integrated coverage and connectivity configuration in wireless sensor networks pdf

Integrated Coverage And Connectivity Configuration In Wireless Sensor Networks Pdf

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On Coverage Problems of Directional Sensor Networks

Wireless sensor networks typically consist of a great number of tiny low-cost electronic devices with limited sensing and computing capabilities which cooperatively communicate to collect some kind of information from an area of interest.

When wireless nodes of such networks are equipped with a low-power camera, visual data can be retrieved, facilitating a new set of novel applications. The nature of video-based wireless sensor networks demands new algorithms and solutions, since traditional wireless sensor networks approaches are not feasible or even efficient for that specialized communication scenario.

The coverage problem is a crucial issue of wireless sensor networks, requiring specific solutions when video-based sensors are employed. In this paper, it is surveyed the state of the art of this particular issue, regarding strategies, algorithms and general computational solutions.

Open research areas are also discussed, envisaging promising investigation considering coverage in video-based wireless sensor networks. In the last decade, wireless sensor networks WSNs were one of the main research topics in computer communications. Composed of low-cost powered-restricted devices with sensing and computing capabilities which cooperatively communicate in a wireless manner, WSNs allowed a variety of innovative applications for sensing in wide, hostile or even hard access areas, as in battlefield surveillance, environmental monitoring, rescue operations, home entertainment and pollution detection, among others [ 1 ].

To achieve such applicability, many aspects of these networks, ranging from energy efficiency to sensor deployment and mobile communications, have been addressed in numerous research projects [ 2 ]. Nodes in WSNs are disposable electronic devices equipped with a transceiver, an energy supply typically a battery and a sensing unity, although others modules can also be found [ 3 ]. In traditional WSNs, sensors collect scalar data such as humidity, temperature, pressure and seismic variations [ 4 ].

When inexpensive low-resolution cameras are embedded in wireless sensors, visual data can be retrieved from the environment too, allowing a new scope of applications. For the resulting Video-based Wireless Sensor Networks VWSNs , or simply Visual Sensor Networks VSNs , new researches had to be conducted, since many traditional WSN algorithms, architectures and computational solutions are not feasible or even efficient for that specific communication scenario [ 5 , 6 ].

A crucial point in WSNs is the coverage problem [ 7 ]. The coverage concept is subject to a wide range of interpretations. Coverage can be formulated based on the subject to be covered, the sensor deployment mechanism, the network connectivity and energy consumption [ 8 , 9 ].

All these issues will be surveyed in this paper, particularly considering VWSNs. Typically, sensors will be randomly scattered in a target area, what can result in regions densely or sparsely covered by sensor nodes. For many applications, the quality of the deployed sensor network will be a direct function of how well an area of interest is covered by the sensor nodes. Maximum coverage as a result of optimal placement of sensor nodes is only feasible when deterministic deployment is considered.

Others important issues directed related to the coverage problem are connectivity and energy preservation. Wireless sensor networks, no matter the kind of sensors employed, are energy constrained. To maximize the network lifetime, the role of each wireless node has to be efficiently set along the time, since it can be sensing, relaying other nodes packets or sleeping for energy saving [ 10 ]. When a wireless node fails due to energy depletion or physical damage, a sleeping node is turned on, potentially prolonging the lifetime of the network and preserving the coverage of a region.

On the other hand, nodes with low energy can be selected for sensing, while wireless nodes with more energy are elected for routing, or the opposite, depending on the algorithms employed and the application requirements. In traditional WSNs, different aspects of the coverage problem have been addressed by many works. For example, in [ 7 ] polynomial time algorithms are presented to determine if a set of wireless nodes can properly cover a target area. This problem is expanded in [ 11 ], which regards a three-dimensional sensing model.

The 3D sensing range of the nodes is calculated by a low-complexity algorithm, in polynomial time. The protocol proposed in [ 12 ] preserves nodes with higher importance for the sensing application, electing them for sensing instead of routing in sparsely covered areas. Due to the nature of directional sensing traditional solutions for the coverage problem should not be employed for video-based wireless sensor networks. A reasonable discussion of the problems of using traditional coverage algorithms in VWSNs is offered in [ 13 ].

Several papers can be found in the literature surveying wireless sensor networks [ 2 , 3 , 14 ] and visual sensor networks [ 6 , 15 ] as their main research areas. A specific survey on the coverage problem in traditional wireless sensor networks can be found in [ 16 ]. In a different way, in this survey we present the recent developments, challenging issues and open research areas of the coverage problem in video-based wireless sensor networks.

This crucial part of VWSNs is surveyed in a structured way, comprising directional sensing, node deployment, coverage metrics and energy efficient solutions, besides complementary issues related to directional coverage. The rest of the paper is organized as follows. Section 2 provides a short description about the main concepts of VWSNs.

In Section 3, the fundaments of directional coverage are presented. Deterministic deployment of video-based sensors is discussed in Section 4, along with the resulting coverage of optimal camera and sensor placement.

Algorithms for coverage management, coverage metrics and node localization in randomly deployed VWSNs are presented in Section 5. Section 6 brings algorithms and strategies for coverage, connectivity and energy preservation. Section 7 presents others relevant issues in the coverage problem. Open research areas are discussed in Section 8. At last, conclusions and references are presented. Recent advances in CMOS technology have allowed the development of low-power cameras that can be embedded in wireless nodes for a whole new set of sensing functions.

Inexpensive visual sensors could be developed by the reduction of the hardware to a single integrated chip that can capture and process an optimal image [ 15 ]. Sensors equipped with a CMOS camera can collect visual data, attending applications unassisted by Internet and traditional wireless sensor networks technologies. The resolution, viewing angle and cost of video-based sensors can significantly differ depending on the application type and the network budget.

Figure 1 presents some typical low-resolution cameras for visual sensor networks. VWSNs are an emerging ad-hoc network technology that employs autonomous wireless sensors equipped with a low-power cameras to wirelessly retrieve visual data from the monitored field. In the last years, the demand for VWSN applications has significantly increased, fostered by vision-based applications as traffic monitoring and surveillance.

For an increasing group of applications, scalar data collected from traditional wireless sensor networks are insufficient, even if a large number of sensors are deployed [ 5 ]. Other promising applications for video-based wireless sensor networks are environment monitoring, wildlife observation, automated assistance for elderly and disabled people, person location service and industrial process control [ 15 ].

Besides the use of some well-known algorithms and strategies from traditional wireless sensor networks, VWSNs demand new solutions for challenging questions, due to their particular sensing operations. For traditional WSNs, coverage and connectivity are coupled issues, since the wireless sensors collect data from their vicinity [ 5 ] and a single area is likely to be monitored by neighboring nodes.

Thus, for visual sensor networks, a different concept of sensing range is created [ 14 ]. Others relevant issues emerge when the wireless sensors are equipped with cameras. Given the large amount of data generated by camera nodes, data transmission requires more bandwidth and energy in VWSNs.

To reduce the amount of data transmitted, many works suggest and propose algorithms for local processing of visual data. Data aggregation is addressed as a crucial point in video-based wireless sensor networks, requiring complex and sometimes costly algorithms to process visual data, when compared with scalar data provided by traditional WSNs [ 17 ]. In fact, video-based wireless sensor networks may expect more energy consumption in local processing than in data transmission, contrary to traditional wireless sensor networks [ 6 ].

In addition, the nature of visual data imposes time constraints to the multi-hop communication among wireless nodes. Therefore, new protocols and network topologies were created or adapted to couple with the transmission of such data type [ 18 ]. Moreover, QoS has been pointed as a valuable resource for video-based wireless sensor networks [ 15 ]. Multimedia in-network processing is another key design requirement of VWSNs, addressed by many works [ 15 ].

It is expected cooperative processing of multimedia data by intermediate nodes, potentially reducing the amount of data transmitted throughout the network and prolonging the overall network lifetime by saving energy, since the communication latency is kept in an acceptable level.

Many others relevant issues are associated to video-based wireless sensor networks, comprising a diversity of interdisciplinary aspects with particular challenges. However, this work is focused on the coverage problem in VWSNs, which have a direct impact in the quality of the deployed network and the user application, but also is influenced by many aspects as node deployment and energy preservation approaches.

The coverage is a crucial issue directed related to the final quality of the application, also impacting in the way ad-hoc networks operate. In fact, for most WSN applications, how well a monitored field is covered is a fundamental concern that should be properly addressed. But such concern varies according to the nature of the applications. However, such restriction cannot be so strict for some kind of applications, which are concerned with the sensing of large areas, as in weather monitoring, where there is no need for collecting data from every single subspace of the field.

When dealing with coverage, we wish to determine how well an area of interest is covered by wireless nodes and how redundant nodes can be used to prolong the network lifetime, keeping a minimum level of coverage quality required by the application and connectivity of the nodes. For these challenging issues, a sensing model has to be created according to the way sensors collect date from the monitored field.

In traditional wireless sensor networks, the sensing range of wireless nodes can be approximated to the radius of a circumference [ 7 ]. Scalar data is collected according to the type of the sensor, its accuracy and the sensing range. For this sensing structure, neighboring nodes are likely to collect similar data.

For visual sensor networks, video-based sensors collect data in a different way, creating a directional sensing model.

Additionally, some aspects of digital cameras, such as lens quality and zoom capabilities, can influence the final sensing and coverage. Although there are many options and configurations, cost will often limit the quality and additional features of video-based sensors.

The maximum volume visible from a camera is defined as the Field of View FoV. The spatial resolution of a camera is the ratio between the total number of pixels used to represent an object and its size.

More detailed images are directly proportional to higher spatial resolution [ 19 ], but with direct influence in the final cost of the camera. The Depth of Field DoF is the amount of distance between the nearest and farthest targets that can be properly viewed by a camera [ 19 ]. Due to limited resolution and distortion of lenses, cameras in real-world VWSNs have a limited depth of field [ 20 ]. In fact, targets too far from the optical center may not be observed. The viewing angle is the maximum angle at which an object of the scene can be observed [ 19 ].

As each camera has a viewing direction, it is very acceptable to conceive that each sensor in a VWSN has a unique perspective of the monitored field [ 13 ]. Figure 2 b shows a configuration where seven sensors are employed to cover eight targets. The same eight targets are now covered by only four sensors, reducing the cost of the deployed network and allowing the using of redundant nodes to prolong network lifetime.

Directional sensing model. When video-based sensors are deployed, others important issues have to be considered. When the FoV of two or more cameras intersect, the same object or scene is viewed by more than one wireless node, even under different directions and perspectives. This overlapping area can be processed for image compression or utilized for localization and optimal deployment purposes. On the other hand, the occlusion occurs when the field of view of a camera is blocked by some obstacle, which can be statically positioned or moving across the monitored field.

In a modeled monitored field, a prohibited area is a region where moving or static objects cannot be placed e. When computing coverage, such regions may not be processed, saving time and computational resources. The coverage in VWSNs can be also influenced by the type of the cameras.

Integrated coverage and connectivity configuration for energy conservation in sensor networks

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes, while the remaining nodes stay active to provide continuous service. This analysis yields key insights for treating coverage and connectivity in a unified… Expand Abstract. View on ACM. Save to Library.

Raut and S. DOI : Holzinger , On Topological Data Mining , pp. Fan and S. Bai, S. Kuma, D.

In conventional sensor networks, the sensors often are based on omni-sensing model. However, directional sensing range and sensors are great application chances, typically in video sensor networks. Toward this end, this paper evaluates the requirements of deploying directional sensors for a given coverage probability. Moreover, the paper proposes how to solve the connectivity problem for randomly deployed sensors under the directional communication model. The paper proposes a method for checking and repairing the connectivity of directional sensor networks for two typical cases. We design efficient protocols to implement our idea.


For the sensor network to operate successfully, the active nodes must maintain both sensing coverage and network connectivity. Furthermore, the network must​.


Integrated coverage and connectivity configuration for energy conservation in sensor networks

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes while the remaining nodes stay active to provide continuous service.

Wireless Sensor Networks - Technology and Protocols.

On Coverage Problems of Directional Sensor Networks

Nowadays, wireless sensor networks WSNs emerge as an active research area in which challenging topics involve energy consumption, routing algorithms, selection of sensors location according to a given premise, robustness, efficiency, and so forth. Despite the open problems in WSNs, there are already a high number of applications available. In all cases for the design of any application, one of the main objectives is to keep the WSN alive and functional as long as possible. A key factor in this is the way the network is formed. This survey presents most recent formation techniques and mechanisms for the WSNs.

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. A FoI is said to be covered if each point in the FoI is monitored by at least one sensor node. Due to small size, battery power supply, simple architecture, and light weight Operating System of the sensor nodes, maintaining the desired coverage of the FoI consists various issues and challenges in a connected WSN.

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Wireless sensor networks typically consist of a great number of tiny low-cost electronic devices with limited sensing and computing capabilities which cooperatively communicate to collect some kind of information from an area of interest.

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