مسیریابی آگاهانه انرژی قابل پیش بینی مبتنی بر تئوری بازی دینامیکی در شبکه های حسگر بی سیم

A routing algorithm named Sub-Game Energy Aware Routing (SGEAR) modeled by Dynamic Game Theory is proposed in this paper to make better routing choices. SGEAR takes the residual energy of the nodes and the energy consumption of the path into consideration and achieves Nash Equilibrium using Backward Induction. Compared with Energy Aware Routing, SGEAR can provide stable routing choices for relaying nodes and the energy of the network can still burn evenly. Moreover, this algorithm is more suitable for being combined with sleeping scheduling scheme and thus prolongs the lifetime of Wireless Sensor Networks. Simulation results show that, combined with sleeping scheduling scheme, SGEAR has an increase of 20% in energy saving compared with Energy Aware Routing.

Severe energy limitation is one of the most important differences between traditional network and Wireless Sensor Networks (WSNs). It is not feasible to supply energy with new battery to the sensor nodes because there are plenty of very cheap sensors deployed in a wide and complex area, some of which human cannot reach. So how to prolong the lifetime of the WSN is a challenging job researchers now face. A lot of work has been done to solve this energy-awareness problem, the principal goal of which is the efficient use of energy [1], [2], [3] and [4]. In this paper, energy efficient technologies are analyzed first and then the deficiency of the Energy Aware Routing is pointed out, leaving topics for further investigation. We assume that an ideal network model is adopted which means nodes’ detecting neighbors is supported [5]. We set a simple example to illustrate the deficiency and also give a guidance of the improvement. Because the process of the routing scheme proposed in this paper is just a mapping of Dynamic Game Theory model, Dynamic Game Theory is therefore used in order to give a more persuasive conclusion and the strategies are given to choose and adjust path in finding the solution of sub-game Nash Equilibrium. Moreover, simulations show that the solution has a more predictable feature which endows itself with the advantage of combining with sleep scheduling mechanism.

This paper proposed a new routing scheme named SGEAR based on the fact that the optimizing problem of routing can be mapped into a problem of Dynamic Game, and thus can be solved by the method of Backward Induction of Dynamic Game Theory. Analysis shows that SGEAR differs from Energy Aware Routing only in the strategies of choosing the next hop, so it does not add to the overhead complexity. Moreover, SGEAR can make stable routing choices comparing with Energy Aware Routing, because it chooses the routing path according to generalized survivability factors, which is a determined manner. Such a feature can be used to be combined with sleep scheduling to get energy efficient results. Simulations show that SGEAR can still maintain the feature that the energy of the network burns evenly as it burns in Energy Aware Routing. Although SGEAR does not outperform Energy Aware Routing too much in energy saving, it does have a more predictable feature of choosing the next hop, which is more stable and suitable for combining with sleep scheduling schemes and thus prolongs the lifetime of WSN. So SGEAR combined with sleep scheduling is further explored in subsequent simulations. Combined with a simple sleep scheduling scheme, simulations show that SGEAR has an increase of 20% in average energy saving than Energy Aware Routing, which implies an obvious improvement in the energy saving issue.