On Data Fusion and Lifetime Constraints in Wireless Sensor Networks Xiaodong Wang, Demin Wang, Yun Wang and Dharma P. Agrawal Center for Distributed and Mobile Computing Computer Science Department, Univ. of Cincinnati Email: {wangxd, wangdm, wany6, dpa}@ececs.uc.edu Amitabh Mishra ECE Department Virginia Tech Email: mishra@vt.edu Abstract—The problems of energy efficient sensor network configuration and local fusion of sensed data have been addressed separately in the previous works. In this paper, we propose to analyze the sensing accuracy with the consideration of the node distribution and fusion overhead in terms of the energy con- sumption. By extending existing sensing model in the literature, we quantify how the sensing ranges and node densities of sensor nodes impact the sensing accuracy, and how it affects the fusion overhead and the sensing lifetime of the network. We identify the tradeoff between sensing accuracy and energy consumption in an analytical framework. It can be used to choose optimal parameters of sensor networks to meet the specified sensing or lifetime requirements. I. I NTRODUCTION Equipped with different sensing hardware, sensors can per- form detection and sensing on different environment properties for different applications. Meanwhile, advanced wireless tech- nologies have enabled sensors self-organize and communicate with the infrastructure. With the enabling wireless technolo- gies, a wireless sensor network provides a viable approach for human interfacing with the environment. Wireless sensor networks are useful in many applications like environment monitoring and battle-field management [1]. There exist unique challenges in designing large-scale sen- sor networks for sensing tasks [2]. These challenges can be summarized into two fundamental problems: achieving effi- cient sensing, and efficient communication under the stringent power constraint as well as the sheer network size. These two problems have been addressed separately in the literature. Local fusion (i.e. aggregation) has been recognized as an effective approach in sensor network designs [3], [4]. Sensors process sensed data locally before transmitting it. This can save energy since it can reduce dramatically the amount of data to transport through the network. Obviously, the sensing accuracy can be improved by combining sensed data from multiple local sensors sensing the event. Sensing accuracy and fault tolerance have been researched from a signal detection’s perspective in [3], [5], where the detection missing probability and false alarm probability are used as sensing accuracy metrics. Intuitively, available number of sensor nodes to perform local fusion is dependent on the node density, sensing range and the transmission range; and the number of sensor nodes to perform fusion determines the effectiveness of local fusion. On the other hand, performing local fusion on too many local sensor nodes will introduce high communication overhead, since the sensed data from individual node needs to be transmitted to share with local neighbors. To the best of our knowledge, how the network parameters, such as transmission range, sensing range, and node density, affects the sensing accuracy and sensing lifetime has not been fully investigated in the literature. In this paper, we characterize how the number of coop- erating local sensor nodes in a randomly deployed sensor network impacts the overall sensing accuracy. The tradeoff in sensing accuracy and sensing lifetime is identified. Based on our analytical framework, we can choose optimal network parameters to achieve the best lifetime under the sensing accuracy constraint or achieve the best sensing accuracy under the sensing lifetime constraint. This paper is organized as follows. In the next section, we introduce the sensing and fusion model, and characterize the sensing accuracy. In section III, we analyze the sensing lifetime. Section IV is application of our results in sensor net- work design under lifetime and sensing accuracy constraints. We discuss related works in section V. Conclusion and future works are the last part. II. SENSING ACCURACY ANALYSIS A. Local sensing fusion Due to the large size of the sensor network, it is energy- expensive to transport the sensed data or sensed decisions indi- vidually to the centralized processor. Decentralized processing of sensed data has been shown as a viable approach to reduce the burden of data transportation [3]. On the other hand, exploiting the spatial and temporal corre- lation among sensed data by combining the sensed information from local neighbors, and performing local data fusion can improve the sensing accuracy. We consider a sensor network with homogenous sensor nodes. To simplify the analysis, we assume a simple deter- ministic sensing model [6], where a neighbor node can be reached when it is within the transmission range (denoted by r c ), and a target can be sensed if the target is located within the sensing range (denoted by r s ). As shown in Fig. 1, the target is sensed by three nearby sensors. r c ≥ 2r s can ensure the sensors sensing the event to communicate with each other. Since the power consumption 1-4244-0353-7/07/$25.00 ©2007 IEEE This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings.