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.
Highlights
With the development of industry and the acceleration of urbanization, we must pay attention to environmental monitoring, because air quality has a paramount impact on human health.The collection and processing of environmental data are prerequisites for environmental monitoring and pollution warning
In an environmental monitoring system based on Internet of Things (IoT) and cloud computing technology, the environmental data collected by the sensors are processed by the
Combined genetic algorithm with task scheduling strategy is denoted by combined genetic algorithm (CGA)&Q
Summary
With the development of industry and the acceleration of urbanization, we must pay attention to environmental monitoring, because air quality has a paramount impact on human health. The authors in [15] took dependencies among subtasks into account, and proposed an multistage greedy adjustment (MSGA) algorithm to solve the task allocation problem They minimized the completion time of application by jointly considering the network flows and tasks. Adopt Non-dominated Sorting Genetic Algorithm II to shorten the resource allocating time of the computing tasks and reduce the energy consumption of the edge computing servers, but this work did not consider the dependencies among tasks. The impact of dependency among subtasks on completion time has been discussed in the contribution above To solve this problem, a task scheduling strategy proposed that reordering the task queue on edge computing server according to the priority of subtasks can reduce the average task completion latency when considering emergency task.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
