Wireless sensor networks are becoming increasingly important in both the civilian and military fields. The object of this study is the process of collecting data by a telecommunication aerial platform from network nodes under conditions of their remoteness from the telecommunication infrastructure. Most available papers consider a solution to partial problems related to the process of data acquisition by a telecommunication aerial platform: clustering of the network, search for the shortest flight route, minimization of energy costs of nodes, etc. Therefore, an improved method of direct data collection by a telecommunication air platform is proposed, which consistently and comprehensively solves these problems. Unlike existing methods, it takes into consideration several objective functions (optimization of data collection time by a telecommunication air platform and a network functioning time), parameters of the state of nodes and clusters, as well as makes it possible to obtain solutions in real time. A special feature of the proposed method is the search for the optimal solution according to the hierarchy: network – cluster – node. At the network level, the following is optimized: the number of clusters of a certain dimensionality and the trajectory of the cluster flyby. At the cluster level, the points (intervals) of data collection during the freezing (in motion) of the telecommunication air platform and the trajectory of its flight within a cluster are determined. At the node level, its energy consumption is minimized by reducing the distance to the telecommunication aerial platform. The trajectory of the platform within a cluster is calculated according to the developed rule base. The rules implement the method of situational management. The conditions of application are the parameters of the state of the nodes, the solutions are the parameters of the trajectory of a telecommunication aerial platform, and the intervals of data acquisition. The rules take into consideration the priority of the objective functions, the state of the parameters of the cluster nodes, and the previously made basic decision on the trajectory of the flyby. The simulation results show that the application of the method reduces the time of data collection up to 15 % or increases the network functioning time to 17 %.
A telecommunication aerial platform (TA) based on an unmanned aerial vehicle is considered a promising technology for data collection in wireless sensor networks in the absence of connectivity between network nodes and any communication infrastructure. In fact, TA acts as a mobile gateway and could collect data from several nodes at the same time. The trajectory of its movement, location of points and data exchange intervals significantly affect the efficiency of the data collection process. The article considers a model of situational control of flight trajectory construction for data collection to achieve certain target functions: optimization of data collection time and network operation time. In the work, a set of situations on the network is classified and corresponding production rules for building a TA trajectory are defined, which implement a defined decision-making hierarchy: network, cluster, TA, node, considering the target management functions. At the network level, the rules for determining the number and size of clusters are applied, and a basic solution for determining the collection points and the trajectory of their flight is being built. At the level of each cluster, the TA adjusts the basic solution during the flight based on considering the parameters of the actual state of the cluster nodes. At the level of TA-node interaction, the energy consumption of the node and the speed of data transmission are optimized by reducing the distance of the node to the telecommunication aerial platform. To reduce the number of rules, meta-rules are proposed. This approach allows you to achieve optimization of the target functions of the data collection process and ensure decision-making in real time. The simulation results proved the possibility of reducing data collection time by 10–15 % or increasing network operation time by 12–17 % compared to existing solutions.
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