Edge computing

About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.

Hierarchical data fusion for Smart Healthcare

Abstract: The Internet of Things (IoT) facilitates creation of smart spaces by converting existing environments into sensor-rich data-centric cyber-physical systems with an increasing degree of automation, giving rise to Industry 4.0. When adopted in commercial/industrial contexts, this trend is revolutionising many aspects of our everyday life, including the way people access and receive healthcare services. As we move towards Healthcare Industry 4.0, the underlying IoT systems of Smart Healthcare spaces are growing in size and complexity, making it important to ensure that extreme amounts of collected data are properly processed to provide valuable insights and decisions according to requirements in place. This paper focuses on the Smart Healthcare domain and addresses the issue of data fusion in the context of IoT networks, consisting of edge devices, network and communications units, and Cloud platforms. We propose a distributed hierarchical data fusion architecture, in which different data sources are combined at each level of the IoT taxonomy to produce timely and accurate results. This way, mission-critical decisions, as demonstrated by the presented Smart Healthcare scenario, are taken with minimum time delay, as soon as necessary information is generated and collected. The proposed approach was implemented using the Complex Event Processing technology, which natively supports the hierarchical processing model and specifically focuses on handling streaming data ‘on the fly’—a key requirement for storage-limited IoT devices and time-critical application domains. Initial experiments demonstrate that the proposed approach enables fine-grained decision taking at different data fusion levels and, as a result, improves the overall performance and reaction time of public healthcare services, thus promoting the adoption of the IoT technologies in Healthcare Industry 4.0.

Satellite-Terrestrial Integrated Edge Computing Networks: Architecture, Challenges, and Open Issues

Abstract: STN has been considered a novel network architecture to accommodate a variety of services and applications in future networks. Being a promising paradigm, MEC has been regarded as a key technology-enabler to offer further service innovation and business agility in STN. However, most of the existing research in MEC enabled STN regards a satellite network as a relay network, and the feasibility of tasks processing directly on the satellites is largely ignored. Moreover, the problem of multi-layer edge computing architecture design and heterogeneous edge computing resource co-scheduling, have not been fully considered. Therefore, different from previous works, in this article, we propose a novel architecture named STECN, in which computing resources exist in multi-layer heterogeneous edge computing clusters. The detailed functional components of the proposed STECN are discussed, and we present the promising technical challenges, including meeting QoE requirements, cooperative computation offloading, multi-node task scheduling, mobility management and fault/failure recovery. Finally, some potential research issues for future research are highlighted.

2022 roadmap on neuromorphic computing and engineering

Abstract: Abstract Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10 18 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.

Data-Aware Task Allocation for Achieving Low Latency in Collaborative Edge Computing

Abstract: The recent trend in the Internet of Things (IoT) is to distribute and move the computation from centralized cloud devices to edge devices which are closer to data sources. Researchers have proposed collaborative edge computing for IoT where the data and computation tasks are shared among a network of edge devices. One of the important problems in collaborative edge computing is to schedule tasks among edge devices to minimize latency and other performance metrics. Compared to existing works in wireless sensor networks and IoT, there are two additional challenges while scheduling tasks in collaborative edge computing. First, we need to consider the transfer of input data required by different tasks as the data is generated by sensing devices which are located at different geographical places. Second, existing works solve the problem of task scheduling without considering network flow scheduling which can lead to network congestion and long completion times. In this paper, we study the data-aware task allocation problem to jointly schedule task and network flows in collaborative edge computing. We mathematically model the joint problem to minimize the overall completion time of the application. We have proposed a multistage greedy adjustment (MSGA) algorithm where the task scheduling is done by considering both placement of tasks and adjustment of network flows. Performance comparison done using simulation shows that MSGA leads to up to 27% improvement in completion time as compared to benchmark solutions.

Trust-Oriented IoT Service Placement for Smart Cities in Edge Computing

Abstract: Smart city is gradually forming a large scope of Internet of Things (IoT) networks with diffusely deployed IoT devices that produce quantities of services. Considering the large-scale and widely distributed features of IoT networks, edge computing is emerged as a powerful and suitable paradigm to provide computing abilities for the IoT devices at the edge of the networks. In edge computing, the IoT services could be placed on the edge computing units (ECUs) for execution, which provides low latency and eases the burden of bandwidth. However, it is still challenging to improve the overall ECU execution performance (i.e., the resource usage, the load balance levels, and the power consumption of ECUs) and meanwhile prevent privacy leakage of the IoT devices for service placement. To tackle this challenge, a trust-oriented IoT service placement method, abbreviated as TSP, is proposed for smart cities in edge computing. Technically, improving the strength Pareto evolutionary algorithm (SPEA2) is leveraged to acquire the balanced placement strategies for the tradeoffs among the execution performance metrics with privacy preservation. Additionally, the technique for order preference by similarity to ideal solution (TOPSIS) and multicriteria decision-making (MCDM) techniques are employed to identify the optimal placement strategy among the obtained service placement strategies. Eventually, systematic experiments are conducted to verify the efficiency and reliability of TSP.