With the growth of different design goals and application requirements, wireless sensor networks (WSNs) have been increasingly popular for a wide variety of purposes, e.g., image formation of a target field, intrusion detection, surveillance and environmental monitoring. In this paper, a multi-hop heterogeneous cluster-based optimization algorithm (MHCOA) for WSNs is proposed. The motivation to MHCOA for WSNs is that several higher energy sensor nodes act as cluster heads, which are deployed artificially, while some low energy sensor nodes act as cluster members, which are deployed randomly. In order to realize monitoring task, we complete three major works in this paper. First, MHCOA calculates the number of cluster heads and communication radius of low energy sensor nodes; Second, it finishes monitoring task through data packets transmission with higher energy nodes acting as cluster heads in the form of multi-hop heterogeneous WSNs; Finally, simulation results show that compared to its peers in heterogeneous WSNs, MHCOA reduces the number of cluster heads, which saves the network average energy by up to 16.7 % and extends the network life by up to 38 %, while with less end-to-end delay.
Cross-layer design was proposed in recent years, it was mainly used in network architecture design. Now it is widely introduced in wireless sensor networks. This paper focuses on cross-layer optimization with physical layer, MAC layer and network layer in wireless sensor networks, which is applied to sensor node power control. Based on maximum communication range of sensor nodes, several power control levels are designed. Transmission power of neighbor nodes is classified into different power levels. Neighbor nodes transmit data to central node with designed level of power according to distances. Central node exchanges information with neighbor nodes through cross-layer optimization. Simulation results show that cross-layer optimization for node transmission power control can greatly save node energy consumption, under the condition of nearly same average network throughput with no power control. The most can save energy up to 63.8%.
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