Visualizing Sensor Network Coverage with Location Uncertainty

Notice of Violation of IEEE Publication Principles Critical Density for Coverage and Connectivity in Two-Dimensional Aligned-Orientation Directional Sensor Networks Using Continuum Percolation, by Mohammad Khanjary, Masoud Sabaei, and Mohammad Reza Meybodi in IEEE Sensors Journal, vol. 14, no. 8, pp. 2856-2863, Aug. 2014 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Integrated Coverage and Connectivity in Wireless Sensor Networks: A Two-Dimensional Percolation Problem, by H. M. Ammari and S. K. Das, in IEEE Transactions on Computers, vol. 57, no. 10, pp. 1423-1434, Oct. 2008 Sensing coverage is one of the fundamental design issues in wireless sensor networks, which reflects the surveillance quality provided by them. Moreover, network connectivity enables the gathered data by sensors to reach to the sink node. Given an initially uncovered field, and as more and more directional sensors are continuously added to the sensor network, the size of partial covered areas increases. At some point, the situation abruptly changes from small fragmented covered areas to a single large covered area. We call this abrupt change the sensing-coverage phase transition (SCPT). Likewise, given an originally disconnected sensor network, as more and more sensors are added, the number of connected components changes such that the sensor network suddenly becomes connected at some point. We call this sudden change the network connectivity phase transition (NCPT). The nature of such phase transitions is a central topic in the percolation theory. In this paper, we introduce aligned-orientation directional sensor networks in which nodes are deployed based on Poisson point process and the orientation of all sensor nodes is the same. Then, we propose an approach to compute density of nodes at critical percolation for both of the SCPT and NCPT problems in such networks, for all angles of field-of-view between 0 and π by using continuum percolation. Due to percolation theory, the critical density is infimum density that for densities above it SCPT and NCPT almost surely occur. In addition, we propose a model for percolation in directional sensor networks, which provides a basis for solving the SCPT and NCPT problems together.

It is our great pleasure to welcome you to the 3rd ACM Workshop on Security of Ad Hoc and Sensor Networks (SASN'05), held in conjunction with the 12th ACM Conference on Computer and Communications Security (CCS'05), November 7, 2005, in Alexandria, Virginia.This year we received a total of 39 submissions on various aspects of ad-hoc and sensor network security. The program committee selected 6 as regular papers that cover a variety of topics, including trust establishment in Mobile Ad-Hoc Networks (MANETs), security of vehicular ad-hoc networks, secure group communication, key pre-distribution for sensor networks, jamming attacks against sensor network MAC protocols, and defense against Denial-of-Service (DoS) attacks. This year we continue the tradition of SASN'03 and SASN'04 to encourage the presentation and discussion of work-in-progress on ad-hoc and sensor network security. Among the 39 submissions, the program committee selected 7 papers to present in work-in-progress sessions, covering topics on anonymous routing for MANETs, key distribution for inter-group multicast in ad-hoc networks, improved topology assumptions for threshold cryptography in MANETs, RFID security, tracing in mobile wireless sensor networks, time synchronization attacks in sensor networks, and coordinated deployment for improving secure communications and sensing coverage in wireless sensor networks. In addition, the program includes a panel on Advances in Sensor and Ad-hoc Network Security and an invited keynote talk by Radha Poovandran (University of Washington) on Information Assurance in MANETs and Wireless Sensor Networks. We hope that these proceedings will serve as a valuable reference for security researchers and developers.

BackgroundAttention-deficit/hyperactivity disorder (ADHD) is currently diagnosed by a diagnostic interview, mainly based on subjective reports from parents or teachers. It is necessary to develop methods that rely on objectively measureable neurobiological data to assess brain-behavior relationship in patients with ADHD. We investigated the application of a topological data analysis tool, Mapper, to analyze the brain functional connectivity data from ADHD patients.MethodsTo quantify the disease severity using the neuroimaging data, the decomposition of individual functional networks into normal and disease components by the healthy state model (HSM) was performed, and the magnitude of the disease component (MDC) was computed. Topological data analysis using Mapper was performed to distinguish children with ADHD (n = 196) from typically developing controls (TDC) (n = 214).ResultsIn the topological data analysis, the partial clustering results of patients with ADHD and normal subjects were shown in a chain-like graph. In the correlation analysis, the MDC showed a significant increase with lower intelligence scores in TDC. We also found that the rates of comorbidity in ADHD significantly increased when the deviation of the functional connectivity from HSM was large. In addition, a significant correlation between ADHD symptom severity and MDC was found in part of the dataset.ConclusionsThe application of HSM and topological data analysis methods in assessing the brain functional connectivity seem to be promising tools to quantify ADHD symptom severity and to reveal the hidden relationship between clinical phenotypic variables and brain connectivity.

We study the coverage properties of large-scale sensor networks. Three coverage measures are defined to characterize the fraction of the area covered by sensors (area coverage), the fraction of sensors that can be removed without reducing the covered area (node coverage), and the capability of the sensor network to detect objects moving in the network (detectability), respectively. We approach the coverage problem from a theoretical perspective and explore the fundamental limits of the coverage of a large-scale sensor network. We characterize the asymptotic behavior of the coverage measures for a variety of sensor network scenarios. We find that the coverage of a sensor network exhibits different behaviors for different network configuration and parameters. Based on the analytical characterizations of the network coverage, we further discuss the implications to network planning and protocol performance of sensor networks.

For multilayer mobile sensor network, the issues of improving sensor network coverage by the use of mobile sensors are studied. A coverage-optimized deployment algorithm based on grid-division and bipartite graph matching is proposed. Firstly, the deployment area is divided into many grids and build distribution matrix of sensor nodes. Then construct a bipartite graph G based on the grid-division and solve a matching of maximum cardinality of G. It corresponds to an optimal deployment scheme which maximizes the network coverage and minimizes the total movement cost. Results show that after optimized deployment the network coverage increases, and with the increase in the percentage of mobile nodes it increases accordingly. In the way of distributed calculating the network also can achieve a higher coverage ratio and the movement cost is lower. It is very suitable for deployment of multilayer sensor network.

Wireless ad-hoc sensor networks will provide one of the missing connections between the Internet and the physical world. One of the fundamental problems in sensor networks is the calculation of coverage. Exposure is directly related to coverage in that it is a measure of how well an object, moving on an arbitrary path, can be observed by the sensor network over a period of time.In addition to the informal definition, we formally define exposure and study its properties. We have developed an efficient and effective algorithm for exposure calculation in sensor networks, specifically for finding minimal exposure paths. The minimal exposure path provides valuable information about the worst case exposure-based coverage in sensor networks. The algorithm works for any given distribution of sensors, sensor and intensity models, and characteristics of the network. It provides an unbounded level of accuracy as a function of run time and storage. We provide an extensive collection of experimental results and study the scaling behavior of exposure and the proposed algorithm for its calculation.

Aiming at the uneven random coverage distribution of wireless sensor networks sensor nodes, the sparrow search algorithm (SSA) is used to optimize the node coverage of wireless sensor networks. To improve the global search capability of SSA, the algorithm is improved and applied to the wireless sensor networks of bridge monitoring. For enhancing the coverage of the wireless sensor networks, this article uses two improved methods. One is to use the good point set theory to make the initial population evenly distributed; another is to introduce a weight factor to speed up its convergence. The experiments have proved the reliability and rationality of the algorithm. The improved algorithm is superior to other meta-heuristic algorithms and provides a new idea for optimizing bridge monitoring wireless sensor networks coverage.

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.

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.

Coverage is a fundamental task in sensor networks. We present two binary integer programming models for the effective sensor placement on a grid-structured sensor field when there are more than one type of available sensors with varying sensing quality and price. The first model assumes perfect sensor detection. The second one is more realistic and gives an optimal placement strategy for probabilistic sensing. Both models suffer from the intractability of the binary integer programming formulations. We therefore suggest greedy and Lagrangean heuristics, which are both accurate and efficient. Computational results are also provided.

Wireless Sensor networks have become one of the most widely-used forms of ad-hoc networks which have countless number of applications in the modern life. Due to the unreliable communication medium and the failure prone sensors, coverage is an important functional property of a sensor network. Coverage is represented as a function of sensor density, which in turn depends on sensing range and the deployment function. The sensing range and the deployment function have random nature that can greatly affect sensor coverage. In this study, we subjected a densely deployed sensor network to stochastic variations in sensing range and deployment. Also, we captured deployment variations in space dimension by a random deployment pattern and a random noise. In addition, we considered time dependent random variation in noise and environmental factors. More specifically, we studied the effect of randomness resulting in a certain percentage of uncovered area in the network.

Toward the end of the 20th century, the Internet has been able to provide a large number of users with the ability to move diverse forms of information readily and thus has revolutionized business, industry, defense, science, education, research, and human interactions. In the last 10 years, sensor networking combines the technology of modern microelectronic sensors, embedded computational processing systems, and modern computer networking methodology. It is believed that sensor networking in the 21st century will be equally significant by providing measurement of the spatial-temporal physical phenomena around us, leading to a better understanding and utilization of this information in a wide range of applications. Sensor networking will be able to bring a finer-grained and fuller measurement and characterization of the world around us to be processed and communicated, so the decision makers can utilize the information to take actions in near-real-time. The potential applications enabled by SNs include security and surveillance, environmental monitoring and control, target detection and tracking, etc. Wireless sensor networks utilize the extensive networking concepts of ad hoc networks and apply them to specific sensor network scenarios. This mini-special issue comprises three papers covering three quiet diverse aspects of wireless sensor networks. The first paper, “Lifetime Maximization Based on Coverage and Connectivity in Wireless Sensor Networks,” is by T. Zhao et al. They studied the problem of network lifetime maximization for QoS specific information retrieval for the reconstruction of a spatially correlated signal field in a wireless sensor network for two wireless transmission cases. In one case, they assumed there exist single-hop transmissions between sensors and the access point, and in the other case, the measurements are sent to the access point through multi-hop transmissions. To address both of these problems, they formulated the problems using integer programming based on the theories of coverage and connectivity in sensor networks and then derived upper bounds for the network lifetime that provides benchmarks for the performance of suboptimal methods. Several lowcomplexity suboptimal algorithms for joint node scheduling and data routing were then proposed to approach the performance upper bounds. The second paper, “Using Heterogeneity to Enhance Random Walk-based Queries,” is by M. Zuniga et al. They presented a study of the impact of heterogeneous node connectivity on random walk-based queries. The main contribution of the work is showing that with a small percentage (e.g., 10%) of high-degree nodes in the network and using a simple distributed push-pull mechanism, significant cost savings can be obtained (e.g., between 30% and 70%) depending on the coverage of the high-degree nodes. Their work provides interesting theoretical results for line topologies showing that when cluster-heads have a coverage of k nodes to the right and left and are uniformly distributed, a fraction of 4/5k J Sign Process Syst (2009) 57:381–383 DOI 10.1007/s11265-009-0377-9

To solve the problem of unreasonable random distribution of sensor nodes and low network coverage in sensor networks, a coverage optimization method based on artificial fish swarm algorithm is proposed. Firstly, the current research status of WSN coverage was analyzed, the node coverage and regional coverage in WSN on the basis were analyzed, the corresponding mathematical model was established, the WSN coverage optimization program in view of the artificial fish swarm was obtained. Finally, MATLAB was used for the simulation experiment, and the simulation results showed that the introduction of this method improved the node coverage in WSN effectively, the coverage area was huger at the same amount of nodes. Moreover, the algorithm can get the optimal solution in the global scope, and reach the better network coverage optimization effect with less sensor nodes, and the number of iterations was decreased significantly.

A multi-agent system (MAS) approach on wireless sensor networks (WSNs) comprising sensor-actuator nodes is very promising as it has the potential to tackle the resource constraints inherent in these networks by efficiently coordinating the activities among the nodes. In this paper, we consider the coordinated sensing coverage problem and study the behavior and performance of four distributed reinforcement learning (DRL) algorithms: (i) fully distributed Q-learning, (ii) distributed value function (DVF), (iii) optimistic DRL, and (iv) frequency maximum Q-Iearning (FMQ). We present results from simulation studies and actual implementation of these DRL algorithms on Crossbow Mica2 motes, and compare their performance in terms of incurred communication and computational costs, energy consumption and the achieved level of sensing coverage. Issues such as convergence to local or global optima, as well as speed of convergence are also considered. These implementation results show that the DVF agents outperform other agents in terms of both convergence and energy consumption

This paper investigates the benefits of spatial correlation for the problem of coverage in Wireless Sensor Networks (WSN). Specifically, it studies two detectors, the Mean Detector (MD) and the Covariance Detector (CD) and compares their coverage performance for various scenarios. The main contribution of this paper is to show that one can exploit the possible correlation between measurements of neighboring sensor nodes in order to achieve significantly better coverage. Moreover, the results of this paper have direct implications on the topology of the network. Our results indicate that for the CD the best placement would be pairs of sensor nodes placed on a grid configuration.