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The IoT devices with advanced computational powers supported by Artificial Intelligence (AI) in diversified application domains require infrastructures that can deliver resources and computational services based on application needs. Fog computing has emerged as a platform for applications that can provide flexible and shared computational capabilities to such delay-sensitive services along with maintaining network availability, minimum latency, and Quality of Service (QoS) maximization. The proposed model offers the adaptive offloading of the applications while achieving the maximum QoS in a delay-constrained service environment. Moreover, to minimize the resource starvation condition, a probabilistic application task scheduler algorithm is presented, which considers the task's initial priority and deadline. The tasks within waiting queues are assigned new priorities and used to select the most relevant task, reducing the response time and failure rate. Besides, a cost parameter is included in the proposed model that chooses the cost-efficient option for offloading the applications to the available servers. This work aims to maximize the overall QoS and optimize the service cost, which is demonstrated through simulation results. Our proposed model adaptive application offloading for QoS maximization with delay constraint (AAOQM-DC) yields a performance improvement by getting 0.11-2.49 sec. downfall in overall response time, 0.12- 1.38 sec. decrement in network delay, and enhanced QoS by achieving 0.07-5.12% lower failure rate in comparison to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In the era of technology, compact and low-powered IoT devices have become an integral part of the daily routines of various sectors such as healthcare, education, industries, defense, and agriculture, etc. The IoT has increased the flow of enormous data from one network to another and extended the dependability of the public networks. In health applications, this dependency can cause excessive packets to be lost and prolonged waiting, which can cause sensitive applications to suffer. The authors suggested fog computing as a potential middle layer in the delay-aware computational system in existing works. There are many issues with the cloud-based computation model, such as location awareness, long-distance communication path, and unmanageable network bandwidth. These parameters can be solved using a fog computing model, which helps achieve ultra-low latency and minimum service delivery time. However, task offloading and scheduling are significant open research issues concerning resource management and allocation in a cloud-fog system. This paper presents a delay-aware application offloading focusing on reducing the response time and maximizing the system performance. Our proposed model developed a multiclass open queueing model that maintains the traffic on different queues, aiming to achieve maximum system performance. The simulation results demonstrate that our proposed model DAAO (Delay-aware application offloading) yields a performance improvement of 14.30% service rate and minimized failure rate by 2.0%. The simulation results supported the robustness of the proposed model in terms of minimum response time and maximum success rate. Our proposed model achieves a better tradeoff than existing work in terms of offloading and minimum failure rate. [ABSTRACT FROM AUTHOR]

Recent developments in manufacturing processes and automation have led to the new industrial revolution termed "Industry 4.0". Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies. [ABSTRACT FROM AUTHOR]

Sensor nodes in IoT applications exhibit limited computing power, communication range and energy resource. These are some of the major constraints in the deployment of these systems. This leads to a multivariable optimization problem. Further, the variations in geographic conditions such as ground, terrain, atmosphere and mobility between various nodes introduces severe randomness in received signal strength at particular nodes. To mitigate this random nature of wireless link, probabilistic channel models are explored and analyzed. For more realistic estimation, multiple factors such as fading, shadowing, interference and noise must be considered simultaneously. In this paper, the reliability of wireless link in such environment is analyzed by capturing effect of these parameters through compound probability distributions. Expressions for Link and node outage have been obtained and measured through network simulation for reliability analysis. The comparative study with the other available fading models shows that the proposed model is more suitable in approximating real phenomenon of wireless link design. [ABSTRACT FROM AUTHOR]

This article describes how the Heterogeneous and IoT networks are the most sought solutions to significant coverage and connectivity to the users. This article suggests a model for a heterogeneous and IoT network comprised of grading of the users based on their movement pattern, usage, and applications. Users are then given preferential possible access to the best connected network on the basis of their loyalty factor. The proposed model is customer centric and provides a best suited network to their loyal user on different aspects of the usage. Simulation studies were carried out to establish the suitability of the proposed work. The work also presents a scheme to retain the loyal customers in their network by providing them better services. [ABSTRACT FROM AUTHOR]