Uncertainty-Aware Sensor Network Deployment Senouci Mustapha Reda Military Polytechnic School Algiers, Algeria mrsenouci@gmail.com Mellouk Abdelhamid LiSSi Laboratory Paris, France mellouk@u-pec.fr Oukhellou Latifa LiSSi Laboratory Paris, France latifa.oukhellou@u-pec.fr Aissani Amar LRIA Laboratory Algiers, Algeria amraissani@yahoo.fr Abstract—In this paper, we address the issue of handling uncertainty and information fusion for an efficient WSN de- ployment. We present a flexible framework for collaborative target detection within the transferable belief model. Using the developed framework, we propose an uncertainty-aware deployment algorithm that is able to determine the minimum number of sensors and their locations such that full area coverage is achieved. The issues of connectivity, obstacles, preferential coverage, challenging environments and sensor reliability are also discussed. Experimental results are provided to demonstrate the ability of our approach to achieve an efficient sensor deployment by exploiting a collaborative target detection scheme. I. I NTRODUCTION A Wireless Sensor Network (WSN) is comprised of small sensors with limited computational and communication capa- bilities. A typical application of WSN is target detection. In order to detect a target moving through a specific Region of Interest (RoI), sensors have to make local observations of their surrounding environment and collaborate to produce a global decision that reflects the status of the RoI. This collaboration requires local processing of the observations, communication between nodes, and information fusion [9]. Deployment is a fundamental issue in WSN. The number and locations of sensors determine many intrinsic properties of WSN, such as coverage, connectivity, cost and lifetime. A problem which impinges upon the success of any WSN deployment is the fact that sensory data are marred by the flaw of uncertainty. Indeed, sensors may not always provide reliable information, either due to operational tolerance levels or environmental factors. It is therefore very important to take into account this uncertainty in the deployment process. In this work, we explore the problem of uncertainty-aware WSN deployment. More precisely, we address the issue of how to handle uncertainty and information fusion for an efficient WSN deployment. The original contributions of this work are the following: first, we present a flexible framework for collaborative target detection within the Transferable Belief Model (TBM) [8], a model for the representation of quantified uncertainty based on belief functions. Second, using the de- veloped framework we conceive an uncertainty-aware deploy- ment algorithm, which determines the minimum number of sensors and their locations to ensure full area coverage. Third, we discuss some related issues, such as connectivity, obstacles, preferential coverage, challenging environments and sensor reliability. Experimental results are given to demonstrate the application of our approach. The rest of the paper is organized as follows. Related works will first be recalled in Section II. Section III gives an overview of the TBM. In Section IV, we show how target detection can be performed within the TBM, and we detail our deployment algorithm in Section V. Section VI presents our experiments. Section VII concludes the paper and discusses some future directions for our work. II. RELATED PRIOR WORK There has been much related research on the coverage problem for WSN [10]. In [3], the authors use a sequential deployment of sensors i.e., a limited number of sensors are deployed in each step until the desired probability of detection of a target is achieved. Sensor placement on two- and three- dimensional grids was formulated as a combinatorial optimiza- tion problem, and solved using integer linear programming [2]. The main drawback of these approaches is the fact that the grid coverage approach relies on ”perfect” sensor detection, i.e., a sensor is expected to yield a binary yes/no detection outcome in every case. In [5], [4], [12], [1] sensor detection is modeled probabilisti- cally. In [1], [5], the authors formulate the sensor placement as an optimization problem and propose heuristics to optimize the number of sensors and determine their placement to provide sufficient grid coverage of the RoI. In [4], the authors proposed two deterministic deployment algorithms. Uncertainty associ- ated with the predetermined sensor locations is considered in [12]. The authors propose two sensor placement algorithms, where the sensor location is modeled as a random variable with a Gaussian probability distribution. To the best of our knowledge, all related works assume either a binary or a probabilistic coverage model. The binary coverage model is overly simplistic and does not reflect reality, while the probabilistic coverage model is limited and does not allow the easy integration of some related issues, such as sensor reliability. In this work, we define an evidence-based coverage model, which is a generalization of the probabilistic model. The proposed model reflects reality well and offers flexibility and capabilities that are not available in the proba- bilistic model. Before we define our model, let us first present the TBM. III. OVERVIEW OF THE TBM The Transferable Belief Model (TBM) introduced in [8], is a particular interpretation of the so-called Dempster-Shafer 978-1-4244-9268-8/11/$26.00 ©2011 IEEE This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE Globecom 2011 proceedings.