Sensor placement and resource allocation for energy harvesting IoT networks

Abstract

The paper studies optimal sensor selection for source estimation in energy harvesting Internet of Things (IoT) networks. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and digital transmission schemes from the selected sensors to the fusion center. In the digital transmission case, an information theoretic approach is used to model the transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the digital system outperforms the analog system with a possibly different sensor selection.Two source models are studied in this paper: static source estimation for a vector of correlated sources and dynamic state estimation for a scalar source. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. We propose a randomized rounding algorithm which generalizes the existing algorithm. The proposed randomized rounding algorithm takes the joint sensor location, power and bandwidth selection into account to efficiently round the obtained relaxed solution.

Energy‑harvesting and energy aware routing algorithm for heterogeneous energy WSNs

“…The energy harvested from solar radiation can be estimated on the basis of seasonal and diurnal cycles and weather prediction for the region. There are several predictive algorithms available to forecast potential available solar energy over time [24], [26], [27]. Sharma and Bhondekar [28], proposed the production of electricity from solar energy is unregulated, and it can be predicted for the majority of SEH sensor network scenarios.…”

A New Task Scheduling Approach for Energy Conservation in Internet of Things

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Distributed Sensor Management Based on Target Losing Probability for Maneuvering Multi-Target Tracking

“…An objective function should be established firstly and after that, the optimal sensor management schemes should be calculated. The calculated sensor management scheme is carried out to get observations, which will be used to estimate motion states of targets [1]- [4].…”