An Energy Effective Adaptive Spatial Sampling Algorithm for WSNs

Abstract: The objective of environmental observation with WSNs (wireless sensor networks) is to extract the synoptic structures (spatio-temporal sequence) of the phenomena of ROI (region of interest) in order to make effective predictive and analytical characterizations. Energy limitation is one of the main obstacles to the universal application of WSNs and therefore there are a large mass of researches on energy conservation for WSNs. Among them, adaptive sampling strategy is regarded as a promising method to improve energy efficiency in recent years, therefore, many researches are concerning to different kinds of energy efficient sampling scheme for WSNs. In this paper, we dedicate to investigating how to schedule sensor nodes in the spatial region domain by our adaptive sampling scheme so as to reduce energy consumption of sensor nodes. The key idea of this paper is to schedule sensor nodes to achieve the desired level of accuracy by activating sensor system only when necessary to acquire a new set of samples and then prepare to power it off immediately afterwards. By adaptively sampling the region of interest, fewer sensors are activated at the same time. Moreover, only the necessary communications are remaining with this algorithm, so as to achieve significant energy conservation than before. The algorithm proposed in this literature is named as Adaptive Spatial Sampling (ASS) algorithm in short. The simulation results verified that ASS algorithm can outperform traditional fixed sampling strategy

Active node determination for correlated data gathering in wireless sensor networks

Cataloged from PDF version of article.In wireless sensor network applications where data gathered by different sensor nodes is correlated, not all sensor nodes need to be active for the wireless sensor network to be functional. Given that the sensor nodes that are selected as active form a connected wireless network, the inactive sensor nodes can be turned off. Allowing some sensor nodes to be active and some sensor nodes inactive interchangably during the lifecycle of the application helps the wireless sensor network to have a longer lifetime. The problem of determining a set of active sensor nodes in a correlated data environment for a fully operational wireless sensor network can be formulated as an instance of the connected correlation-dominating set problem. In this work, our contribution is twofold; we propose an effective and runtime-efficient iterative improvement heuristic to solve the active sensor node determination problem, and a benefit function that aims to minimize the number of active sensor nodes while maximizing the residual energy levels of the selected active sensor nodes. Extensive simulations we performed show that the proposed approach achieves a good performance in terms of both network lifetime and runtime efficiency. © 2012 Elsevier B.V. All rights reserved

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

Effect of Partially Correlated Data on Clustering in Wireless Sensor Networks

In wireless sensor networks, clustering allows the aggregation of sensor data. It is well known that leveraging the correlation between different samples of the observed data will lead to better utilization of energy reserve. However, no previous work has analyzed the effect of non-ideal data aggregation in multi-hop sensor networks. In this paper, we propose a novel analytical framework to study how partially correlated data affect the performance of clustering algorithms. We analyze the behavior of multi-hop routing and, by combining random geometry techniques and rate distortion theory, predict the total energy consumption and network lifetime. We show that when a moderate amount of correlation is available, the optimal probabilities that lead to minimum energy consumption are far from optimality in terms of network lifetime. In addition, we study the sensitivity of the total energy consumption and network lifetime to the amount of correlation and compression distortion constraint