Energy-efficient Edge Computing Framework for Decentralized Sensing in WSN-assisted IoT
ABSTARCT :
This paper addresses the problem of decentralized sensor selection in an energy-constrained wireless sensor network-based Internet-of-Things, for monitoring a spatio-temporally varying process. To do so, an adaptive edge computing framework and its variants are proposed which distributedly optimize a critical trade-off between sensing quality and remaining energy of the sensor nodes (SNs). Unlike the existing distributed sensing approaches, the proposed one aims to maintain energy balance among the SNs. The original sensor selection problem is decoupled into multiple sub-problems, each solvable at an edge node elected as head of a coverage region containing a set of SNs. The sub-problem in each coverage region is adapted to variations of the underlying process. In each region, the process is estimated using PCA-SBL (principal component analysis-sparse Bayesian learning) on noisy signal measured by the respective active SNs. Further, to correctly adapt to the process and estimate the signal, a novel logic is designed that indicates requirement of network retraining in the next measurement cycle. The results from extensive simulation studies illustrate improved energy efficiency and network energy balance of the proposed framework over the existing closest competitive centralized and decentralized approaches. The proposed framework is tested on synthetic as well as real data-sets of a sensor network.
EXISTING SYSTEM :
? Their solution addresses very important issues in this area, such as the existence of multiple IoT standards, the heterogeneity of IoT devices and non-interoperability of existing solutions.
? Their solution overcomes some drawbacks of existing designs such as interoperability and scalability.
? It monitors the real-time location of workers, their activity, as well as the existing temperature and lighting in the workplace according to the external weather conditions.
? This has been the case with this new proposal, where an existing energy efficiency system has been improved notably with the application of CAFCLA and GECA, reducing the data traffic that enters the Cloud, especially lessening the amount of information that needs to be transmitted from the ZigBee beacons to the locating engine running in the Cloud.
DISADVANTAGE :
? These issues can be controlled with increased application management load, where context- and location-awareness provides knowledge of application-specific resource availability.
? This issue can be addressed with optimized agent execution environments and interaction protocols, that propose an IoT software framework for mobile agents based on standardized Web technologies.
? End devices join and part systems any time due to connection issues and network bandwidth may become insufficient due to high utilization.
? Fog addresses networking issues with the dense deployment of edge nodes that provide high resource availability, bandwidth and LAN connections.
PROPOSED SYSTEM :
• Several authors have proposed solutions to motivate, educate and raise awareness among users about the importance of smart energy consumption.
• In this regard, serious games are one of the solutions proposed to improve user behaviour and these have been presented and analyzed in numerous research.
• Due to the wide variety of the proposed solutions, and above all, the multiple application areas, the state of the art presented here includes the most outstanding works in three different areas, Smart Grids, Industrial and commercial environments and Domestic environments.
• In connection to this statement, their research proposes the deployment of SmartLocalGrids (SLG) as a communication paradigm between microgrids.
ADVANTAGE :
? Fluctuating resource availability and communication latencies reduce the system performance and quality of service.
? With in-network data processing, mobile agents can address the energy efficiency of the host devices, and simultaneously optimize resource usage in a MAS with regard to local network conditions.
? In general, large volumes of data can be energy efficiently processed by agents at the data producing devices and later aggregated to avoid communication overhead in the layers.
? This application scenario was selected to demonstrate the energy efficiency of decentralized and autonomous agent-based control of end device operation at the device layer in IoT edge computing.
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