A Survey on Evaluating the Quality of Autonomic Internet of Things Applications

Fizza, Kaneez; Banerjee, Abhik; Jayaraman, Prem Prakash; Auluck, Nitin; Ranjan, Rajiv; Mitra, Karan; Georgakopoulos, Dimitrios

doi:10.1109/comst.2022.3205377

Cited by 23 publications

(11 citation statements)

References 179 publications

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“…With digitization revolutionizing the world at an expeditious rate, IoT is emerging as a broad and multifaceted term encompassing several components and protocols, leading to a dominant technological shift. This rapid evolution is being utilized in domains such as smart factories, structural healthcare, smart cities, smart transportation, supply chain control, intelligent shopping applications, smart agriculture and many more in the form of autonomous IoT applications [3]. This paradigm shift has empowered self-driving drones to carry out home deliveries of groceries, enabled healthcare experts to conduct continuous health monitoring through wearable sensors, facilitated real-time monitoring of equipment and processes in supply chain management, and many more.…”

Section: A Unfolding Emerging Computing Paradigmmentioning

confidence: 99%

AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives

Walia,

Kumar,

Gill

2024

IEEE Commun. Surv. Tutorials

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The proliferation of ubiquitous Internet of Things (IoT) sensors and smart devices in several domains embracing healthcare, Industry 4.0, transportation and agriculture are giving rise to a prodigious amount of data requiring everincreasing computations and services from cloud to the edge of the network. Fog/Edge computing is a promising and distributed computing paradigm that has drawn extensive attention from both industry and academia. The infrastructural efficiency of these computing paradigms necessitates adaptive resource management mechanisms for offloading decisions and efficient scheduling. Resource Management (RM) is a non-trivial issue whose complexity is the result of heterogeneous resources, incoming transactional workload, edge node discovery, and Quality of Service (QoS) parameters at the same time, which makes the efficacy of resources even more challenging. Hence, the researchers have adopted Artificial Intelligence (AI)-based techniques to resolve the abovementioned issues. This paper offers a comprehensive review of resource management issues and challenges in Fog/Edge paradigm by categorizing them into provisioning of computing resources, task offloading, resource scheduling, service placement, and load balancing. In addition, existing AI and non-AI based state-of-the-art solutions have been discussed, along with their QoS metrics, datasets analysed, limitations and challenges. The survey provides mathematical formulation corresponding to each categorized resource management issue. Our work sheds light on promising research directions on cutting-edge technologies such as Serverless computing, 5G, Industrial IoT (IIoT), blockchain, digital twins, quantum computing, and Software-Defined Networking (SDN), which can be integrated with the existing frameworks of fog/edge-of-things paradigms to improve business intelligence and analytics amongst IoT-based applications.

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Section: A Unfolding Emerging Computing Paradigmmentioning

confidence: 99%

AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives

Walia,

Kumar,

Gill

2024

IEEE Commun. Surv. Tutorials

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“…The internet of things (IoT) has a broad development prospect in the field of distribution grid energy dispatch. By connecting distribution grid operators with energy users and electric equipment (Tariq et al, 2020;Liao et al, 2023a;Fizza et al, 2023;Liu et al, 2023;Safdar Malik et al, 2023;Zhu et al, 2024), IoT provides dynamic data acquisition and realtime state perception of key electric equipment. Then, these collected data can be uploaded to the edge server or cloud server for deep state analysis and intelligent energy dispatch Yao et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Delay and cost-balanced communication resource management for IoT-empowered distribution grid energy dispatch

Shi

2024

Front. Energy Res.

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The combination of internet of things (IoT), 5G, and power line communication (PLC) provides real-time and low-cost data transmission services to meet the quality of service (QoS) requirements for distribution grid energy dispatch. However, the IoT-empowered communication resource management system faces challenges in optimizing delay and traffic cost for distribution grid energy dispatch. There is a contradiction between the long-term performance guarantee and short-term optimization objectives, compounded by competition for communication resources. In this paper, we construct the minimization problem of the weighted sum of data transmission delay and traffic cost. Utilizing Lyapunov optimization, we aim to decouple the long-term constraint of average queuing delay with the short-term optimization objective. Then, a delay and cost-balanced communication resource management algorithm based on two-layer iterative matching is proposed. It optimizes the communication mode selection by I-to-2 bidding matching in a large timescale and subchannel allocation by many-to-many deferred acceptance matching in a small timescale. The simulation data present that the proposed algorithm excels in reducing data transmission delay, minimizing traffic cost, and decreasing queuing delay.

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“…As the vision of an internet-centric and things-centric world for the near future has been progressively unfolding, numerous IoT applications across many domains have hitherto made successful market entries, or some of their prototypes have already been implemented [1]. This technological development underpins the projection that the IoT innovations will add about $5.5 trillion to $12.6 trillion in value to the global economy by 2030 [2]. Notably, some of the areas of modern life that have already witnessed the application of the IoT include smart homes and cities [3][4][5], Industrial IoT (IIoT) [6,7], smart agriculture [3,8], intelligent medicine [8][9][10][11], smart transportation and autonomous vehicles [3,4,12], and wearable fitness [4,8,10] among others.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Safety and Security Analysis Frameworks for the Internet of Things

et al. 2023

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The rapid progress of the Internet of Things (IoT) has continued to offer humanity numerous benefits, including many security and safety-critical applications. However, unlocking the full potential of IoT applications, especially in high-consequence domains, requires the assurance that IoT devices will not constitute risk hazards to the users or the environment. To design safe, secure, and reliable IoT systems, numerous frameworks have been proposed to analyse the safety and security, among other properties. This paper reviews some of the prominent classical and model-based system engineering (MBSE) approaches for IoT systems’ safety and security analysis. The review established that most analysis frameworks are based on classical manual approaches, which independently evaluate the two properties. The manual frameworks tend to inherit the natural limitations of informal system modelling, such as human error, a cumbersome processes, time consumption, and a lack of support for reusability. Model-based approaches have been incorporated into the safety and security analysis process to simplify the analysis process and improve the system design’s efficiency and manageability. Conversely, the existing MBSE safety and security analysis approaches in the IoT environment are still in their infancy. The limited number of proposed MBSE approaches have only considered limited and simple scenarios, which are yet to adequately evaluate the complex interactions between the two properties in the IoT domain. The findings of this survey are that the existing methods have not adequately addressed the analysis of safety/security interdependencies, detailed cyber security quantification analysis, and the unified treatment of safety and security properties. The existing classical and MBSE frameworks’ limitations obviously create gaps for a meaningful assessment of IoT dependability. To address some of the gaps, we proposed a possible research direction for developing a novel MBSE approach for the IoT domain’s safety and security coanalysis framework.

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A Survey on Evaluating the Quality of Autonomic Internet of Things Applications

Cited by 23 publications

References 179 publications

AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives

AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives

Delay and cost-balanced communication resource management for IoT-empowered distribution grid energy dispatch

An Overview of Safety and Security Analysis Frameworks for the Internet of Things

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