Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Achieving Crossed Strong Barrier Coverage in Wireless Sensor Network

Barrier coverage has been widely used to detect intrusions in wireless sensor networks (WSNs). It can fulfill the monitoring task while extending the lifetime of the network. Though barrier coverage in WSNs has been intensively studied in recent years, previous research failed to consider the problem of intrusion in transversal directions. If an intruder knows the deployment configuration of sensor nodes, then there is a high probability that it may traverse the whole target region from particular directions, without being detected. In this paper, we introduce the concept of crossed barrier coverage that can overcome this defect. We prove that the problem of finding the maximum number of crossed barriers is NP-hard and integer linear programming (ILP) is used to formulate the optimization problem. The branch-and-bound algorithm is adopted to determine the maximum number of crossed barriers. In addition, we also propose a multi-round shortest path algorithm (MSPA) to solve the optimization problem, which works heuristically to guarantee efficiency while maintaining near-optimal solutions. Several conventional algorithms for finding the maximum number of disjoint strong barriers are also modified to solve the crossed barrier problem and for the purpose of comparison. Extensive simulation studies demonstrate the effectiveness of MSPA

Barrier Coverage in Wireless Sensor Networks

Barrier coverage is a critical issue in wireless sensor networks (WSNs) for security applications, which aims to detect intruders attempting to penetrate protected areas. However, it is difficult to achieve desired barrier coverage after initial random deployment of sensors because their locations cannot be controlled or predicted. In this dissertation, we explore how to leverage the mobility capacity of mobile sensors to improve the quality of barrier coverage. We first study the 1-barrier coverage formation problem in heterogeneous sensor networks and explore how to efficiently use different types of mobile sensors to form a barrier with pre-deployed different types of stationary sensors. We introduce a novel directional barrier graph model and prove that the minimum cost 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 graph. In addition, we 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. We further study the k-barrier coverage formation problem in sensor networks. We introduce a novel weighted barrier graph model and prove that determining the minimum number of mobile sensors required to form k-barrier coverage is related with but not equal to finding k vertex-disjoint paths with the minimum total length on the WBG. With this observation, we propose an optimal algorithm and a faster greedy algorithm to find the minimum number of mobile sensors required to form k-barrier coverage. Finally, we study the barrier coverage formation problem when sensors have location errors. We derive the minimum number of mobile sensors needed to fill in a gap with a guarantee when location errors exist and propose a progressive method for mobile sensor deployment. Furthermore, we propose a fault tolerant weighted barrier graph to find the minimum number of mobile sensors needed to form barrier coverage with a guarantee. Both analytical and experimental studies demonstrated the effectiveness of our proposed algorithms

A study of sensor movement and selection strategies for strong barrier coverage

Intruder detection and border surveillance are some of the many applications of sensor networks. In these applications, sensors are deployed along the perimeter of a protected area such that no intruder can cross the perimeter without being detected. The arrangement of sensors for this purpose is referred to as the barrier coverage problem in sensor networks. A primary question centering such a problem is: How to achieve barrier coverage? On the other hand, sensor nodes are usually battery-powered and have limited energy. It is critical to design energy-efficient barrier construction schemes while satisfying the coverage requirement. First, we studied how to achieve strong barrier coverage with mobile sensors. We leverage the mobility of sensors and relocate them to designated destinations to form a strong horizontal barrier after the random deployment. Algorithms were proposed to calculate the optimal relocating destinations such that the maximum moving distance of sensors is minimized. Depending on the number of sensors on the final barrier, two problems were investigated: (1) constructing a barrier with the minimum number of sensors on the final barrier, and (2) constructing a barrier with any number of sensors on the final barrier. For both problems, we optimized the barrier location instead of fixing it a priori as other works. We proposed algorithms which first identify a set of discrete candidates for the barrier location, then check the candidates iteratively. Both problems could be solved in polynomial time. Second, we investigated how to achieve strong barrier coverage by selectively activating randomly deployed static sensors. We aimed to select the minimum number of sensors to be active to achieve barrier coverage under a practical probabilistic model. The system false alarm probability and detection probability were jointly considered, and a (P_D^{min}, P_F^{max})-barrier coverage was defined where P_D^{min} is the minimum system detection probability and P_F^{max} is the maximum system false alarm probability. Our analysis showed that with the constraint on the system false alarm probability, the number of active sensors affects the detection capability of sensors, which would bring new challenges to the min-num sensor selection problem. We proposed an iterative framework to solve the sensor selection problem under the probabilistic model. Depending on whether the decision fusion was applied, different detection capability evaluation methods were used in the iterative framework. Finally, we studied how to achieve strong barrier coverage in a hybrid network with a mix of mobile and static sensors. A two-step deployment strategy was adopted where static sensors are first randomly deployed, and then mobile sensors are deployed to merge the coverage gap left by the static sensors. We aimed to find the proper coverage gaps to deploy mobile sensors such that (P_D^{min}, P_F^{max})-barrier coverage is achieved, and the total cost of the barrier is minimized. Under the probabilistic model, we solved the problem by iteratively trying multiple assumptions of the number of active sensors, and obtained the min-cost deployment strategy with the help of graph algorithms

Local phenomena, chapter 3, part C

Oceanic and coastal phenomena with dimensions ranging to 100 km are dealt with. The two major categories discussed are waves, their generation and dynamics and ocean-land related problems. The dynamics, of surface waves in both capillary and gravity ranges indicates that microwave technology provides a superior means of measuring simultaneously the spatial and temporal properties of ocean waves. The need for basic studies of physical phenomena in support of active microwave sensing is indicated. Active microwave scattering from surface waves is discussed in terms of wave dynamics

WiFi-based PCL for monitoring private airfields

In this article, the potential exploitation of WiFi-based PCL systems is investigated with reference to a real-world civil application in which these sensors are expected to nicely complement the existing technologies adopted for monitoring purposes, especially when operating against noncooperative targets. In particular, we consider the monitoring application of small private airstrips or airfields. With this terminology, we refer to open areas designated for the takeoff and landing of small aircrafts that, unlike an airport, have generally short and possibly unpaved runways (e.g., grass, dirt, sand, or gravel surfaces) and do not necessarily have terminals. More important, such areas usually are devoid of conventional technologies, equipment, or procedures adopted to guarantee safety and security in large aerodromes.There exist a huge number of small, privately owned, and unlicensed airfields around the world. Private aircraft owners mainly use these “airports” for recreational, single-person, or private flights for small groups and training flight purposes. In addition, residential airparks have proliferated in recent years, especially inthe United States, Canada, and South Africa. A residential airpark, or “fly-in community,” features common airstrips where homes with attached hangars allow owners to taxi from their hangar to a shared runway. In many cases, roads are dual use for both cars and planes.In such scenarios, the possibility to employ low-cost, compact, nonintrusive, and nontransmitting sensors as a way to improve safety and security with limited impact on the airstrips' users would be of great potential interest. To this purpose, WiFi-based passive radar sensors appear to be good candidates [23]. Therefore, we investigate their application against typical operative conditions experienced in the scenarios described earlier. The aim is to assess the capability to detect, localize, and track authorized and unauthorized targets that can be occupying the runway and the surrounding areas

Belt-Barrier Construction Algorithm for WVSNs

[[abstract]]Previous research of barrier coverage did not consider breadth of coverage in Wireless Visual Sensor Networks (WVSNs). In this paper, we consider breadth to increase the Quality of Monitor (QoM) of WVSNs. The proposed algorithm is called Distributed β-Breadth Belt-Barrier construction algorithm (D-TriB). D-TriB constructs a belt-barrier with β breadth to offer β level of QoM, we call β-QoM. D-TriB can not only reduce the number of camera sensors required to construct a barrier but also ensure that any barrier with β-QoM in the network can be identified. Finally, the successful rate of the proposed algorithm is evaluated through simulations.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20120401~20120404[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Shanghai, Chin

Validating an integer non-linear program optimization model of a wireless sensor network using agent-based simulation

Deploying wireless sensor networks (WSN) along a barrier line to provide surveillance against illegal intruders is a fundamental sensor-allocation problem. To maximize the detection probability of intruders with a limited number of sensors, we propose an integer non-linear program optimization model which considers multiple types of sensors and targets, probabilistic detection functions and sensor-reliability issues. An agent-based simulation (ABS) model is used to validate the analytic results and evaluate the performance of the WSN under more realistic conditions, such as intruders moving along random paths. Our experiment shows that the results from the optimization model are consistent with the results from the ABS model. This increases our confidence in the ABS model and allows us to conduct a further experiment using moving intruders, which is more realistic, but it is challenging to find an analytic solution. This experiment shows the complementary benefits of using optimization and ABS models