Abstract-Existing geographic routing algorithms depend on the planarization of the network connectivity graph for correctness, and the planarization process gives rise to a well-defined notion of "faces". In this paper, we demonstrate that we can improve routing performance by storing a small amount of local face information at each node. We present a protocol, Path Vector Exchange Protocol (PVEX), that maintains local face information at each node efficiently, and a new geographic routing algorithm, Greedy Path Vector Face Routing (GPVFR), that achieves better routing performance in terms of both path stretch and hop stretch than existing geographic routing algorithms by exploiting available local face information. Our simulations demonstrate that GPVFR/PVEX achieves significantly reduced path and hop stretch than Greedy Perimeter Stateless Routing (GPSR) and somewhat better performance than Greedy Other Adaptive Face Routing (GOAFR+) over a wide range of network topologies. The cost of this improved performance is a small amount of additional storage, and the bandwidth required for our algorithm is comparable to GPSR and GOAFR+ in quasi-static networks.

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.

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