Big Bang-Big Crunch Algorithm for Dynamic Deployment of Wireless Sensor Network

This paper proposes soft computing technique Big Bang-Big Crunch (BB-BC) to address the main issue of deployment of wireless sensor networks. Deployment is the main factor that significantly affects the performance of the wireless sensor network. This approach maximizes the coverage area of the given set of sensors. We implemented our approach in MATLAB and compared it with ABC approach and found that the proposed approach is much better than the said approach

K-coverage in regular deterministic sensor deployments

An area is k-covered if every point of the area is covered by at least k sensors. K-coverage is necessary for many applications, such as intrusion detection, data gathering, and object tracking. It is also desirable in situations where a stronger environmental monitoring capability is desired, such as military applications. In this paper, we study the problem of k-coverage in deterministic homogeneous deployments of sensors. We examine the three regular sensor deployments - triangular, square and hexagonal deployments - for k-coverage of the deployment area, for k ≥ 1. We compare the three regular deployments in terms of sensor density. For each deployment, we compute an upper bound and a lower bound on the optimal distance of sensors from each other that ensure k-coverage of the area. We present the results for each k from 1 to 20 and show that the required number of sensors to k-cover the area using uniform random deployment is approximately 3-10 times higher than regular deployments

Is Deterministic Deployment Worse than Random Deployment for Wireless Sensor Networks?

Before a sensor network is deployed, it is important to determine how many sensors are required to achieve a certain coverage degree. The number of sensor required for maintaining $k$-coverage depends on the area of the monitored region, the probability that a node fails or powers off (to save energy), and the deployment strategy. In this paper, we derive the density required to maintain $k$-coverage under three deployment strategies: (i) nodes are deployed as a Poisson point process, (ii) nodes are uniformly randomly distributed, (iii) nodes are deployed on regular grids. Our results show that under most circumstances, grid deployment renders asymptotically lower node density than random deployment. These results override a previous conclusion that grid deployment may render higher node density than random node distributions

Random vs. Deterministic Deployment of Sensors in the Presence of Failures and Placement Errors

Abstract—Although random deployment is widely used in theoretical analysis of coverage and connectivity, and evaluation of various algorithms (e.g., sleep-wakeup), it has often been considered too expensive as compared to optimal deterministic deployment patterns when deploying sensors in real-life. Roughly speaking, a factor of log n additional sensors are needed in random deployment as compared to optimal deterministic de-ployment if n sensors are needed in a random deployment. This may be an illusion however, since all real-life large-scale deployments strategies result in some randomness, two prime sources being placement errors and sensor failures, either at the time of deployment or afterwards. In this paper, we consider the effects of placement errors and random failures on the density needed to achieve full coverage when sensors are deployed randomly versus deterministically. We compare three popular strategies for deployment. In the first strategy, sensors are deployed in an optimal lattice but enough sensors are colocated at each lattice point to compensate for failure and placement errors. In the second, only one sensor is deployed at each lattice point but lattice spacing is sufficiently shrunk to achieve a desired quality of coverage in the presence of failure and placement errors. In the third, a random deployment is used with appropriate density. We derive explicit expressions for the density needed for each of the three strategies to achieve a given quality of coverage, which are of independent interest. In comparing the three deployments, we find that if errors in placement are half the sensing range and failure probability is 50%, random deployment needs only around 10 % higher density to provide a similar quality of coverage as the other two. We provide a comprehensive comparison to help a practitioner decide the lowest cost deployment strategy in real-life. I

Chapter Efficient Node Placement for Congestion Control in Wireless Sensor Networks

Geology & the lithospher

Empirical Approach in Topology Control of Sensor Networks for Urban Environment, Journal of Telecommunications and Information Technology, 2019, nr 1

Research into the topology control of Wireless Sensor Networks (WSNs) is geared towards modeling and analysis of methods that may be potentially harnessed to optimize the structure of connections. However, in practice, the ideas and concepts provided by researchers have actually been rarely used by network designers, while sensor systems that have already been deployed and are under continued development in urban environments frequently differ from the patterns and research models available. Moreover, easy access to diversified wireless technologies enabling new solutions to be empirically developed and popularized has also been conducive to strengthening this particular trend

Mobile Search Strategies and Detection Analysis of Nuclear Radiation Sources

This work focuses on detection analysis and search strategies for nuclear radiation sources in metropolitan areas with mobile sensor networks. A mobile sensor detecting a stationary nuclear source experiences continually changing statistics. In this work we provide an analysis of the probability of detection of a nuclear source that incorporates these continual changes. We apply the analysis technique to several patterns of motion including linear and circular paths. Analysis is also presented for cases in which there is a significant vertical offset between source and mobile sensor (the three-dimensional problem). The resulting expressions are computationally simple to evaluate and have application to both analysis and simulation of nuclear detection systems in a variety of scenarios. In metropolitan areas, with vehicles equipped with detectors and Global Position System (GPS) devices, we consider the design of a robust detection system to provide consistent surveillance. Various strategies for providing this surveillance with a mobile sensor network are considered and the results are compared. Both time-from-last-visit based algorithms and detection algorithms that utilize both time and probability-of-miss estimates are considered. The algorithms are shown to perform well in a variety of scenarios, and it is further shown that the algorithms that utilize probability information outperform those that do not

Is Deterministic Deployment Worse than Random Deployment for Wireless Sensor Networks?

Abstract — Before a sensor network is deployed, it is important to determine how many sensors are required to achieve a certain coverage degree. The number of sensor required for maintaining k-coverage depends on the area of the monitored region, the probability that a node fails or powers off (to save energy), and the deployment strategy. In this paper, we derive the density required to maintain k-coverage under three deployment strategies: (i) nodes are deployed as a Poisson point process, (ii) nodes are uniformly randomly distributed, (iii) nodes are deployed on regular grids. Our results show that under most circumstances, grid deployment renders asymptotically lower node density than random deployment. These results override a previous conclusion that grid deployment may render higher node density than random node distributions. I