Geometric Algorithm for Finding Time-Sensitive Data Gathering Path in Energy Harvesting Sensor Networks
ABSTARCT :
To perform large-scale monitoring of sensitive events, energy harvesting wireless sensor network is considered where a mobile data sink MS collects data while travelling on a fixed path Pms. The sensor nodes sense environmental data continuously at a pre-specified rate. The sensors close to Pms are referred as gateways. Sensors forward their data to the MS through the gateways. In practice, the usage of MS is not suitable for time-sensitive applications due to its long data gathering delay. Time-bound data gathering for path constrained environment is not accounted in literature. We aim at finding energy-efficient maximum data gathering sub-path for the MS for a given data gathering period T. To deal with the problem a novel optimal deterministic data collection sub-path finding algorithm is proposed which is based on the geometric properties of the sensors' communication disks and the data gathering path. It maximizes the data collection and reduces the energy consumption by jointly optimizing the data gathering sub-path selection and the data forwarding path optimization. The performance of the proposed algorithm is compared with an existing baseline algorithm DDGA and a heuristic algorithm H-DGSPF. The simulation results show that our proposed algorithm outperforms DDGA and H-DGSPF in terms of data collection, data delivery success ratio, and energy consumption.
EXISTING SYSTEM :
? It can be inferred that the existing surveys do not encompass the subject completely.
? They also surveyed some existing techniques based on evolutionary algorithm to achieve various trade-offs between multiple conflicting requirements for prolonging the lifetime of the WSNs.
? The existing work in this research area has been classified with respect to different network types, different applications, different solution types and different conflicting objectives.
? The authors argued that the proposed clustering algorithm based on particle swarm optimization gave better results as compared to the other existing methods.
DISADVANTAGE :
? RF has a problem because the nodes may present very restricted space for antennas, thus requiring very short-wavelengths (for example, high-frequency) transmission, which must then tolerate very high attenuation.
? The main problem of this approach is the considerable loads on sink’s neighbors which may lead to their failure.
? This way, the problem of a node starving and losing energy quickly is overcome when the majority of the data are of priority type.
? For wireless communications, the main alternative technologies are based on RF and optical transmission methods which each have their own benefits and disadvantages.
PROPOSED SYSTEM :
• Researchers have proposed and adopted various techniques in order to utilize the resource constrained WSNs efficiently.
• A multi-objective optimization approach has been proposed in for the modular design architecture of QoS aware routing protocol to ensure the homogeneous depletion rate of energy.
• A scheme for the minimization of energy consumption has been proposed in by treating the design problem of beam pattern optimization as a multi-objective formulation.
• Space-based applications of wireless sensor networks are considered in , where authors have proposed a multi-objective formulation to address the problems of maximization of lifetime, minimization of energy consumption and maximization of the coverage.
ADVANTAGE :
? In order to choose the best next-hop node in terms of energy-efficiency, ERB-GR calculates metrics called Progress and Cost Progress for each neighbor.
? On the other side, Real-time Data-aware Anycasting policy, by doing some computations, decides which next hop node achieves better aggregation performance while satisfying real-time requirements.
? The energy efficiency of a solar cell ranges from 10 to 30 percent in contemporary technologies, offering 300 mW in full sunlight in the ideal case scenario for a 1-mm solar cell working at 1 V.
? Data aggregation is one of the most significant techniques which can be used to achieve energy efficiency and traffic optimization in routing operation.
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