An Efficient Resource Allocation Strategy for Edge-Computing Based Environmental Monitoring System

Abstract

The cloud computing and microsensor technology has greatly changed environmental monitoring, but it is difficult for cloud-computing based monitoring system to meet the computation demand of smaller monitoring granularity and increasing monitoring applications. As a novel computing paradigm, edge computing deals with this problem by deploying resource on edge network. However, the particularity of environmental monitoring applications is ignored by most previous studies. In this paper, we proposed a resource allocation algorithm and a task scheduling strategy to reduce the average completion latency of environmental monitoring application, when considering the characteristic of environmental monitoring system and dependency among task. Simulations are conducted, and the results show that compared with the traditional algorithms. With considering the emergency task, the proposed methods decrease the average completion latency by 21.6% in the best scenario.

An Internet-of-Things based Real-time Monitoring System for Smart Classroom

“…Generally, several schemes have been proposed for managing or allocating resources in smart systems. For example, resource allocation algorithm and task scheduling strategy were used for edgecomputing based environmental monitoring system in [19]. In addition, the development of a smart parking system suitable for urban environment was proposed.…”

Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme

“…Hence, several applications may miss their deadline due to low bandwidth data transmission, causing delays that are unacceptable in certain cases [5,14]. It is assumed in [6,15] that a possible way to mitigate the challenge of unacceptable delay is to make sure computational resources are at the proximity of end-users.…”

“…Recently, mobile edge clouds (MECs) emerged as a promising solution to offer a service environment that is characterized by proximity, low latency, and high data rate access to mobile subscribers via a radio access network [16,17]. Although the scale of the edge cloud seems limited in capacity to execute high computational tasks, the remote cloud is used to compliment resource scarcity for mobile devices [15,18]. One of the challenges faced by MECs is limited resources.…”