Wireless charging provides dynamic power supply for Wireless Sensor Networks (WSNs). Such systems, are typically considered under the scenario of Wireless Rechargeable Sensor Networks (WRSNs). With the use of mobile chargers (MCs), the flexibility of WRSNs is further enhanced. However, the use of MCs poses several challenges during the system design. The coordination process has to simultaneously optimize the scheduling, the moving time and the charging time of multiple MCs under limited system resources (time and energy). Efficient methods that jointly solve these challenges are generally lacking in the literature. In this paper, we address the multiple MCs coordination problem under multiple system requirements. Firstly, we aim at minimizing the energy consumption of MCs, guaranteeing that every sensor will not run out of energy. We formulate the multiple MCs coordination problem as a mixed-integer linear programming and derive a set of desired network properties. Secondly, we propose a novel decomposition method to optimally solve the problem, as well as to reduce the computation time. Our approach divides the problem into a subproblem for the MC scheduling and a subproblem for the MC moving time and charging time, and solves them iteratively by utilizing the solution of one into the other. The convergence of proposed method is analyzed theoretically. Simulation results demonstrate the effectiveness and scalability of the proposed method in terms of solution quality and computation time.

International audienceWhen integrating mixed critical systems on a multi/many-core, one challenge is to ensure predictability for high crit-icality tasks and an increased utilization for low criticality tasks. In this paper, we address this problem when several high criticality tasks with different deadlines, periods and offsets are concurrently executed on the system. We pro-pose a distributed run-time WCET controller that works as follows: (1) locally, each critical task regularly checks if the interferences due to the low criticality tasks can be toler-ated, otherwise it decides their suspension; (2) globally, a master suspends and restarts the low criticality tasks based on the received requests from the critical tasks. Our ap-proach has been implemented as a software controller on a real multi-core COTS system with significant gains 1