Energy-Efficient Methods for Highly Correlated Spatio-Temporal Environments in Wireless Sensor Network Communications

Continuous-monitoring (CM) of natural phenomenon is one of the major streams of applications in wireless sensor networks (WSNs), where aggregation and clustering techniques are beneficial as correlation dominates in both spatial and temporal aspects of sensed phenomenon. Conversely, in Event Driven Reporting (EDR), the efficient transmission of sensitive data related to some predefined alarm cases is of major importance. As such, reporting latency is a more important performance parameter. However, in some applications, the transmission of both CM and EDR data is encouraged or even required. For either CM or EDR applications, system performance can be greatly improved when both the number of packets to be transmitted as well as the packet size is reduced. This is especially true for highly dense sensor networks where many nodes detect the same values for the sensed phenomenon. Building on this, this paper focuses on studying and proposing compression techniques to improve the system performance in terms of energy consumption and reporting latency in both CM and EDR applications. Furthermore, we extend our analysis to hybrid networks where CM and EDR are required simultaneously. Specifically, this paper presents a simple aggregation technique named smart aggregation (SAG) for the CM applications and an event driven scheme named compression cluster scheme in spatial correlated region (CC_SCR). The proposed SAG exploits both spatial and temporal correlations where CC_SCR exploits the spatial correlation of such networks by data compression. Rationalizing the developments is attained by simulations that compare energy efficiency of the proposed SAG with k-hop