Abstract—In directional sensor networks, sensors can steer around to serve multiple target points. Most previous works assume there are always enough deployed sensors so that all target points can be served simultaneously. However, this assumption may not hold when the mission requirement changes or when more target points need to be served. Since it is not always practical to deploy new sensors, we propose to reconfigure the network by letting existing sensors steer and serve the targets periodically. As a result, targets may not be served continuously, and the service delay affects the quality of service. One important problem is how to choose the optimal set of targets to serve by each sensor such that the maximum service delay is minimized. We first show that this problem is NP-complete, and then we propose a centralized protocol whose performance is bounded by a logarithm factor of the optimal solution. We also propose a distributed protocol which achieves the same performance as the centralized protocol. Finally, we extend the optimization model and the protocols by considering the rotation delay, which is critical for some applications but ignored by previous work. I.

Abstract—Camera sensors are different from traditional scalar sensors as different cameras from different positions can form distinct views of the object. However, traditional disk sensing model does not consider this intrinsic property of camera sensors. To this end, we propose a novel model called full-view coverage. An object is considered to be full-view covered if for any direction from 0 to 2π (object’s facing direction), there is always a sensor such that the object is within the sensor’s range and more importantly the sensor’s viewing direction is sufficiently close to the object’s facing direction. With this model, we propose an efficient method for full-view coverage detection in any given camera sensor networks. We also derive a sufficient condition on the sensor density needed for full-view coverage in a random uniform deployment. Finally, we show a necessary and sufficient condition on the sensor density for full-view coverage in a triangular lattice based deployment. I.

Static wireless networks are by now quite well understood mathematically through the random geometric graph model. By contrast, there are relatively few rigorous results on the practically important case of mobile networks. In this paper we consider a natural extension of the random geometric graph model to the mobile setting by allowing nodes to move in space according to Brownian motion. We study three fundamental questionsinthismodel: detection (the time until a given target point—which may be either fixed or moving—is detected by the network), coverage (the time until all points inside a finite box are detected by the network), and percolation (the time until a given node is able to communicate with the giant component of the network). We derive precise asymptotics for these problems by combining ideas from stochastic geometry, coupling and multi-scale analysis. We also give an application of our results to analyze the time to broadcast a message in a mobile network.

With over 60 % of the world’s population using cellphones, the need for ubiquitous and broadband wireless network connectivity has become ever more important. Limitations and the time-varying nature of resources such as network capacity and battery capacity pose significant challenges in achieving such objectives. Further, the deployment and management costs of large infrastructures impose additional constraints. With the increasing array of sensors that are becoming standard features on cellphones, these devices are also uniquely positioned to sense our surrounding environment at scale. These capabilities are also being embedded in many smart pervasive devices in a wide range of scientific disciplines. The problem of sensing at scale, however is complicated by various additional factors including renewable energy resources, wireless interference, and physical faults. My research focuses on systematically analyzing such challenges, developing the foundations, and then designing practical systems with the vision of making ubiquitous broadband coverage and large-scale sensing a reality.

In this paper, we introduce a new concept of coverage for wireless sensor networks, called Directed Coverage (D-Coverage). D-Coverage is the coverage provided by a sensor network monitoring an area between two boundaries, through which the intruder attempts to penetrates the area. We also study how to measure the quality of D-Coverage. Our first measurement approach is a projection-based simple approach. Our second one is more comprehensive, which is based on Markov chain. These approaches can accurately evaluate the quality of D-Coverage, as well as provide sound guidelines for sensor network deployment and run-time repair to achieve desired D-Coverage quality.

Abstract—Coverage problem is a fundamental issue in wireless ad hoc and sensor networks. Previous techniques for coverage scheduling often require accurate location information or range measurements, which cannot be easily obtained in resource-limited ad hoc and sensor networks. Recently, a method based on algebraic topology has been proposed to achieve coverage verification using only connectivity information. The topological method sheds some light on the issue of location-free coverage. Unfortunately, the needs of centralized computation and rigorous restriction on sensing and communication ranges greatly limit the applicability in practical large-scale distributed sensor networks. In this work, we make the first attempt towards establishing a graph theoretical framework for connectivity-based coverage with configurable coverage granularity. We propose a novel coverage criterion and scheduling method based on cycle partition. Our method is able to construct a sparse coverage set in a distributed manner, using purely connectivity information. Compared with existing methods, our design has a particular advantage, which permits us to configure or adjust the quality of coverage by adequately exploiting diverse sensing ranges and specific requirements of different applications. We formally prove the correctness and evaluate the effectiveness of our approach through extensive simulations and comparisons with the state-of-the-art approaches. Keywords-wireless ad hoc and sensor networks; coverage; distributed; connectivity; topological graph; cycle partition; I.

Abstract—As sensors are energy constrained devices, one challenge in wireless sensor networks (WSNs) is to guarantee coverage and meanwhile maximize network lifetime. In this article, we leverage prediction to solve this challenging problem, by exploiting temporal-spatial correlations among sensory data. The basic idea lies in that a sensor node can be turned off safely when its sensory information can be inferred through some prediction methods, like Bayesian inference. We adopt the concept of entropy in information theory to evaluate the information uncertainty about the region of interest (RoI). We formulate the problem as a minimum weight submodular set cover problem, which is known to be NP hard. To address this problem, an efficient centralized truncated greedy algorithm (TGA) is proposed. We prove the performance guarantee of TGA in terms of the ratio of aggregate weight obtained by TGA to that by the optimal algorithm. Considering the decentralization nature of WSNs, we further present a distributed version of TGA, denoted as DTGA, which can obtain the same solution as TGA. The implementation issues such as network connectivity and communication cost are extensively discussed. We perform real data experiments as well as simulations to demonstrate the advantage of DTGA over the only existing competing algorithm [1] and the impacts of different parameters associated with data correlations on the network lifetime.