The Coverage Problem in Video-Based Wireless Sensor Networks: A Survey

Abstract

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.

Self-Organising Zooms for Decentralised Redundancy Management in Visual Sensor Networks

“…Hoffman et al [10] present a selforganising approach to camera pan-tilt-zoom configurations, however they are concerned with minimising overlap between FOVs, rather than seeing this as an opportunity to make use of redundancy. Further information on camera placement algorithms and coverage optimisation is given in [2], [15], [19]. However, our aim is not maximal coverage.…”

Cryptography in Wireless Multimedia Sensor Networks: A Survey and Research Directions

“…This particular scenario leads us to survey the state-of-the-art of this subject, comparing different promising solutions. Actually, recent papers have proposed cryptography-based solutions for wireless multimedia sensor networks [4,13,14], but to the best of our knowledge, no survey has been written focusing specifically on cryptography issues in wireless multimedia sensor networks.…”

A routing mechanism based on the sensing relevancies of source nodes for time-critical applications in visual sensor networks

“…Group of relevance 2 available paths (p 1 < p 2 ) p 1 Maximum and high relevance packets p 2 Medium and low relevance packets 3 available paths (p 1 < p 2 < p 3 ) p 1 Maximum relevance packets p 2 High relevance packets p 3 Medium and low relevance packets 4 available paths (p 1 < p 2 < p 3 < p 4 ) p 1 Maximum relevance packets p 2 High relevance packets p 3 Medium relevance packets p 4 Low relevance packets 5 or more available paths (p 1 < p 2 < p 3 < p 4 < p (4 + i) ) p 1 Maximum relevance packets p 2 High relevance packets p 3 Medium relevance packets p 4 Low relevance packets p (4 + i) Backup paths…”

“…The quality of visual sensor networks will be a function of how well an area of interest is viewed by the source nodes [2] [7]. And such quality depends on the actual application requirements, which dictate what, when and with which constraints a set of static or moving targets or even an area of interest must be monitored by the deployed source sensors.…”

“…Visual sensor networks (VSNs) are composed of resourceconstrained self-organizing nodes where some or all of them are endowed with a low-power camera for a series of innovative multimedia sensing functions [1] [2]. Visual information retrieved from the monitored field in the form of video streaming, conventional snapshots, infrared or thermal images can significantly enhance a large set of monitoring applications, besides fostering the development of new ones [3] [4].…”