Prognostic and Condition-based Maintenance of Lithium-Ion batteries is a fundamental topic which is rapidly expanding since a long battery lifetime is required to ensure economic viability and minimize the life cycle cost. Remaining Useful Life (RUL) estimation is an essential tool for Prognostic and Health Management of batteries. In this paper a hybrid approach based on both condition monitoring and physic model is presented to improve the accuracy and precision of RUL estimation for Lithium-Ion battery. An Artificial Intelligence estimation method based on Recurrent Neural Network (RNN) is integrated with a state space estimation technique which is typical of filtering-based approach. The state space estimation is used to generate a big dataset for the training of the RNN. Some additional deep layers are used to improve the prediction of nonlinear trends (typical of batteries) while the performance optimization of the RNN is ensured using a genetic algorithm. The performances of the proposed method have been tested using a battery degradation dataset from the data repository of Prognostics Center of Excellence at NASA. Two different degradation models are compared, the widely known empirical double exponential model and an innovative single exponential model which allows to ensure optimal performance with fewer parameters required to be estimated.

Fog Computing is an emerging paradigm that extends Cloud Computing towards the edge of the network. In particular, Fog Computing refers to a distributed computing infrastructure confined on a limited geographical area within which some Internet of Things (IoT) applications/services run directly at the network edge on smart devices having computing, storage, and network connectivity, named Fog Nodes, with the goal of improving efficiency and reducing the amount of data that needs to be sent to the Cloud for massive data processing, analysis and storage. This paper proposes an efficient strategy to offload computationally intensive tasks from end-user devices to Fog Nodes. The computation offload problem is formulated here as a matching game with externalities, with the aim of minimizing the worst case service time by taking into account both computational and communications costs. In particular, this paper proposes a strategy based on the deferred acceptance algorithm to achieve the efficient allocation in a distributed mode and ensuring stability over the matching outcome. The performance of the proposed method is evaluated by resorting to computer simulations in terms of worst total completion time, mean waiting and mean total completion time per task. Moreover, with the aim of highlighting the advantages of the proposed method, performance comparisons with different alternatives are also presented and critically discussed. Finally, a fairness analysis of the proposed allocation strategy is also provided on the basis of the evaluation of the Jain’s index.

The continuous growth of smart devices needing processing has led to moving storage and computation from cloud to the network edges, giving rise to the edge computing paradigm. Owing to the limited capacity of edge computing nodes, the presence of popular applications in the edge nodes results in significant improvements in users’ satisfaction and service accomplishment. However, the high variability in the content requests makes prediction demand not trivial and, typically, the majority of the classical prediction approaches require the gathering of personal users' information at a central unit, giving rise to many users' privacy issues. In this context, federated learning gained attention as a solution to perform learning procedures from data disseminated across multiple users, keeping the sensitive data protected. This study applies federated learning to the demand prediction problem, to accurately forecast the more popular application types in the network. The proposed framework reaches high accuracy levels on the predicted applications demand, aggregating in a global and weighted model the feedback received by users, after their local training. The validity of the proposed approach is verified by performing a virtual machine replica copies and comparison with the alternative forecasting approach based on chaos theory and deep learning.

The recent growth in intensive services and applications demand has triggered the functional integration of cloud computing with edge computing capabilities. One of the main goals is to allow a fast processing to tasks with strict real time constraints in order to lower the task dropping probability due to expiration of the associated deadlines. This paper deals with the performance evaluation and optimization of a three layers cloud-fog-edge computing infrastructure by resorting to the use of queueing theory results. In particular, a Markov queueing system model with reneging is proposed for the cloud subsystem, in order to consider the premature computation requests departure due to their deadline expiration. Furthermore, a computational resources allocation method is proposed with the aim at maximizing the social welfare metric, constrained to specific quality of service requirements. Finally, the proposed queueing theory analysis as well as of the computational resources allocation approach is validated by comparing the obtained analytical predictions with simulation results.

The forthcoming sixth-generation (6G) network technology will trigger tremendous changes across several application scenarios and even in the way we work, communicate, and, more in general, organize our everyday life. In particular, in such a new digital era, virtual reality (VR) technology will play a fundamental role, carrying with it numerous challenges as regards the increasing demand of high rate, highly reliable, and low-latency communications, especially in reference to Internet-of-Things environments. In this sense, an effective end-to-end (e2e) delay analysis becomes imperative to pursue an efficient design of the foreseen VR services. Toward this goal, this article proposes the e2e delay investigation through the martingale theoretical bound and considering the stochastic network calculus principles for its formulation. The performance discussion focuses on the achieved communications reliability, comparing the analytical predictions obtained via the martingale bound with the simulation results, and the Markov queuing theory-based alternative. The presented results confirm the validity of the proposed approach, exhibiting noticeable closeness between the theoretical bound and the simulation outcomes.

Nowadays, Industrial Internet of Things (IIoTs) has gained attention as an emerging application area of the Internet of Things paradigm to improve efficiency and reliability in a wide class of manufacture processes. This paper focuses on a combined edge-cloud computing architecture for IIoT systems, and addresses the minimization of both the mean system response time and the number of requests dropping due to a response time greater than their time deadline. The problem is formulated as a matching game with externalities between the EC servers (ECSs), and the applications themselves. The proposed strategy adopts a discard policy with the aim at favoring the IIoT-devices requests with a not-expired deadline. Moreover, a suitable discussion of the matching stability is proposed. Finally, the strategy validity has been confirmed by system performance expressed in terms of mean and worst system response time and outage probability, in comparison to methods recently proposed in literature.