Sensor networks are increasingly used to control and monitor industrial and manufacturing processes. In this paper, we consider the problem of optimizing a cost function for wireless sensor networks of this kind under energy consumption constraints. We focus, in particular, on the problem of coverage optimization through scheduling. Following existing approaches, we use a mixed integer linear program formulation. We show how to use partitioning techniques to decompose the problem into separate subproblems, solved individually, overcoming the exponential complexity typical of integer linear programming, while minimizing the loss in optimality. In addition, we evaluate the achieved degree of optimality by computing relatively tight bounds with respect to the optimal solution. Finally, we employ simple but effective heuristics to further improve our solution. The results show that our procedure is very efficient and scalable, and is able to find solutions that are very close to optimal. These characteristics make our approach a perfect fit for large and fixed deployments of wireless sensors, typical in factory automation and industrial applications. To show the generality of the approach, we apply our methodology to three different models of varying complexity.

Scalable Offline Optimization of Industrial Wireless Sensor Networks

Wireless sensor networks (WSNs) have a broad range of applications, such as battlefield surveillance, environmental monitoring, and disaster relief. These networks usually have stringent constraints on the system resources, making data-extraction and aggregation techniques critically important. However, accurate data extraction and aggregation is difficult, due to significant variations in sensor readings and frequent link and node failures. To address these challenges, we propose data-aggregation techniques based on statistical information extraction that capture the effects of aggregation over different scales. We also design, in this paper, an accurate estimation of the distribution parameters of sensory data using the expectation-maximization (EM) algorithm. We demonstrate that the proposed techniques not only greatly reduce the communication cost but also retain valuable statistical information that is otherwise lost in many existing data-aggregation approaches for sensor networks. Moreover, simulation results show that the proposed techniques are robust against link and node failures and perform consistently well in broad scenarios with various network configurations.

Parameter-Based Data Aggregation for Statistical Information Extraction in Wireless Sensor Networks

Target counting in sensor-based surveillance systems is an interesting task that potentially could have many important applications in practice. In such a system, each sensor outputs the number of targets in its sensing region, and the problem is how one can combine all the reported numbers from sensors to provide an estimate of the total number of targets present in the entire monitored area. The main challenge of the problem is how to handle different sensors' outputs that contain some counts of the same targets falling into the overlapped area from these sensors' sensing regions. This paper introduces a statistical approach to estimate the target count in such a surveillance system. Our approach avoids direct handling of the overlapping issue by adopting statistical methods. First, depending on whether or not certain prior knowledge is available regarding the target distribution, the procedure in minimizing the residual sum of squares or kernel regression is used to estimate the distribution of targets. Then the estimated count of the total targets is obtained by the method of likelihood estimation based on a sequence of binomial distributions that are derived from a sampling procedure. Comparisons based on simulations show that our proposed counting approach outperform the state of art counting algorithms. Extensive simulations also show that our proposed approach is very fast and very promising in estimating the target count in sensor-based surveillance systems.

https://www.seas.gwu.edu/~cheng/Publications/2012/TargetCounting-INFOCOM12.pdf

A statistical approach for target counting in sensor-based surveillance systems

Target counting is an established challenge for sensor networks: given a set of sensors that can count (but not identify) targets, how many targets are there? The problem is complicated because of the need to disambiguate duplicate observations of the same target by different sensors. A number of approaches have been proposed in the literature, and in this paper we take an existing technique based on Euler integration and develop a fully-distributed, self-stabilising solution. We derive our algorithm within the field calculus from the centralised presentation of the underlying integration technique, and analyse the precision of the counting through simulation of several network configurations.

/pdf/self-stabilising-target-counting-in-wireless-sensor-networks-5dghrhandl.pdf

Self-Stabilising Target Counting in Wireless Sensor Networks Using Euler Integration

Target counting in wireless sensor networks has attracted a lot of attention in recent years from both academia and industry. In this chapter, we review various problem formulations and technical approaches proposed in recent literature for target counting. Major existing works are classified into the following four categories: binary counting, numeric counting, energy counting, and compressive counting, based on the sensing capabilities of the network and the underlying theoretical foundations of the technical approaches. Within each category, we summarize the representative works according to their objectives, technical methods, performances, and advantages and disadvantages. Comparative evaluations are provided to illustrate the influence of different sensor network settings on the target counting accuracy. The applicable environments of these algorithms are also discussed at the end of the chapter.

Target Counting in Wireless Sensor Networks

Fast track article: On accurate and efficient statistical counting in sensor-based surveillance systems

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Fast track article: On accurate and efficient statistical counting in sensor-based surveillance systems