Lydia Ray

Sensor-centric quality of routing in sensor networks

Standard embedded sensor network models emphasize energy efficiency and distributed decision-making by considering untethered and unattended sensors. To this we add two constraints - the possibility of sensor failure and the fact that each sensor must tradeoff its own resource consumption with overall network objectives. In this paper, we develop an analytical model of data-centric information routing in sensor networks under all the above constraints. Unlike existing techniques, we use game theory to model intelligent sensors thereby making our approach sensor-centric. Sensors behave as rational players in an N-player routing game, where they tradeoff individual communication and other costs with network wide benefits. The outcome of the sensor behavior is a sequence of communication link establishments, resulting in routing paths from reporting to querying sensors. We show that the optimal routing architecture is the Nash equilibrium of the N-player routing game and that computing the optimal paths (which maximizes payoffs of the individual sensors) is NP-hard with and without data-aggregation. We develop a game-theoretic metric called path weakness to measure the qualitative performance of different routing mechanisms. This sensor-centric concept which is based on the contribution of individual sensors to the overall routing objective is used to define the quality of routing (QoR) paths. Simulation results are used to compare the QoR of different routing paths derived using various energy-constrained routing algorithms.

Max-Min Length-Energy-Constrained Routing in Wireless Sensor Networks

We consider the problem of inter-cluster routing between cluster heads via intermediate sensor nodes in a hierarchical sensor network. Sensor nodes having limited and unreplenishable power resources, both path length and path energy cost are important metrics affecting sensor lifetime. In this paper, we model the formation of length and energy constrained paths using a game theoretic paradigm. The Nash equilibrium of our routing game corresponds to the optimal length-energy-constrained (LEC) path. We show that this path can be computed in polynomial time in sensor networks operating under a geographic routing regime. We then define a simpler team version of this routing game and propose a distributed nearly-stateless energy efficient inter-cluster routing protocol that finds optimal routes. This protocol balances energy consumption across the network by periodically determining a new optimal path consistent with associated energy distributions. Simulation results testify to the effectiveness of the protocol in producing a longer network lifetime.