Coverage Aware Scheduling Strategies for 3D Wireless Video Sensor Nodes to Enhance Network Lifetime

Abstract

High-density wireless video sensor nodes (VSNs) having limited battery power are deployed randomly in the disaster-hit area for capturing visual data, but its local processing and transmission consume high energy. High deployment density of those VSNs results in a larger overlap in the coverage area across VSNs that can be utilized to cover the sensing region of some VSNs and shut off such VSNs to decrease energy consumption and increase network lifetime without losing much area coverage. Two advanced approaches (APP_5 and APP_6) with realistic 3D rectangular pyramid camera coverage of VSN monitoring 2D target area is proposed in this paper. These approaches reduce the number of active VSNs in the target area and energy consumption maintaining the overall coverage area above some threshold value ensuring network connectivity. The approaches are compared with the three state-of-the-art approaches EX_1, EX_2 and EX_3 in the same simulation setup. Observed that for 150 deployed VSNs over the target area of size 75x75 square meters, APP_5 and APP_6 reduce energy consumption by 6.98% and 18.6% respectively from the existing approach EX_3 (producing a better result among three existing approaches in terms of energy consumption). Reducing the number of active VSNs helps decrease energy consumption at the expense of reduced area coverage. For the same node density, both APP_5 and APP_6 lose a little amount of area coverage (i.e. 0.93% and 0.95%) than the existing approach EX_2 (producing a better result among three existing approaches in terms of percentage of area coverage). Additionally, both the proposed approaches (having the same communication overhead) establish superiority by 3.19%/7.83%/4.25% from EX_1/EX_2/(EX_3) in terms of communication overhead for 100 deployed VSNs on the same target area. Finally, APP_6 substantiates superiority in terms of reduced energy consumption (11.97%) than APP_5 losing a very little percentage (0.02%) of area coverage for 150 deployed VSNs.

Efficient approaches to optimize energy consumption in 3D wireless video sensor network under the coverage and connectivity constraints

“…But heuristics in [13] have used the 2D Omni-directional sensing model of WSNs, not a realistic camera coverage model. Two centralized approaches (APP_5 and APP_6) having an advanced duty-cycling technique are suggested in [14]. These two approaches are successful in lowering the number of active VSNs, total energy consumption by the active VSNs more compared to the existing approaches (EX_1, EX_2, EX_3).…”

“…These two approaches are successful in lowering the number of active VSNs, total energy consumption by the active VSNs more compared to the existing approaches (EX_1, EX_2, EX_3). EX_1, EX_2 and EX_3 are the upgraded 3D version of [1], [2] and [13] respectively as revealed in [14]. Consequently, the loss of coverage by the active VSNs is also more in [14] compared to EX_1, EX_2 and EX_3.…”

“…EX_1, EX_2 and EX_3 are the upgraded 3D version of [1], [2] and [13] respectively as revealed in [14]. Consequently, the loss of coverage by the active VSNs is also more in [14] compared to EX_1, EX_2 and EX_3. But, the scheduling schemes in [1-9, 13, 14] are able to reduce the number of active VSNs and in turn energy consumption at the cost of a reduction in the percentage of coverage.…”

“…A set of video sensor nodes (VSNs) furnished with miniature video cameras called CMOS [14] cameras forms a wireless video sensor network (WVSN). Such sensors possess the function of capturing video and image and are extensively utilized in various applications like surveillance work, monitoring in the affected area with natural disasters, tracking, environment monitoring etc.…”

“…The performances of PA_1 and PA_2 are compared with the three existing approaches, APP_5 [14], APP_6 [14] and ET_3 (upgraded 3D version of [13]). ET_3 are evolved by replacing 2D VSNs with 3D VSNs.…”