In wireless sensor networks, a few sensor nodes end up being vulnerable to potentially rapid depletion of the battery reserves due to either their central location or just the traffic patterns generated by the application. Traditional energy management strategies, such as those which use topology control algorithms, reduce the energy consumed at each node to the minimum necessary. In this paper, we use a different approach that balances the energy consumption at each of the nodes, thus increasing the functional lifetime of the network. We propose a new distributed dynamic topology control algorithm called Energy Balanced Topology Control (EBTC) which considers the actual energy consumed for each transmission and reception to achieve the goal of an increased functional lifetime. We analyze the algorithm's computational and communication complexity and show that it is equivalent or lower in complexity to other dynamic topology control algorithms. Using an empirical model of energy consumption, we show that the EBTC algorithm increases the lifetime of a wireless sensor network by over 40 compared to the best of previously known algorithms.
But the WDTC algorithm does not allow for the fact that, for any sensor node, the energy consumed for sending and receiving a packet may be different. The energy consumed in receiving a packet is usually less than that of the energy consumed for sending the same packet @cite_7 @cite_4 @cite_15 @cite_9 . In other words, if the sender and the receiver node have the same amount of energy, the sender node will very likely to be the node that runs out of energy first. Therefore, the current energy level alone does not tell us enough to estimate the remaining lifetime of a wireless sensor node.
@cite_9 @cite_15 @cite_4 @cite_7
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Game theory has been used for decades in fields of science such as economics and biology, but recently it was used to model routing and packet forwarding in wireless ad-hoc and sensor networks. However, the clustering problem, related to self-organization of nodes into large groups, has not been studied under this framework. In this work our objective is to provide a game theoretical modeling of clustering for ad-hoc and sensor networks. The analysis is based on a non-cooperative game approach where each sensor behaves selfishly in order to conserve its energy and thus maximize its lifespan. We prove the Nash Equilibria of the game for pure and mixed strategies, the expected payoffs and the price of anarchy corresponding to these equilibria. Then, we use this analysis to formulate a clustering mechanism (which we called Clustered Routing for Selfish Sensors--CROSS), that can be applied to sensor networks in practice. Comparing this mechanism to a popular clustering technique, we show via simulations that CROSS achieves a performance similar to that of a very popular clustering algorithm. In wireless sensor networks that consist of a large number of low-power, short-lived, unreliable sensors, one of the main design challenges is to obtain long system lifetime without sacrificing system original performances (sensing coverage and sensing reliability). In this paper, we propose a node-scheduling scheme, which can reduce system overall energy consumption, therefore increasing system lifetime, by identifying redundant nodes in respect of sensing coverage and then assigning them an off-duty operation mode that has lower energy consumption than the normal on-duty one. Our scheme aims to completely preserve original sensing coverage theoretically. Practically, sensing coverage degradation caused by location error, packet loss and node failure is very limited, not more than 1 as shown by our experimental results. In addition, the experimental results illustrate that certain redundancy is still guaranteed after node-scheduling, which we believe can provide enough sensing reliability in many applications. We implement the proposed scheme in NS-2 as an extension of the LEACH protocol and compare its energy consumption with the original LEACH. Simulation results exhibit noticeably longer system lifetime after introducing our scheme than before. Copyright © 2003 John Wiley & Sons, Ltd. Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.