Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Next century challenges: mobile networking for “Smart Dust”
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
GPSR: greedy perimeter stateless routing for wireless networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Energy-Efficient Link Layer for Wireless Microsensor Networks
WVLSI '01 Proceedings of the IEEE Computer Society Workshop on VLSI 2001
Breeding swarms: a GA/PSO hybrid
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Wireless Communications & Mobile Computing
Loss minimization control of induction motor using GA-PSO
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Communications Magazine
On maximizing the throughput of convergecast in wireless sensor networks
GPC'08 Proceedings of the 3rd international conference on Advances in grid and pervasive computing
TDMA scheduling algorithms for wireless sensor networks
Wireless Networks
Review: A survey on cross-layer solutions for wireless sensor networks
Journal of Network and Computer Applications
A cyclic MAC scheduler for collecting data from heterogeneous sensors
Computer Communications
A decentralized minislot scheduling protocol (DMSP) in TDMA-based wireless mesh networks
Journal of Network and Computer Applications
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In wireless sensor networks, time division multiple access (TDMA)-based MAC can potentially reduce the delay and provide real-time guarantees as well as save power by eliminating collisions. In this kind of MAC, a common energy-saving strategy is to allow the sensors to turn their radio off when not engaged. However, too much state transitions between the active and sleep modes would also waste energy. In order to save this part of energy and further improve the time performance, a multi-objective TDMA scheduling problem for many-to-one sensor networks is presented. An effective optimization framework is then proposed, where genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are hybridized to enhance the searching ability. Simulation results with different network sizes are given. Three algorithms are used for comparisons, which are PSO algorithm, max degree first coloring algorithm and node based scheduling algorithm. It is shown that the proposed hybrid algorithm is superior over these three algorithms on a specified objective, which can be the total time or the total energy for data collection. In addition, the results reveal that the proposed optimization framework can flexibly deal with a multi-objective optimization problem.