Networking reliability approach for energy analysis in wireless sensor nodes with edge computing techniques

The pivotal objective is to compute the capacity and reliability at the edge server nodes with considering the contingency time in failure in using wireless resources. The proposed data driven approach contextually discriminates reliable and unreliable sensor data at the edge server via numerical metrics. The reliability metrics provides an alternative in which rather than dumping the whole data in cloud it process only unreliable data with its computational service. Thus the data acquisition from sensor and processing based on edge node provides improvement in terms of reducing latency for reliable data. Further, instance of unreliability in data is processed with computational storage capacity and processing at cloud.

1. Introduction

Edge computing has been preferred solution when compared to cloud computing in resource metrics utilized such as response time and bandwidth utilized [1]. The data management strategy has been classified into "placement" or "access" [24]. The placement denotes where to store data in a distributed manner at the optimum node. The later denote the process of accounting consistency via read and write operation. The edge devices are the primary point of contact when it comes to IoT devices used for various applications [3]. The acoustic signal processing data has been used in wireless sensor networks involving edge computing. The trade-off describes where to perform edge computing at edge node or at the server. This has been evaluated based on the load relating it to compute the task, the time critical deadline for which the application of sensor is used [4]. So interfacing a cyber physical system to a cloud has been discussed in [5] via edge computing platform. Two buffers deployed for handling sensors request sequentially. The conflict of resource from sensors with priority is obtained priorly at edges node which routes it to cloud and gets response. Furthermore, the process provides merging request and feasible response reducing latency. Discussion of aggregation functions in sensor network which assume a symmetric communication does not work in all scenarios. The main limitation is the lack of location dependant information from sensor which changes as per the feature of interest as considered in [16]. The consistency of sensed data has been measured at hotspot with possibility in failure duration. The reliability has been estimated with response time in forwarding data for the deployed sensors at hot spot [21]. Data locality in [25] states that it reduces the resources and discriminates the unprocessed data in reaching the cloud. However, calculating reliability at edge nodes provides alternatively significant improvement where the raw data is processed and sent to cloud. Pre-processing the data has been used in this work to ensure reliability via local execution to enable faster response. The Processing of sensor data at cloud to enable unreliable link and flows of data to ensures fairness by satisfaction ratio.