Localization is the process of determining the position of an entity in a given coordinate system. Due to its wide range of applications (e.g. autonomous driving, Internet-of-Things), it has gained much focus from the industry and academia. Channel State Information (CSI) has overtaken Received Signal Strength Indicator (RSSI) to achieve localization given its temporal stability and rich information. In this paper, we extend our previous work by combining classical and deep learning methods in an attempt to improve the localization accuracy using CSI. We then test the generalization aspect of both approaches in different environments by splitting the training and test sets such that their intersection is reduced when compared with uniform random splitting. The deep learning approach is a Multi Layer Perceptron Neural Network (MLP NN) and the classical machine learning method is based on K-nearest neighbors (KNN). The estimation results of both approaches outperform state-of-the-art performance on the same dataset. We illustrate that while the accuracy of both approaches deteriorates when tested for generalization, deep learning exhibits a higher potential to perform better beyond the training set. This conclusion supports recent state-of-the-art attempts to understand the behaviour of deep learning models.

In modern wireless systems such as ZigBee, sensitive information which is produced by the network is transmitted through different wired or wireless nodes. Providing the requisites of communication between diverse communication system types, such as mobiles, laptops, and desktop computers, does increase the risk of being attacked by outside nodes. Malicious (or unintentional) threats, such as trying to obtain unauthorized accessibility to the network, increase the requirements of data security against the rogue devices trying to tamper with the identity of authorized devices. In such manner, focusing on Radio Frequency Distinct Native Attributes (RF-DNA) of features extracted from physical layer responses (referred to as preambles) of ZigBee devices, a dataset of distinguishable features of all devices can be produced which can be exploited for the detection and rejection of spoofing/rogue devices. Through this procedure, distinction of devices manufactured by the different/same producer(s) can be realized resulting in an improvement of classification system accuracy. The two most challenging problems in initiating RF-DNA are (1) the mechanism of features extraction in the generation of a dataset in the most effective way for model classification and (2) the design of an efficient model for device discrimination of spoofing/rogue devices. In this paper, we analyze the physical layer features of ZigBee devices and present methods based on deep learning algorithms to achieve high classification accuracy, based on wavelet decomposition and on the autoencoder representation of the original dataset.

Medical IoT devices that use miniature sensors to collect patient's bio-signals and connected medical applications are playing a crucial role in providing pervasive and personalized healthcare. This technological improvement has also created opportunities for the better management of personal health information. The Personal Health Information Management System (PHIMS) supports activities such as acquisition, storage, organization, integration, and privacy-sensitive retrieval of consumer's health information. For usability and wide acceptance, the PHIMS should follow the design principles that guarantee privacy-aware health information sharing, individual information control, integration of information obtained from multiple medical IoT devices, health information security, and flexibility. Recently, blockchain technology has emerged as a lucrative option for the management of personal health information. In this paper, we propose eHealthChain-a blockchain-based PHIMS for managing health data originating from medical IoT devices and connected applications. The eHealthChain architecture consists of four layers, which are a blockchain layer for hosting a blockchain database, an IoT device layer for obtaining personal health data, an application layer for facilitating health data sharing, and an adapter layer, which interfaces the blockchain layer with an application layer. Compared to existing systems, eHealthChain provides complete control to the user in terms of personal health data acquisition, sharing, and self-management. We also present a detailed implementation of a Proof of Concept (PoC) prototype of eHealthChain system built using Hyperledger Fabric platform.

Named Data Networking (NDN) is a data-driven networking model that proposes to fetch data using names instead of source addresses. This new architecture is considered attractive for the Internet of Things (IoT) due to its salient features, such as naming, caching, and stateful forwarding, which allow it to support the major requirements of IoT environments natively. Nevertheless, some NDN mechanisms, such as forwarding, need to be optimized to accommodate the constraints of IoT devices and networks. This paper presents LAFS, a Learning-based Adaptive Forwarding Strategy for NDN-based IoT networks. LAFS enhances network performances while alleviating the use of its resources. The proposed strategy is based on a learning process that provides the necessary knowledge allowing network nodes to collaborate smartly and offer a lightweight and adaptive forwarding scheme, best suited for IoT environments. LAFS is implemented in ndnSIM and compared with state-of-the-art NDN forwarding schemes. As the obtained results demonstrate, LAFS outperforms the benchmarked solutions in terms of content retrieval time, request satisfactory rate, and energy consumption.

Digital forensic analysis of videoconferencing applications has received considerable attention recently, owing to the wider adoption and diffusion of such applications following the recent COVID-19 pandemic. In this contribution, we present a detailed forensic analysis of Cisco WebEx which is among the top three videoconferencing applications available today. More precisely, we present the results of the forensic investigation of Cisco WebEx desktop client, web, and Android smartphone applications. We focus on three digital forensic areas, namely memory, disk space, and network forensics. From the extracted artifacts, it is evident that valuable user data can be retrieved from different data localities. These include user credentials, emails, user IDs, profile photos, chat messages, shared media, meeting information including meeting passwords, contacts, Advanced Encryption Standard (AES) keys, keyword searches, timestamps, and call logs. We develop a memory parsing tool for Cisco WebEx based on the extracted artifacts. Additionally, we identify anti-forensic artifacts such as deleted chat messages. Although network communications are encrypted, we successfully retrieve useful artifacts such as IPs of server domains and host devices along with message/event timestamps.

ICT hold significant potential to increase resource and energy efficiencies and contribute to a circular economy. Yet unresolved is whether the aggregated net effect of ICT overall mitigates or aggravates environmental burdens. While the savings potentials have been explored, drivers that prevent these and possible counter measures have not been researched thoroughly. The concept digital sufficiency constitutes a basis to understand how ICT can become part of the essential environmental transformation. Digital sufficiency consists of four dimensions, each suggesting a set of strategies and policy proposals: (a) hardware sufficiency, which aims for fewer devices needing to be produced and their absolute energy demand being kept to the lowest level possible to perform the desired tasks; (b) software sufficiency, which covers ensuring that data traffic and hardware utilization during application are kept as low as possible; (c) user sufficiency, which strives for users applying digital devices frugally and using ICT in a way that promotes sustainable lifestyles; and (d) economic sufficiency, which aspires to digitalization supporting a transition to an economy characterized not by economic growth as the primary goal but by sufficient production and consumption within planetary boundaries. The policies for hardware and software sufficiency are relatively easily conceivable and executable. Policies for user and economic sufficiency are politically more difficult to implement and relate strongly to policies for environmental transformation in general. This article argues for comprehensive policies for digital sufficiency, which are indispensible if ICT are to play a beneficial role in overall environmental transformation.