Energy Sharing for Multiple Sensor Nodes With Finite Buffers

Abstract

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes in order to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the -greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization in order to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.

Harvested Energy Scavenging and Transfer capabilities in Opportunistic Ring Routing

“…Padakandla et al [53] investigated the optimal energy sharing policies in EHWSNs to maximize the network performance for the scenarios involving multiple sensor nodes and only one energy harvesting node. Prasad et al [54] presented a detailed survey on various energy harvesting techniques especially covering topics such as power management and networking in EHWSNs.…”

Energy Management in RFID-Sensor Networks: Taxonomy and Challenges

“…The aim is to attain considerable network throughput and optimal lifetime of channel bandwidth access by mutually optimising the harvestable energy and power consumption processes in Nano sensors. Sindhu et al had developed an efficient energy distribution algorithms, Q-learning algorithm, with exploration mechanisms based on the e-greedy method as well as Upper Confidence Bound (UCB) [90]. An energy consumption model based on energy consumption analysis for WNSNs by unitedly reckoning the energy consumption of both sender and receiver has also been developed and implemented [91].…”

Supply and Demand Oriented Energy Management in the Internet of Things

“…Such energy buffering strategies based on deferrable operations achieve the intelligent dispatch of distributed energy storage with long-term benefit maximization. The optimal decision problem of energy buffering can be formulated as a discounted-cost MDP over an infinite horizon [5] [6]. In particular, in a system with multiple sensor nodes and a single energy-harvesting source, energy buffering can be used to store the energy harvested from ambient energy sources and to share energy among the sensor nodes such that the long-run average delay during data transmission is minimized.…”