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Item BA: Game Theoretical Approach for Energy-Delay Balancing in Distributed Duty-Cycled MAC Protocols of Wireless Networks(ACM, 2014-07-14) Doudou, Messaoud; M. Barcelo-Ordinas, Jose; Djenouri, Djamel; Garcia-Vidal, Jorge; Badache, NadjibOptimizing energy consumption and end-to-end (e2e) packet delay in energy constrained distributed wireless networks is a conflicting multi-objective optimization problem. This paper investigates this trade-off from a game-theoretic perspective, where the two optimization objectives are considered as virtual game players that attempt to optimize their utility values. The cost model of each player is mapped through a generalized optimization framework onto protocol specific MAC parameters. A cooperative game is then defined, in which the Nash Bargaining solution assures the balance between energy consumption and e2e packet delay. For illustration, this formulation is applied to three state-of-the-art wireless sensor network MAC protocols; X-MAC, DMAC, and LMAC as representatives of preamble sampling, slotted contention-based, and frame-based MAC categories, respectively. The paper shows the effectiveness of such framework in optimizing protocol parameters for achieving a fair energy-delay performance trade-off, under the application requirements in terms of initial energy budget and maximum e2e packet delay. The proposed framework is scalable with the increase in the number of nodes, as the players represent the optimization metrics instead of nodes.Item Game Theory Framework for MAC Parameter Optimization in Energy-Delay Constrained Sensor Networks(ACM, 2016-05-15) Doudou, Messaoud; M. Barcelo-Ordinas, Jose; Djenouri, Djamel; Garcia-Vidal, Jorge; Bouabdallah, Abdelmadjid; Badache, NadjibOptimizing energy consumption and end-to-end (e2e) packet delay in energy-constrained, delay-sensitive wireless sensor networks is a conflicting multi-objective optimization problem. We investigate the problem from a game theory perspective, where the two optimization objectives are considered as game players. The cost model of each player is mapped through a generalized optimization framework onto protocol specific MAC parameters. From the optimization framework, a game is first defined by the Nash Bargaining Solution (NBS) to assure energy-consumption and e2e delay balancing. Secondly, the Kalai-Smorodinsky Bargaining Solution (KSBS) is used to find equal proportion of gain between players. Both methods offer a bargaining solution to the duty-cycle MAC protocol under different axioms. As a result, given the two performance requirements, i.e., the maximum latency tolerated by the application and the initial energy budget of nodes, the proposed framework allows to set tunable system parameters to reach a fair equilibrium point which dually minimizes the system latency and energy consumption. For illustration, this formulation is applied to six state-of-the-art Wireless Sensor Network (WSN) MAC protocols; B-MAC, X-MAC, RI-MAC, SMAC, DMAC, and LMAC. The paper shows the effectiveness and scalability of such framework in optimizing protocol parameters that achieve a fair energy-delay performance trade-off under the application requirements.Item Game Theoretical Approach for Energy-Delay Balancing in Distributed Duty-Cycled MAC Protocols of Wireless Networks(ACM, 2014-07-15) Doudou, Messaoud; M. Barcelo-Ordinas, Jose; Djenouri, Djamel; Garcia-Vidal, Jorge; Badache, NadjibOptimizing energy consumption and end-to-end (e2e) packet delay in energy constrained distributed wireless networks is a conflicting multi-objective optimization problem. This paper investigates this trade-off from a game-theoretic perspective, where the two optimization objectives are considered as virtual game players that attempt to optimize their utility values. The cost model of each player is mapped through a generalized optimization framework onto protocol specific MAC parameters. From the optimization framework, a cooperative game is defined in which the Nash Bargaining solution assures the balance between energy consumption and e2e packet delay. For illustration, this formulation is applied to three state-of-the-art wireless sensor network MAC protocols; X-MAC, DMAC, and LMAC as representatives of preamble sampling, slotted contention-based, and frame-based MAC categories, respectively. The paper shows the effectiveness of such framework in optimizing protocol parameters that achieve a fair energy-delay performance trade-off under the application requirements in terms of initial energy budget and maximum e2e packet delay. The proposed framework is scalable with the increase in the number of nodes, as the players represent the optimization metrics instead of nodes.Item Game Theoretical Approach for Energy-Delay Balancing in Distributed Duty-Cycled MAC Protocols of Wireless Networks(CERIST, 2014-04-24) Doudou, Messaoud; M. Barcelo-Ordinas, Jose; Djenouri, Djamel; Garcia-Vidal, Jorge; Badache, NadjibOptimizing energy consumption and end-to-end (e2e) packet delay in energy constrained distributed wireless networks is a conflicting multi-objective optimization problem. This paper investigates this trade-off from a game-theoretic perspective, where the two optimization objectives are considered as virtual game players that attempt to optimize their utility values. The cost model of each player is mapped through a generalized optimization framework onto protocol specific MAC parameters. From the optimization framework, a cooperative game is defined in which the Nash Bargaining solution assures the balance between energy consumption and e2e packet delay. For illustration, this formulation is applied to three state-of-the-art wireless sensor network MAC protocols; X-MAC, DMAC, and LMAC as representatives of preamble sampling, slotted contention-based, and frame-based MAC categories, respectively. The paper shows the effectiveness of such framework in optimizing protocol parameters that achieve a fair energy-delay performance trade-off under the application requirements in terms of initial energy budget and maximum e2e packet delay. The proposed framework is scalable with the increase in the number of nodes, as the players represent the optimization metrics instead of nodes.