EAR: An Energy and Activity-Aware Routing Protocol for Wireless Sensor Networks in Smart Environments

Abstract

Sensor network, unlike traditional communication network, is deeply embedded in physical environments and its operation is mainly driven by the event activities in the environment. In long-term operations, the event activities usually show certain patterns which can be learned and exploited to optimize network design. However, this has been underexplored in the literature. One work related to this is using ATPG for radio duty cycling ([1]). In this paper we present a novel Energy and Activity aware Routing (EAR) protocol for sensor networks. As a case study, we have evaluated EAR with the data trace of real Smart Environments. In EAR an Activity Transition Probability Graph (ATPG) is learned and built from the event activity patterns. EAR is an online routing protocol, which chooses the next-hop relay node by utilizing: activity pattern information in the ATPG graph and a novel index of energy balance in the network. EAR extends network lifetime by maintaining an energy balance across the nodes in the network, while meeting application performance with desired throughput and low data delivery latency. We theoretically prove that: (a) the network throughput with EAR achieves a competitive ratio (i.e., the ratio of the performance of any offline algorithm that has knowledge of all past and future packet arrivals to the performance of our online algorithm) which is asymptotically optimal, and (b) EAR achieves a lower bound in network lifetime. Extensive experimental results from: (a) 82 node Motelab sensor network testbed [2] and (b) varying size network (20-100) in sensor network simulator TOSSIM, validate that EAR outperforms the existing methods both in terms of network performance (network lifetime, network energy consumption) and application performance (low latency, desired throughput) for an energy-constrained sensor network.