Coverage and Connectivity Issues in Wireless Sensor Networks

Abstract

Wireless sensor networks [33,34] have inspired tremendous research interest in since the mid-1990s. Advancement in wireless communication and microelectromechanical systems (MEMSs) have enabled the development of low-cost, lowpower, multifunctional, tiny sensor nodes that can sense the environment, perform data processing, and communicate with each other untethered over short distances. A typical wireless sensor network consists of thousands of sensor nodes, deployed either randomly or according to some predefined statistical distribution, over a geographic region of interest. A sensor node by itself has severe resource constraints, such as low battery power, limited signal processing, limited computation and communication capabilities, and a small amount of memory; hence it can sense only a limited portion of the environment. However, when a group of sensor nodes collaborate with each other, they can accomplish a much bigger task efficiently. One of the primary advantages of deploying a wireless sensor network is its low deployment cost and freedom from requiring a messy wired communication backbone, which is often infeasible or economically inconvenient.Wireless sensor networks ensure a wide range of applications [2], starting from security surveillance in military and battlefields, monitoring previously unobserved environmental phenomena, smart homes and offices, improved healthcare, industrial diagnosis, and many more. For instance, a sensor network can be deployed in a remote island for monitoring wildlife habitat and animal behavior [25], or near the crater of a volcano to measure temperature, pressure, and seismic activities. In many of these applications the environment can be hostile where human Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions

Network Performance Enhancement with Optimization Sensor Placement in Wireless Sensor Network

“…In some studies coverage of each sensor will decrease with increasing the distance from sensor. Equation (1) shows sensor sensitivity values S of sensor S i at point P; ( , ) d S P i is the Euclidean distance between sensor and the point, λ is a positive parameter and γ is between 2 to 5 related to environment features [13].…”

Robustness and Performance Analysis of a Dynamic Sensor Network Scheduling Algorithm

“…For the k-coverage problem, formulated as a decision problem, polynomial-time algorithms (in terms of the number of sensors) are presented in [9]. A comprehensive study on both coverage and connectivity issues can be found in [8].…”

Coevrage Estimation of Geosensor in 3d Vector Environments

“…They are usually deployed in a wireless network to monitor and collect physical and environmental information such as motion, temperature, humidity, pollutants, and traffic flow in a given area (Romer and Mattern, 2004) . The information is then communicated to a processing center, where they are integrated and analyzed for different applications (Ghosh and Das, 2006). Capturing information of the environment is an important function of Wireless geoSensor Network (WSN) applications.…”

“…On the other hand, in the area-based coverage problem, which is the concern of this paper, the objective is to obtain the maximum region covered by geosensors, and is usually evaluated as the ratio of the covered area to the whole area (Huang and Tseng, 2005). The area-based coverage calculation methods are classified into: (i) the methods that consider a raster environment (Akbarzadeh et al, 2013;Argany et al, 2012;Cortés et al, 2004), which are limited by the spatial resolution; and (ii) the methods that model the environment as a vector dataset (Ghosh and Das, 2006;Guvensan and Yavuz, 2011;Ma et al, 2009;Cao, 2006, 2011), which have been mostly proposed for 2D spaces and do not consider the earth topography and human-made obstacles. Furthermore, the sensing models (i.e., binary or probabilistic, omnidirectional or directional, etc.)…”