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The vehicular sensor network (VSN) is a bright candidate for many applications owing to its particular advantages. Such an application is air pollution monitoring, a critical issue associated directly with public health. We also notice that public transports are well-suited to deploy the network for this type of application. In this article, we propose a practical approach that leverages public buses to build the vehicle network with attached sensors for air pollution monitoring toward minimizing the deployment cost. Therefore, we hereafter call this problem vehicular sensor deployment (VSD). We first convert the VSD issue to a flow network described by a directed graph. Then, based on air sampling requirements, capacity constraints, and flow conservation of the flow network, we formulate the problem as an integer linear programming (ILP) model that yields the optimal solution. In addition, we suggest an approximation algorithm implemented by the linear programming relaxation technique combined with the randomized rounding algorithm (LPR-RRA), which provides approximate solutions to large-scale VSD problems. We also propose a greedy-based algorithm (GBA) to solve the problem against the most effective sampling in each iteration. Finally, we conduct experiments to evaluate the performance of the proposed algorithms as well as the influences of associated factors on the deployment cost. Simulation results show that the approximation algorithm approaches the optimal and outperforms the greedy. [ABSTRACT FROM AUTHOR]

Recently, the problem of scheduling mobile sensors to cover all targets and maintain network connectivity such that the total movement distance is minimized, termed the mobile sensor deployment (MSD) problem, has received a great deal of attention. However, the complexity of the MSD problem remains unknown because no exact proof has been provided before. In this paper, we show that not only the MSD problem, but also its special case, termed the target coverage problem, are NP-hard. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), the sensed data by sensors need to be gathered, so that one very important application is periodical data collection. There is much effort which aimed at the data collection scheduling algorithm development to minimize the latency. Most of previous works investigating the minimum latency of data collection issue have an ideal assumption that the network is a centralized system, in which the entire network is completely synchronized with full knowledge of components. In addition, most of existing works often assume that any (or no) data in the network are allowed to be aggregated into one packet and the network models are often treated as tree structures. However, in practical, WSNs are more likely to be distributed systems, since each sensor’s knowledge is disjointed to each other, and a fixed number of data are allowed to be aggregated into one packet. This is a formidable motivation for us to investigate the problem of minimum latency for the data aggregation without data collision in the distributed WSNs when the sensors are considered to be assigned the channels and the data are compressed with a flexible aggregation ratio, termed the minimum-latency collision-avoidance multiple-data-aggregation scheduling with multi-channel (MLCAMDAS-MC) problem. A new distributed algorithm, termed the distributed collision-avoidance scheduling (DCAS) algorithm, is proposed to address the MLCAMDAS-MC. Finally, we provide the theoretical analyses of DCAS and conduct extensive simulations to demonstrate the performance of DCAS. [ABSTRACT FROM AUTHOR]

In the tracking system, a better prediction model can significantly reduce power consumption in a wireless sensor network because fewer redundant sensors will be activated to keep monitoring the object. The Gauss-Markov mobility model is one of the best mobility models to describe object trajectory because it can capture the correlation of object velocity in time. Traditionally, the Gauss-Markov parameters are estimated using an autocorrelation technique or a recursive least-squares estimation technique; either of these techniques, however, requires a large amount of historical movement information of the mobile object, which is not suitable for tracking objects in a wireless sensor network because they demand a considerable amount of message communication overhead between wireless sensors which are usually battery powered. In this paper, we develop a Gauss-Markov parameter estimator for wireless sensor networks (GMPE_MLH) using a maximum likelihood technique. The GMPE_MLH model estimates the Gauss-Markov parameters with few requirements in terms of message communication overhead. Simulations demonstrate that the GMPE_MLH model generates negligible differences between the actual and estimated values of the Gauss-Markov parameters and provides comparable prediction of the mobile object's location to the Gauss-Markov parameter estimators using an autocorrelation technique or a recursive least-squares estimation. [ABSTRACT FROM PUBLISHER]