Data Fusion Based Coverage Optimization in Heterogeneous Sensor Networks: A Survey
基于数据融合的异构传感器网络覆盖优化
Information Fusion, In press, accepted manuscript, Available online 1 December 2018
Xianjun Deng, Yalan Jiang, Laurence T. Yang, Man Lin, Minghua Wang
摘要：Sensor networks, as a promising network paradigm, have been widely applied in a great deal of critical real-world applications. A key challenge in sensor networks is how to improve and optimize coverage quality which is a fundamental metric to characterize how well a point or a region or a barrier can be sensed by the geographically deployed heterogeneous sensors. Because of the resource-limited, battery-powered and type-diverse features of the sensors, maintaining and optimizing coverage quality includes a significant amount of challenges in heterogeneous sensor networks. Many researchers from both academic and industrial communities have performed numerous significant works on coverage optimization problem in the past decades. Some of them also have surveyed the current models, theories and solutions on the problem of coverage optimization. However, most of the existing surveys and analytical studies ignore how to exploit data fusion and cooperation of the deployed sensors to enhance coverage performance. In this paper, we provide an insightful and comprehensive summarization and classification on the data fusion based coverage optimization problem and techniques. Aiming at overcoming the shortcomings existed in current solutions, we also discuss the future issues and challenges in this area and sketch a general research framework in the context of reinforcement learning.
QoS guaranteed surveillance algorithms for directional wireless sensor networks
具有QoS保证的定向无线传感器网络监控算法
Ad Hoc Networks, Volume 81, December 2018, Pages 71-85
Chih-Yung Chang, Chih-Yao Hsiao, Chao-Tsun Chang
摘要：A directional wireless sensor network (DWSN) consists of a number of directional sensors. Many types of directional sensors have been widely used in constructing a wireless sensor network for IoT applications. These types of sensors include infrared sensors, microwave sensors, ultrasonic sensors, cameras, and radar. Compared with an omni-directional sensor, a directional sensor can achieve better performance because it can additionally report the direction of an intruder. Unfortunately, most existing barrier-coverage mechanisms adopt omni-directional sensor networks. They cannot be efficiently applied to DWSNs because the sensing area of each directional sensor is fan-shaped. This paper investigates the surveillance service problem which supports the surveillance quality of k-barrier coverage in DWSNs. Three algorithms, called BA, BTA and BRA, are proposed which aim at finding a maximal number of different defense barriers, each of which supports k-coverage. Using these algorithms, at least k directional sensors can detect intruders intending to cross the monitoring area. Performance analyses of the proposed algorithms are conducted in Section 5 to verify the performance improvement from a theoretical point of view. In the performance study, the proposed algorithms are compared with other existing algorithms. The experimental study shows that the proposed k-barrier coverage algorithm achieves similar performance with the optimal solution but has fewer control packets. Furthermore, the proposed BRA achieves better performance in terms of the numbers of control packets and constructed defense barriers, as compared with existing algorithms.
A data sample algorithm applied to wireless sensor network with disruptive connections
数据样本算法在颠覆性连接无线传感器网络中的应用
Computer Networks, Volume 146, 9 December 2018, Pages 1-11
Israel L. C. Vasconcelos, Ivan C. Martins, Carlos M. S. Figueiredo, Andre L. L. Aquino
要：This paper presents a data sample algorithm applied to wireless sensor network applications with disruptive connections. Additionally, it defines a model for delay-tolerant sensor network where drop strategies are applied to improve the phenomenon coverage in an application that monitors the forest temperature incidence for wildlife observation. The environmental application model comprises: i) Phenomenon generation based on a Gaussian random field along with the Matern covariance model; ii) Sensing nodes deployment based on simple sequential inhibition process with a mobile sink node following a random walk process; iii) Data collection and processing based on a data-aware drop strategy; and iv) Phenomenon reconstruction based on simple kriging interpolation. This research employed the data-aware drop strategy and compared it with the others, reported in the literature. Besides the satisfactory application of this model, the results show that the performance of data-aware drop strategy is twice better than conventional ones in all evaluated scenarios.
ECOCS: Energy consumption optimized compressive sensing in group sensor networks
ECOCS: 组传感器网络的能量消耗优化压缩感知
Computer Networks, Volume 146, 9 December 2018, Pages 159-166
Hao Yang, Xiwei Wang
摘要：Compressive sensing (CS) is a widely employed technique in sensor networks for energy-efficient data transmission. In recent years, the group-based network structures, e.g., regionalized and clustered networks, have been proposed to work with compressive sensing to reduce the energy cost of boundary sensors. Studies in previous literatures including exploring the relationships among samples in sensor groups, the techniques for grouping, etc. However, several issues may surface after the group structure is established. To extend the state-of-the-art techniques, we propose an energy consumption optimization approach based on CS, ECOCS, in group sensor networks. Three challenges are addressed in this paper: 1) we show the design principle of group measurement matrix and analyze the expected size of measurements; 2) we present two schemes to obtain candidate sensors that facilitate group collector election and cost reduction when establishing routing schemes based on hyperbolic Ricci flow; 3) we give the reachable probability of accurate reconstruction to avoid unnecessary sampling. The experiments demonstrate that our solutions to these challenges are superior to existing approaches.
Maximizing heterogeneous coverage in over and under provisioned visual sensor networks
充分及不充分提供视觉传感器网络的异构覆盖最大化
Journal of Network and Computer Applications, Volume 124, 15 December 2018, Pages 44-62
Abdullah Al Zishan, Imtiaz Karim, Sudipta Saha Shubha, Ashikur Rahman
摘要：We address “heterogeneous coverage” in visual sensor networks where coverage requirements of some randomly deployed targets vary from target to target. The main objective is to maximize the coverage of all the targets to achieve their respective coverage requirement by activating minimal sensors. The problem can be viewed as an interesting variation of the classical Max-Min problem (i.e., Maximum Coverage with Minimum Sensors (MCMS)). Therefore, we study the existing Integer Linear Programming (ILP) formulation for single and k-coverage MCMS problem in the state-of-the-art and modify them to solve the heterogeneous coverage problem. We also propose a novel Integer Quadratic Programming (IQP) formulation that minimizes the Euclidean distance between the achieved and the required coverage vectors. Both ILP and IQP give exact solution when the problem is solvable but as they are non-scalable due to their computational complexity, we devise a Sensor Oriented Greedy Algorithm (SOGA) that approximates the formulations. For under-provisioned networks where there exist insufficient number of sensors to meet the coverage requirements, we propose prioritized IQP and reduced-variance IQP formulations to capture prioritized and group wise balanced coverage respectively. Moreover, we develop greedy heuristics to tackle under provisioned networks. Extensive evaluations based on simulation illustrate the efficiency and efficacy of the proposed formulations and heuristics under various network settings. Additionally, we compare our methodologies and algorithm with two state-of-the-art algorithms available for target coverage and show that our methodologies and algorithm substantially outperform both the algorithms.
Simultaneous target tracking and sensor location refinement in distributed sensor networks
分布式传感器网络同时目标跟踪及传感器定位求精
Signal Processing, Volume 153, December 2018, Pages 123-131
Kai Shen, Zhongliang Jing, Peng Dong
摘要：The conventional consensus filter is an effective tool for distributed fusion but so far the literature has paid little attention to take the uncertainty of sensor position into consideration. In this paper, we address this problem of sensor position uncertainty and propose variational Bayesian and consensus based filters for simultaneous target tracking and sensor location refinement in distributed sensor networks. The variational Bayesian method is employed to jointly estimate the target state and local sensor position while the consistent global target state can be approached by consensus scheme at each node. The filter for linear measurement model is first derived and then extended to nonlinear measurement models exploiting the extended Kalman filter paradigm. Simulations are performed in order to demonstrate the effectiveness of the proposed algorithms.
