Barrier Coverage for 3D Camera Sensor Networks

Barrier coverage is a critical issue in wireless sensor networks for security applications (e.g., border protection) where directional sensors (e.g., cameras) are becoming more popular and advantageous than omni-directional scalar sensors for the extra dimensional information they provide. However, barrier coverage can not be guaranteed after initial random deployment of sensors, especially for directional sensors with limited sensing angles. In this paper, we study how to efficiently achieve barrier coverage in hybrid directional sensor networks by moving mobile sensors to fill in gaps and form a barrier with stationary sensors. In specific, we introduce the notion of directional barrier graph to model the barrier coverage formation problem. We prove that the minimum number of mobile sensors required to form a barrier with stationary sensors is the length of the shortest path from the source node to the destination node on the directional barrier graph. We then formulate the problem of minimizing the cost of moving mobile sensors to fill in the gaps on the shortest path as a minimum cost bipartite assignment problem, and solve it in polynomial time using the Hungarian algorithm. Both analytical and experimental studies demonstrate the effectiveness of the proposed algorithm.

By taking advantage of active radio waves, radar sensors can provide high-accuracy target detection over traditional passive sensors. In this paper, we study barrier coverage in bistatic radar sensor networks (BRSNs), which consist of a set of transmitter radars and receiver radars. Barrier coverage in BRSNs is much more difficult than that in traditional sensor networks as the sensing area of a bistatic radar depends on the positions of both transmitter and receiver, and is typically a Cassini oval. Moreover, different transmitters and receivers can pair with each other by choosing the same frequency and thus the sensing network topology can be quite different in different time slots. To tackle this challenge, we first investigate the characteristic of the ε-covered area of a bistatic radar, then we represent a bistatic radar with a virtual point at the middle point of the line segment formed by the transmitter and receiver. With these representations, we formulate the barrier coverage problem in BRSNs as (k, ε)-Minimum Weight Barrier Coverage Problem ((k, ε)-MWBCP). By constructing a directed coverage graph, we transform the (k, ε)-MWBCP into finding k node-disjoint shortest paths and propose an energy-efficient algorithm called (k, ε)-MWBCA to solve the problem within polynomial time. Extensive simulations are conducted to demonstrate the performance of our proposed algorithm.

Leveraging data fusion to improve barrier coverage in wireless sensor networks

This paper concerns an issue that, given a certain amount of sensors, how to organize sparse coverage so that the sensor network has greater ability of event detection and fault tolerance. Existing sparse coverage sensor networks often concern stochastic coverage. Unauthorized travelers may pass through the randomly deployed sensors without being detected. Barrier coverage is a model of in-belt stochastic coverage. We apply barrier coverage on a network scale to form sparse coverage. Our approach constructs a lattice coverage sensor network, in which a Field of Interest is enclosed into multiple sub-spaces using barrier coverage. We present an evaluation of the event detection by modeling the event generation and target mobility. Comparing with stochastic coverage, the lattice coverage exhibits better performance concerning event detection ability and efficiency.

Wireless sensor networks are an important research area that has attracted a lot of attention in the past few years. Most of this attention, however, has focused on wireless sensor network collect scalar data such as temperature and vibration. Scalar data is insufficient for many applications such as video surveillance and traffic monitoring. Whereas, camera sensors collect video data, which are rich in information and offer tremendous potential when used in wireless sensor networks. Video sensor networks continue to gain increase interest due to their ability to collect video information for a wide range of applications in recent years. However, knowledge about these types of networks is mostly related to visual algorithms, leaving the issues such as the coverage of video sensor network and the transmission of video data aside. In this paper, we first highlight the issues and opportunities of video sensor network. And then we discuss key research issue on video sensor network, specifically camera coverage problem, network architecture, and low-power video data processing and communication. Finally, we identify enabling approaches in this area.

Wireless Sensor Networks (WSNs) have a wide range of applications, and barrier coverage is a crucial feature out of them, in which intruders who are trying to penetrate any protected areas can be easily detected. However, because the locations of mobile sensors cannot be controlled or predicted once they are deployed into the field of interest, achieving required barrier coverage after the initial random deployment of mobile sensors is difficult. In wireless sensor network, generally static sensor nodes are used but in our case, we have used mobile sensors to provide better connectivity, effective coverage, energy efficient and good topology management. Sensor deployment strategies are directly related to barrier coverage problem. To improve initial random deployment for ensuring coverage and connectivity in the sensor network, algorithms based on virtual forces are used. Virtual Force Algorithm (VFA) is improved with the factors like- the boundary in the Region of Interest (RoI), the effective distance of acting forces, useless moving, etc. We mainly focus on how to minimize the number of sensors required to construct the barrier so that it could be energy-constrained as well as cost effective. In this paper, we have discussed about barrier coverage problem, it's applications & design issues and finally proposed an optimized-VFA approach with the boundary constraint, where a wise combination of attractive and repulsive forces is used to determine virtual motion paths to create a rectangular barrier.

Forest fire detection system using barrier coverage in wireless sensor networks

In this paper, we study the barrier lifetime maximisation (BLM) and barrier gap mending (BGM) problem in a sensor network, where sensors can choose to use different sensing ranges when constructing a sensor barrier. In the BLM problem, for small–scale sensor networks, we propose a linear programming optimisation method. For large–scale sensor networks, we propose two distributed heuristics to select a next node to construct barriers. Simulation results show that compared with the randomised one, the greedy scheme can achieve longer lifetime. In the BGM problem, we study how to efficiently mend barrier gaps in a hybrid sensor network consisting of both stationary and mobile sensors. We propose two gap mending schemes: the min–max scheme to minimise the maximal energy consumption to move sensors, and the max–lifetime scheme to maximise the lifetime of barrier coverage after mending all gaps. Simulation results show that the min–max scheme can achieve a lower maximal moving distance and the max–lifetime scheme can efficiently extend the barrier lifetime.

This paper is to study the tracking of mobile objects and associated coverage problems in visual sensor networks. When sensors equipped with cameras are randomly deployed in a monitoring environment, many cameras are involved in covering the same mobile object. Images of the object may be captured by different camera nodes in different orientations simultaneously, and the captured images are then sent back to a base station or server. However, achieving characteristic point coverage of objects invariably involves a great deal of redundant image data consuming the transmission energy of the visual sensor network. One approach is proposed to overcome this problem. The minimum number of camera nodes required for set coverage can be determined by predicting the direction and speed of the mobile object. The simulation results show that this protocol reduces transmission cost while preserving the maximum coverage range of mobile objects.

Wireless Sensor Networks (WSNs) is composed of sinks and sensor nodes, which can monitor large-scale environment. The sensor nodes have several advantages, such as low cost, small size, and capability of mobility, and they can do data sensing, data processing and wireless communication. However, the greatest challenge is the constraints of energy. The most critical issue is how to minimize the energy consumption and keep tracking the mobile target in WSNs. In this paper, we propose a tracking approach for tracking mobile target in hybrid wireless sensor network which consisting of static and mobile node. While static nodes are random distributed in the Field of Interested (FoI) and mobile nodes are moved to fill the coverage hole. We predict the area where the target may cross and construction the local barrier coverage to detect the target. In order to reduce the energy consumption, we wakeup the portion of constructed local barrier coverage to tracking the target. The simulation shows the proposed scheme can reduce the energy consumption and missing rate.

Barrier coverage is an important problem in wireless sensor networks and is used to guarantee the detection of an intruder that tries to cross a protected region. Most of previous studies on barrier coverage are based on the disk coverage model or sector coverage model. Recently, barrier coverage based on the bistatic radar sensor model has received more attention. The coverage area of abistatic radar is different from that of the disk and sector coverage and is dependent on the distance between the transmitter and the receiver. In particular, in [8], Wang et al. studied belt barrier coverage in bistatic radar sensor model and proposed algorithms that minimize the total placement cost spent in constructing the barrier. Note that belt barrier is a line barrier with the breadth not smaller than a predefined threshold so that the coverage quality can be improved. The purpose of this paper is to study belt barrier coverage in bistatic radar sensor model. We first propose a new deployment method that further reduces the total placement cost of [8]. We then study the fault-tolerance issue and the energy-saving issue. To the best of our knowledge, our work is the first paper that studies these two issues in belt barrier coverage in bistatic radar sensor model.

Modern developments in image technology enabled easy access to an innovative type of sensor-based networks, Camera or Visual Sensor Networks (VSN). Nevertheless, more sensor data sources bring about the problem of overload information. To solve this problem, some researchers have been carried out on the techniques to counteract the data overload caused by sensors without losing useful data. The aim of fusion in each application is to combine images from several sensors, which leads to the decreased amount of input image data, producing an image with more accurate data. This paper proposes a noisy feature removal scheme for multi-focus image fusion combining the decision information of optimized individual features. The proposed scheme is developed in two main steps. In the first step, the diverse types of features are extracted from each block of input blurred images. The useful information of these individual features indicates which image block is more focused among corresponding blocks in source images. After that, noisy features are removed using binary Genetic Grey wolf optimizer (GGWO) algorithm. The ensemble decision based on individual features is employed to fuse blurred images in the second step. The experimentation is evaluated on different multi-focus images and it reveals that GGWO based proposed method performs better visual quality than other methods.

Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.

Self-orienting the cameras for maximizing the view-coverage ratio in camera sensor networks

In camera sensor network research, physical camera sensor network platforms with low power and low cost are needed for testing and validating algorithms. It is also necessary that the camera nodes be calibrated precisely. In this work, we first develop and evaluate a low power and low cost wireless camera sensor network platform. The camera sensor nodes in this platform transmit a grayscale image over a wireless channel to a master control station. Then we propose a simple, light-weight algorithm to perform distributed calibration of the camera sensor nodes. The camera sensor nodes use their imaging abilities in collaboration with a cooperative moving target to determine their own positions and orientations. The proposed algorithm requires simple arithmetic calculations and hence it can be realized on low power processors. This calibration algorithm has been implemented and evaluated on the developed physical camera sensor network platform.