NDCMC: A Hybrid Data Collection Approach for Large-Scale WSNs Using Mobile Element and Hierarchical Clustering

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To collect data from large-scale wireless sensor networks (WSNs) is a challenging issue and there are mainly two approaches to increase the efficiency: 1) by hierarchical routing based on node clustering and 2) by mobile elements (MEs). Since either method has pros and cons, this paper presents a hybrid approach, called node-density-based clustering and mobile collection (NDCMC), to combine the hierarchical routing and ME data collection in WSNs. A number of cluster heads (CHs) gather information from cluster members and then an ME visits these CHs to collect data. First, for a randomly deployed WSN, a new CH selection scheme based on the node density is proposed. The advantage is that the nodes which are surrounded by more deployed nodes are more likely to be CHs. Thus, the efficiency of both intracluster routing and ME data collection is improved. Second, a low-complexity traveling track planning algorithm is designed for an ME to pass by all CHs. The analytical model of NDCMC is also developed and the expectation of the sensor power consumption and network lifetime are derived. In addition, a simple random clustering and mobile collection (RCMC) scheme is introduced by which a number of CHs are selected randomly in a WSN. Although RCMC yields performance degradation, it has much less complexity. Extensive simulations show that the proposed hybrid NDCMC scheme leads to not only remarkable performance improvement but also convenient tradeoff between the network energy saving and the data collection latency.