The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to Edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations, particularly security and privacy challenges. This systematic literature review aims to identify similarities, differences, main attacks, and countermeasures in the various paradigms mentioned. The main determining outcome points out the essential security and privacy threats. The presented results also outline important similarities and differences in Cloud, Edge, and Fog computing paradigms. Finally, the work identified that the heterogeneity of such an ecosystem does have issues and poses a great setback in the deployment of security and privacy mechanisms to counter security attacks and privacy leakages. Different deployment techniques were found in the review studies as ways to mitigate and enhance security and privacy shortcomings.

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.

Autonomous driving services depends on active sensing from modules such as camera, LiDAR, radar, and communication units. Traditionally, these modules process the sensed data on high-performance computing units inside the vehicle, which can deploy intelligent algorithms and AI models. The sensors mentioned above can produce large volumes of data, potentially reaching up to 20 Terabytes. This data size is influenced by factors such as the duration of driving, the data rate, and the sensor specifications. Consequently, this substantial amount of data can lead to significant power consumption on the vehicle. Similarly, a substantial amount of data will be exchanged between infrastructure sensors and vehicles for collaborative vehicle applications or fully connected autonomous vehicles. This communication process generates an additional surge of energy consumption. Although the autonomous vehicle domain has seen advancements in sensory technologies, wireless communication, computing and AI/ML algorithms, the challenge still exists in how to apply and integrate these technology innovations to achieve energy efficiency. This survey reviews and compares the connected vehicular applications, vehicular communications, approximation and Edge AI techniques. The focus is on energy efficiency by covering newly proposed approximation and enabling frameworks. To the best of our knowledge, this survey is the first to review the latest approximate Edge AI frameworks and publicly available datasets in energy-efficient autonomous driving. The insights from this survey can benefit the collaborative driving service development on low-power and memory-constrained systems and the energy optimization of autonomous vehicles.

Personal mobile devices such as smartwatches, smart jewelry, and smart clothes have launched a new trend in the Internet of Things (IoT) era, namely the Internet of Wearable Things (IoWT). These wearables are small IoT devices capable of sensing, storing, processing, and exchanging data to assist users by improving their everyday life tasks through various applications. However, the IoWT has also brought new challenges for the research community to address such as increasing demand for enhanced computational power, better communication capabilities, improved security and privacy features, reduced form factor, minimal weight, and better comfort. Most wearables are battery-powered devices that need to be recharged – therefore, the limited battery life remains the bottleneck leading to the need to enhance the energy efficiency of wearables, thus, becoming an active research area. This paper presents a survey of energy-efficient solutions proposed for diverse IoWT applications by following the systematic literature review method. The available techniques published from 2010 to 2020 are scrutinized, and the taxonomy of the available solutions is presented based on the targeted application area. Moreover, a comprehensive qualitative analysis compares the proposed studies in each application area in terms of their advantages, disadvantages, and main contributions. Furthermore, a list of the most significant performance parameters is provided. A more in-depth discussion of the main techniques to enhance wearables’ energy efficiency is presented by highlighting the trade-offs involved. Finally, some potential future research directions are highlighted.

With the miniaturization of electronics, Global Navigation Satellite Systems (GNSS) receivers are getting more and more embedded into devices with harsh energy constraints. This process has led to new signal processing challenges due to the limited processing power on battery-operated devices and to challenging wireless environments, such as deep urban canyons, tunnels and bridges, forest canopies, increased jamming and spoofing. The latter is typically tackled via new GNSS constellations and modernization of the GNSS signals. However, the increase in signal complexity leads to higher computation requirements to recover the signals; thus, the trade-off between precision and energy should be evaluated for each application. This paper dives into low-power GNSS, focusing on the energy consumption of satellite-based positioning receivers used in battery-operated consumer devices and Internet of Things (IoT) sensors.We briefly overview the GNSS basics and the differences between legacy and modernized signals. Factors dominating the energy consumption of GNSS receivers are then reviewed, with special attention given to the complexity of the processing algorithms. Onboard and offloaded (Cloud/Edge) processing strategies are explored and compared. Finally, we highlight the current challenges of today’s research in low-power GNSS.

In this digitized world, data has become an integral part in any domain, including healthcare. The healthcare industry produces a huge amount of digital data, by utilizing information from all sources of healthcare, including the patients' demographics, medications, vital signs, physician's observations, laboratory data, billing data, data from various wearable sensors, etc. With the rapid growth of the wireless technology applications, there has also been a significant increase in the digital health data. New medical discoveries and new eHealth-related technologies, such as mobile apps, novel sensors, and wearable technology have contributed as important data sources for healthcare data. Nowadays, there is a huge potential to improve the healthcare quality and customer satisfaction with the help of machine learning (ML) algorithms applied on time-domain and frequency-domain healthcare data obtained from wearables and sensors. This systematic literature review examines in depth how health data from sensors can be processed and analyzed using ML techniques. The review focuses on the following diseases for obtaining the eHealth data: diabetes mellitus type 1 and type 2, hypertension and hypotension, atrial fibrillation, bradykinesia, dyskinesia, and fever related diseases. The data for the systematic literature review was collected from four databases, Medline, Proquest, Scopus, and Web of Science. We selected 67 studies for the final in-depth review out of the initial 1530 pre-selected papers. Our study identified that the major part of eHealth data is obtained from the sensors such as accelerometer, gyroscopes, ECG (Electrocardiogram), EEG (Electroencephalogram) monitors, and blood glucose sensors. This study also examines the different feature types, feature extraction methods, and ML algorithms used for eHealth data analysis. Our review also shows that neural network (NN) algorithms and support vector machines (SVM) have shown so far the best performance for analyzing the healthcare data among other ML algorithms studied in the literature.