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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

Multi-objective Task Assignment and Multiagent Planning with Hybrid GP by Thomas Robinson and Guoxin Su

Allocation and planning with a collection of tasks and a group of agents is an important problem in multiagent systems. One commonly faced bottleneck is scalability, as in general the multiagent model increases exponentially in size with the number of agents. We consider the combination of random task assignment and multiagent planning under multiple-objective constraints, and show that this problem can be decentralised to individual agent-task models. We present an algorithm of point-oriented Pareto computation, which checks whether a point corresponding to given cost and probability thresholds for our formal problem is feasible or not. If the given point is infeasible, our algorithm finds a Pareto-optimal point which is closest to the given point. We provide the first multi-objective model checking framework that simultaneously uses GPU and multi-core acceleration. Our framework manages CPU and GPU devices as a load balancing problem for parallel computation. Our experiments demonstr

Ex-MSD leader Thompson still a principal — but has no school

Ex-MSD leader Thompson still a principal — but has no school
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Former Stoneman Douglas leader Ty Thompson still a principal — but with no school

Four years after he stopped leading Marjory Stoneman Douglas High School, Ty Thompson has been a principal without a school. Thompson was moved out as principal of Stoneman Douglas in June 2019, a year after the mass shooting at the Parkland school that brought his leadership skills into question. Since then, he has kept the title of principal as well as the corresponding salary — currently .

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