Wireless Camera Sensor Networks Research Articles

Enhancement of Performance Metrics of Heterogeneous Wireless Camera Sensor Network with Functionalization of Extensive Zonal Stable Election Protocol using Threshold Amplification and Residual Energy

We report a new improved energy competent and optimized data packet flow protocol with Hierarchical Clustering utilized in Wireless camera Sensor Network.The existing Extensive Zonal Stable Election Protocol has been modified along with the threshold parameters amplification and residual energy. It incorporates dynamic hybrid method with finite number of Member Sensor nodes (MSN) in proximity with the base station share their data directly , while the rest of the farther nodes form a cluster for data transference using Cluster Head. The performance metrics accompanied by heterogeneity, longer network survival and better throughput have been improved. The network field was divided into 4 zones with a gateway for defined region 2, 3, and 4. The criterion for zone division remained on the energy status (residual) of the MSNs and distance from the BS and the formulated field characteristics in the simulation were kept unknown. The obtained results demonstrate that our proposed modified version of EZSE protocol considerably performs better than existing EZ-SEP, Z-SEP, SEP, LEACH, Mod-Leach protocols during entire stability timeframe. The notable achievement is also reported in throughput as the same is enhanced more than by ~ 39%, 43%, 49% ,56%, 53% while total packets communicated with base station has been increased more than by ~ 127%, 131%, 147%, 151%, 148% stability of the network is also improved more than by ~ 37%, 42%, 45%, 49%, 51% with the corresponding increase in the heterogeneity of networks.

Low-Power Low-Area Near-Lossless Image Compressor for Wireless Capsule Endoscopy

The paper presents the concept of a low-power, low-area, near-lossless image compressor for resource-constrained devices such as wireless capsule endoscopy (WCE). The compressor directly processes the raw data from the Bayer Color Filter Array (CFA) imager to avoid the high cost of color interpolation. To improve the efficiency of the compressor in terms of energy consumption, silicon area and compression ratio, the main part of the compressor, i.e., the entropy encoder, uses the existing correlations between the color components of a captured CFA image. The proposed image compressor requires only 12.4% of the memory needed by other high-quality CFA compressors based on the JPEG-LS standard. Despite this significant reduction in memory size, the proposed image compressor outperforms other state-of-the-art coding schemes on capsule endoscopy images. At the same time, it offers only slightly lower performance on standard test images. The proposed image compressor has been implemented as an intellectual property (IP) core using two different low-cost CMOS processes. The design, implemented in UMC 180 nm CMOS process, requires a very low silicon area (534 times 426 upmu m^{2}) and consumes very low energy (22 upmu J per a single 512 times 512 image frame). Even higher energy efficiency (12 upmu J per the same image frame) has the IP core implemented in the TSMC 130 nm CMOS process. Both of the selected technologies are low-cost and well-suited to implement a radio frequency transmitter and a low-power successive approximation register analog-to-digital converter in addition to the compressor to provide a cost-effective System on Chip for resource-constrained devices like WCE or wireless camera sensor network.

Shooting Utility Maximization in UAV-Assisted Wireless Camera Sensor Networks.

Recently, wireless camera sensor networks (WCSNs) have entered an era of rapid development, and WCSNs assisted by unmanned aerial vehicles (UAVs) are capable of providing enhanced flexibility, robustness and efficiency when executing missions such as shooting targets. Existing research has mainly focused on back-end image processing to improve the quality of captured images, but it has neglected the question of attaining quality images on the front-end, which is significantly influenced by the location and hovering time of the UAV. Therefore, in this paper, we conceive a novel shooting utility model to quantify shooting quality, which is maximized by simultaneously considering the UAV’s trajectory planning, hovering time and shooting point selection. To expound further, we prove the submodularity of the utility function, whereby the original problem can be expressed as a submodular maximization problem with path constraints, and we propose a utility-cost ratio (UCR) algorithm to maximize shooting utility through two-level optimization. Then, by using the relaxation of the cost function, we analyze the gap between the proposed algorithm and the optimal algorithm (OPT) and prove that the UCR algorithm has a bi-criterion approximation ratio of . Simulation results show that the algorithm outperforms both the random algorithm (RAN) and the maximum shooting utility point selection algorithm (MSU) in terms of shooting utility and time utilization efficiency, improving shooting utility by 51% and 21% compared to the RAN and MSU algorithms, respectively, and achieving at least 88.2% of the OPT algorithm in terms of time utilization efficiency.

Entropy encoder for low-power low-resources high-quality CFA image compression

An entropy encoder for high-quality image compression in low-power, low-resources devices like wireless capsule endoscopy (WCE) or wireless camera sensor network (WCSN) is proposed. The proposed entropy encoder is optimized for encoding data resulting from transformation and quantization of images captured with a color filter array (CFA) digital camera. It efficiently exploits existing correlations among color components in CFA images to improve the performance of the image compressor in terms of power consumption, silicon area, and compression ratio. The image compressor based on the new encoder requires only 22% of the memory size needed by the JPEG-LS-based CFA compressor (for an image with 512 columns). Despite this significant reduction in memory size, the average compression ratio in relation to the image quality is superior to other state-of-the-art algorithms of similar computational complexity and memory requirements. The image compressor based on the proposed encoder has been synthesized using a low-cost UMC 180 nm CMOS process as an intellectual property (IP) core. The design requires a very small silicon area (0.54 × 0.50 mm) and achieves excellent energy efficiency (30μJ per a single 512 × 512 image frame and 20 fps). The selected technology for the implementation is not only low-cost but also well suited to implement an RF transmitter in addition to the compressor to provide a cost effective System on Chip (SoC) for resource-constrained devices like WCE or WCSN.

MAXIMIZING LIFETIME OF HETEROGENEOUS WIRELESS TURNABLE CAMERA SENSOR NETWORKS ENSURING STRONG BARRIER COVERAGE

Barrier coverage in wireless camera sensor networks (WCSNs) has drawn the attention of research community since it promises an extremely large potential in applications involve movement detection and surveillance. As the battery resources are limited, improving the efficiency of energy is one of the key drivers for prolonging the lifetime of barrier coverage. However, most prior studies on this problem only worked on the networks with homogeneous sensors as well as omni-directional sensing coverage that have not been taken into account in WCSNs research. This paper thus investigates the problem of maximizing the network lifetime to ensure strong barrier coverage for heterogeneous case (MLBC-HWCSN). We formulate the problem, and then propose a Modify Maximum Flow Algorithm (MMFA) consisting of three stages: constructing the flow-network, finding the maximum flow and refining the solution to solve this problem. Experimental results on extensive instances show that the proposed methodology is suitable for the studied problem and more efficient than existing algorithms.

A Distributed Surveillance System With Full Coverage Guarantee Using Positive Orthogonal Codes

Surveillance systems provide continual coverage of target area(s) using several cameras through different angles. Conventionally, a central unit controls the system by adjusting the coverage rate of the cameras. However, in large-scale environments, such a centralized system is costly and energy inefficient, as the central unit should exchange a lot of control messages with the cameras. Centralized systems also are not resilient because they fail when the central unit is faulty. In this paper, we propose a distributed surveillance system in which each camera independently decides its activation sequence without exchanging control messages while providing guaranteed coverage of the target areas and ensuring a fault-tolerant system. Our goal is to provide full coverage of all target areas with reduced number of activations of each camera to reduce energy consumption and increase the system lifetime in case of wireless camera sensor network (WCSN) where cameras are battery-powered. To achieve that goal, each camera is activated according to positive orthogonal codes (POCs). POCs have been used for medium access control (MAC) in vehicular sensor networks as a distributed solution for medium access to reduce packet collisions. To the best of our knowledge, this is the first work that uses POCs to create a distributed surveillance system. This work proposes the system design, modeling, and simulation study of a POC-based distributed surveillance system. Moreover, we present a performance evaluation for the coverage percentage and coverage cost metrics using simulations and theoretical analysis. The results indicate that the use of POCs can achieve full coverage of the target areas with a significantly reduced number of cameras compared to a benchmark scheme that uses synchronous fixed repetition (SFR) codes.

Cooperative MIMO-OFDM-Based Exposure-Path Prevention Over 3D Clustered Wireless Camera Sensor Networks

As compared with 2D wireless camera sensor networks (WCSNs), 3D WCSNs can capture more accurate and comprehensive information for exposure-path prevention in supervisory and military applications. However, 3D WCSNs impose many new challenges for energy-efficiency and interference-mitigation subject to required coverage rate constraint due to extensive power consumption over time-varying wireless channels. To overcome the above-mentioned problems, in this paper we propose the AQ-DBPSK/DS-CDMA (alternating quadratures differential binary phase shift keying/direct-sequence code division multiple access) based cooperative MIMO energy-efficient and interference-mitigating scheme under the constraint of optimal tradeoff between power consumption and coverage rate over multi-hop clustered WCSNs. In particular, we build sensing models to characterize the minimum coverage rate constraint and formulate the exposure-path prevention problem using the percolation theory. Then, we derive the critical density of camera sensors subject to minimum exposure-path prevention probability constraint. We develop the AQ-DBPSK/DS-CDMA scheme and also derive the optimal data rate to optimize transmit-power and data-rate trade-off. By deriving the critical density of camera sensors over 3D WCSNs, we apply the cooperative MIMO based NEW LEACH architecture for our multi-hop cooperative MIMO scheme. Also conducted is a set of simulations which show that our proposed scheme can outperform other existing schemes in terms of energy efficiency and interference-mitigation over multi-hop 3D clustered WCSNs.

Lightweight Server-Assisted H-K Compression for Image-Based Embedded Wireless Sensor Network

Embedded wireless imaging systems based on wireless camera sensor network can be used to observe biological phenomena unobtrusively in various environmental monitoring applications. In our prior work, we have shown the feasibility and benefits of such a system through actual deployments. We have also shown that low-complexity data compression schemes can be employed to improve image transfer rate or to lower the energy costs of communication. In this paper, we extend upon our prior work and propose a scheme called as H–K compression , a simple lightweight image compression algorithm combining the ideas of Huffman coding and K-means clustering. Specifically, H–K compression applies K-means clustering for pixel color grouping and Huffman coding for the group color encoding, but combines the two schemes into one algorithm which can reduce the computational cost. Using 100 000 images collected from our pilot deployments at James Reserve, we study the applicability and impact of the proposed algorithm. Our results suggest that the cost of running the learning steps on an embedded sensor node may outweigh the benefit of compression, but offloading the learning can provide significant energy gains. Evaluations using the data set show that our proposed scheme compresses the data by $\sim$ 57% and reduces power usage by $\sim$ 43% when sending image updates from a bird nest periodically every 15 min.

Mobile target tracking algorithm for wireless camera sensor networks with adjustable monitoring direction of nodes

SummaryWireless camera sensor networks (WCSNs) possess a powerful physical environment monitoring capability. Camera nodes with adjustable monitoring directions further improve their flexibility. This study focuses on tracking multiple mobile targets to investigate the node scheduling and target location evaluation strategy of WCSNs on the basis of rotating nodes. By referring to existing research, this study improves the camera node monitoring and rotation model and proposes three network performance evaluation indicators. The proposed algorithm schedules nodes and their monitoring directions by using the unutilized energy of the nodes and the number of monitored targets. It also predicts the moving trends of the targets and selects active nodes by using the locations and linear speeds of the targets. Experimental results show that the proposed algorithm has a high target tracking accuracy. Compared with traditional target tracking algorithms, the proposed algorithm can effectively reduce the number of active nodes, balance the energy consumption between nodes, and prolong network lifetime.

Full-view barrier coverage in mobile camera sensor networks

Barrier coverage is an important problem in wireless camera sensor networks for many security applications such as border monitoring and target detection, where camera sensors are used for detecting targets that penetrate the protected area. The full-view coverage is an effective approach of camera barrier coverage which can capture multiple viewpoints of the target crossing the protected area. Existing works on full-view barrier coverage mainly consider static and rotatable camera sensors, which leads to a waste of camera sensors under the limitation of sensing angles and coverage areas. In this paper, we study full-view barrier coverage in mobile camera sensor networks, where camera sensors can randomly move within the deployed area. Specially, we first propose full-view covered model of mobile camera sensors. With this model, we then divide the deployed area into some connected grids and present grid-based deployment strategy to deploy camera sensors for each grid. Finally, we construct a weighted directed graph to model these connected grids and their relationship. Based on the graph, we employ Dijkstras algorithm to obtain a shortest coverage barrier, which is a connected full-view coverage zone across the entire target area. Extensive simulation and theoretical results are provided to illustrate the efficient of our covered models as well as our proposed algorithm in reducing the number of camera sensors and improving coverage probability.

Cascading Target Tracking Control in Wireless Camera Sensor and Actuator Networks

AbstractRecent years have witnessed the fast development of camera sensor networks (CSNs), but most of the existing research methods are based on homogeneous CSNs, which are not cost‐effective and are hard to evolve from traditional wireless sensor networks (WSNs). In this paper, we focus on the target tracking problem in heterogeneous wireless camera sensor and actuator networks which consists of a large number of common static sensors and a small number of mobile camera sensors. The objective is to control camera sensors to track targets dependably with maximized effective monitoring time and short moving distance. We first design a novel tracking algorithm for tracking one target where multiple camera sensors cooperate with each other to track the target in a cascading scheme. Moreover, this cascading algorithm is extended for a multi‐target scenario in consideration of tracking performance and loading balance for multiple targets. The effectiveness of proposed control algorithms is validated by extensive simulation results, which show that they can achieve high monitoring efficiency and comparatively short moving distance.

A Genetic Algorithm for the Generation of Packetization Masks for Robust Image Communication.

Image interleaving has proven to be an effective solution to provide the robustness of image communication systems when resource limitations make reliable protocols unsuitable (e.g., in wireless camera sensor networks); however, the search for optimal interleaving patterns is scarcely tackled in the literature. In 2008, Rombaut et al. presented an interesting approach introducing a packetization mask generator based in Simulated Annealing (SA), including a cost function, which allows assessing the suitability of a packetization pattern, avoiding extensive simulations. In this work, we present a complementary study about the non-trivial problem of generating optimal packetization patterns. We propose a genetic algorithm, as an alternative to the cited work, adopting the mentioned cost function, then comparing it to the SA approach and a torus automorphism interleaver. In addition, we engage the validation of the cost function and provide results attempting to conclude about its implication in the quality of reconstructed images. Several scenarios based on visual sensor networks applications were tested in a computer application. Results in terms of the selected cost function and image quality metric PSNR show that our algorithm presents similar results to the other approaches. Finally, we discuss the obtained results and comment about open research challenges.

An Effective and Robust Decentralized Target Tracking Scheme in Wireless Camera Sensor Networks.

In this paper, we propose an effective and robust decentralized tracking scheme based on the square root cubature information filter (SRCIF) to balance the energy consumption and tracking accuracy in wireless camera sensor networks (WCNs). More specifically, regarding the characteristics and constraints of camera nodes in WCNs, some special mechanisms are put forward and integrated in this tracking scheme. First, a decentralized tracking approach is adopted so that the tracking can be implemented energy-efficiently and steadily. Subsequently, task cluster nodes are dynamically selected by adopting a greedy on-line decision approach based on the defined contribution decision (CD) considering the limited energy of camera nodes. Additionally, we design an efficient cluster head (CH) selection mechanism that casts such selection problem as an optimization problem based on the remaining energy and distance-to-target. Finally, we also perform analysis on the target detection probability when selecting the task cluster nodes and their CH, owing to the directional sensing and observation limitations in field of view (FOV) of camera nodes in WCNs. From simulation results, the proposed tracking scheme shows an obvious improvement in balancing the energy consumption and tracking accuracy over the existing methods.

실내 환경에서의 이동로봇의 위치추정을 위한 카메라 센서 네트워크 기반의 실내 위치 확인 시스템

This paper proposes a novel indoor positioning system (IPS) that uses a calibrated camera sensor network and dense 3D map information. The proposed IPS information is obtained by generating a bird’s-eye image from multiple camera images; thus, our proposed IPS can provide accurate position information when objects (e.g., the mobile robot or pedestrians) are detected from multiple camera views. We evaluate the proposed IPS in a real environment with moving objects in a wireless camera sensor network. The results demonstrate that the proposed IPS can provide accurate position information for moving objects. This can improve the localization performance for mobile robot operation.

Automatic calibration of camera sensor networks based on 3D texture map information

To construct an intelligent space with a distributed camera sensor network, pre-calibration of all cameras (i.e., determining the absolute poses of each camera) is an essential task that is extremely tedious. This paper considers the automatic calibration method for camera sensor networks based on 3D texture map information of a given environment. In other words, this paper solves a global localization problem for the poses of the camera sensor networks given the 3D texture map information. To manage the complete calibration problem, we propose a novel image descriptor based on quantized line parameters in the Hough space (QLH) to perform a particle filter-based matching process between line features extracted from both a distributed camera image and the 3D texture map information. We evaluate the proposed method in a simulation environment with a virtual camera network and in a real environment with a wireless camera sensor network. The results demonstrate that the proposed system can calibrate complete external camera parameters successfully.

Enhancing barrier coverage with β quality of monitoring in wireless camera sensor networks

Over years, barrier coverage problems in wireless camera sensor networks have received an amount of attention for their important applications such as intrusion detection and battlefield surveillance. Though much work has been done in this field, few works focus on providing high surveillance quality efficiently. Recently, Cheng and Tsai pointed out the breadth of barrier-coverage of camera sensor network was significant to guarantee high quality of monitoring (QoM), and they proposed a β-breadth belt-barrier problem under 2D circumstances whose goal was to select camera sensors as few as possible to construct a β-breadth belt-barrier. In this paper, we study the problem of constructing a β-breadth belt-barrier with the minimum number of sensors thoroughly, both under 2D circumstances and 3D circumstances. Besides, we extend this problem and study how to build up k disjoint β-breadth belt-barriers under both 2D and 3D circumstances. We set many application scenarios in simulation and thoroughly study how our algorithms’ performances are affected by different environmental parameters, including the number of sensors and the breadth of a barrier.

Minimum (k, ω)-angle barrier coverage in wireless camera sensor networks

Barrier coverage is an important issue in wireless sensor networks, which guarantees to detect any intruder attempting to cross a barrier or penetrating a protected region monitored by sensors. However, the barrier coverage problem in wireless camera sensor networks WCSNs is different from that in scalar sensor networks. In this paper, based on k, ω-angle coverage, we study the minimum k, ω-angle barrier coverage problem in WCSNs. We first present the technique to deploy minimum number of camera sensors to form a k, ω-angle barrier. Then propose a novel method to select the minimum number of camera sensors from an arbitrary deployment to form a k, ω-angle barrier. Though our simulation, we confirm that our algorithms reduce the number of sensors required comparing to the state-of-art algorithm.

Minimum (k, ω)-angle barrier coverage in wireless camera sensor networks

Barrier coverage is an important issue in wireless sensor networks, which guarantees to detect any intruder attempting to cross a barrier or penetrating a protected region monitored by sensors. How...

Security of Surveillance Wireless Camera Sensor Network Systems using Embedding Techniques for Authentication

Security in today's wireless camera sensor networks is very crucial for authentication of video feeds from appropriate sources. This will ensure the safety and security of the digital video feeds as well as provide means of authenticating the video. These authentication approaches should be able to identify tamper detection. In this paper, we propose an embedding technique for authentication of video feeds obtained from wireless camera sensor networks. These approaches are able make the signals more secured and easily verifiable. General Terms Digital signal processing, image processing, security, multimedia, sensor networks.

Algorithm for Autonomous Reorganization of Mobile Wireless Camera Sensor Networks to Improve Coverage

In this paper, sensing coverage by wireless camera-embedded sensor networks (WCSNs), a class of directional sensors is studied. The proposed work facilitates the autonomous tuning of orientation parameters and displacement of camera-sensor nodes in the bounded field of interest (FoI), where the network coverage in terms of every point in the FoI is important. The proposed work is first of its kind to study the problem of maximizing coverage of randomly deployed mobile WCSNs which exploits their mobility. We propose an algorithm uncovered region exploration algorithm (UREA-CS) that can be executed in centralized and distributed modes. Further, the work is extended for two special scenarios: 1) to suit autonomous combing operations after initial random WCSN deployments and 2) to improve the network coverage with occlusions in the FoI. The extensive simulation results show that the performance of UREA-CS is consistent, robust, and versatile to achieve maximum coverage, both in centralized and distributed modes. The centralized and distributed modes are further analyzed with respect to the computational and communicational overheads.