25]. En consecuencia, las redes de sensores presentan un amplio rango de aplicaciones: seguridad en el hogar, monitorización del medio, análisis y predicción de condiciones climáticas, biomedicina [79], etc. A diferencia de las redes convencionales, las redes de sensores sus propias limitaciones, como la cantidad de energía disponible, el corto alcance de sus comunicaciones, su bajo ancho de band y sus limitaciones en el procesado de información y el almacenamiento de la misma. Por otro parte, existen limitaciones en el diseño que dependerán directamente de la aplicación que se le quiera dar a la red, como por ejemplo el tamaño de la red, el esquema de despliegue o la topología de la red..........Presidente: Jesús Cid Sueiro; Vocal: Mónica F. Bugallo; Secretario: Sancho Salcedo San

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society