Habib M. Ammari

Coverage in Wireless Sensor Networks: A Survey

Wireless sensor networks are a rapidly growing area for research and commercial development. Wireless sensor networks are used to monitor a given field of interest for changes in the environment. They are very useful for military, environmental, and scientific applications to name a few. One of the most active areas of research in wireless sensor networks is that of coverage. Coverage in wireless sensor networks is usually defined as a measure of how well and for how long the sensors are able to observe the physical space. In this paper, we take a representative survey of the current work that has been done in this area. We define several terms and concepts and then show how they are being utilized in various research works.

Centralized and Clustered k-Coverage Protocols for Wireless Sensor Networks

Sensing coverage is an essential functionality of wireless sensor networks (WSNs). However, it is also well known that coverage alone in WSNs is not sufficient, and hence network connectivity should also be considered for the correct operation of WSNs. In this paper, we address the problem of k-coverage in WSNs such that in each scheduling round, every location in a monitored field (or simply field) is covered by at least k active sensors while all active sensors are being connected. Precisely, we study sensors duty-cycling strategies for generating k-coverage configurations in WSNs. First, we model the k-coverage problem in WSNs. Second, we derive a sufficient condition of the sensor spatial density for complete k-coverage of a field. We also provide a relationship between the communication and sensing ranges of sensors to maintain both k-coverage of a field and connectivity among all active sensors. Third, we propose four configuration protocols to solve the problem of k-coverage in WSNs. We prove that our protocols select a minimum number of sensors to achieve full k-coverage of a field while guaranteeing connectivity between them. Then, we relax some widely used assumptions for coverage configuration in WSNs, to promote the use of our proposed protocols in real-world sensing applications. Our simulation results show that our protocols outperform an existing distributed k-coverage configuration protocol.

Promoting Heterogeneity, Mobility, and Energy-Aware Voronoi Diagram in Wireless Sensor Networks

Static always-on wireless sensor networks (WSNs) are affected by the energy sink-hole problem, where sensors nearer a central gathering node, called the sink, suffer from significant depletion of their battery power (or energy). It has been shown through analysis and simulation that it is impossible to guarantee uniform energy depletion of all the sensors in static uniformly distributed always-on WSNs with constant data reporting to the sink when the sensors use their nominal communication range to transmit data to the sink. We prove that the energy sink-hole problem can be solved provided that the sensors adjust their communication ranges. This solution, however, imposes a severe restriction on the size of a sensor field. To overcome this limitation, we propose a sensor deployment strategy based on energy heterogeneity with a goal that all the sensors deplete their energy at the same time. Simulation results show that such a deployment strategy helps achieve this goal. To solve the energy sink-hole problem for homogeneous WSNs, we propose a localized energy-aware-Voronoi-diagram-based data forwarding (EVEN) protocol. EVEN combines sink mobility with a new concept, called energy-aware Voronoi diagram. Through simulations, we show that EVEN outperforms similar greedy geographical data forwarding protocols and has performance that is comparable to that of an existing data collection protocol that uses a joint mobility and routing strategy. Precisely, we find that EVEN yields an improvement of more than 430 percent in terms of network lifetime.

A Study of k-Coverage and Measures of Connectivity in 3D Wireless Sensor Networks

In a wireless sensor network (WSN), connectivity enables the sensors to communicate with each other, while sensing coverage reflects the quality of surveillance. Although the majority of studies on coverage and connectivity in WSNs consider 2D space, 3D settings represent more accurately the network design for real-world applications. As an example, underwater sensor networks require design in 3D rather than 2D space. In this paper, we focus on the connectivity and k-coverage issues in 3D WSNs, where each point is covered by at least k sensors (the maximum value of k is called the coverage degree). Precisely, we propose the Reuleaux tetrahedron model to characterize k-coverage of a 3D field and investigate the corresponding minimum sensor spatial density. We prove that a 3D field is guaranteed to be k-covered if any Reuleaux tetrahedron region of the field contains at least k sensors. We also compute the connectivity of 3D k-covered WSNs. Based on the concepts of conditional connectivity and forbidden faulty sensor set, which cannot include all the neighbors of a sensor, we prove that 3D k-covered WSNs can sustain a large number of sensor failures. Precisely, we prove that 3D k-covered WSNs have connectivity higher than their coverage degree k. Then, we relax some widely used assumptions in coverage and connectivity in WSNs, such as sensor homogeneity and unit sensing and communication model, so as to promote the practicality of our results in real-world scenarios. Also, we propose a placement strategy of sensors to achieve full k-coverage of a 3D field. This strategy can be used in the design of energy-efficient scheduling protocols for 3D k-covered WSNs to extend the network lifetime.

Integrated Coverage and Connectivity in Wireless Sensor Networks: A Two-Dimensional Percolation Problem

While sensing coverage reflects the surveillance quality provided by a wireless sensor network (WSN), network connectivity enables data gathered by sensors to reach a central node, called the sink. Given an initially uncovered field and as more and more sensors are continuously added to a WSN, 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 as the sensing-coverage phase transition (SCPT). Also, given an originally disconnected WSN and as more and more sensors are added, the number of connected components changes such that the WSN suddenly becomes connected at some point. We call this sudden change as the network-connectivity phase transition (NCPT). The nature of such phase transitions is a central topic in percolation theory of Boolean models. In this paper, we propose a probabilistic approach to compute the covered area fraction at critical percolation for both of the SCPT and NCPT problems. Because sensing coverage and network connectivity are not totally orthogonal, we also propose a model for percolation in WSNs, called correlated disk model, which provides a basis for solving the SCPT and NCPT problems together.

Critical Density for Coverage and Connectivity in Three-Dimensional Wireless Sensor Networks Using Continuum Percolation

Although most of the studies on coverage and connectivity in wireless sensor networks (WSNs) considered two-dimensional (2D) settings, such networks can in reality be accurately modeled in a three-dimensional (3D) space. The concepts of continuum percolation theory best fit the problem of connectivity in WSNs to find out whether the network provides long-distance multihop communication. In this paper, we focus on percolation in coverage and connectivity in 3D WSNs. We say that the network exhibits a coverage percolation (respectively, connectivity percolation) when a giant covered region (respectively, giant connected component) almost surely spans the entire network for the first time. Because of the dependency between coverage and connectivity, the problem is not only a continuum percolation problem but also an integrated continuum percolation problem. Thus, we propose an integrated-concentric-sphere model to address coverage and connectivity in 3D WSNs in an integrated way. First, we compute the critical density lambdaC con above which coverage percolation in 3D WSNs will almost surely occur. Second, we compute the critical density lambdac con above which connectivity percolation in 3D WSNs will almost surely occur. Third, we compute the critical density lambdac cov-con above which both coverage and connectivity percolation in 3D WSNs will almost surely occur. For each of these three problems, we also compute their corresponding critical network degree. Our results can be helpful in the design of energy-efficient topology control protocols for 3D WSNs in terms of coverage and connectivity.

Integration of mobile ad hoc networks and the Internet using mobile gateways

Summary form only given. Mobile ad hoc networks (MANET) and the Internet exhibit differences in their network architecture. These differences concern the various sorts of assumptions imposed not only on the structure and topology of the underlying networks, but also on communication patterns of mobile nodes in both networks. Integrating MANET and the Internet into a hybrid network is a challenging problem due to these differences. We propose a three-layer approach that uses both mobile IP and dynamic destination-sequenced distance vector (DSDV) to integrate these two types of networks into a hybrid environment, in order to provide MANET nodes with Internet connectivity and access to the Internet resources. Our approach is based on the use of mobile gateways as an interface between MANET and the Internet. These mobile gateways can use mobile IP when they communicate with the Internet and DSDV when they interact with MANET. We also show the results of several simulation experiments that were conducted to study the integrated environment.

A trade-off between energy and delay in data dissemination for wireless sensor networks using transmission range slicing

Data dissemination is an essential function in wireless sensor networks (WSNs). A WSN consists of a large number of unattended sensors with limited storage, battery power, computation, and communication capabilities, where battery power (or energy) is the most crucial resource for sensor nodes. Because delay time is also a critical metric for certain applications, data dissemination between source sensors (or simply sources) and a sink (or central gathering point) should be done in an energy-efficient and timely manner. In this paper, we present an approach that characterizes a trade-off between energy and source-to-sink delay (or simply delay). Specifically, we decompose the transmission range of sensors into concentric circular bands (CCBs) based on a minimum transmission distance between any pair of sensors. Our decomposition strategy provides a classification of these CCBs that helps a sensor express its degree of interest (DoI) in minimizing two conflicting metrics, namely energy consumption and delay. We also propose a data dissemination protocol that exploits the above-mentioned decomposition to meet the specific requirements of a sensing application in terms of energy and delay. We prove that the use of sensors nodes, which lie on or closely to the shortest path between a source and a sink, as proxy forwarders in data dissemination from sources to a sink, helps simultaneously minimize energy consumption and delay. Also, we compute theoretical lower and upper bounds on these two metrics. Our simulation results are found to be consistent with our theoretical results, and show that the first CCB minimizes energy consumption; the last CCB minimizes delay; and the middle CCBs trade-off energy consumption with delay in data dissemination in WSNs.

On the Connected k-Coverage Problem in Heterogeneous Sensor Nets: The Curse of Randomness and Heterogeneity

Coverage is an essential task in sensor deployment for the design of wireless sensor networks. While most existing studies on coverage consider homogeneous sensors, the deployment of heterogeneous sensors represents more accurately the network design for real-world applications. In this paper, we focus on the problem of connected k-coverage in heterogeneous wireless sensor networks. Precisely, we distinguish two deployment strategies, where heterogeneous sensors are either randomly or pseudo-randomly distributed in a field. While the first deployment approach considers a single layer of heterogeneous sensors, the second one proposes a multi-tier architecture of heterogeneous sensors to better address the problems introduced by pure randomness and heterogeneity.

Trade-off between energy savings and source-to-sink delay in data dissemination for wireless sensor networks

Wireless sensor networks (WSNs) consist of large numbers of unattended sensors with limited storage, energy (battery power) and computational and communication capabilities. Because battery power is the most crucial resource for sensor nodes and delay time is a critical metric for certain WSN applications that require fast response time, data dissemination between source sensors and sinks, which is an essential activity in WSNs, should be done in an energy efficient and timely manner. In this paper, we characterize the trade-off between energy savings and source-to-sink delay in order to extend the operation of individual sensors and hence increase the lifetime of the WSN, and enable sinks to receive sensed data in a timely fashion and make appropriate decisions quickly. To this end, the proposed data dissemination protocol decomposes the transmission range of sensors into a certain number of concentric circular bands (CCBs) based on a minimal distance between consecutive forwarding sensors. Then, it provides a classification of these CCBs based on their exterior radii which will help a source sensor express its degree of interest (DoI) in minimizing two metrics, namely energy consumption and source-to-sink delay. We prove that the use of sensors nodes, which lie on or closely to the shortest path between a source and the sink, as proxy forwarders, helps minimize these two metrics. Our numerical results show that the second CCB minimizes energy consumption; the last CCB minimizes source-to-sink delay; and the middle CCBs trade off between the two metrics in disseminating the monitored data towards the sink.