Clustering is one of the important methods for prolonging the network lifetime in wireless sensor networks (WSNs). It involves grouping of sensor nodes into clusters and electing cluster heads (CHs) for all the clusters. CHs collect the data from respective cluster’s nodes and forward the aggregated data to base station. A major challenge in WSNs is to select appropriate cluster heads. In this paper, we present a fuzzy decision-making approach for the selection of cluster heads. Fuzzy multiple attribute decision-making (MADM) approach is used to select CHs using three criteria including residual energy, number of neighbors, and the distance from the base station of the nodes. The simulation results demonstrate that this approach is more effective in prolonging the network lifetime than the distributed hierarchical agglomerative clustering (DHAC) protocol in homogeneous environments.

The presence of cluster heads (CHs) in a clustered wireless sensor network (WSN) leads to improved data aggregation and enhanced network lifetime. Thus, the selection of appropriate CHs in WSNs is a challenging task, which needs to be addressed. A multicriterion decision-making approach for the selection of CHs is presented using Pareto-optimal theory and technique for order preference by similarity to ideal solution (TOPSIS) methods. CHs are selected using three criteria including energy, cluster density and distance from the sink. The overall network lifetime in this method with 50% data aggregation after simulations is 81% higher than that of distributed hierarchical agglomerative clustering in similar environment and with same set of parameters. Optimum number of clusters is estimated using TOPSIS technique and found to be 9–11 for effective energy usage in WSNs.

This study presents and tests the accuracy of a low‐cost triboelectric sensor for speed measurement and weight estimation of passenger vehicles in motion. Polytetrafluoroethylene and aluminium films are employed as sensing elements for generating voltage pulses due to movement of vehicles in real time. A number of road tests by 880 and 1020 kg cars in the speed range from 10 to 60 kmph show that the sensor is effective for measuring speeds with more than 95% accuracy and 3% mean absolute percentage error. For weight estimation, it is found that the voltage signals obtained from sensor due to impact between the tires of the vehicles and sensor varies linearly with speed ensuring the reliability of the sensor. On road experimental results reveal that the weight can be predicted from voltage signals by maintaining a database of different class of vehicles. The results and the overall concept indicate that the triboelectric sensor and associated wireless setup is promising for smart traffic‐monitoring applications at a very low cost.