On the selection of connectivity-based metrics for WSNs using a classification of application behaviour

This paper addresses a subset of Wireless Sensor Network (WSN) applications in which data is produced by a set of resource-constrained source nodes and forwarded to one or more sink nodes. The performance of such applications is affected by the connectivity of the WSN, since nodes must remain connected in order to transfer data from sources to sinks. Designers use metrics to measure and improve the efficacy of WSN applications. We aim to facilitate the choice of connectivity-based metrics by introducing a classification of WSN applications based on their data collection behaviour and indicating the metrics best suited to the evaluation of particular application classes. We argue that no suitable metric currently exists for a significant class of applications with the following characteristics: 1) application data is periodically routed or disseminated from source nodes to one or more sink nodes, and 2) the application can continue to function with the loss of source nodes although its useful network lifetime diminishes as a result. We present a new metric, known as Connectivity Weighted Transfer, which may be used to evaluate WSN applications with these characteristics.Preprin

Aggregate node placement for maximizing network lifetime in sensor networks

Sensor networks have been receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In this paper, we consider the design issue of sensor networks by placing a few powerful aggregate nodes into a dense sensor network such that the network lifetime is significantly prolonged when performing data gathering. Specifically, given K aggregate nodes and a dense sensor network consisting of n sensors with K ≪ n, the problem is to place the K aggregate nodes into the network such that the lifetime of the resulting network is maximized, subject to the distortion constraints that both the maximum transmission range of an aggregate node and the maximum transmission delay between an aggregate node and its covered sensor are met. This problem is a joint optimization problem of aggregate node placement and the communication structure, which is NP-hard. In this paper, we first give a non-linear programming solution for it. We then devise a novel heuristic algorithm. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithm in terms of network lifetime. The experimental results show that the proposed algorithm outperforms a commonly used uniform placement schema - equal distance placement schema significantly

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