Abstract
Several algorithms have already been provided for problems of data aggregation in wireless sensor networks, which somehow tried to increase networks lifetimes. In this study, we dealt with this problem using a more efficient method by taking parameters such as the distance between two sensors into account. In this paper, we presented a heuristic algorithm based on distributed learning automata with variable actions set for solving data aggregation problems within stochastic graphs where the weights of edges change with time. To aggregate data, the algorithm, in fact, creates a stochastic minimum spanning tree (SMST) in networks where variable distances of links are considered as edges, and sends data in the form of a single packet to central node after data was processed inside networks. To understand this subject better, we modeled the problem for a stochastic graph having edges with changing weights. Although this assumption that edges weights change with time makes our task difficult, the results of simulations indicate relatively optimal performance of this method.
Highlights
Wireless sensor networks consist of a large number of inexpensive sensor nodes distributed densely in the environment, having limited energy and on the other hand, consuming a great deal of energy in order to send information to central node directly
We presented a heuristic algorithm based on distributed learning automata with variable actions set for solving data aggregation problems within stochastic graphs where the weights of edges change with time
The algorithm, creates a stochastic minimum spanning tree (SMST) in networks where variable distances of links are considered as edges, and sends data in the form of a single packet to central node after data was processed inside networks
Summary
A learning automata-based algorithm using stochastic minimum spanning tree for improving life time in wireless sensor networks. We presented a heuristic algorithm based on distributed learning automata with variable actions set for solving data aggregation problems within stochastic graphs where the weights of edges change with time. The algorithm, creates a stochastic minimum spanning tree (SMST) in networks where variable distances of links are considered as edges, and sends data in the form of a single packet to central node after data was processed inside networks. To understand this subject better, we modeled the problem for a stochastic graph having edges with changing weights.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
