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Wireless sensor networks (WSNs) are being applied widely for data collection, especially to carry out the mission-critical tasks. Therefore, one of the most challenging tasks of mission-critical sensors and sensor networks is the development of energy efficient (EE) routing protocols. Comparing with flat routing protocols, more EE can be achieved in hierarchical routing protocols. In this paper, we propose an enhanced balanced energy efficient network-integrated super-heterogeneous (E-BEENISH) routing protocol, by analyzing communication energy consumption of the clusters and a large range of energy levels in heterogeneous WSNs. E-BEENISH is based on weighted election probabilities of each node to become a cluster head according to the remaining energy and the distance from the sink to the node. Moreover, we also study the impact of the heterogeneity of nodes in terms of energy. Studying the sensitivity of our stable election protocol, we conclude heterogeneity parameters capturing energy imbalance in the network and find that the E-BEENISH yields the longer stability region for the suitable weight of energy and distance. Our results show by simulation that the E-BEENISH can improve system lifetime by an order of magnitude compared to obtained using current clustering protocols, which is crucial for many applications. INDEX TERMS Wireless sensor networks, residual energy, heterogeneity, routing protocol.

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks.

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