Energy-aware, dynamic hierarchy routing protocols for maximizing the lifetime of wireless sensor networks
Abstract
Wireless Sensor Networks (WSNs) have received considerable attention in recent years because of their numerous applications. Typically, a WSN consists of a large number of sensor nodes which can communicate with each other and with external base stations responsible for collecting the sensed data. In the sensor, a battery provides the essential power which is usually non-replenishable. As a result, the maximizing lifetime problem has driven most issues in research and practice concerning WSNs. Routing is an important means that enables to save energy and therefore prolong the lifetime. Another method that has been widely used to enhance the lifetime and achieve network scalability is data aggregation, which is known to be tightly coupled with routing topology. The research on routing combined with data aggregation in WSNs has previously uncovered new opportunities and challenges. In this research we develop three routing protocols that incorporate the data aggregation, under the hierarchical routing topology. First, we propose a distributed cluster-based routing protocol: Predictive and Adaptive Routing Protocol using Energy Welfare (PARPEW). PARPEW applies the notion of Energy Welfare (EW) in choosing cluster heads whose role is to relay data from sensors to the base station, in an effort to achieve both energy efficiency and energy balance at the same time. Second, we further develop the PARPEW by devising an iterative mechanism that enables to identify better cluster heads. Third, we develop a tree-based routing protocol: Spanning Tree Of Residual Energy (STORE). STORE is flexibly applicable to scenarios that take into account (1) energy cost for data aggregation and (2) partial data aggregation, which are important considerations in WSNs that process multimedia data. Experimental results demonstrate that these protocols are capable of significantly prolonging the lifetime of WSNs over existing approaches under various scenarios.
Degree
Ph.D.
Advisors
Lee, Purdue University.
Subject Area
Industrial engineering
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