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Blockchain-Based Nash Bargaining for Task Scheduling in iot edge computing Environments

IEEE INTERNET OF THINGS JOURNAL 2024年第8期11卷 13851-13864页

作者： Chen, Yishan Li, Bo Li, Wei Zeng, Bowen Yin, Jianwei Deng, Shuiguang Jiangxi Univ Sci & Technol Coll Informat Engn Ganzhou 341000 Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310058 Peoples R China

Modern iot industry cannot operate well without task scheduling, while the merging of edge computing and blockchain can empower more secure task scheduling models in distributed iot environments. Due to various desirable properties (self-verifying, self-executing, immutability, reliability, confidentiality, etc.) provided by blockchain, the tasks generated from iot devices can be safely scheduled. However, merely incorporating a blockchain framework into an iot edge computing environment cannot guarantee the most desirable rewards for task participants. So in this article, we propose to use a Nash bargaining method in blockchain smart contract designing to strive the rewards for task participants and stimulate their proactivity while extending the above properties. The proposed blockchain-based Nash bargaining architecture can be applied in many distributed iot scenarios, such as smart logistics, health data exchange, etc. For a practical implementation, a configurable blockchain architecture is developed consisting of both regular and constrained iot devices. We additionally implement mutual authentication protocol and proof of authority to maintain the privacy and security for scheduling. Further, we experimentally study the feasibility of such architecture built in static and mobile iot devices. Evaluations demonstrate that, the proposed architecture can well support a task scheduling process with tamper resistance, which is suitable for iot edge computing with high-level security and creditability.

关键词： Task analysis Internet of Things Security Processor scheduling Blockchains Computer architecture edge computing Blockchain Nash bargaining iot edge computing rewards task scheduling

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Priority-based DAG task offloading and secondary resource allocation in iot edge computing environments

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computing 2024年第10期106卷 3229-3254页

作者： Chen, Yishan Luo, Xiansong Liang, Peng Han, Junxiao Xu, Zhonghui Jiangxi Univ Sci & Technol Ganzhou 341000 Peoples R China Hangzhou City Univ Hangzhou 310015 Peoples R China

With the development of iot, the concept of intelligent services has gradually come to the fore. Intelligent services usually involve a large number of computation intensive tasks with data dependencies that are often modelled as directed acyclic graphs (DAGs), and the offloading of DAG tasks is complex and has proven to be an NP hard challenge. As a key research issue, the task offloading process migrates the computation intensive tasks from resource-constrained iot devices to nearby edge servers, and pursuing a lower delay and energy consumption. However, data dependencies among tasks are complex, and it is challenging to coordinate the computation intensive tasks among multiple edge servers. In this paper, a flexible and generic DAG task model is built to support the associative task offloading process with complex data dependencies in iot edge computing environments. Additionally, a priority-based DAG task offloading algorithm and a secondary resource allocation algorithm are proposed to minimize the response delay and improve the resource utilization of edge servers. Experimental results demonstrate that the proposed method can well support the DAG task offloading process with the shortest response delay, while outperforming all the benchmark policies, which is suitable for iot edge computing environments.

关键词： Task offloading DAG Secondary resource allocation Response delay iot edge computing

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Deep Reinforcement Learning for Containerized edge Intelligence Inference Request Processing in iot edge computing

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IEEE TRANSACTIONS ON SERVICES computing 2023年第6期16卷 4328-4344页

作者： Nkenyereye, Lionel Baeg, Kang-Jun Chung, Wan-Young Pukyong Natl Univ AI Convergence Educ & Res Grp Busan 48513 South Korea Pukyong Natl Univ Dept Nanotechnol Engn Busan 48547 South Korea Pukyong Natl Univ Dept AI Convergence Busan 48513 South Korea Pukyong Natl Univ Dept Elect Engn Busan 48513 South Korea

edge intelligence (EI) refers to a set of connected systems and devices for artificial intelligence (AI) data collected and learned near the data collection site. The EI model inference phase has been improved through edge caching technologies such as intelligent models (IMs). IM inference across heterogeneously distributed edge nodes is worthy of discussion. The present focuses on software-defined infrastructure (SDI) and introduces a containerized EI framework for a mobile wearable Internet-of-Things (iot) system. This framework, called the containerized edge intelligence framework (CEIF), is an inter-working architecture that allows the provisioning of containerized EI processing intelligent services related to mobile wearable iot systems. CEIF enables dynamic instantiation of the inference services of AI models that have been pre-trained on clouds. It also accommodates edge computing devices (ECDs) running the container virtualization technique. Dynamic AI learning policies can also help with workload optimization, thereby reducing the response time of the requests of the EI inference. To stall the rapid increase in user workload when inferring the collected data for analysis, we then propose a deep q-learning algorithm in which the container cluster platform learns the varying user workload at the location of each ECD. The requests of the EI inference are scaled with the learned value and are processed successfully without overloading the ECD. When evaluated in a case study, the proposed algorithm enabled scaling of the processing requests of the EI inference in a containerized EI system while minimizing the number of instantiated container EI instances. The EI inference's requests are completed in an under-loaded container EI cluster system.

关键词： Internet of Things iot service layer iot edge computing deep reinforcement learning edge intelligence artificial intelligence AI model inference

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A Reliable and Efficient Task Offloading Strategy Based on Multifeedback Trust Mechanism for iot edge computing

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IEEE INTERNET OF THINGS JOURNAL 2022年第15期9卷 13927-13941页

作者： Kong, Wenping Li, Xiaoyong Hou, Liyang Yuan, Jie Gao, Yali Yu, Shui Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Minist Educ Beijing 100876 Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW 2007 Australia

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 k-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.

关键词： iot edge computing task offloading trust evaluation trust weight k-means (TWK-means) clustering

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Eiot-PBFT: A multi-stage consensus algorithm for iot edge computing based on PBFT

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MICROPROCESSORS AND MICROSYSTEMS 2022年 95卷

作者： Gan, Bo Wang, Yaojie Wu, Qiwu Zhou, Yang Jiang, Lingzhi Naval Univ Engn Natl Key Lab Sci & Technol Vessel Integrated Power Wuhan 430033 Peoples R China Engn Univ PAP Coll Informat Engn Xian 710086 Peoples R China Engn Univ PAP Coll Equipment Management & Support Xian 710086 Peoples R China

With the introduction of edge computing into the field of Internet of Things (iot), the Cognitive Internet of Things (Ciot) has emerged as the next-generation solution for trust and intelligent reasoning in the iot. That also puts blockchain, with its unique consensus mechanism, transparency and trustworthiness, on the stage of iot applications. At present, not much research is focused on blockchain's application in Ciot, whose development is to a large extent restricted by the inefficiency of the consensus algorithm. Considering the characteristics of Ciot, a multi-stage consensus algorithm of Eiot-PBFT is proposed on the basis of PBFT algorithm, which includes the Grouping stage, Scoring stage and Consensus reaching stage. Eiot-PBFT meets the iot edge computing setup by adopting a two-phase improved PBFT algorithm and a scoring mechanism based on both location and reputation, thus achieving a great increase in consensus efficiency. Evaluation results show that Eiot-PBFT takes 36.4% less time than PBFT for a single consensus, and the performance remains stable over the 2500 node configurations we set up. Moreover, at a scale of 1000 nodes, the number of edge nodes to be configured to reduce the number of system communications by 90% compared to the PBFT algorithm is only 5, making blockchain more customized for Ciot settings.

关键词： iot edge computing Blockchain PBFT algorithm Geographical location Trust score

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Hierarchical Security Paradigm for iot Multiaccess edge computing

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IEEE INTERNET OF THINGS JOURNAL 2021年第7期8卷 5794-5805页

作者： Singh, Jaspreet Bello, Yahuza Hussein, Ahmed Refaey Erbad, Aiman Mohamed, Amr Manhattan Coll Dept Elect & Comp Engn Riverdale NY 10471 USA Western Univ Dept Elect & Comp Engn London ON N6A 3K7 Canada Hamad Bin Khalifa Univ Coll Sci & Engn Doha Qatar Qatar Univ Dept Comp Sci & Engn Doha Qatar

The rise in embedded and iot device usage comes with an increase in LTE usage as well. About 70% of an estimated 18 billion iot devices will be using cellular LTE networks for efficient connections. This introduces several challenges, such as security, latency, scalability, and quality of service, for which reason edge computing or fog computing has been introduced. The edge is capable of offloading resources to the edge to reduce workload at the cloud. Several security challenges come with multiaccess edge computing (MEC), such as location-based attacks, the man- in-the-middle attacks, and sniffing. This article proposes a software-defined perimeter (SDP) framework to supplement MEC and provide added security. The SDP is capable of protecting the cloud from the edge by only authorizing authenticated users at the edge to access services in the cloud. The SDP is implemented within a mobile-edge LTE network. Delay analysis of the implementation is performed, followed by a Denial-of-Service (DoS) attack to demonstrate the resilience of the proposed SDP. Further analyses, such as CPU usage and port scanning were performed to verify the efficiency of the proposed SDP. This analysis is followed by concluding remarks with insight into the future of the SDP in MEC.

关键词： edge computing Computer architecture Internet of Things Cloud computing Long Term Evolution Denial-of-service attack Denial of Service (DoS) fog computing iot edge computing LTE multiaccess edge computing (MEC) security software-defined perimeter (SDP)

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DanceFormer: Hybrid transformer model for real-time dance pose estimation and feedback

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ALEXANDRIA ENGINEERING JOURNAL 2025年 121卷 66-76页

作者： Zhao, Hong Du, Bojing Jia, Yongju Zhao, Hui Shandong Univ Sch Arts Weihai 264209 Peoples R China Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China Qingdao Univ Conservatory Mus Qingdao 266071 Peoples R China

Dance pose estimation holds significant value in teaching, training, and performance within the dance domain. However, challenges such as multi-target tracking in complex dynamic scenes, real-time demands, and computational resource constraints have posed difficulties for traditional methods. This paper introduces DanceFormer, a Transformer-based model for dance pose estimation that integrates the Vision Transformer (ViT), Time Series Transformer (TST), and an edge computing layer to achieve deep fusion of multimodal features, enhancing accuracy and real-time feedback capabilities. Experiments on the AIST and DanceTrack datasets show DanceFormer achieves pose estimation accuracy (MPJPE) of 18.4 mm and 20.1 mm, and multi- object tracking accuracy (MOTA) of 92.3% and 89.5%, outperforming other models. The edge computing design reduces average latency to 35.2ms, providing robust real-time processing suitable for low-resource edge devices. DanceFormer offers an efficient, precise, and real-time solution for complex dance scenarios, with broad application potential in dance education and real-time motion analysis. Future work will focus on optimizing the edge computing module and enhancing model generalization through data augmentation and dataset diversification.

关键词： Dance pose estimation Transformer iot edge computing Motion analysis Deep learning

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An analysis of architecture, framework, security and challenging aspects for data aggregation and routing techniques in iot WSNs

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THEORETICAL COMPUTER SCIENCE 2022年 929卷 95-113页

作者： Chandnani, Neeraj Khairnar, Chandrakant N. Devi Ahilya Vishwavidyalaya Mil Coll Telecommun Engn Res Ctr Mhow Indore Madhya Pradesh India Mil Coll Telecommun Engn Fac Commun Engn Mhow Madhya Pradesh India

Internet has evolved into a promising technology after going through various transitional phases in the past decades. It was invented in the early nineties, with the web being static, public and shared. The final transitional phase is the internet of services, where the content, web services, Extensible Markup Language (XML), productivity and commerce tools were created by the user bringing improved websites and services. Later on, with affordable mobile broadband, Android phones and tablets, people could access the internet and be in touch with the world through social media platforms and mobile applications. iot is a network of physical components, vehicles, household appliances and other items embedded with devices, software, sensor systems, actuators and connectivity, connecting and transferring data. The process of gathering data from heterogeneous sensors for preventing recurrent transmissions simultaneously offering quality aggregated information at the sink node is collectively called as data aggregation and routing process. There is a transmission of only most complex information to the sink node. The continuous use of data aggregation and routing technique increases the energy, bandwidth and memory requirements. The challenging aspects in iot WSN are energy consumption, bandwidth and memory utilized for data aggregation and routing process. So, it is very much important to focus on these parameters so that the network has a greater lifetime and Quality of service. The tradeoff between energy saving, precision of data and latency can be achieved only by utilizing data redundancy, data similarity, data aggregation and routing algorithms. The aim of the research is to do the analysis of the iot WSNs on the basis of the architecture, framework, and challenges related to security, data aggregation and routing techniques. This research work motivates the researchers to know about the challenges in data aggregation and routing in iot WSNs. An efficient data aggre

关键词： Data Aggregation Routing Internet of Things iot Security iot Architecture Data Analytics iot edge computing QoS iot WSNs

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Tiny-UKSIE: An Optimized Lightweight Semantic Inference Engine for Reasoning Uncertain Knowledge

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INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS 2022年第1期18卷 1页

作者： Geng, Daoqu Li, Haiyang Liu, Chang Chongqing Univ Posts & Telecommun Coll Automat Chongqing Peoples R China

The application of semantic web technologies such as semantic inference to the field of the internet of things (iot) can realize data semantic information enhancement and semantic knowledge discovery, which plays a key role in enhancing data value and application intelligence. However, mainstream semantic inference engines cannot be applied to iot computing devices with limited storage resources and weak computing power and cannot reason about uncertain knowledge. To solve this problem, the authors propose a lightweight semantic inference engine, Tiny-UKSIE, based on the RETE algorithm. The genetic algorithm (GA) is adopted to optimize the Alpha network sequence, and the inference time can be reduced by 8.73% before and after optimization. Moreover, a four-tuple knowledge representation method with probability factors is proposed, and probabilistic inference rules are constructed to enable the inference engine to infer uncertain knowledge. Compared with mainstream inference engines, storage resource usage is reduced by up to 97.37%, and inference time is reduced by up to 24.55%.

关键词： iot edge computing Rete Algorithm Semantic Inference Engine Uncertain Knowledge

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Study on cyber-security for iot edge utilizing pattern match accelerator

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ELECTRICAL ENGINEERING IN JAPAN 2021年第2期214卷 e23333-e23333页

作者： Kashiyama, Masamori Kashiyama, Reo Seki, Hiroto Hosono, Hiroyuki Nihon Univ Grad Sch Sci & Technol Tokyo Japan Tokyo Inst Lbchnol Sch Engn Dept Mech Engn Tokyo Japan Nihon Univ Coll Sci & Technol Funabashi Chiba Japan

A malware detection algorithm that can be embedded in iot edge computing is proposed in this study and validated using an emulator. This algorithm, with a pattern match accelerator, reduces the computing cost while maintaining a relatively high detection accuracy. For autonomous driving, complicated iot edge computing must have a huge amount of embedded program codes. In such a situation, the invasion of malware can lead to compromised cybersecurity. In this study, a pattern match accelerator is implemented for such issues, thereby offering iot edge computing that detects malware automatically. edge computing is designed to apply simply structural level analysis algorithms using HLAC mask pattern. We developed a pseudo-emulator system environment and conducted performance confirmation of the proposed technique using 641 chosen samples from six types of malware families. The algorithm's efficiencies demonstrated an identification performance of approximately 80%. In comparison to characteristic extraction using AI, the computing cost was reduced and these processes enable edge computing with high cybersecurity features.

关键词： cybersecurity Higher-order Local Auto Correlation (HLAC) iot edge computing malware pattern match accelerator texture image

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