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"On Virtualizing Targets Coverage in Energy Harvesting IoT Systems" by Longji Zhang, Kwan Wu Chin et al.

This paper considers targets coverage in energy harvesting Internet of Things (IoT) networks. Specifically, solar-powered sensor devices employ network virtualization technology to partition their resources, such as energy, memory, and computation workload, in order to serve requests with different coverage requirements. Our objective is to maximize the revenue from completing requests. To this end, we outline a mixed integer linear program (MILP) to optimize the start time of each request and the set of nodes that serve a request. We also propose a heuristic, called energy harvesting aware request placement (EHARP), to determine requests to be deployed in each time slot based on energy harvesting conditions and the resource state of sensor nodes. Furthermore, we propose two model predictive control (MPC) approaches, called MPC-MILP and MPC-EHARP, respectively, which deploy requests based on energy arrival at devices over a given time window as predicted by a Gaussian mixture model (GM ....

Energy Harvesting , Nternet Of Things , Mathematical Optimization , Receding Horizon Control , Soft Sensors , Task Analysis , Task Assignment , Virtual Network Function , Wireless Sensor Networks ,

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