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.

Vehicular communication networks have emerged to enable numerous vehicular data services and applications. Conventional vehicular ad hoc networks (VANETs) are often operated in the ad hoc mode and mainly focus on road safety applications based on the connection between vehicles and roadside units (RSUs). To support vehicular communications, dedicated shortrange communication (DSRC) and car-to-car communication consortium (C2C-CC) have been initiated in the United States and Europe, respectively. With the new era of the Internet of Things (IoT), the conventional VANETs have evolved to the Internet of Vehicles (IoV). In IoV, each vehicle is envisioned as an intelligent object, equipped with sensing platforms, computing facilities, control units, and storages and is connected to any entity (other vehicles, RSUs, charging/gas stations, cloud, and so on) via vehicle-to-everything (V2X) communications. Intelligent vehicles can take different roles, i.e., being both a client and a server, taking and providing big data services, leading to numerous new IoV applications, from assisted/autonomous driving and platooning, secure information sharing and learning to traffic control and optimization.