Minimum Number Of Active Sensors Research Articles

Contribution to the Maximum Coverage Detection in a Heterogeneous Network

This article has addressed the problem of area coverage in surveillance camera networks using a minimum number of active sensors. The dense and random deployment of cameras creates many problems, among which the same portion of the area of interest is cited and monitored by several sensors. This redundancy of information generates unnecessary energy consumption, which increases the cost of installation. This work contributed to the extension of a surveillance algorithm, and the authors presented in this work a distributed algorithm of perimeter surveillance and made this contribution allowing the maintenance of total coverage in heterogeneous camera networks. The proposed solution is based on the search for minimum sets that completely cover a surface by scheduling the activity of the sensors. The proposed approach consists of calculating the distance between the center and the furthest point not covered and subtracting a fixed step from it; the coverage of these circles is done in the same way as the coverage of the first perimeter. The results of the simulations show that this approach ensures maximum coverage with a minimum number of cameras.

Energy-efficient scheduling strategies for minimizing big data collection in cluster-based sensor networks

Today, wireless sensor networks (WSNs) have been widely used in monitoring various applications, such as environment, military and health-care, etc. The explosive growth of the data volume generated in these applications has led to one of the most challenging research issues of the big data era. To deal with such amounts of data, exploring data correlation and scheduling strategies have received great attention in WSNs. In this paper, we propose an efficient mechanism based on the Euclidean distance for searching the spatial-temporal correlation between sensor nodes in periodic applications. Based on this correlation, we propose two sleep/active strategies for scheduling sensors in the network. The first one searches the minimum number of active sensors based on the set covering problem while the second one takes advantages from the correlation degree and the residual energy of the sensors for scheduling them in the network. Our mechanism with the proposed strategies were successfully tested on real sensor data. Compared to other existing techniques, the simulation results show that our mechanism significantly extends the lifetime of the network while conserving the quality of the collected data and the coverage of the monitored area.

A Learning Automata-based Algorithm for Area Coverage Problem in Directional Sensor Networks

Coverage problem is a research hot spot in directional sensor networks (DSNs). However, the major problem affecting the performance of the current coverage-enhancing strategies is that they just optimize the coverage of networks, but ignore the maximum number of sleep sensors to save more energy. Aiming to find an approximate optimal method that can cover maximum area with minimum number of active sensors, in this paper, a new scheduling algorithm based on learning automata is proposed to enhance area coverage, and shut off redundant sensors as many as possible. To evaluate the performance of the proposed algorithm, several experiments are conducted. Simulation results indicate that the proposed algorithm have effective performance in terms of coverage enhancement and sleeping sensors compared to the existing algorithms.

Full Coverage Deployment in Wireless Sensor Network

In wireless sensor network very less work has been done on deployment. As we know in random deployment we can’t achieve the maximum coverage in communication which result in short life time of the battery. So in this paper we discuss few benefit of the Uniform deployment in which we can achieve the full coverage with uniformity. In this paper we can achieve the maximum efficiency and increase lifetime time of the sensors which result in the long battery life which is the back bone of any Wireless Sensor Network for Remote Sensing like in Oceanographic, Glacier study and Metrological Department for weather study which are nearly inaccessible regions. Wireless sensor networks (WSNs) are composed of large number of sensor nodes that have capability of sensing, data processing and communication functionalities. The nodes are usually accoutered with power-strained batteries, which are often difficult, extravagant and even impossible to be restored once the nodes are deployed. Therefore energy aliveness becomes the key research challenge for sensor network protocols. The energy consumed by a node depends on its state. Each node could be in one of four states: transmit, receive, idle (when the node keeps listening to the medium even when no messages are being transmitted) and finally sleep state (where the radio module is switched off: no communication is possible). Recent research showed that accurate energy savings can be achieved by scheduling node’s activities in high-density WSNs. Specifically, some nodes are scheduled to sleep whereas the remaining ones provide continuous monitoring. The main issue here is how to minimize the number of active nodes in order to maximize the network lifetime. Sensing coverage is also a significant issue for sensor networks and is viewed as one of the critical measures of the supervision quality provided by a WSN. Sensor deployment is important in WSNs, as it not only determines the cost for creating the network but also affects how well a region is supervised by sensors. One frequently used method to decrease the undesirable energy consumption is to implement an energy-efficient configuration protocol where sensors operate under a well-defined activating schedule, that is, some unnecessary sensors switch to off mode or low-power listening mode. The coverage problem in WSNs is how one can be sure that the deployment of active sensors provides the necessary coverage level and how to determine the minimum number of active sensors required and their locations in the interested area to ensure desirable coverage. Regarding sensors activating or deployment control problem, some work have been investigated how some sensors can be arranged to go to sleeping modes to elongate the network lifetime while maintaining desirable coverage of sensing field. On the other hand how these active sensors can be selected to maintain the desirable coverage of sensing field. And provide a near-optimal grid-based sensor placement algorithm to achieve the complete coverage. In this method, the interested area is divided into grid points uniformly. If one grid point can be detected by at least one sensor, the grid point is covered. The coverage of the area is defined as the ratio of detected grid points to the total

Energy-efficient communication in wireless cable sensor networks

In this paper, we introduce a new type of sensor: cable sensor. Unlike traditional point sensors, this type of sensor has a rectangular sensing region with a processor installed on it to do processing and communication. The wireless network formed by this kind of sensor is called wireless cable sensor network (WCSN). We study energy-efficient communication algorithms in WCSNs. We address it in two ways: one is through reducing the total transmission power of processors while maintaining the connectivity of the network and the other is through scheduling cable sensors to let them take turns to go to sleep without affecting the coverage and connectivity of the network. In the first approach, we initially develop a distributed algorithm called DTRNG based on the relative neighbourhood graph. Later we enhance it to Algorithm determine the transmission power by removing the largest edge in CYCles (DTCYC). Mathematical proofs show that Algorithm DTCYC provides an optimal solution that can not only minimise the total processor transmission power but maintain the connectivity of the network as well. In the second approach, we propose a cable mode transition algorithm which determines the minimum number of active sensors to maintain K-coverage as well as K-connectivity required by the application. We discuss the relationship between coverage and connectivity and prove the theorems that lay the foundation for our algorithm. Simulation results demonstrate that our algorithm is efficient in saving energy.

A learning automata-based algorithm for solving coverage problem in directional sensor networks

Wireless sensor networks have been used in a wide variety of applications. Recently, networks consisting of directional sensors have gained prominence. An important challenge facing directional sensor networks (DSNs) is maximizing the network lifetime while covering all the targets in an area. One effective method for saving the sensors' energy and extending the network lifetime is to partition the DSN into several covers, each of which can cover all targets, and then to activate these covers successively. This paper first proposes a fully distributed algorithm based on irregular cellular learning automata to find a near-optimal solution for selecting each sensor's appropriate working direction. Then, to find a near-optimal solution that can cover all targets with the minimum number of active sensors, a centralized approximation algorithm is proposed based on distributed learning automata. This algorithm takes advantage of learning automata (LA) to determine the sensors that must be activated at each stage. As the presented algorithm proceeds, the activation process is focused on the sensor nodes that constitute the cover set with the minimum number of active sensors. Through simulations, we indicate that the scheduling algorithm based on LA has better performance than the greedy algorithm-based scheme in terms of maximizing network lifetime.

Cluster-based Energy-efficient k-Coverage for Wireless Sensor Networks

Normal 0 21 false false false MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tableau Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} Coverage preservation and prolonging lifetime are the fundamental issues in wireless sensor networks. Due to the large variety of applications, coverage is subject to a wide range of interpretations. Some applications require that every point in the area is monitored by only one sensor while other applications may require that each point is covered by at least k sensors (k>1) to achieve fault tolerance. Hence, it is desirable to active a minimum number of sensors able to ensure coverage area and turn off some redundant sensors to save energy and therefore extend network lifetime. Furthermore, determining a minimum number of active sensors is based on the degree coverage required and its level. In this paper, we propose a cluster-based efficient-energy coverage scheme called CSA_VS (Cluster-based Scheduling Algorithm–Virtual Sensor) to ensure the full coverage of a monitored area while saving energy. CSA_VS uses a novel node scheduling scheme based on the position and the remaining energy of each sensor to determine the state of all the deployed sensors to be either active or sleep as well as the state durations. Simulation results show that CSA_VS provides better performance in terms of the number and the percentage of active sensors to guarantee the area coverage compared to others algorithms.

Weaving a proper net to catch large objects in wireless sensor networks

Wireless sensor networks consist of a large number of sensors and have been broadly used for intrusion detection in surveillance systems. To guarantee detection quality, such networks are usually over-engineered, i.e., more than required sensors are deployed and remain active in order to cover each point in the monitored field with a high probability at any time instance. Existing sensor scheduling schemes based on the point coverage model tightly weave a sensor that is unnecessarily dense. Intuitively, when the size and the shape of intrusion objects are considered, any net with holes no smaller than the size of the intrusion object would work fine. With this design philosophy in mind, we build a new mathematical model to investigate the impact of size and shape of intrusion objects on network configuration. We derive analytical results that provide practitioners with insights on how to weave an effective sensor for intrusion object detection with minimum number of active sensors.

Innovations Diffusion: A Spatial Sampling Scheme for Distributed Estimation and Detection

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We consider a wireless network with distributed processing capabilities for estimation or detection applications. Due to limited communication resources, the network selects only a subset of sensor measurements for estimation or detection as long as the resulting fidelity is tolerable. We present a distributed sampling scheme based on the concept of <emphasis emphasistype="italic">innovations diffusion</emphasis> to select the sensor nodes. In the proposed scheme, sensor selection is accomplished through local communication and signal processing. In order to conserve energy and prolong system lifetime, the proposed algorithm selects a nearly minimum number of active sensors to ensure a desired fidelity for each working period. Extensive simulations illustrate the effectiveness of the proposed sampling scheme. </para>