Telecom

This paper investigates the impact of several hyperparameters on static malware detection using deep learning, including the number of epochs, batch size, number of layers and neurons, optimisation method, dropout rate, type of activation function, and learning rate. We employed the cAgen tool and grid search optimisation from the scikit-learn Python library to identify the best hyperparameters for our Keras deep learning model. Our experiments reveal that cAgen is more efficient than grid search in finding the optimal parameters, and we find that the selection of hyperparameter values has a significant impact on the model’s accuracy. Specifically, our approach leads to significant improvements in the neural network model’s accuracy for static malware detection on the Ember dataset (from 81.2% to 95.7%) and the Kaggle dataset (from 94% to 98.6%). These results demonstrate the effectiveness of our proposed approach, and have important implications for the field of static malware detection. Full article

In Vehicular Ad Hoc Networks (VANETs), promoting cooperative behavior is a challenging problem for mechanism designers. Cooperative actions, such as disseminating data, can seem at odds with rationality and may benefit other vehicles at a cost to oneself. Without additional mechanisms, it is expected that cooperative behavior in the population will decrease and eventually disappear. Classical game theoretical models for cooperation, such as the public goods game, predict this outcome, but they assume fixed population sizes and overlook the ecological dynamics of the interacting vehicles. In this paper, we propose an evolutionary public goods game that incorporates VANET ecological dynamics and offers new insights for promoting cooperation. Our model considers free spaces, population density, departure rates of vehicles, and randomly composed groups for each data sender. Theoretical analysis and simulation results show that higher population densities and departure rates, due to minimum differences between pay-offs of vehicles, promote cooperative behavior. This feedback between ecological dynamics and evolutionary game dynamics leads to interesting results. Our proposed model demonstrates a new extension of evolutionary dynamics to vehicles of varying densities. We show that it is possible to promote cooperation in VANETs without the need for any supporting mechanisms. Future research can investigate the potential for using this model in practical settings. Full article

There has been a fast deployment of wireless networks in recent years, which has been accompanied by significant impacts on the environment. Among the solutions that have been proven to be effective in reducing the energy consumption of wireless networks is the use of machine learning algorithms in cell traffic management. However, despite promising results, it should be noted that the computations required by machine learning algorithms have increased at an exponential rate. Massive computing has a surprisingly large carbon footprint, which could affect its real-world deployment. Thus, additional attention needs to be paid to the design and parameterization of these algorithms applied in order to reduce the energy consumption of wireless networks. In this article, we analyze the impact of hyperparameters on the energy consumption and performance of machine learning algorithms used for cell traffic prediction. For each hyperparameter (number of layers, number of neurons per layer, optimizer algorithm, batch size, and dropout) we identified a set of feasible values. Then, for each combination of hyperparameters, we trained our model and analyzed energy consumption and the resulting performance. The results from this study reveal a great correlation between hyperparameters and energy consumption, confirming the paramount importance of selecting optimal hyperparameters. A tradeoff between the minimization of energy consumption and the maximization of machine learning performance is suggested. Full article

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

Link adaptation (LA) matches transmission parameters to conditions on the radio link, and therefore plays a major role in telecommunications. Improving LA is within the requirements for next-generation mobile telecommunication systems, and by refining link adaptation, a higher channel efficiency can be achieved (i.e., an increased data rate thanks to lower required bandwidth). Furthermore, by replacing traditional LA algorithms, radio transmission systems can better adapt themselves to a dynamic environment. There are several drawbacks to current state-of-the-art approaches, including predefined and static decision boundaries or relying on a single, low-dimensional metric. Nowadays, a broadly used approach to handle a variety of related input variables is a neural network (NN). NNs are able to make use of multiple inputs, and when combined with reinforcement learning (RL), the so-called deep reinforcement learning (DRL) approach emerges. Using DRL, more complex parameter relationships can be considered in order to recommend the modulation and coding scheme (MCS) used in LA. Hence, this work examines the potential of DRL and includes experiments on different channels. The main contribution of this work lies in using DRL algorithms for LA, optimized for throughput based on a subcarrier observation matrix and a packet success rate feedback system. We apply Natural Actor-Critic (NAC) and Proximal Policy Optimization (PPO) algorithms on simulated channels with a subsequent feasibility study on a prerecorded real-world channel. Empirical results produced by experiments on the examined channels hint that Deep Reinforcement Learning for Link Adaptation (DRLLA) offers good performance indicated by a promising data rate on the additive white Gaussian noise (AWGN) channel, the non-line-of-sight (NLOS) channel, and a prerecorded real-world channel. No matter the channel impairment, the agent is able to respond to changing signal-to-interference-plus-noise-ratio (SINR) levels, as exhibited by expected changes in the effective data rate. Full article

In this paper, an embedded system is used as a host for the intercom and as a chatbot server for this system. The chatbot server controls door locks, cameras, buzzers, and related devices through web services on the WoT (Web of Things) to provide residents and visitors with better functionality and integrational services. This system can greatly improve the security and convenience of the system compared with the traditional intercom system. The resident uses the instant messaging software of the smartphone to replace the handset function, and there is no need to install and learn new apps, reducing the cost of the handset and the wiring indoors and outdoors. Whether or not the residents are at home, they can check whether there are visitors and check the status of their doors through their smartphones. Conversely, any visitor can also contact the resident through this intercom, while there is no way to confirm whether the resident is at home or not, which enhances the security of the house. This system provides flexibility in wireless installation and use and sufficient mobility for residents. The system architecture strikes a good balance between user convenience and home security and between performance and cost, effectively improving home security and reducing costs. Full article

Over the past fifty years, telecommunications has transformed, in an unprecedented manner, the way we live, work and communicate. For telecommunications, fixed networks are the one pillar and wireless networks the other. The wireless networks and their generations have been extensively investigated in the literature; however, the fixed networks lack a consistent exploration of their evolution. For this purpose, our motivation is to present a review of the evolution of fixed networks in a holistic manner, from various different perspectives, which can assist engineers and students to understand, in a better way, how broadband networks developed and the main features of the current networking environment. Specifically, in this review, we aim to shed light on the most important technologies, standards and milestones of the fixed networks from various perspectives, such as the service perspective, the networking perspective (both access and core part) and the physical layer perspective. Our study follows the timeline of the five generations of fixed networks defined by the European Telecommunications Standards Institute (ETSI) F5G group and discusses the key achievements and limitations of each generation for each different infrastructure layer and segment, designating the most important issues for the most widely-adopted technologies and standards. Finally, our analysis helps to reveal the technical challenges that need to be addressed by the fixed telecom network community, such as energy efficiency, capacity scaling, cost-efficiency, etc., while also revealing potential future directions. Full article

Smart meters (SM) with wireless capabilities are one of the most meaningful applications of the Internet of Things. Standards like Zigbee have found a niche in transmitting data on energy usage to the user and the supplier wirelessly via these meters and communication hubs. There are still certain difficulties, notably in delivering wireless connectivity to meters situated in difficult-to-reach locations such as basements or deep indoors. To solve this issue, this paper investigates the usage of mesh networks at 868 MHz, particularly to increase coverage, and proposes an additional mounted antenna to significantly increase outside coverage while providing the necessary coverage extension for hard-to-reach indoor locations. Extensive measurements were made in Newbury in both suburban and open environments for validation and delivery of a simple statistical model for the 868 MHz band in United Kingdom conurbations. Results presented in this paper estimate that mesh networks at 868 MHz can accommodate large areas constituting several SMs with the proposed coverage extension method. With our findings and proposed methods on mesh connectivity, only 1% of UK premises will require mesh radios to achieve the desired coverage. Full article