Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Optimization of WSN using Biological Inspired Self-Organized Secure Autonomous Routing Protocol

Since last three decade, Wireless Sensor Network is one of the biggest innovative technologies; it provides facility of heavy data traffic and management telecommunication by sensing, computation and communication into a small device. Main threat for this type of data transfer is data security in terms of maintains data integrity, high consumption of energy, end-to-end delay and high cost of nodes i.e. sensor. Handling all h issue at same time is the difficult task. SRTLD and BIOSARP are two routing protocol which helps in improving performance of the WSN. This paper is a detail description of secure architecture which is based on SRTLD and BIOSARP protocol. The main objective of this architecture is to provide high security by taking into account low energy consumption, low end-to-end delay and low node level cost. This mechanism uses concept of ACO (Ant Colony Optimization) which helps in achieving objective of the architectur

Enhanced Ant-Based Routing for Improving Performance of Wireless Sensor Network

Routing packets from the source node to the destination node in wireless sensor networks WSN is complicated due to the distributed and heterogeneous nature of sensor nodes. An ant colony system algorithm for packet routing in WSN that focuses on a pheromone update technique is proposed in this paper. The proposed algorithm will determine the best path to be used in the submission of packets while considering the capacity of each sensor node such as the remaining energy and distance to the destination node. Global pheromone update and local pheromone update are used in the proposed algorithm with the aim to distribute the packets fairly and to prevent the energy depletion of the sensor nodes. Performance of the proposed algorithm has outperformed three (3) other common algorithms in static WSN environment in terms of throughput, energy consumption and energy efficiency which will result to reduction of packet loss rate during packet routing and increase of network lifetime

Design of an intelligent waterway ambient infrastructure based on Multiagent Systems and Wireless Sensor Networks

Lately Maritime research areas have moved their interests from traditional ship studies and traffic systems to new areas that confer a more general character to them as, for example, environmental monitoring. BOYAS project is proposed including these new perspectives as well as more classical ones. Trying to get this integral character for the waterway ambient and its activities management, the confluence between two recent research areas is studied. The convergence of Multiagent Systems and Wireless Sensor Networks constitutes a good framework and scenario in which this new research activities may be studied and develop.Ministerio de Industria, Turismo y Comercio FIT-340000-2006-2

Multiagent Systems and Wireless Sensor Networks convergence for an intelligent waterway ambient

Texto completo accesible previo pago en: http://www.worldses.org/books/index.html Índice de las ponencias del congreso en: http://www.wseas.us/e-library/conferences/2008/malta/fb-mn/fb-mn00.pdfLately Maritime research areas have moved their interests to cover new areas conferring a more general character to them. BOYAS project is proposed including these new perspectives as well as the classical ones. Trying to get this integral character for the waterway ambient and its activities management, the convergence between Multiagent Systems and Wireless Sensor Networks is studied