Telecom, Volume 4, Issue 1 (March 2023) – 12 articles

The Connected Automated Vehicle (CAV)’s deployment is proof of the wide evolution of autonomous driving technologies enabling vehicles to gradually dispose of their drivers. Within the scope of smart cities, such innovation has given rise to a new type of CAV: the Automated City Shuttle (ACS). Foreseen as the new paradigm aiming to shape the public transport model, the ACS elicits a plurality of new applications, such as the on-demand service in which a driverless shuttle offers the desired ride without human intervention. However, such a model raises cybersecurity concerns through the numerous attack surfaces and vehicle hyperconnection. This phenomenon was highlighted in several studies on CAVs, but very few research works tackled the specific case of ACSs, whose challenges and risks far exceed those of personal vehicles. The present work offers a comprehensive investigation of cybersecurity attacks, demonstrates a performed risk assessment based on the ISO/SAE 21434 standard, and showcases a penetration test over a real ACS of automation level four (L4) according to the Society of Automotive Engineering (SAE)’s ranking. Based on our experiments, we leverage fundamental cybersecurity recommendations with a focus on the ACS’s physical security. Full article

Micro-electromechanical systems (MEMS) technology-based sensors have found diverse fields of application due to the advancement in semiconductor manufacturing technology, which produces sensitive, low-cost, and powerful sensors. Due to the fabrication of different electrical and mechanical components on a single chip and complex process steps, MEMS sensors are prone to deterministic and random errors. Thus, testing, calibration, and quality control have become obligatory to maintain the quality and reliability of the sensors. This is where Artificial Intelligence (AI) can provide significant benefits, such as handling complex data, performing root cause analysis, efficient feature estimation, process optimization, product improvement, time-saving, automation, fault diagnosis and detection, drift compensation, signal de-noising, etc. Despite several benefits, the embodiment of AI poses multiple challenges. This review paper provides a systematic, in-depth analysis of AI applications in the MEMS-based sensors field for both the product and the system level adaptability by analyzing more than 100 articles. This paper summarizes the state-of-the-art, current trends of AI applications in MEMS sensors and outlines the challenges of AI incorporation in an industrial setting to improve manufacturing processes. Finally, we reflect upon all the findings based on the three proposed research questions to discover the future research scope. Full article

In this paper, a monopole patch antenna is designed, and the structure of the antenna is analyzed. The manufacturing process adopts TSMC 0.18 μm CMOS process technology. An artificial magnetic conductor (AMC) on the M1 layer is proposed in this paper to increase the radiation gain and reduce the reflection coefficient (S11) magnitude for impedance matching and antenna performance. This method can make up for the radiation efficiency and benefits of the antenna-on-chip that are affected by the high dielectric constant and low resistivity of the silicon substrate of the CMOS process. The antenna designed in this paper obtains a simulated bandwidth of 37.5 GHz to 69.5 GHz using the Electromagnetic Simulation Software, and the fractional bandwidth of the design is 60%. Among them, 62 GHz shows a maximum gain value of −2.64 dBi. Actual measurements have confirmed that the reflection coefficient of the antenna on the chip proposed in this paper is the same as the simulation trend, and a wider bandwidth is obtained from 20.9 GHz to 67 GHz, with a fractional bandwidth of 104.89%. This bandwidth covers millimeter wave 28 GHz, 38 GHz, and 60 GHz application frequencies. Full article

This study aims to analyze and discuss the risks facing construction projects by reviewing some of the processes and procedures that address risks through the use of the digital twin technology. The paper studies generic risks and their treatment, and it develops a proposal for risk management systematization using the Digital Twin for Construction Projects approach, previously developed by the authors. It addresses how to classify risks so that the digital system is fed with the proper information and data, which is based on processing and analysis, to reach understandable decisions and overcome anomalies. The research reached a set of results, the most prominent of which is that the digital twin can be used to enhance risk management in construction projects through adapted techniques such as the ones proposed in the paper; namely, a risk treatment procedure and a custom risk matrix. In addition, risk management treated according to a digital approach helps to improve the prediction capabilities, and this helps human decision-makers to avoid potential unplanned costs and failures, and to maximize efficiency. The study also recommends new investigations in the field of safeguarding shared information and data to protect from intentional and accidental mismanagement in order to reach a comprehensive digital system. Full article

Nowadays, e-learning and web-based learning are the most integrated new learning methods in schools, colleges, and higher educational institutions. The recent web-search-based learning methodological approach has helped online users (learners) to search for the required topics from the available online resources. The learners extracted knowledge from textual, video, and image formats through web searching. This research analyzes the learner’s significant attention to searching for the required information online and develops a new recommendation system using machine learning (ML) to perform the web searching. The learner’s navigation and eye movements are recorded using sensors. The proposed model automatically analyzes the learners’ interests while performing online searches and the origin of the acquired and learned information. The ML model maps the text and video contents and obtains a better recommendation. The proposed model analyzes and tracks online resource usage and comprises the following steps: information logging, information processing, and word mapping operations. The learner’s knowledge of the captured online resources using the sensors is analyzed to enhance the response time, selectivity, and sensitivity. On average, the learners spent more hours accessing the video and the textual information and fewer hours accessing the images. The percentage of participants addressing the two different subject quizzes, Q1 and Q2, increased when the learners attempted the quiz after the web search; 43.67% of the learners addressed the quiz Q1 before completing the web search, and 75.92% addressed the quiz Q2 after the web search. The average word counts analysis corresponding to text, videos, overlapping text or video, and comprehensive resources indicates that the proposed model can also apply for a continuous multi sessions online search learning environment. The experimental analysis indicates that better measures are obtained for the proposed recommender using sensors and ML compared with other methods in terms of recall, ranking score, and precision. The proposed model achieves a precision of 27% when the recommendation size becomes 100. The root mean square error (RMSE) lies between 8% and 16% when the number of learners < 500, and the maximum value of RMSE is 21% when the number of learners reaches 1500. The proposed recommendation model achieves better results than the state-of-the-art methods. Full article

This paper proposes a MAC protocol for ad hoc networks using In-band Full-duplex (IBFD) wireless communications, which are named as AdHoc-FDMAC. To utilize IBFD communications in ad hoc networks, this protocol modifies a number of control frames in the IEEE 802.11 Distributed Coordination Function (DCF) MAC standard. Here, the detailed time sequences for all types of IBFD communications are shown for the data transmission and routing. In this paper, the probability and throughput equations for IBFD communications in different situations have been derived. The performance of the proposed AdHoc-FDMAC has been analysed in terms of probability, throughput, and routing time. The Maximum throughput of AdHoc-FDMAC has been found to be 48.34 Mbps, and it is compared with a recently published ad hoc MAC as well as with the conventional HD MAC. The AdHoc-FDMAC outperforms the recently published ad hoc MAC and conventional HD MAC by 16.80% and 66.50% throughput gain, respectively. AdHoc-FDMAC incorporates the existing Ad hoc On-demand Distance Vector (AODV) routing protocol, but this AODV routing is utilized here using IBFD communications. This paper also compares the routing time of the IBFD-based AODV with the conventional AODV. The result shows that the IBFD-based AODV requires 33.33% less routing time than that of the conventional AODV for 3-hop distance between the transmitter and receiver. This paper suggests that the AdHoc-FDMAC protocol provides much more throughput in ad hoc networks by utilizing IBFD communications. Full article

5G and beyond mobile networks are envisioned as the fundamental components that drive business and societal transformation. The deterioration of the natural environment and climate change have raised questions regarding the role of the mobile network ecosystem and its potential to accelerate innovations in industrial and societal sustainability. This paper describes the challenges facing 5G/6G mobile networks from sectors essential for the sustainable use of natural resources, which include smart agriculture and forestry, biodiversity monitoring, and water management. Based on recent advancements in the above-mentioned domains, the identification of use cases and their requirements are performed together with the evaluation of current and expected future support provided by 5G and 6G networks. Finally, a list of open issues and challenges to be tackled to enable the implementation of carrier-grade services for these sectors using 5G and 6G platforms is presented. Full article

Oceanographic data collection, disaster prevention, aided navigation, critical observation sub-missions, contaminant screening, and seaward scanning are just a few of the submissions that use underwater sensor hubs. Unmanned submerged vehicles (USVs) or autonomous acoustic underwater vehicles (AUVs) through sensors would similarly be able to explore unique underwater resources and gather data when utilized in conjunction with integrated screen operations. The most advanced technological method of oceanic observation is wireless information routing beneath the ocean or generally underwater. Water bottoms are typically observed using oceanographic sensors that collect data at certain ocean zones. Most research on UWSNs focuses on physical levels, even though the localization level, such as guiding processes, is a more recent zone. Analyzing the presenting metrics of the current direction conventions for UWSNs is crucial for considering additional enhancements in a procedure employing underwater wireless sensor networks for locating sensors (UWSNs). Due to their severely constrained propagation, radio frequency (RF) transmissions are inappropriate for underwater environments. This makes it difficult to maintain network connectivity and localization. This provided a plan for employing adequate reliability and improved communication and is used to locate the node exactly using a variety of methods. In order to minimize inaccuracies, specific techniques are utilized to calculate the distance to the destination. It has a variety of qualities, such as limited bandwidth, high latency, low energy, and a high error probability. Both nodes enable technical professionals stationed on land to communicate data from the chosen oceanic zones rapidly. This study investigates the significance, uses, network architecture, requirements, and difficulties of undersea sensors. Full article

This paper studies the formulation of dark and singular stationary optical solitons that stem from quadratic-cubic and generalized quadratic-cubic forms of nonlinear refractive index coupled with nonlinear chromatic dispersion. The temporal evolution is taken to be of both kinds, namely linear and generalized. The enhanced Kudryashov’s approach enables this retrieval possible. Full article

DEVS (Discrete Event System Specification) is widely used in modeling and simulation fields to design, validate, and implement complex response systems. DEVS provides a robust formalism for system design using event-driven, state-based models with explicitly defined temporal information. We extend the RTL-DEVS model based on DEVS formalism to enable part of Verilog simulation in DEVS-based simulation tools. The simulation based on RTL-DEVS methodology, which imitates Verilog’s testbench and behavioral module, confirmed through experiments that RTL simulation can be performed sufficiently through the code elaboration process. In multiple simulation results, Verilog simulation and RTL-DEVS-based simulation were able to output equivalent results under limited conditions. DEVS formalism-based modeling can be extended to other DEVS-based simulators when using model-type exchange tools, and this means that the advanced functions or classes of RTL simulation tools can be applied using higher-level language tools. Full article

Clustering algorithms are commonly used in the mining of static data. Some examples include data mining for relationships between variables and data segmentation into components. The use of a clustering algorithm for real-time data is much less common. This is due to a variety of factors, including the algorithm’s high computation cost. In other words, the algorithm may be impractical for real-time or near-real-time implementation. Furthermore, clustering algorithms necessitate the tuning of hyperparameters in order to fit the dataset. In this paper, we approach clustering moving points using our proposed Adaptive Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, which is an implementation of an adaptive approach to building the minimum spanning tree. We switch between the Boruvka and the Prim algorithms as a means to build the minimum spanning tree, which is one of the most expensive components of the HDBSCAN. The Adaptive HDBSCAN yields an improvement in execution time by 5.31% without depreciating the accuracy of the algorithm. The motivation for this research stems from the desire to cluster moving points on video. Cameras are used to monitor crowds and improve public safety. We can identify potential risks due to overcrowding and movements of groups of people by understanding the movements and flow of crowds. Surveillance equipment combined with deep learning algorithms can assist in addressing this issue by detecting people or objects, and the Adaptive HDBSCAN is used to cluster these items in real time to generate information about the clusters. Full article