A framework for optimal battery management for wireless nodes

Abstract

The focus of this paper is to extend the lifetime of a battery powered node in wireless context. The lifetime of a battery depends on both the manner of discharge and the transmission power requirements. We present a framework for computing the optimal discharge strategy which maximizes the lifetime of a node by exploiting the battery characteristics and adapting to the varying power requirements for wireless operations. The complexity of the optimal computation is linear in the number of system states. However, since the number of states can be large, the optimal strategy can only be computed offline and executed via a table lookup. We present a simple discharge strategy which can be executed online without any This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Abstract-The focus of this paper is to extend the lifetime of a battery powered node in wireless context. The lifetime of a battery depends on both the manner of discharge and the transmission power requirements. We present a framework for computing the optimal discharge strategy which maximizes the lifetime of a node by exploiting the battery characteristics and adapting to the varying power requirements for wireless operations. The complexity of the optimal computation is linear in the number of system states. However, since the number of states can be large, the optimal strategy can only be computed offline and executed via a table lookup. We present a simple discharge strategy which can be executed online without any table lookup and attains near maximum lifetime.

A Battery Power Scheduling Policy with Hardware Support In Mobile Devices

“…In [4], a framework has been developed to compute the optimal dicharge policy that maximizes the battery lifetime. But this strategy requires significant time and memory for computation.…”

“…But this strategy requires significant time and memory for computation. Hence, a strategy known as Maximum Charge scheduling policy which aims to efficiently choose the cell to be discharged, so as to approximate the optimal is proposed in [4]. Here, the total energy delivered is computed using the theory of stochastic Dynamic programming.…”

“…The size of the packet is specified in terms of number of charge units to be discharged from a battery [4]. In [4], the size of the packets corresponding to each traffic burst is assumed to be poisson distributed.…”

“…In [4], the size of the packets corresponding to each traffic burst is assumed to be poisson distributed. The poisson process is the oldest process that has been used to model interarrival times of traffic streams.…”

“…Hence implementing the above mentioned idea in an efficient and optimized manner presents a big challenge. Fortunately the problem of satisfying optimally the request of size K contains an optimal substructure and hence is solvable by the strategy of dynamic programming(DP) as proposed in [4] and [5]. Dynamic Programming is typically applied to optimization problems.…”