Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks

Abstract

We propose localized, self organizing, robust, and energy-efficient data aggregation tree approaches for sensor networks, which we call Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs). They are based on topologies, such as LMST and RNG, that can approximate minimum spanning tree and can be efficiently computed using only position or distance information of one-hop neighbors. The actual routing tree is constructed over these topologies. We also consider different parent selection strategies while constructing a routing tree. We compare each topology and parent selection strategy and conclude that the best among them is the shortest path strategy over LMST structure. Our solution also involves route maintenance procedures that will be executed when a sensor node fails or a new node is added to the network. The proposed solution is also adapted to consider the remaining power levels of nodes in order to increase the network lifetime. Our simulation results show that by using our power-aware localized approach, we can almost have the same performance of a centralized solution in terms of network lifetime, and close to 90 percent of an upper bound derived here.

Energy-Efficient Node Selection Algorithms with Correlation Optimization in Wireless Sensor Networks

“…Secondarily, nodes in a given cover set CS are sorted into a sequence with descending order of their deviation to (Line 15), and they are moved to another cover set with smaller size (Line [16][17][18][19]. This process continues until all nodes in the cover set are checked (Line [14][15][16][17][18][19][20].…”

“…In-network data aggregation [12][13][14][15][16][17][18] is another approach to reduce the amount of data transmitted by the nodes and prolong the network lifetime. It performs data aggregation in network to reduce the amount of data transmission by constructing a routing tree.…”

“…Al-Karaki et al [14] present a Grid-based Routing and Aggregator Selection Scheme (GRASS), which achieves low-energy dissipation and low-latency without sacrificing quality. Seyin et al [15] propose a localized and energy-efficient data aggregation tree approach called Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs) for sensor networks. Gao et al [16] jointly adopt the cooperative multiple-input-multiple-output and data-aggregation techniques to reduce the energy consumption per bit in wireless sensor network by reducing the amount of data for transmission and better using network resources through cooperative communication.…”

COSE: A Query-Centric Framework of Collaborative Heterogeneous Sensor Networks

“…Note that COSE determines the schedule of sensor networks for query processing along the pipeline. It does not affect the original transfer/aggregation mechanism [7], [8] of any sensor network.…”

Leveraging Prediction to Improve the Coverage of Wireless Sensor Networks

“…L AST decade has witnessed the rapid development of wireless sensor networks (WSNs) [2], [3], [4], [5], [6]. Consisting of a large number of small-size limitedcapability sensor nodes, WSNs are widely used in a variety of application scenarios such as surveillance and environment monitoring.…”