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

Decoder design and decoding models for joint source-network coding

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Universidade do Porto. Faculdade de Engenharia. 201

Routing Protocols for Large-Scale Wireless Sensor Networks: A Review

With the advances in micro-electronics, wireless sensor gadgets have been made substantially littler and more coordinated, and large-scale wireless sensor networks (WSNs) based the participation among the noteworthy measure of nodes have turned into a hotly debated issue. "Large-scale" implies for the most part large region or high thickness of a system. As needs be the routing protocols must scale well to the system scope augmentation and node thickness increments. A sensor node is regularly energy-constrained and can't be energized, and in this manner its energy utilization has a very critical impact on the adaptability of the protocol. To the best of our insight, at present the standard strategies to tackle the energy issue in large-scale WSNs are the various leveled routing protocols. In a progressive routing protocol, every one of the nodes are separated into a few gatherings with various task levels. The nodes inside the abnormal state are in charge of data aggregation and administration work, and the low level nodes for detecting their environment and gathering data. The progressive routing protocols are ended up being more energy-proficient than level ones in which every one of the nodes assume a similar part, particularly as far as the data aggregation and the flooding of the control bundles. With concentrate on the various leveled structure, in this paper we give an understanding into routing protocols planned particularly for large-scale WSNs. As per the distinctive goals, the protocols are by and large ordered in light of various criteria, for example, control overhead decrease, energy utilization mitigation and energy adjust. Keeping in mind the end goal to pick up a thorough comprehension of every protocol, we feature their imaginative thoughts, portray the basic standards in detail and break down their points of interest and hindrances. Also a correlation of each routing protocol is led to exhibit the contrasts between the protocols as far as message unpredictability, memory necessities, localization, data aggregation, bunching way and different measurements. At last some open issues in routing protocol plan in large-scale wireless sensor networks and conclusions are proposed