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Top1. Introduction
Advances in wireless communication and digital electronics have enabled a new generation of distributed computing environment that is wireless sensor networks. A wireless sensor network consists of a number of wireless sensor nodes. Thus recent progress in wireless communications, micro-electro-mechanical systems and electronics have enabled the development of low-cost, low power, multifunctional sensor nodes that are small in size and communicate unchained in short distances. These tiny sensor nodes, which consist of sensing, data processing, and communicating components, leverage the idea of sensor networks. Sensor networks represent a significant improvement over traditional sensors. A sensor network is composed of a large number of sensor nodes that are densely deployed either inside the phenomenon or very close to it.
A Wireless Sensor Network (WSN) is designed to gather and process data from the environment in order to understand behavior of the monitored area. The areas of applications of Wireless Sensor Networks (WSNs) vary from civil, healthcare and environmental to military. Examples of applications include target tracking in battlefields, habitat monitoring, civil structure monitoring, forest fire detection, vehicle tracking and forest maintenance (T. Bokareva et al., 2006; A. Mainwaring et al., 2002; N. Xu et al., 2004)
Low power consumption is a critical factor to be considered in the hardware and architectural design and also in the design of algorithms and network protocols at all layers of the network architecture. Therefore, an important design objective is to maximize the network lifetime by decreasing the network energy consumption. The network protocols designed have to deal with collaboration, redundancy, data fusion, and node mobility issues in the network topology, noisy environment and uncertainty issues in the real world. Data Fusion a discipline that is concerned with how data collected from multiple sources can be processed to obtain improved data or greater relevance (F. Nakamura et al., 2007). There exists general literature on data fusion (Bahador Khaleghi et al., 2013; C.Intanagonwiwat et al., 2000) relies on the specific data related challenging aspects addressed and explores each method on a data centric taxonomy.
The coverage algorithms proposed are either centralized, or distributed and localized. In routing driven algorithms as indicated by researchers (B.Krishnamachari et al., 2002; C.Instanagonwiwat et al., 2000) data is routed through shortest paths to the sink, with aggregation taking place opportunistically when data flows. In aggregation driven routing algorithm routing paths are heavily dependent on data correlation in order to fully benefit from information reduction resulted from data aggregation.
The proposed Adaptive Data Fusion for Energy Efficient Routing (ADFER) in Wireless Sensor Network (H Luo et al., 2006) uses an aggregation based distributed routing algorithm for data aggregation i.e. Adaptive Fusion Steiner Tree (AFST) which jointly optimizes over transmission as well as aggregation cost, and aggregation benefit is calculated according to data correlation, aggregation cost and transmission cost. It is implemented by individual sensor nodes in a distributed fashion relying on local information and also adaptively adjusts along the information routes and decides whether fusion shall be performed at a particular node or not. Adaptive Fusion Steiner Tree (AFST) entails centralized aggregation decision and routing is computed at the sink node and needs plenty of communication to distribute the results to source nodes.