Abstract:Visual sensor networks are receiving significant attention due to their potential applications ranging from surveillance to tracking domains. Nevertheless, due to the funneling effect, the unbalanced energy usage among visual sensor nodes (SNs) increases and leads to premature decrease in network lifetime. Firstly, considering Rayleigh fading channel and routing models, we analyze the optimization of network lifetime by balancing the energy consumption among different SNs. From analysis it is revealed that the… Show more
“…When visual sensor networks are employed, the planning optimization includes not only the location of the sensors but also their orientation (as the field of view is not omnidirectional). In many of these visual sensor networks, there is a goal to place the network in a configuration so as to optimize network lifetime [9], [10]. In other applications, network lifetime is treated as a constraint [11].…”
Section: Background On Sensor Network Configuration Optimizationmentioning
We develop an optimization approach for the planning problem of configuring the positions of sensors within a sensor network for minimization of the travel length required to service the sensor locations. This is in contrast to existing approaches whereby the coverage of the sensor network is an objective of the optimization; in this new optimization approach the level of coverage is treated as a constraint. By beginning with an over-populated sensor network and then alternating between sensor repositioning and sensor removal, we create an optimization procedure that solves this difficult practical planning problem. A formal rule for switching between the repositioning and removal components of the optimization strategy is developed. Numerical example problems are presented to illustrate the method and show its performance against a heuristic optimization approach.
“…When visual sensor networks are employed, the planning optimization includes not only the location of the sensors but also their orientation (as the field of view is not omnidirectional). In many of these visual sensor networks, there is a goal to place the network in a configuration so as to optimize network lifetime [9], [10]. In other applications, network lifetime is treated as a constraint [11].…”
Section: Background On Sensor Network Configuration Optimizationmentioning
We develop an optimization approach for the planning problem of configuring the positions of sensors within a sensor network for minimization of the travel length required to service the sensor locations. This is in contrast to existing approaches whereby the coverage of the sensor network is an objective of the optimization; in this new optimization approach the level of coverage is treated as a constraint. By beginning with an over-populated sensor network and then alternating between sensor repositioning and sensor removal, we create an optimization procedure that solves this difficult practical planning problem. A formal rule for switching between the repositioning and removal components of the optimization strategy is developed. Numerical example problems are presented to illustrate the method and show its performance against a heuristic optimization approach.
“…In (1), the selfishness term is used to drive mobile units to visit those recently unexplored areas in the AoI to maximize the cumulative area coverage. It is defined as…”
“…S URVEILLANCE systems often require manual deployment of stationary sensory units to eliminate coverage holes [1]. Due to the inability of stationary sensory units to self-organize themselves in a given area of interest (AoI), they fail to cope with dynamic changes of the environment.…”
Anti-flocking controlled mobile sensor networks (MSNs) have demonstrated impressive dynamic area coverage performances. Even though MSNs are often utilized in outdoor environments that consist of uneven terrains, existing antiflocking control protocols are designed for flat terrain navigation. Thus they tend to maneuver mobile sensory units along shortest paths between navigation goals in an area of interest. Even though navigating along shortest paths can be both time-and energyefficient on flat terrains, such motions can often result in excessive energy consumptions on uneven terrains. This paper proposes an energy-efficient anti-flocking control protocol for MSNs based on a terrain adaptation force and a navigation goal selection method. The proposed control protocol encourages mobile sensory units to follow terrain contours whenever feasible. Test results show that the proposed control protocol is a promising energy-efficient solution for MSNs operating on uneven terrains.
“…The next issue is to determine an optimum coverage of the sensing area. The sensors' coverage area depends on characteristics of the camera such as position, orientation, and angular view depth [9] . In WMSN, geographically neighbor nodes do not necessarily sense adjacent visual areas.…”
Abstract-Coverage area and network lifetime are major problems in wireless multimedia sensor networks (WMSN). In order to optimize the network coverage and maximize the network's lifetime, we propose a coverage-enhancing algorithm based on sensors' priority. To increase the lifetime of the network, the density of distributed sensors could be such that a subset of sensors can cover the required air space. The sensors are prioritized based on their visual and Communicative properties and the selection is performed according to the prioritizing function. Simulation results show that the proposed algorithm achieves more effective enhancement on network coverage compared to the existing algorithm.
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