Today is the era of the Internet of Things (IoT). The recent advances in hardware and information technology have accelerated the deployment of billions of interconnected, smart and adaptive devices, in critical infrastructures like health, transportation, environmental control and home automation. Transferring data over a network without requiring any kind of human-to-computer or human-to-human interaction, brings reliability and convenience to consumers, but also opens a new world of opportunity for intruders, and introduces a whole set of unique and complicated questions to the field of Digital Forensics. Although IoT data could be a rich source of evidence, forensics professionals cope with diverse problems, starting from the huge variety of IoT devices and non-standard formats, to the multi-tenant cloud infrastructure and the resulting multi-jurisdictional litigations. A further challenge is the end-to-end encryption which represents a trade-off between users’ right to privacy and the success of the forensics investigation. Due to its volatile nature, digital evidence has to be acquired and analysed using validated tools and techniques that ensure the maintenance of the Chain of Custody. Therefore, the purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges. Furthermore, this work provides an overview of the past and current theoretical models in the digital forensics science. Special attention is paid to frameworks that aim to extract data in a privacy-preserving manner or secure the evidence integrity using decentralized blockchain-based solutions. In addition, the present paper addresses the ongoing Forensics-as-a-Service (FaaS) paradigm, as well as some promising cross-cutting data reduction and forensics intelligence techniques. Finally, several other research trends and open issues are presented, with emphasis on the need for proactive Forensics Readiness strategies and generally agreed-upon standards.

This article presents a unified computing, caching, and communication (3C) solution for the upcoming 5G environment that will allow service, content, and function providers to deploy their services/ content/functions near the end users (EUs); to allow network providers to virtually deploy their connectivity services over commodity hardware; and to enable end users to renounce their role as passive 5G stakeholders and become active ones by offering their 3C resources to the 5G ecosystem. In this direction, we foresee the exploitation of a peer-topeer- like middleware/app solution that upon installation will enhance the end user devices with the ability to form virtual fogs capable of providing their 3C resources to the 5G ecosystem. Additionally, we propose the introduction of heterogeneous nodes (e.g., FPGAs and GPUs) at the networks edge, which will boost the processing capabilities without paying a premium in power consumption. This will enable efficient and thorough filtering of the information that makes it all the way up to the cloud. In summary, this article proposes an architecture that exploits and advances the edge and extreme edge 3C paradigms toward enabling the 5G ecosystem to meet its own criterion for low end-to-end latencies and, as such, enable it to provide and sustain high QoS/QoE levels.

It is anticipated that the backbone of Smart Cities concerning automation and networking will be formed by Unmanned Aerial Vehicles in the imminent future. Therefore, our research focuses on developing advanced microcontrollers embedded with Artificial Intelligence techniques for self-governing Unmanned Aerial Vehicles. The main objective of this research was to enable full automation for the execution of flight paths with non-trivial sequences that will be performed with centimetre-level accuracy. Also, by utilising dynamic flight plans and trajectories, we aim to secure autonomous aviation based on norms, with control loops and fundamental constraints. More specifically, we evolved a novel algorithmic technique for trajectory optimisation, which deploys a modification to the A* search algorithm, implemented by the Haversine formula and enhances accuracy using Vincenty's formula. Furthermore, realistic values for trajectory optimisation and obstacle avoidance were found through the implementation of a simulative investigation. The outcomes of our methodology indicate that the safety constraints associated with the integration of Unmanned Aerial Vehicles in the urban environment can be significantly mitigated. Consequently, their effectiveness will be increased in realising their diverse operations and capabilities.

The Internet of Things (IoT) is an emerging Internet-based architecture, enabling the exchange of data and services in a global network. With the advent of the Internet of Things, more and more devices are connecting to the Internet in order to help people get and share data or program actions. In this paper, we introduce an IoT Agent, a Web application for monitoring and controlling a smart home remotely. The IoT Agent integrates a chat bot that can understand text or voice commands using natural language processing (NLP). With the use of NLP, home devices are more user-friendly and controlling them is easier, since even when a command or question/command is different from the presets, the system understands the user’s wishes and responds accordingly. Our solution exploits several available Application Programming Interfaces (APIs), namely: the Dialogflow API for the efficient integration of NLP to our IoT system, the Web Speech API for enriching user experience with voice recognition and synthesis features, MQTT (Message Queuing Telemetry Transport) for the lightweight control of actuators and Firebase for dynamic data storage. This is the most significant innovation it brings: the integration of several third-party APIs and open source technologies into one mash-up, highlighting how a new IoT application can be built today using a multi-tier architecture. We believe that such a tiered architecture can be very useful for the rapid development of smart home applications.

Currently there is an active debate about how the existing cloud paradigm can cope with the volume, variety, and velocity of the data generated by end devices (e.g., Internet of Things sensors). It is expected that there will be over 50 billion of these devices by 2020, which will create more than two Exabytes worth of data each day. Additionally, the vast number of edge devices create a huge ocean of digital resources close to the data source, which, however, remain so far unexploited to their full extent. EXEGESIS proposes to harness these unutilized resources via a three-layer architecture that encompasses the mist, fog, and cloud. The mist network is located at the very bottom, where interconnected objects (Internet of Things devices, small servers, etc.) create neighborhoods of objects. This arrangement is enhanced by a virtual fog layer, which allows for dynamic, ad hoc interconnections among the various neighborhoods. At the top layer resides the cloud with its abundant resources that can also be included in one or more virtual fog neighborhoods. Thus, this article complements and leverages existing cloud architectures, enabling them to interact with this new edge-centric ecosystem of devices/resources, and benefit from the fact that critical data are available where they can add the most value.