The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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SUMMARYThe range of applications of wireless sensor networks is so wide that it tends to invade our every day life. In the future, a sensor network will survey our health, our home, the roads we follow, the office or the industry we work in or even the aircrafts we use, in an attempt to enhance our safety. However, the wireless sensor networks themselves are prone to security attacks. The list of security attacks, although already very long, continues to augment impeding the expansion of these networks. The trust management schemes consist of a powerful tool for the detection of unexpected node behaviours (either faulty or malicious). Once misbehaving nodes are detected, their neighbours can use this information to avoid cooperating with them, either for data forwarding, data aggregation or any other cooperative function. A variety of trust models which follow different directions regarding the distribution of measurement functionality, the monitored behaviours and the way measurements are used to calculate/define the node's trustworthiness has been presented in the literature. In this paper, we survey trust models in an attempt to explore the interplay among the implementation requirements, the resource consumption and the achieved security. Our goal is to draw guidelines for the design of deployable trust model designs with respect to the available node and network capabilities and application peculiarities.

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The goal of the study presented in this article is to provide a general overview of the various aspects related to electric vehicles (EVs), along with all associated emerging challenges and perspectives. In this context, the basic types of EVs and the corresponding charging technologies are analyzed. Since EVs are expected to be a key component of future smart electrical grids (SEG), connection to the grid issues, along with advanced charging techniques (i.e., wireless power transfer), are analyzed as well. To this end, the main features, the requirements of vehicle to grid (V2G) communications, as well as future developments and scenarios of electrification, are also presented and analyzed. Moreover, integration issues with currently deployed fifth generation (5G) mobile wireless networks are also outlined, in order to ensure optimum transmission and reception quality in V2G communications and improved user experience. This integration is also expanded in autonomous vehicles (AVs) technology (self-driving objects), since optimized information processing from various diverse sources is required in order to ensure advanced traffic management aspects.In this context, two major categories can be classified: a. Battery electric vehicles (BEV), and b. Hybrid electric vehicles (HEV). BEVs use batteries as a source of energy and they are also called "green vehicles, or clean vehicles, or eco-friendly vehicles" because they have zero emissions. In order to cover a travel distance, they are equipped with larger storage batteries than HEVs. However, the limited traveling distance of BEVs is an important drawback because it is often necessary to recharge the battery by connecting to an external power source (in city cars, autonomy starts from 100 to 120 km and reaches 500 km or more in high power cars-Tesla Model). A HEV is classified as a car that uses two or more different technologies to achieve its movement. These technologies usually include the classic internal combustion engine and a more "mild" environmentally-friendly technology, usually an electric motor. However, the electric motor is used as a supplementary power source in cases where the HEV requires more power.It is apparent from the above that proper energy management is of vital importance for the smooth operation of EVs. A challenging research field includes the design and implementation of efficient charging schemes that ensure fast and reliable EV charging in order to increase vehicle autonomy. In this concept, the vehicle-to-grid (V2G) approach aims to optimize the way we transport, use, and produce electricity by turning electric cars into "virtual power plants" [9]. V2G technology refers to a bi-directional flow system operation, in which plug-in battery electric vehicles communicate with a recipient and allow the reciprocal flow between the EV and an electric grid [10,11]. Under this relatively new concept, electric cars would store and dispatch electrical energy stored in networked vehicle batteries which together act as one collective battery fleet ...