IoT Edge Computing Research Articles

Adaptive Knowledge Distillation-Based Lightweight Intelligent Fault Diagnosis Framework in IoT Edge Computing

Adaptive Knowledge Distillation-Based Lightweight Intelligent Fault Diagnosis Framework in IoT Edge Computing

FPGA-Based High-Speed Energy-Efficient 32-Bit Fixed-Point MAC Architecture for DSP Application in IoT Edge Computing

Designing high-speed and energy-efficient blocks for image and digital signal processing (DSP) architecture is an evolving research field. This work designs a high-speed and energy-efficient multiply-accumulate (MAC) unit to augment the performance of field-programmable gate array (FPGA)-based accelerators and softcore processors. In this work, three discrete 32-bit fixed-point signed MAC architectures were designed in Verilog and synthesized for the Zynq 7000 ZedBoard to obtain efficient MAC architecture. The ultimate goal of this work is to design a fast and energy-efficient MAC unit that can achieve speed up to the DSP48 block to reduce the latency of IoT edge computing. Energy efficiency was achieved in PPG and partial product addition (PPA) for the proposed Booth radix-4 Dadda (BR4D)-based MAC. At PPG, the width of the partial product (PP) terms was optimized with Bewick’s signed extension to reduce the power consumption. At PPA, the number of PP rows reduces the critical path delay (CPD) with Dadda-based PPA. The proposed BR4D MAC unit offers a reduction in dynamic power, CPD, power-delay product (PDP) and energy-delay product (EDP) by 22%, 9%, 29% and 36%, respectively, compared to standard Booth radix-4 Wallace tree (BR4WT) based MAC. Furthermore, hybrid MACs (BR4WT and BR4D) were compared with the current state-of-the-art (SoA) designs, and it was found that the proposed BR4D MAC is 47% faster compared to the same design in SoA. The proposed BR4D was tested for frequency scaling technique by reducing the frequency in steps of 10 MHz from a maximum usable frequency (MUF) of 64 MHz to 10 MHz to evaluate the performance for low-power applications. Reducing clock frequency by 84% will reduce the power consumption at the same proportion and speed by 38%. Additionally, the proposed design helps to improve the battery life of IoT end nodes with a reduction in energy consumption and EDP by 76% and 61%, respectively.

Reliable edge-to-core optical networks: An optimal algorithm for maximal path diversity

With the emergence of IoT applications, 5G, and edge computing, network resource allocation has shifted toward the edge, bringing services closer to the end users. These applications often require communication with the core network for purposes that include cloud storage, compute offloading, 5G-and-Beyond transport communication between centralized unit (CU), distributed unit (DU) and core network, centralized network monitoring and management, etc. As the number of these services increases, efficient and reliable connectivity between the edge and core networks is of the essence. Wavelength Division Multiplexing (WDM) is a well-suited technology for transferring large amounts of data by simultaneously transmitting several wavelength-multiplexed data streams over each single fiber optics link. WDM is the technology of choice in mid-haul and long-haul transmission networks, including edge-to-core networks, to offer increased transport capacity.Optical networks are prone to failures of components such as network fiber links, sites, and transmission ports. A single network element failure alone can cause significant traffic loss due to the disruption of many active data flows. Thus, fault-tolerant and reliable network designs remain a priority. The architecture called “dual-hub and dual-spoke” is often used in metro area networks (MANs). A dual-hub, or in general a multi-hub network, consists of a set of designated destination nodes (hubs) in which the data traffic from all other nodes (the peripherals) should be directed to the hubs. Multiple hubs offer redundant connectivity to and from the core or wide area network (WAN) through geographical diversity. The routing of the connections (also known as lightpaths) between the peripheral node and the hubs has to be carefully computed to maximize path diversity across the edge-to-core network. This means that whenever possible the established redundant lightpaths must not contain a common Shared Risk Link Group (SRLG).An algorithm is proposed to compute the most reliable set of SRLG disjoint shortest paths from any peripheral to all hubs. The proposed algorithm can also be used to evaluate the overall edge-to-core network reliability quantified through a newly introduced figure of merit.

Proactive Edge Computing for Smart City: A Novel Case for ML-Powered IoT

Purpose: In response to the challenges posed by traditional cloud-centric IoT architectures, this research explores the integration of Proactive Edge Computing (PEC) in context of smart cities. The purpose addresses privacy concerns, enhance system capabilities, and introduce machine learning powered anticipation to revolutionize urban city management.
 Methodology: The research employs a comprehensive methodology that includes a thorough review of existing literature on use of IoT devices, edge computing and machine learning in context of smart cities. It introduces the concept of PEC to advocate for a shift from cloud-centric to on-chip computing. The methodology is based on several case studies in various domains of smart city management focusing on the improvement of public life.
 Findings: This research reveal that the integration of PEC in various smart city domains leads to a significant improvement. Real time data analysis, and machine learning predictions contributes to reduced congestion, enhance public safety, sustainable energy practices, efficient waste management, and personalized healthcare.
 Unique Contribution to Theory, Policy and Practice: The research makes a unique contribution to the field of theory, policy and practice by proposing a paradigm shift associated with IoT for smart cities. The suggested shift not only ensures data security but also offers a more efficient and proactive approach to urban challenges. The case studies provide actionable insights for policymakers and practitioners, fostering a holistic understanding of the complexities associated with deploying IoT devices in smart cities. The research lays the foundation for a more secure, efficient, and anticipatory ecosystem, aligning technological advancements with societal needs in the dynamic landscape of smart cities.

Employing Collective Intelligence at the IoT Edge for Spatial Decisions

This paper explores the synergies between collective intelligence and edge computing, presenting a novel paradigm that harnesses decentralized processing power for collaborative problem-solving. Edge devices, such as sensors and IoT devices, collect spatial data in real time from their local environments. They can incorporate machine learning algorithms to analyze spatial data, enabling quicker and more context-aware decision-making. Spatial clustering, a pivotal strategy in IoT edge computing, is examined to optimize localized data processing, enhance resource efficiency, and enable real-time analytics in decentralized environments. By leveraging the physical proximity of devices, spatial clustering contributes to the effectiveness and sustainability of IoT deployments at the edge. The paper introduces an innovative approach to adaptive spatial clustering by adopting swarm intelligence, drawing inspiration from the collective behavior of a flock of birds. Building upon the classical flock model of Reynolds, our extended model incorporates movement in a multi-dimensional space and introduces different types of birds. In this context, the birds serve as agents for discovering points with specific characteristics in a multidimensional space. The integration of swarm intelligence into spatial clustering presents a promising avenue for addressing the challenges of decentralized processing in edge computing environments, paving the way for more efficient and responsive IoT deployments.

Application and Effectiveness of IoT Edge and Fog Computing Technologies in Smart Energy Development with the Use of Encryption Algorithms and Security Systems

Application and Effectiveness of IoT Edge and Fog Computing Technologies in Smart Energy Development with the Use of Encryption Algorithms and Security Systems

TCEC: Integrity Protection for Containers by Trusted Chip on IoT Edge Computing Nodes

TCEC: Integrity Protection for Containers by Trusted Chip on IoT Edge Computing Nodes

Clustering-Based Resource Management for Consumer Cost Optimization in IoT Edge Computing Environments

Clustering-Based Resource Management for Consumer Cost Optimization in IoT Edge Computing Environments

Heuristically Modified LSTM-Based Reinforcement Learning for Task offloading in Industrial IoT Edge Computing

Heuristically Modified LSTM-Based Reinforcement Learning for Task offloading in Industrial IoT Edge Computing

PPO-Based Autonomous Transmission Period Control System in IoT Edge Computing

PPO-Based Autonomous Transmission Period Control System in IoT Edge Computing

MedGCN: An IoT-edge thrombus graph convolutional network for accurate prediction and prescription diagnosis of vascular occlusive diseases from unstructured clinical reports

The timely and accurate prediction and diagnosis of vascular occlusive diseases are pivotal in enhancing patient outcomes. This research addresses this critical healthcare challenge by introducing MedGCN, a novel thrombus graph convolutional network model. MedGCN is specifically designed for the precise prediction of prescriptions and diagnoses based on unstructured clinical diagnostic reports. The model synergistically blends OpenAI’s GPT4 and a uniquely designed Cross Fusion Graph Convolutional Network (CF-GCN) to ensure the meticulous fusion of knowledge. We delve into nine distinct learning tasks, encompassing both prescription and diagnosis and employ a multi-label classification GCN pretraining technique to assess them. Our evaluation underscores MedGCN’s robust predictive prowess across various tasks. By amalgamating cutting-edge AI paradigms with IoT edge computing, this research not only bolsters the efficacy of medical decision-making but also champions patient privacy. The methodologies and findings presented herein hold immense potential for deployment in IoT frameworks, thus proffering swift and precise assistance in medical decision-making and addressing a paramount need in the contemporary healthcare landscape.

Retracted: Thermal Straightening Control System for Variable-Section Automotive Leaf Springs Rolling Based on IoT Edge Computing

Retracted: Thermal Straightening Control System for Variable-Section Automotive Leaf Springs Rolling Based on IoT Edge Computing

Towards 6G Internet of Things: Recent advances, use cases, and open challenges

Smart services based on the Internet of Everything (IoE) are gaining considerable popularity due to the ever-increasing demands of wireless networks. This demands the appraisal of the wireless networks with enhanced properties as next-generation communication systems. Although 5G networks show great potential to support numerous IoE based services, it is not adequate to meet the complete requirements of the new smart applications. Therefore, there is an increased demand for envisioning the 6G wireless communication systems to overcome the major limitations in the existing 5G networks. Moreover, incorporating artificial intelligence in 6G will provide solutions for very complex problems relevant to network optimization. Furthermore, to add further value to the future 6G networks, researchers are investigating new technologies, such as THz and quantum communications. The requirements of future 6G wireless communications demand to support massive data-driven applications and the increasing number of users. Unlike existing works, this paper highlights the recent activities and trends toward 6G technology, network requirement, essential enabling technologies for 6G networks, and a detailed use case analysis between 5G and 6G networks. Moreover, this paper surveys emerging 6G connectivity solutions, such as holographic beamforming, artificial intelligence-enabled IoT networks, edge computing, and backscatter communications to serve smart communities. Furthermore, several future research directions to accomplish 6G-based IoT networks are also highlighted.

An Empirical Analysis of the Impact of Environmental Protection Tax on Corporate Earnings Management

The environmental protection tax (EPT) is an important environmental regulation measure in China and an important guarantee for high-quality economic development. An important measure for the construction of ecological civilization is that local governments promote the construction and improvement of EPT in the way of “Fee-to-Tax.” The EPT regulates pollution control, protects and improves the environment through taxation, and forms an effective mechanism for polluters to promote ecological civilization. This study made use of the data collected from different listed companies in Shanghai and Shenzhen provinces from 2010 to 2019, discussing the impacts of EPT on enterprise earnings management. An enterprise analysis model based on IoT-edge computing is suggested to decrease the processing time. The results of the empirical analysis completed in this study showed a significant positive correlation between EPT and enterprise earnings management. In other words, EPT increases the motivation of enterprise earnings management. Moreover, there is heterogeneity in the driving force of EPT on enterprise earnings management. For example, we observed that state-owned enterprises are significantly higher than non-state-owned enterprises. Moreover, pollution-intensive enterprises are significantly higher than non-pollution-intensive enterprises, and economically developed areas are significantly better than economically underdeveloped areas. In order to give better play to the effectiveness of EPT in modern environmental governance, the government should continue to improve the construction of EPT and speed up the construction process of ecological civilization in the republic of China.

Research on Edge Detection and Image Segmentation of Cabinet Region Based on Edge Computing Joint Image Detection Algorithm

Image segmentation (IE) in several disciplines of image processing and computer vision is an essential topic. Segmentation splits a picture into the areas or items that it constitutes. Image segmentation may be achieved with many approaches, some easier than others because of sophisticated programming requirements. The most common technique for segmenting pictures is edge detection (ED) based on sudden (locomotive) intensity fluctuations. This paper aims to study edge detection approaches for the division of images and acquired experimental findings, Sobel, Prewitt, Robert, CannyLoG (Laplacian of Gaussian). It is vital to ensure that picture segmentation algorithms deliver correct results quickly and efficiently for computer vision to reach its full potential. Computer vision approaches require more investigation in hierarchical architectural IoT networks created for seeing the world. In this work, the new way to provide joint image detection (JID) algorithm is to provide multi-scaling approaches for edge detection and segmentation using IoT edge computing (EC). This JID-EC method avoids the requirement to choose and track the edge explicitly. This study provides an overview of fundamental ideas, techniques, and algorithms common to segment images and edge detection, focusing on the segmentation and visualization of joint-articular cartilage images. The reason for this failure is that it is an image noise-sensitive high pass filter. The need for improved algorithms to meet a suitable value of low and high thresholds should thus be stressed for picture noise such as a canny edge, and the performance is achieved with an efficiency of 95.2%.

A Reliable and Efficient Task Offloading Strategy Based on Multifeedback Trust Mechanism for IoT Edge Computing

Facing multidemand tasks and massive heterogeneous resources in an IoT edge computing environment, it is a challenge to obtain reliable and quick response service and allocate application tasks to resource nodes that meet task requirements and user preference. Since IoT edge computing is facing different types of severe attacks, such as message attacks, swing attacks, collusion attack, node attacks, etc., providing a reliable service environment, trust evaluation between edge nodes is necessary. Existing trust computing schemes, however, suffer from a long response period and low malicious detection rate in a dynamic environment. To alleviate these issues, we propose a reliable and efficient task offloading strategy based on the multifeedback trust mechanism (TOSMFTM). First, a reliable and efficient architecture of TOSMFTM is established, which can effectively improve the ability of trust computing and task offloading. Second, according to the broker’s dynamic monitoring of data, a multifeedback trust aggregation model based on time attenuation and interaction frequency is proposed to provide a trusted running environment. Third, a trust weight <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means (TWK-means) clustering algorithm is designed based on resource attributes to enhance the reliability of service, and quickly and accurately cluster out resource nodes required by the task. Finally, we construct a task offloading model based on trust clustering to ensure user experience quality and promote system efficiency. Different from existing task processing models, which only focus on task offloading, our method also carries out resource preprocessing, trust evaluation, and resource clustering before task processing. The experiment verifies the effectiveness and feasibility of our TOSMFTM scheme.

Edge computing

IoT edge computing is a new computing paradigm “in the IoT domain” for performing calculations and processing at the edge of the network, closer to the user and the source of the data. This paradigm is relatively recent, and, together with cloud and fog computing, there may be some confusion about its meaning and implications. This paper aims to help practitioners and researchers better understand what the industry thinks about what IoT edge computing is, and the expected benefits and challenges associated with this paradigm. We conducted a survey using a semi-structured in-depth questionnaire to collect qualitative data from relevant stakeholders from 29 multinational companies and qualitatively analyzed these data using the Constructivist Grounded Theory (Charmaz) method. Several researchers participated in the coding process (collaborative coding). To ensure consensus on the constructs that support the theory and thus improve the rigor of qualitative research, we conducted an intercoder agreement analysis. From the analysis, we have derived a substantive and analytic theory of what companies perceive about IoT edge computing, its benefits and challenges. The theory is substantive in that the scope of validity refers to the 29 surveys processed and analytic in that it analyzes “what is” rather than explaining causality or attempting predictive generalizations. A public repository with all the data related to the information capture process and the products resulting from the analysis of this information is publicly available. This study aims to strengthen the evidence and support practitioners in making better informed decisions about why companies are adopting edge computing and the current challenges they face. Additionally, the testing theory phase shows that the results are aligned with the ISO/IEC TR 30164 standard.

Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning

Resource allocation is a fundamental research issue in IoT edge computing, and reinforcement learning is fast becoming a common solution. The majority of the current techniques involve decision makers who determine how and where resources should be distributed. In a standard cloud system, this decision maker is a central server. In an edge system, the decision maker is an edge host. Both approaches have drawbacks. Edge hosts do not always have access to enough global information to create the most optimal resource allocation strategy. Central servers do but at the cost of privacy. A solution is needed that can do both. This article, therefore, presents a novel resource allocation method called concurrent federated reinforcement learning. The scheme inherits the privacy protection of federated learning, the complex problem solving power of reinforcement learning and adds concurrency in the form of joint decision making so the resource allocation strategies work to the benefit of the global system. The experiments demonstrate that the approach provides the state-of-the-art performance in system-wide utility, speed of task completion, and resource utilization.

IoT Edge Computing and Blockchain for High-Performance and Decentralized Health Monitoring System

IoT Edge Computing and Blockchain for High-Performance and Decentralized Health Monitoring System

Vibration Edge Computing in Maritime IoT

IoT and the Cloud are among the most disruptive changes in the way we use data today. These changes have not significantly influenced practices in condition monitoring for shipping. This is partly due to the cost of continuous data transmission. Several vessels are already equipped with a network of sensors. However, continuous monitoring is often not utilised and onshore visibility is obscured. Edge computing is a promising solution but there is a challenge sustaining the required accuracy for predictive maintenance. We investigate the use of IoT systems and Edge computing, evaluating the impact of the proposed solution on the decision making process. Data from a sensor and the NASA-IMS open repository were used to show the effectiveness of the proposed system and to evaluate it in a realistic maritime application. The results demonstrate our real-time dynamic intelligent reduction of transmitted data volume by without sacrificing specificity or sensitivity in decision making. The output of the Decision Support System fully corresponds to the monitored system's actual operating condition and the output when the raw data are used instead. The results demonstrate that the proposed more efficient approach is just as effective for the decision making process.