The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.

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A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Enabling technologies for fog computing in healthcare IoT systems

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

The Industrial Internet of Things (IIoT) is a crucial research field spawned by the Internet of Things (IoT). IIoT links all types of industrial equipment through the network; establishes data acquisition, exchange, and analysis systems; and optimizes processes and services, so as to reduce cost and enhance productivity. The introduction of edge computing in IIoT can significantly reduce the decision-making latency, save bandwidth resources, and to some extent, protect privacy. This paper outlines the research progress concerning edge computing in IIoT. First, the concepts of IIoT and edge computing are discussed, and subsequently, the research progress of edge computing is discussed and summarized in detail. Next, the future architecture from the perspective of edge computing in IIoT is proposed, and its technical progress in routing, task scheduling, data storage and analytics, security, and standardization is analyzed. Furthermore, we discuss the opportunities and challenges of edge computing in IIoT in terms of 5G-based edge communication, load balancing and data offloading, edge intelligence, as well as data sharing security. Finally, we introduce some typical application scenarios of edge computing in IIoT, such as prognostics and health management (PHM), smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics.

Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges

Cloud computing plays a vital role in processing a large amount of data. However, with the arrival of the Internet of Things, huge data are generated from these devices. Thus, there is the need for bringing characteristics of cloud closer to the request generator, so that processing of these huge data takes place at one-hop distance closer to that end user. This led to the emergence of fog computing with the aim to provide storage and computation at the edge of the network that reduces network traffic and overcomes many cloud computing drawbacks. Fog computing technology helps to overcome challenges of big data processing. The paper discusses the taxonomy of fog computing, how this is different from cloud computing and edge computing technologies, its applications, emerging key technologies (i.e., communication technologies and storage technologies) and various challenges involved in fog technology.

Fog computing: from architecture to edge computing and big data processing

With the development of medical sensors and IoT, personalized service assisted elder and patient living is a critical service in IoT-based healthcare application. However, the scale and complexity of personalized service is increasing because of ubiquitous deployment of various kinds of medical sensors, which cause response time increase and resource waste. Therefore, leveraging the advantage of complex event processing (CEP) in data stream processing, we propose a hierarchical fog-cloud computing CEP architecture for personalized service to accelerate response time and reduce resource waste. Firstly, we introduce the proposed architecture, which includes sensor layer, fog layer and cloud layer. Secondly, we propose a series of optimizations for the architecture, there are a partitioning and clustering approach and a communication and parallel processing policy to optimize the fog and cloud computing. Finally, we implement a prototype system based on the architecture named FogCepCare. Experimental result shows that FogCepCare is superior to the traditional IoT-based healthcare application.

Proactive personalized services through fog-cloud computing in large-scale IoT-based healthcare application

With the scale of big data increasing in large-scale IoT application, fog computing is a recent computing paradigm that is extending cloud computing towards the edge of network in the field. There are a large number of storage resources placed on the edge of the network to form a geographical distributed storage system in fog computing system (FCS). It is used to store the big data collected by the fog computing nodes and to reduce the management costs for moving big data to the cloud. However, the storage of fog nodes at the edge of the network faces a direct attack of external threats. In order to improve the security of the storage of fog nodes in FCS, in this paper, we proposed a data security storage model for fog computing (FCDSSM) to realize the integration of storage and security management in large-scale IoT application. We designed a detail of the FCDSSM system architecture, gave a design of the multi-level trusted domain, cooperative working mechanism, data synchronization and key management strategy for the FCDSSM. Experimental results show that the loss of computing and communication performance caused by data security storage in the FCDSSM is within the acceptable range, and the FCDSSM has good scalability. It can be adapted to big data security storage in large-scale IoT application.

Data security storage model for fog computing in large-scale IoT application

With the rapid development of smart IoT devices, it is a trend for complex event processing in large-scale IoT application to be intelligent capacity, complex event processing will involve more intelligent collaboration and evolution. However, the studies on traditional complex event processing focus more on performance and efficiency, and fewer consider intelligent collaboration and evolution. In this paper, aiming at the edge and cloud computing in large-scale IoT application, a wide-deep event model and its suitable complex event processing architecture are proposed to mainly improve intelligent collaboration and evolution. The model and architecture fully consider the feature of the edge and cloud computing, combine with deep learning and wide-deep learning and make them seamless integration, making the architecture have better intelligent collaboration and evolution.

A wide-deep event model for complex event processing in edge and cloud computing environment

With the development of IoT and 5G technologies, edge computing has become a key driver for providing compute, network and storage services. The dramatic increase in data size and the complexity of AI computation models have put higher demands on the performance of edge computing. Rational and optimal scheduling of AI data-intensive computation tasks can greatly improve the overall performance of edge computing. To this end, a particle swarm algorithm based on objective ranking is proposed to optimize task execution time and scheduling cost by designing a task scheduling model to achieve task scheduling in an edge computing environment. It is necessary to fully understand the concept of symmetry of resource utilization and task execution cost indicators. The method utilizes nonlinear inertia weights and shrinkage factor update mechanisms to improve the optimization-seeking ability and convergence speed of the particle-to-task scheduling solution space. The task execution time and scheduling cost are greatly reduced. Simulation experiments are conducted using the Cloudsim toolkit to experimentally compare the proposed algorithm TS-MOPSO with three other particle swarm improvement algorithms, and the experimental results show that the task execution time, maximum completion time and total task scheduling cost are reduced by 31.6%, 23.1% and 16.6%, respectively. The method is suitable for handling large and complex AI data-intensive task scheduling optimization efforts.

https://www.mdpi.com/2073-8994/14/12/2565/pdf?version=1670226042

A Particle Swarm Optimization Method for AI Stream Scheduling in Edge Environments

With the development of intelligent applications of the Internet of things, the sharp increase of data scale and the improvement of computational model complexity in the edge computing environment put forward higher requirements for the performance of edge computing. Reasonably optimizing the scheduling of AI data intensive computing tasks can greatly improve the overall performance of edge computing. Therefore, a novel particle swarm optimization algorithm is proposed to realize task scheduling in edge computing environment by calculating task scheduling to optimize task execution time and scheduling cost. Improve the speed formula of particle swarm optimization algorithm to improve the convergence speed and row performance of the algorithm. Finally, on the Cloudsim simulation platform, the proposed particle group algorithm LK-PSO was compared with the other four benchmark algorithms. The results showed that the method task scheduling cost and execution time were effectively optimized. This method can effectively improve the resource utilization rate of marginal computing and improve the efficiency of marginal computing power.

Haifeng Wang

Papers

Proactive personalized services through fog-cloud computing in large-scale IoT-based healthcare application

Data security storage model for fog computing in large-scale IoT application

A wide-deep event model for complex event processing in edge and cloud computing environment

A Particle Swarm Optimization Method for AI Stream Scheduling in Edge Environments

Task Scheduling Model of Edge Computing for AI Flow Computing in Internet of Things