Location Estimation of Nodes in Underwater Acoustic Sensor Networks, Journal of Telecommunications and Information Technology, 2021, nr 1

The paper presents a location estimation scheme for underwater acoustic sensor networks. During the first phase, the sink node begins the trapezoid formation process by activating the trapezoid formation agent. It stores relevant information in the sink’s knowledge base and in the node’s knowledge base, and also develops the search data structure required for locating the node. During the second phase, the position of the node is determined by utilizing the search data structure. Identification of the location of all nodes by traveling across the trajectory may be performed as well, as an alternative approach. When identifying the location of one node, the estimation is performed based on the search data structure. When determining the position of all nodes, the sink node agent travels along the defined trajectory and transmits beacon messages which contain the real-time location at specific points. The anchor node agent measures the signal strength and localizes itself and begins estimating the locations of other nodes within the trapezoids, using location estimation techniques. Various performance parameters are used to validate the proposed scheme

Location Estimation of Nodes in Underwater Acoustic Sensor Networks

The paper presents a location estimation scheme for underwater acoustic sensor networks. During the first phase, the sink node begins the trapezoid formation process by activating the trapezoid formation agent. It stores relevant information in the sink’s knowledge base and in the node’s knowledge base, and also develops the search data structure required for locating the node. During the second phase, the position of the node is determined by utilizing the search data structure. Identification of the location of all nodes by traveling across the trajectory may be performed as well, as an alternative approach. When identifying the location of one node, the estimation is performed based on the search data structure. When determining the position of all nodes, the sink node agent travels along the defined trajectory and transmits beacon messages which contain the real-time location at specific points. The anchor node agent measures the signal strength and localizes itself and begins estimating the locations of other nodes within the trapezoids, using location estimation techniques. Various performance parameters are used to validate the proposed scheme

Tree Based Aggregation and Routing in WSN: A Multi-agent System Approach

In WSN one of the issues is to route the data from the sensor nodes to sink node. The tree-based approach provides an efficient solution for establishing the path and in network aggregation. Tree is nonlinear structures, which have hierarchical levels in terms of parent-child combination. In this work the tree is constructed using the software agents. The proposed scheme uses multiagent system that comprises of both static and mobile agents. On every sensor node of WSN agent platform is running that coordinates the agent communication. Tree Construction Agent (TCA) is mobile agent that is generated at the sink node. TCA uses the angle of constraint for the construction of the tree. In the proposed work along the tree in network aggregation is done that saves the energy and reduces the delay. Aggregation Agent (AA) gets the routing information and visits the nodes along the path (tree) for data aggregation based the correlation of the sensor node data. Finally the aggregation agent routes the aggregated data to the sink node