Traditional Wireless Sensor Networks (WSNs) based on carrier sense methods for channel access suffer from reduced bandwidth utilization, increase energy consumptions and latency problems in networks with high traffic. In this work, a novel Evolutionary Slot Assignment (ESA) algorithm has been developed to in-crease the throughput of large wireless mesh networks with no centralized controller. In the presented scheme, the sensor nodes self-adapt to the traffic patterns of the network by selecting transmission slots us-ing evolutionary learning methods. Each sensor node evolves an independent transmission schedule. Unlike traditional evolutionary methods, fitness evaluation of every node impacts fitness of every other sensor node in the network. The ESA algorithm has been simulated using Network Simulator-2 and compared with the IEEE 802.15.4 CSMA-CA, a Static Slot Assignment (SSA) and a Random Slot Assignment schemes (RSA). Results show a remarkable improvement in the network throughput using the proposed ESA method as op-posed to other compared methods.
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