Abstract
Optimization of energy usage in wireless sensor networks (WSN) has been an active research field for the last decades and various approaches have been explored. In fact, A well designed energy consumption model is the foundation for developing and evaluating a power management scheme in network of energy constrained devices such as: WSN. We are interested in developing optimal centralized power control policies for energy harvesting wireless multimedia sensor networks (WMSN) equipped with photovoltaic cells. We propose a new complete information Markov decision process model to characterize sensor's battery discharge/recharge process and inspect the structural properties of optimal transmit policies.
Highlights
1 Introduction The recent technological advances in the fields of micro-electronic, wireless communication along with reduction of production costs have motivated the development of a novel generation of wireless networks. wireless sensor networks (WSN) are articulated over a set of miniaturized battery powered devices with communication capabilities and are expected to become highly integrated into our daily activities
6 Concluding remarks In this article we considered the problem of dynamic centralized power allocation for energy harvesting wireless multimedia sensor networks (WMSN)
We focus on solar powered sensors and provide a stochastic model for the associated battery discharge/recharge process
Summary
The recent technological advances in the fields of micro-electronic, wireless communication along with reduction of production costs have motivated the development of a novel generation of wireless networks. wireless sensor networks (WSN) are articulated over a set of miniaturized battery powered devices (sensors) with communication capabilities and are expected to become highly integrated into our daily activities. The authors of [13] address the problem of network resource allocation for energy-harvesting sensor platforms with time-varying battery recharging rates. The formulated problem fits within the MDP framework with full information and infinite planning horizon, where the base station will compute and provide each sensor with its optimal transmission strategy, given the fact that it has access to all sensors’ information i.e environment, battery and radio channel status. At each time slot t, depending on the remaining energy off the battery, the sensor j makes a decision on its transmit power pjt. The immediate reward reflects a balance between maximizing the expected sojourn in a given battery state and the corresponding achieved throughput for the chosen transmit power: rt(sj,t, pjt) =. (2) The immediate reward rj for sensor j is superadditive
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