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.

Maximizing Network Lifetime of Broadcasting Over Wireless Stationary Ad Hoc Networks

“…(iii) The dynamic network lifetime increases linearly as a function of network density, which is mainly due to increase in available energy pool of the network. (iv) The metrics of the form (37) or (38) including the initial energy have negative impact on on-line optimization and should be avoided. (v) When control overhead is considered in energy dissipation, the choice of optimization metric favors toward minimizing the maximum cost.…”

“…Note that we do not consider in our battery model the nonlinear behavior of voltage as a function of remaining capacity [7] or the battery charge recovery effect due to diffusion process [38], [39] but use a simplified linear battery discharge model. We intend to study the effect of different battery models in the future.…”

Novel algorithms for the network lifetime problem in wireless settings

“…Segal [7] improved the running time of the MLB problem for the broadcast protocol and also showed an optimal polynomial-time algorithm for convergecast with aggregation. Additional results may be found in [8,6]. By allowing single source/multiple topology, the broadcast and multicast become NP-Hard [5], while convergecast and unicast have polynomial-time optimal solutions.…”

A framework for optimal battery management for wireless nodes

“…More specifically, we will use the theory of "stochastic shortest path" problem, presented in [4]. We give an overview of the related theory and computational techniques in technical report [10]. In this paper, we show that the optimal battery management problem falls within the purview of the stochastic shortest path problem.…”