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detect its own status; master or slave; based on identifier without adding extra overheads or exchanging packets that slow down the network. We propose a baseband design that implements algorithm functions and we detail the hardware implementation using Matlab/Simulink and Ettus B210 USRP. Tests held in laboratory prove that algorithm works as expected.
Clustering in wireless sensor networks groups all the nodesin a region, uses a single node as a cluster head, and communicates with the sink. However, the resource-constrained nodes’ lifetime reduces in the communication process. Toimprove the network lifetime, an efficient cluster head selection process is widely adopted.Similarly, identifying energy-efficient routing reduces the node energy requirements andenhances the network lifetime. Considering these two characteristics as objective, thisresearch work proposes a fuzzy neural network-based clustering with dolphin swarm optimization routing and congestion control (FNDSCC), where an energy-efficient cluster headselection using a deep fuzzy neural network (DFNN) model and an energy-aware optimalrouting using an improved dolphin swarm optimization (DSO) enhance the network life-time by reducing the energy consumption of the nodes. Moreover, novel rate adjustmenttechniques to overcome the congestion inside the network are introduced. Proposed modelperformance is experimentally verified and compared with conventional methods such asgenetic based efficient clustering (GEC), hybrid particle swarm optimization (HPSO), andartificial bee colony (ABC) optimization and rate-controlled reliable transport (RCRT)protocol in terms of latency, reliability, packet delivery ratio, network lifetime and ef-ficiency. The results demonstrate that the proposed multi-objective approach performsbetter than conventional models.
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