Power Adaptation for Distributed Detection in Energy Harvesting WSNs with Finite-Capacity Battery

Abstract

We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), that is tasked with solving a binary distributed detection problem. Each sensor is capable of harvesting randomly arrived energy and storing it in a finite capacity battery. Sensors are informed of their fading channel states, via a bandwidth-limited feedback channel from the FC. Each sensor has the knowledge of its current battery state and its channel state (quantized channel gain). Our goal is to study how sensors should choose their transmit powers such that J-divergence of the received signal densities under two hypotheses at the FC is maximized, subject to certain (battery and power) constraints. We derive the optimal power map, which depends on the energy arrival rate, the battery capacity, and the battery states probabilities at the steady state. Using the optimal power map, each sensor optimally adapts its transmit power, based on its battery state and its channel state. Our simulation results demonstrate the performance of our proposed power adaptation scheme for different system parameters.

Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks

“…• We provide two approximations for the error probability corresponding to the optimal Bayesian fusion rule at the FC, relying on low signal-to-noise ratio (SNR) approximation for the communication channel noise, and Lindeberg Central Limit Theorem (CLT) for large N . This work is different from our previous works in [24], [28]. In particular, in [24] we approached the detection problem from Neyman-Pearson perspective.…”

“…, ∞. Note that parameter ρ is the average number of arriving energy units 2 Suppose each arriving energy unit measured in Joules is bu Joules.…”

“…Let µ zn|h and σ 2 zn|h , denote the mean and variance of z n in (33) given h, respectively. To find µ zn|h , σ 2 zn,h we recall the following fact.…”

“…Power/energy management in EH-enabled WSNs with finite size batteries is necessary, in order to balance the rates of energy harvesting and energy consumption for transmission. If the energy management policy is overly aggressive, sensors may stop functioning, due Parts of this work were presented in GlobalSIP 2018 [1] and GLOBECOM 2019 [2]. to energy outage.…”

Link-Correlation-Aware Opportunistic Routing in Low-Duty-Cycle Wireless Networks

“…Currently, more and more applications (e.g., industrial Internet of Things, structural health monitoring [1] and habitat monitoring [2]) are required to work for a long period of time. As a result, low-duty-cycle wireless networks have become increasingly common due to the increasing gap between rapidly growing lifetime requirements and slow progress in battery capacity [3]. To fill this gap, wireless devices operate in extremely low-dutycycle in which a node keeps an active state briefly and schedules itself dormant for a long time in a period [4].…”