Scheduling algorithms for multihop radio networks
IEEE/ACM Transactions on Networking (TON)
A Unified Framework and Algorithm for (T/F/C)DMA Channel Assignment in Wireless Networks
INFOCOM '97 Proceedings of the INFOCOM '97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Broadcast scheduling in wireless sensor networks using fuzzy Hopfield neural network
Expert Systems with Applications: An International Journal
TDMA Protocol Requirements for Wireless Sensor Networks
SENSORCOMM '08 Proceedings of the 2008 Second International Conference on Sensor Technologies and Applications
A sequential approach for optimal broadcast scheduling in packet radio networks
IEEE Transactions on Communications
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
Energy efficient spatial TDMA scheduling in wireless networks
Computers and Operations Research
An overview of scheduling algorithms in wireless multimedia networks
IEEE Wireless Communications
A mixed neural-genetic algorithm for the broadcast scheduling problem
IEEE Transactions on Wireless Communications
A novel broadcast scheduling strategy using factor graphs and the sum-product algorithm
IEEE Transactions on Wireless Communications
A heuristic algorithm for optimum transmission schedule in broadcast packet radio networks
Computer Communications
Optimal broadcast scheduling in packet radio networks using mean field annealing
IEEE Journal on Selected Areas in Communications
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In wireless ad-hoc networks, the broadcast scheduling problem (BSP) is commonly viewed as a well-known NP-complete combinatorial optimization problem. The purpose of the BSP is to achieve a transmission schedule with collision-free time slots in a time division multiple access ad-hoc network. The transmission schedule is optimized by minimizing the frame length of the node transmissions and maximizing the utilization of the shared channel. In this work, we propose a new evolutionary algorithm to solve the BSP by adopting the rock-paper-scissors dynamics found in natural systems. We use three types of species with strategies of different levels of intensification and diversification to simulate the rock-paper-scissors dynamics. Based on this evolutionary game, in which the success of one species relies on the behavior of others, the dynamic coexistence of three species can be achieved to control the balance between intensification and diversification. The experimental results demonstrate that our algorithm is efficient and effective at solving large instances of the BSP.