Data Aggregation Tree Research Articles

Energy efficient routing and scheduling for real-time data aggregation in WSNs

In this paper, we study the problem of constructing an energy efficient data aggregation tree for data gathering in wireless sensor networks. We consider a real-time scenario where the data aggregation must be performed within a specified latency constraint. The objective is to minimize the overall energy cost of the sensor nodes in the aggregation tree subject to the latency constraint. We propose an algorithm that can construct a data aggregation tree with theoretically bounded energy cost under the latency constraint. Extensive simulations show that the proposed algorithm is very efficient in energy-saving under the latency constraint and has better performance than other existing algorithms.

Energy-Efficient Data-Centric Routing in Wireless Sensor Networks

Incorporating sensor nodes with data aggregation capability to transmit less data flow in wireless sensor networks could reduce the total energy consumption. This calls for the efficient and effective data-centric routing algorithm to facilitate this advantage. In the first part of this paper, we model the data-centric routing problem by rigorous mixed integer and linear mathematical formulation, where the objective function is to minimize the total transmission cost subject to multicast tree constraints. With the advancement of sensor network technology, sensor nodes with configurable transmission radius capability could further reduce energy consumption. The second part of this paper considers the transmission radius assignment of each sensor node and the data-centric routing assignment jointly. The objective function is to minimize the total power consumption together with consideration of construction of a data aggregation tree and sensor node transmission radius assignment. The solution approach is based on Lagrangean relaxation in conjunction with the novel optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithms calculate better solution than other existing heuristics with improvement ratio up to 169% and 59% with respect to fixed transmission radius and configurable transmission radius for network with 300 random generated nodes.