Wireless sensor networks (WSNs) have been used extensively in a range of applications, which realizes data acquisition,\nprocessing, transmission, and analysis in an interesting area. Harsh surroundings and their inherent vulnerability often\nmean that these networks suffer from simultaneous node failure possibly causing the network to become partitioned\ninto multiple disjointed segments. This in turn can prevent the gathering of data from the sensors and subsequent\ntransmission to the sink, causing the whole network to fail. In this paper, a strategy is presented for restoring\nmulti-objective optimization connectivity of these segments using mobile data collectors (MDCs), by considering the\nsegments as collections of sensor nodes and not as some representative node. Different from existing uses of MDCs\nfor restoration, the delay in data collection and task balance is considered, and the network connectivity and data\nacquisition path optimization problem are transformed into an improved multi-travelling salesman problem (iMTSP).\nAn improved multi-objective optimization genetic algorithm for solving the optimal collection data collector position\nand moving paths is proposed, which introduces virtual segments and hierarchical chromosome structure, improved\npopulation diversity, and custom coding and decoding. The simulation results show that the proposed method can\neffectively solve the iMTSP of the Pareto optimal solution and can provide a new strategy for connectivity-restoring\ntechnology in WSNs. Compared with NSGA-II, the diversity of the proposed gene algorithm represents a clear\nimprovement.
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