Optimal Sensor Association and Data Collection in Power Materials Warehouse Based on Internet of Things

Abstract

In order to realize the intelligent management of a power materials warehouse, the Internet of Things based on wireless sensor networks (WSNs) is a promising effective solution. Considering the limited battery capacity of sensor nodes, the optimization of the topology control and the determination of the amount of collected data are critical for prolonging the survival time of WSNs and increasing the satisfaction of the warehouse supplier. Therefore, in this paper, an optimization problem on sensor association and acquisition data satisfaction is proposed, and the subproblem of the sensor association is modeled as the knapsack problem. To cope with it, the block coordinate descent method is used to obtain the suboptimal solution. A sensor association scheme based on the ant colony algorithm (ACO) is proposed, and the upper and lower bounds of this optimization problem are also obtained. After this, a cluster head selection algorithm is given to find the optimal cluster head. Finally, the experimental simulations show that the algorithms proposed in this paper can effectively improve the energy utilization of WSNs to ensure the intelligent management of a power materials warehouse.

A Two-Stage Decision-Making Method Based on WebGIS for Bulk Material Transportation of Hydropower Construction

“…Among existing literature on the transportation problem of engineering materials, studies about the path optimization problem mainly focus on the optimization algorithm [21,22]. The commonly-applied path optimization methods include the shortest path search al-gorithm [23], the ant colony intelligent algorithm [24], the Dijkstra algorithm [25], the Floyd algorithm [26], etc. For example, Wu et al [27] designed a new fuzzy scheduling optimization system based on the ant colony algorithm for multi-objective transportation paths.…”