In-network data aggregation is a widely used method for collecting data efficiently in wireless sensor networks (WSNs). The authors focus on how to achieve high aggregation efficiency and prolonging networks' lifetime. Firstly, this paper proposes an adaptive spanning tree algorithm (AST), which can adaptively build and adjust an aggregation spanning tree. Owing to the strategies of random waiting and alternative father nodes, AST can achieve a relatively balanced spanning tree and flexible tree adjustment. Then a redundant aggregation scheme (RAG) is illustrated. In RAG, interior nodes help to forward data for their sibling nodes and thus provide reliable data transmission for WSN. Finally, the simulations demonstrate that (1) AST can prolong the lifetime and (2) RAG makes a better trade-off between storage and aggregation ratio, comparing to other aggregation schemes. Wireless sensor networks, as a promising technology, have been attracting more and more interest from the research community for a broad range of applications. Typical WSN applications involve lots of distribute-deployed sensors and one or more base stations (each of which is called the sink). Each sensor can sense, collect, and transmit information to the sink periodically or in events-driven style. However, WSN is challenged by its limited capacity of communication and computation, limited power supply, and high vulnerability to failures. Among all the challenges, power supply is a big issue, especially under the wretched and inaccessible network environments. Thus, it is critical to save energy and collect data efficiently and correctly. On the ground, it has attracted much attention to design communication protocols for topology management and in-network aggregation [1] [2] [3] . The design of in-network aggregation [4-8] is a wellstudied energy efficient data collection method and has been applied in other cases [9] . This owes to the following two aspects. First, if sensors send their original readings directly to the sink without local process, it will usually occupy excessive bandwidth and consume too much energy because of the redundancy in these original readings. Second, what the applications need usually are not the original sensor readings but the processed data. These two aspects make in-network aggregation possible and necessary. The basic idea of innetwork aggregation is that redundant and irrelevant data are discarded and the relevant data are fused into an aggregation result at interior nodes along the transmission paths. Thus, data size and traffic are reduced, which will save considerable energy. 3 4 5 7 10 J 8 J 8 J 4 J 3 J 9 J 7 J 6 J (a) TAG tree 2 1 8 6 3 4 5 7 10 J 8 J 8 J 4 J 3 J 9 J 7 J 6 J (b) E-span tree 2 1 8 6 3 4 5 7 10 J 8 J 8 J 4 J 3 J 9 J 7 J 6 J (c) EE-span tree 2 1 8 6 3 4 5 7 10 J 8 J 8 J 4 J 3 J 9 J 7 J 6 J
In REF , an adaptive spanning tree algorithm (AST) is proposed, which adaptively builds and adjusts an aggregation spanning tree.
An Adaptive Spanning Tree-Based Data Collection Scheme in Wireless Sensor Networks
International Journal of Distributed Sensor Networks
Computer Science