Distributed Task Processing Platform for Infrastructure-Less IoT Networks: A Multi-Dimensional Optimization Approach

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Qiushi Zheng;Jiong Jin;Zhishu Shen;Libing Wu;Iftekhar Ahmad;Yong Xiang
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引用次数: 0

Abstract

With the rapid development of artificial intelligence (AI) and the Internet of Things (IoT), intelligent information services have showcased unprecedented capabilities in acquiring and analysing information. The conventional task processing platforms rely on centralised Cloud processing, which encounters challenges in infrastructure-less environments with unstable or disrupted electrical grids and cellular networks. These challenges hinder the deployment of intelligent information services in such environments. To address these challenges, we propose a distributed task processing platform ( ${DTPP}$ ) designed to provide satisfactory performance for executing computationally intensive applications in infrastructure-less environments. This platform leverages numerous distributed homogeneous nodes to process the arriving task locally or collaboratively. Based on this platform, a distributed task allocation algorithm is developed to achieve high task processing performance with limited energy and bandwidth resources. To validate our approach, ${DTPP}$ has been tested in an experimental environment utilising real-world experimental data to simulate IoT network services in infrastructure-less environments. Extensive experiments demonstrate that our proposed solution surpasses comparative algorithms in key performance metrics, including task processing ratio, task processing accuracy, algorithm processing time, and energy consumption.
无基础设施物联网网络的分布式任务处理平台:多维优化方法
随着人工智能(AI)和物联网(IoT)的快速发展,智能信息服务在获取和分析信息方面展现出前所未有的能力。传统的任务处理平台依赖于集中式云处理,这在电网和蜂窝网络不稳定或中断的无基础设施环境中遇到了挑战。这些挑战阻碍了智能信息服务在此类环境中的部署。为应对这些挑战,我们提出了分布式任务处理平台({DTPP}$),旨在为在无基础设施环境中执行计算密集型应用提供令人满意的性能。该平台利用众多分布式同构节点,对到达的任务进行本地或协作处理。基于该平台,我们开发了一种分布式任务分配算法,以便在能源和带宽资源有限的情况下实现较高的任务处理性能。为了验证我们的方法,我们在实验环境中测试了 ${DTPP}$,利用真实世界的实验数据来模拟无基础设施环境中的物联网网络服务。广泛的实验证明,我们提出的解决方案在关键性能指标(包括任务处理率、任务处理准确性、算法处理时间和能耗)上超越了同类算法。
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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