Neuro‐fuzzy and fuzzy schemes for cooperative communication in wireless sensor network: a military battlefield scenario

Abstract

In the military battlefield, maintaining high survivability of energy constraint wireless sensor network (WSN) when some nodes are destroyed by the foe troops is one of the prominent issues for the researchers. This study proposes the fuzzy and neuro‐fuzzy based relay selection schemes for cooperative WSN which reduce the bit error rate (BER) while improving the network lifetime. Meanwhile, the proposed model maintains the uninterrupted communication between military nodes in a warfare situation. The proposed proposed strategies are compared with the maximum residual energy‐based relay selection (MRERS), minimum energy consumption‐based relay selection (MECRS), and random relay selection (RRS) strategies. The performance of the proposed and existing strategies is evaluated on the basis of BER, network lifetime, number of dead nodes, and average energy of nodes in the network. The simulation results demonstrate that proposed schemes has 13.19–19.28% improvement in BER than the existing strategies. Moreover, 76.00–90.88% network lifetime improves when compared with the MECRS and RRS strategies. However, 6–7.76% is less than MRERS strategy. Furthermore, the results show that network survivability and average network energy is also better when the number of dead nodes in the network increases.

A Method of Relay Node Selection for UAV Cluster Networks Based on Distance and Energy Constraints

“…The Kalman filter algorithm is used to predict the position of the UAV at the next moment [25][26][27]. Assuming a constant flight speed, the distance of the UAV can be obtained iteratively, the research calculates the link distance of relay forwarding and selects the nodes whose link distance d xi is less than the average distance d to form the relay alternative set J.…”