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

EMMON - EMbedded MONitoring

Despite the steady increase in experimental deployments, most of research work on WSNs has focused only on communication protocols and algorithms, with a clear lack of effective, feasible and usable system architectures, integrated in a modular platform able to address both functional and non–functional requirements. In this paper, we outline EMMON [1], a full WSN-based system architecture for large–scale, dense and real–time embedded monitoring [3] applications. EMMON provides a hierarchical communication architecture together with integrated middleware and command and control software. Then, EM-Set, the EMMON engineering toolset will be presented. EM-Set includes a network deployment planning, worst–case analysis and dimensioning, protocol simulation and automatic remote programming and hardware testing tools. This toolset was crucial for the development of EMMON which was designed to use standard commercially available technologies, while maintaining as much flexibility as possible to meet specific applications requirements. Finally, the EMMON architecture has been validated through extensive simulation and experimental evaluation, including a 300+ nodes testbed

Improving practical sensitivity of energy optimized wake-up receivers: proof of concept in 65nm CMOS

We present a high performance low-power digital base-band architecture, specially designed for an energy optimized duty-cycled wake-up receiver scheme. Based on a careful wake-up beacon design, a structured wake-up beacon detection technique leads to an architecture that compensates for the implementation loss of a low-power wake-up receiver front-end at low energy and area costs. Design parameters are selected by energy optimization and the architecture is easily scalable to support various network sizes. Fabricated in 65nm CMOS, the digital base-band consumes 0.9uW (V_DD=0.37V) in sub-threshold operation at 250kbps, with appropriate 97% wake-up beacon detection and 0.04% false alarm probabilities. The circuit is fully functional at a minimum V_DD of 0.23V at f_max=5kHz and 0.018uW power consumption. Based on these results we show that our digital base-band can be used as a companion to compensate for front-end implementation losses resulting from the limited wake-up receiver power budget at a negligible cost. This implies an improvement of the practical sensitivity of the wake-up receiver, compared to what is traditionally reported.Comment: Submitted to IEEE Sensors Journa

A group-based architecture and protocol for wireless sensor networks

There are many works related to wireless sensor networks (WSNs) where authors present new protocols with better or enhanced features, others just compare their performance or present an application, but this work tries to provide a different perspective. Why don¿t we see the network as a whole and split it into groups to give better network performance regardless of the routing protocol? For this reason, in this thesis we demonstrate through simulations that node¿s grouping feature in WSN improves the network¿s behavior. We propose the creation of a group-based architecture, where nodes have the same functionality within the network. Each group has a head node, which defines the area in which the nodes of such group are located. Each node has a unique node identifier (nodeID). First group¿s node makes a group identifier (groupID). New nodes will know their groupID and nodeID of their neighbors. End nodes are, physically, the nodes that define a group. When there is an event on a node, this event is sent to all nodes in its group in order to take an appropriate action. End nodes have connections to other end nodes of neighboring groups and they will be used to send data to other groups or to receive information from other groups and to distribute it within their group. Links between end nodes of different groups are established mainly depending on their position, but if there are multiple possibilities, neighbor nodes could be selected based on their ability ¿, being ¿ a choice parameter taking into account several network and nodes parameters. In order to set group¿s boundaries, we can consider two options, namely: i) limiting the group¿s diameter of a maximum number of hops, and ii) establishing boundaries of covered area. In order to improve the proposed group-based architecture, we add collaboration between groups. A collaborative group-based network gives better performance to the group and to the whole system, thereby avoiding unnecessary message forwarding and additional overheads while saving energy. Grouping nodes also diminishes the average network delay while allowing scaling the network considerably. In order to offer an optimized monitoring process, and in order to offer the best reply in particular environments, group-based collaborative systems are needed. They will simplify the monitoring needs while offering direct control. Finally, we propose a marine application where a variant of this groupbased architecture could be applied and deployed.García Pineda, M. (2013). A group-based architecture and protocol for wireless sensor networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/27599TESISPremios Extraordinarios de tesis doctorale

Distributed clock synchronization for wireless sensor networks using belief propagation

In this paper, we study the global clock synchronization problem for wireless sensor networks. Based on belief propagation, we propose a fully distributed algorithm which has low overhead and can achieve scalable synchronization. It is also shown analytically that the proposed algorithm always converges for strongly connected networks. Simulation results show that the proposed algorithm achieves better accuracy than consensus algorithms. Furthermore, the belief obtained at each sensor provides an accurate prediction on the algorithm's performance in terms of MSE. © 2011 IEEE.published_or_final_versio