Search

Proactive content caching has emerged as a promising solution to cope with exponentially increasing mobile data traffic. The popular user contents can be cached near the network edge for faster retrieval and processing. Current state-of-the-art approaches adopt a centralized model training mechanism that requires high communication and data exchange overheads to predict content popularity. Moreover, these approaches fail to deal with the dynamicity of the environment since they do not take into account the users’ mobility information and are unable to incorporate content offload timings. In this paper, we address these limitations by proposing a novel federated learning-based Mobility and Demand-aware Proactive Content Offloading (MDPCO) framework. MDPCO exploits distributed learning strategies and capitalizes on users’ mobility and demand information for proactive content offloading. Extensive simulations are carried out to validate the efficacy of MDCPO against local and cloud-based models. Our proposed model yields an average performance improvement of 6.7% in comparison to the cloud-based model. Furthermore, with the increase in the number of fog servers, the MDPCO achieves a 9.8% higher data offloading ratio and 1.18% increase in the downlink rates while being more energy-efficient than cloud-based approaches.

For vehicle-to-network communications, handover (HO) management enables vehicles to maintain the connection with the network while transiting through coverage areas of different base stations (BSs). However, the high mobility of vehicles means shorter connection periods with each BS that leads to frequent HOs, hence raises the necessity for optimal HO decision making for high quality infotainment services. Machine learning is capable of capturing underlying pattern via data driven methods to find optimal solutions to complex problems, and much learning-based HO optimization research has been conducted focusing on specific network setups. However, attention still needs to be paid to the actual deployment aspect and standardized datasets or simulation environments for evaluation. This paper proposes a deep reinforcement learning-based HO algorithm using the input parameters that are configurable in the existing measurement report of cellular networks. The performance of the proposed algorithm is evaluated using the well-known network simulator ns-3 with its official LTE module. A realistic network setup in the city center of Glasgow (UK) is configured with vehicle trajectories generated by the routes mobility model using Google Maps Directions API. Evaluation results reveal that the proposed algorithm significantly outperforms the A3 RSRP baseline with an average of 25.72\% packet loss reduction per HO, suggesting significant improvement in quality of service of phone call and video streaming, etc. The proposed algorithm also has a small implementation cost compared to some state-of-the-art and should be deployed by a software update to a local BS controller.

This paper presents a framework for achieving machine learning (ML)‐assisted direction‐of‐arrival (DoA) accuracy enhancement using a millimetre‐wave (mmWave) dynamic aperture. The technique used for the enhanced DoA estimation accuracy leverages an over‐sized lens‐loaded cavity antenna connected to a single RF chain in the physical layer and a computational method in the computational layer of the framework. It is shown for the first time that by introducing a reconfigurable mode‐mixing mechanism inside the over‐sized lens‐loaded cavity hardware, a greater number of spatially orthogonal radiation modes can be achieved giving rise to many cavity states. If the best cavity state is determined and selected by means of design exploration using a contemporary ML‐assisted antenna optimisation method, the computational DoA estimation accuracy can be improved. The mode‐mixing mechanism in this work is a randomly oriented metallic scatterer located inside an over‐sized constant−ϵ r lens‐loaded cavity, connected to a stepper motor that is electronically controlled by inputs from the computational layer of the presented framework. Measurement results in terms of near‐field radiation mode scans are included in this study to verify and validate that the proposed ML‐assisted framework enhances the DoA estimation accuracy. Moreover, this investigation simultaneously provides a simplification in the physical layer implementation of mmWave radio hardware, and DoA accuracy enhancement, which in turn lends itself favourably to the adoption of the proposed framework for channel sounding in mmWave communication systems.

Early diagnosis of dental caries progression can prevent invasive treatment and enable preventive treatment. In this regard, dental radiography is a widely used tool to capture dental visuals that are used for the detection and diagnosis of caries. Different deep learning (DL) techniques have been used to automatically analyse dental images for caries detection. However, most of these techniques require large-scale annotated data to train DL models. On the other hand, in clinical settings, such medical images are scarcely available and annotations are costly and time-consuming. To this end, we present an efficient self-training-based method for caries detection and segmentation that leverages a small set of labelled images for training the teacher model and a large collection of unlabelled images for training the student model. We also propose to use centroid cropped images of the caries region and different augmentation techniques for the training of self-supervised models that provide computational and performance gains as compared to fully supervised learning and standard self-supervised learning methods. We present a fully labelled dental radiographic dataset of 141 images that are used for the evaluation of baseline and proposed models. Our proposed self-supervised learning strategy has provided performance improvement of approximately 6% and 3% in terms of average pixel accuracy and mean intersection over union, respectively as compared to standard self-supervised learning. Data and code will be made available to facilitate future research. 2023, The Author(s). This work is supported in parts by EPSRC grant no: EP/T021063/1 and Ajman University Internal Research Grants No. [2021-IRGDEN-7 and RTG-2022-DEN-01]. The research findings presented in this article are solely the author(s) responsibility. Scopus

While the fifth generation (5G) is bringing an innovative fabric of breakthrough technologies, enabling smart factories, cities, and Internet-of-Things (IoT), the unprecedented strain on communication networks put by these applications, in terms of highly cognitive, agile architectures and the support of massive connectivity, energy efficiency, and extreme ultralow latency, is pushing 5G to their limits. As such, the focus of academic and industrial efforts has shifted toward beyond 5G (B5G) and the conceptualization of sixth generation (6G) systems. This article discusses four main digital and societal use cases (UCs) that will drive the need to reconcile a new breed of network requirements. Based on this, we provide our vision of the fundamental architectural ingredients that will enable the promise of 6G networks of bringing the unification of experiences across the digital, physical, and human worlds. We outline key disruptive technological paradigms that will support 6G materialize a bouquet of unique expectations and redefine how we live and protect our planet. Finally, we adopt the recently envisaged ecosystem of the Internet-of-Musical Things (IoMusT) to depict how the discussed UCs and technological paradigms may be exploited to realize this ecosystem.

Semantic communication (SemCom), as an emerging paradigm focusing on meaning delivery, has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to wireless vehicular networks, which normally consume a tremendous amount of resources to meet stringent reliability and latency requirements. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to simultaneously realize efficient service provisioning for multiple users in vehicle-to-vehicle networks. To this end, this paper identifies and jointly addresses two fundamental problems of knowledge base construction (KBC) and vehicle service pairing (VSP) inherently existing in SemCom-enabled vehicular networks in alignment with the next-generation ultra-reliable and low-latency communication (xURLLC) requirements. Concretely, we first derive the knowledge matching based queuing latency specific for semantic data packets, and then formulate a latency-minimization problem subject to several KBC and VSP related reliability constraints. Afterward, a SemCom-empowered Service Supplying Solution (S$^{\text{4}}$) is proposed along with the theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the superiority of S$^{\text{4}}$ in terms of average queuing latency, semantic data packet throughput, user knowledge matching degree and knowledge preference satisfaction compared with two benchmarks., Comment: This paper has been submitted to IEEE Transactions on Wireless Communications for the second round of peer review after a major revision

Intelligent transportation systems increasingly depend on wireless communication, facilitating real-time vehicular communication. In this context, message authentication is crucial for establishing secure and reliable communication. However, security solutions must consider the dynamic nature of vehicular communication links, which fluctuate between line-of-sight (LoS) and non-line-of-sight (NLoS). In this paper, we propose a lightweight cross-layer authentication scheme that employs public-key infrastructure-based authentication for initial legitimacy detection while using keyed-based physical-layer re-authentication for message verification. However, the latter's detection probability (P_d) decreases with the reduction of the signal-to-noise ratio (SNR). Therefore, we examine using Reconfigurable Intelligent Surface (RIS) to enhance the SNR value directed toward the designated vehicle and consequently improve the P_d, especially for NLoS scenarios. We conducted theoretical analysis and practical implementation of the proposed scheme using a 1-bit RIS, consisting of 64 x 64 reflective units. Experimental results show a significant improvement in the P_d, increasing from 0.82 to 0.96 at SNR = - 6 dB for an orthogonal frequency division multiplexing system with 128 subcarriers. We also conducted informal and formal security analyses, using Burrows-Abadi-Needham (BAN)-logic, to prove the scheme's ability to resist passive and active attacks. Finally, the computation and communication comparisons demonstrate the superior performance of the proposed scheme compared to traditional crypto-based methods., Comment: 12 pages, 12 figures and 5 tables

This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track objects movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency.

In this paper, a compact, efficient and easy to fabricate wearable antenna integrated with Artificial Magnetic Conductor (AMC) is presented. Addition of slots and bevels/cuts in the rectangular monopole patch antenna yield a wide bandwidth along with band notches. The proposed antenna is backed with an AMC metasurface that changes the bidirectional radiation pattern to a unidirectional, thus, considerably reducing the Specific Absorption Ratio (SAR). The demonstrated antenna has a good coverage radiating away from the body and presents reduced radiation towards the body with a front-to-back ratio of 13 dB and maximum gain of 3.54 dB. The proposed design operates over a wide frequency band of 2.9 to 12 GHz (exceeding the designated 3.1−10.6 GHz Ultra-Wideband (UWB) band). The band notches were created using slots on the radiating patch in the sub-bands from 5.50 to 5.67 GHz and 7.16 to 7.74 GHz. The overall dimensions of the structure are 33 × 33 × 6.75 mm3. The antenna's radiation performance increased considerably with the addition of the AMC layer. The SAR values for the antenna are reduced by 85.3% when the AMC is used and are 0.083 W/kg which is well below the FCC SAR limits. The simple design, miniaturized profile, low SAR and wide operating bands with multiple band notches make the presented antenna an appealing choice for several UWB wearable body area network (WBAN) applications.

Securing safe driving for connected and autonomous vehicles (CAVs) continues to be a widespread concern, despite various sophisticated functions delivered by artificial intelligence for in-vehicle devices. Diverse malicious network attacks are ubiquitous, along with the worldwide implementation of the Internet of Vehicles, which exposes a range of reliability and privacy threats for managing data in CAV networks. Combined with the fact that the capability of existing CAVs in handling intensive computation tasks is limited, this implies a need for designing an efficient assessment system to guarantee autonomous driving safety without compromising data security. In this article we propose a novel framework, namely Blockchain-enabled intElligent Safe-driving assessmenT (BEST), which offers a smart and reliable approach for conducting safe driving supervision while protecting vehicular information. Specifically, a promising solution that exploits a long short-term memory model is introduced to assess the safety level of the moving CAVs. Then we investigate how a distributed blockchain obtains adequate trustworthiness and robustness for CAV data by adopting a byzantine fault tolerance-based delegated proof-of-stake consensus mechanism. Simulation results demonstrate that our presented BEST gains better data credibility with a higher prediction accuracy for vehicular safety assessment when compared with existing schemes. Finally, we discuss several open challenges that need to be addressed in future CAV networks., This article has been accepted for publication by IEEE Network. Copyright may be transferred without notice, after which this version may no longer be accessible

Based on people’s enthusiasm for swimming and their pursuit of swimming skill at a higher level, this paper proposes a non-contact motion guidance system based on optical motion capture technology, which aims to improve their swimming technique conveniently, scientifically and quickly. The swimming actions can be captured using one smartphone underwater, and the app on the phone will process the video and give suggestions based on the reference postures derived from statistics of professional swimmers for improvement of the swimming performance. In this system, the Canny operator was applied to detect the edge of the image captured from the swimming video, and the Hough line was implemented to recognize and locate the key position information which refers to the angle between knee and hip joints, and the angle between the trunk and horizontal plane. Furthermore, by comparing the key position information with the data of standard swimming, improvement measures can be given to improve the swimming skills of swimmers. As the result of the experiments, the system can accurately capture the swimmer’s movement state, locate the incorrect posture of the swimmer, and provide the corrective information in real-time. Although the paper only took breaststroke as an example, the proposed method can be extended to the other swimming styles.

The detail is available in the manuscript

No abstract available.

No abstract available.

One of the most significant applications of Internet of Things are smart meters with wireless capabilities. Smart gas and electricity meters can capture half-hourly pricing and consumption data and send automated meter readings to your energy provider, in contrast to regular meters that can only register a running total of energy used. However, the legacy regular meters were not installed with wireless connectivity in mind and are usually found in hard-to-reach places for wireless radio coverage. To understand these scenarios, this paper provides signal strength measurements conducted at the Building Research Establishment determining building penetration losses in both 900 and 2,100 MHz band. We then present a building penetration loss model using these measurements that is practical and cost effective when compared to traditional statistical propagation loss models.

Smart building technologies transform buildings into agile, sustainable, and health-conscious ecosystems by leveraging IoT platforms. In this regard, we have developed a Persuasive Energy Conscious Network (PECN) at the University of Glasgow to understand the user-centric energy consumption patterns in an agile workspace. PECN consists of desk-level energy monitoring sensors that enable us to develop user-centric models that can be exploited to characterize the normal energy usage behavior of an office occupant. In this study, we make use of staked long short-term memory (LSTM) to forecast future energy demands. Moreover, we employed statistical techniques to automate the detection of anomalous power consumption patterns. Our experimental results indicate that post-anomaly resolution leads to 6.37% improvement in the forecasting accuracy.

The dataset is about reading lips in a privacy preserving manner. In particular, radio frequency (RF) sensing was used to capture unique channel variation due to lip movements. USRP x300 was utilised equipped with the VERT2450 omnidirectional antenna and HyperLOG 7040 X used for reception and transmission respectively. Further, the same experiment was repeated with a Xethru UWB radar, where doppler frequency shifts due to lip movements are captured. We consider six classes for lip movements. Five vowels (a, e, i, o, u) and one empty class where lips were not moving. We are able to read lips even under face masks. Three subjects 1 male and 2 females participated in the experiments.

In recent years, research has focused on exploiting the inherent physical (PHY) characteristics of wireless channels to discriminate between different spatially separated network terminals, mitigating the significant costs of signature-based techniques. In this paper, the legitimacy of the corresponding terminal is firstly verified at the protocol stack’s upper layers, and then the re-authentication process is performed at the PHY-layer. In the latter, a unique PHY-layer signature is created for each transmission based on the spatially and temporally correlated channel attributes within the coherence time interval. As part of the verification process, the PHY-layer signature can be used as a message authentication code to prove the packet’s authenticity. Extensive simulation has shown the capability of the proposed scheme to support high detection probability at small signal-to-noise ratios. In addition, security evaluation is conducted against passive and active attacks. Computation and communication comparisons are performed to demonstrate that the proposed scheme provides superior performance compared to conventional cryptographic approaches.

Narrow Band Internet of Things (NB-IoT) is one of the drivers of industry 4.0 and the study of its wireless behavior in Indoor industrial environments has become important. This is because of the unfavorable conditions poised to wireless propagation as a result of the presence of heavy-duty equipment and the physical structure of industrial buildings. In this paper, an indoor industry was modeled to present some of the reflective characteristics and its effects on wireless propagation, particularly large-scale fading. The results obtained which include propagation paths, path loss, and impulse response showed how the environment affected the wireless transmission of NB-IoT. However, to mitigate this challenge, a collaborative scheme is introduced to improve the transmission among the affected NB-IoT terminals. The proposed scheme resulted in a collective improvement in path loss value by 30.44%.

This paper presents a millimeter-wave direction of arrival estimation (DoA) technique powered by dynamic aperture optimization. The frequency-diverse medium in this work is a lens-loaded oversized mmWave cavity that hosts quasi-random wave-chaotic radiation modes. The presence of the lens is shown to confine the radiation within the field of view and improve the gain of each radiation mode; hence, enhancing the accuracy of the DoA estimation. It is also shown, for the first time, that a lens loaded-cavity can be transformed into a lens-loaded dynamic aperture by introducing a mechanically controlled mode-mixing mechanism inside the cavity. This work also proposes a way of optimizing this lens-loaded dynamic aperture by exploiting the mode mixing mechanism governed by a machine learning-assisted evolutionary algorithm. The concept is verified by a series of extensive simulations of the dynamic aperture states obtained via the machine learning-assisted evolutionary optimization technique. The simulation results show a 25$$\%$$ % improvement in the conditioning for the DoA estimation using the proposed technique.

The prosperity of artificial intelligence (AI) has laid a promising paradigm of communication system, i.e., intelligent semantic communication (ISC), where semantic contents, instead of traditional bit sequences, are coded by AI models for efficient communication. Due to the unique demand of background knowledge for semantic recovery, wireless resource management faces new challenges in ISC. In this paper, we address the user association (UA) and bandwidth allocation (BA) problems in an ISC-enabled heterogeneous network (ISC-HetNet). We first introduce the auxiliary knowledge base (KB) into the system model, and develop a new performance metric for the ISC-HetNet, named system throughput in message (STM). Joint optimization of UA and BA is then formulated with the aim of STM maximization subject to KB matching and wireless bandwidth constraints. To this end, we propose a two-stage solution, including a stochastic programming method in the first stage to obtain a deterministic objective with semantic confidence, and a heuristic algorithm in the second stage to reach the optimality of UA and BA. Numerical results show great superiority and reliability of our proposed solution on the STM performance when compared with two baseline algorithms., This work has been accepted for publication by 2022 IEEE International Conference on Computer Communications (INFOCOM). Copyright may be transferred without notice, after which this version may no longer be accessible

The spectrum efficiency can be greatly enhanced by the deployment of machine-to-machine (M2M) communications through cellular networks. Existing resource allocation approaches allocate maximum resource blocks (RBs) for cellular user equipments (CUEs). However, M2M user equipments (MUEs) share the same frequency among themselves within the same tier. This results in generating co-tier interference, which may deteriorate the MUE’s quality-of-service (QoS). To tackle this problem and improve the user experience, in this paper, we propose a novel resource utilization policy, which exploits reinforcement learning (RL) algorithm considering the pointer network (PN). In particular, we design an optimization problem that determines the optimal frequency and power allocation needed to maximize the achievable rate performance of all M2M pairs and CUEs in the network subject to the co-tier interference and QoS constraints. The proposed scheme enables the user equipment (UE) to autonomously select an available channel and optimal power to maximize the network capacity and spectrum efficiency while minimizing co-tier interference. Moreover, the proposed scheme is compared with traditional spectrum allocation schemes. Simulation results demonstrate the superiority of the proposed scheme than that of the traditional schemes. Moreover, the convergence of the proposed scheme is investigated which reduces the computational complexity (CC).

Aiming at the self-association feature of the Hopfield neural network, we can reduce the\ud need for extensive sensor training samples during human behavior recognition. For a\ud training algorithm to obtain a general activity feature template with only one time data\ud preprocessing, this work proposes a data preprocessing framework that is suitable for\ud neuromorphic computing. Based on the preprocessing method of the construction matrix\ud and feature extraction, we achieved simplification and improvement in the classification of\ud output of the Hopfield neuromorphic algorithm. We assigned different samples to neurons\ud by constructing a feature matrix, which changed the weights of different categories to\ud classify sensor data. Meanwhile, the preprocessing realizes the sensor data fusion\ud process, which helps improve the classification accuracy and avoids falling into the\ud local optimal value caused by single sensor data. Experimental results show that the\ud framework has high classification accuracy with necessary robustness. Using the\ud proposed method, the classification and recognition accuracy of the Hopfield\ud neuromorphic algorithm on the three classes of human activities is 96.3%. Compared\ud with traditional machine learning algorithms, the proposed framework only requires\ud learning samples once to get the feature matrix for human activities, complementing\ud the limited sample databases while improving the classification accuracy.

In this paper, we model, analyze and optimize the multi-user and multi-order-reflection (MUMOR) intelligent reflecting surface (IRS) networks. We first derive a complete MUMOR IRS network model applicable for the arbitrary times of reflections, size and number of IRSs/reflectors. The optimal condition for achieving sum-rate upper bound with one IRS in a closed-form function and the analytical condition to achieve interference-free transmission are derived, respectively. Leveraging this optimal condition, we obtain the MUMOR sum-rate upper bound of the IRS network with different network topologies, where the linear graph (LG), complete graph (CG) and null graph (NG) topologies are considered. Simulation results verify our theories and derivations and demonstrate that the sum-rate upper bounds of different network topologies are under a K-fold improvement given K-piece IRS.

This paper presents for the first time a compact three-port Lorentz resonance-based microstrip sensor for permittivity and moisture sensing, as well as refractive index comparison. The proposed sensor is verified by simulations and experiments. The resonant frequency of the designed sensor is 2.24 GHz. The proposed sensor has a quality factor of 102, sensitivity of 45.4 mm/refractive index unit, and figure-of-merit of 34.6. The sensitivity of the proposed sensor is quite superior to other resonance based sensors and a fair comparison is presented in this work. The proposed sensor can be used to find the permittivity of dielectric materials, the moisture content of plants, as well as can be used as a refractive index comparator.

This paper proposes a wideband miniaturized 3 dB 180° rat-race coupler. For ease of fabrication and measurement, the operating frequency of the 180° coupler is designed from 700-960 MHz. Hence, an operating bandwidth of around 30% is achieved at the center frequency of 830 MHz. A size reduction of 95% is accomplished along with a fractional bandwidth of 30%. A meander line configuration is adopted and gaps between the lines are optimized to enhance the capacitance of the structure. The proposed design is fabricated on Rogers 4360 substrate and measured results of the prototype show good agreement with simulated results.

In anticipation of a rapid increase in wireless communication, MIMO is one key technology to be explored for 5G. Conventional approaches are unable to predict many of the key characteristics for MIMO channels, and more detailed methods suffer from significant computational complexity due to the number of antennas in a MIMO array. In this work, the beamforming performance for moving users in a large cell with effective channel throughput has been explored. The Glasgow University campus model is used to estimate channel properties when various beamforming techniques are implemented. The techniques explored are Maximum Ratio Transmission (MRT) (for transmitter), Equal Gain Combining (EGC), Selection Combining (SC), and Max Ratio Combining (MRC) (for receiver) beamforming in 3GPP Long Term Evolution (LTE). Throughput, received signal strength, and signal to noise interference ratio (SINR) are determined. By implementing the beamforming techniques, on average, we are able to improve the throughput from 9 Mbps to 14 Mbps. The best throughput/SINR has been observed with MRT-MRC in comparison to No-beamforming.

This paper proposes a coplanar waveguide feed (CPW-fed) based patch antenna for implantable biomedical applications operating at Industrial, Scientific, and Medical (ISM) band. The antenna has a magnetically symmetric configuration with dimensions of 30 mm ×30 mm l. In free space, the proposed antenna covers a wideband ranging from 2.39 - 2.57 GHz, thereby posing a -10 dB bandwidth of 180 MHz with a peak gain of 2.23 dB. For implant case, the antenna is simulated by creating a three-layered human tissue model in CST MWS 2019. The proposed design also offers -10 dB bandwidth covering the ISM band after implanting inside muscle layer with a peak gain value of -6.46 dB. A prototype of antenna is fabricated for measurement proposes. An ex-vivo testing of proposed design was performed by placing the fabricated antenna inside a fresh pork slab. The measured and simulated results demonstrate the significance of proposed antenna for wearable and implantable biomedical applications.

In recent years, the development of wireless network provides the hardware foundation for wireless perception within indoor environment. The wireless sensing system has been applied in many fields, such as intrusion detection, search rescue, gesture recognition and abnormal human activities detection such as falling, wandering behaviour, breathing and heart rates and so on. Among of them, Wi-Fi sensing has proven to be effective since it exploits off-the-shelf low-cost small wireless devices operating at 2.4 GHz frequency band and also a potential field for real-time sensing method for human recognition. This paper presents foundation for real-time Wi-Fi sensing system to detect various human activities when performed in indoor settings.

This paper presents a platform tolerant ultra-high radio frequency identification (UHF RFID) tag design for hard to tag supply chain items such as large metal and wooden containers. The proposed design consists is backed by multi-resonant surface carefully designed using characteristics mode analysis. As a result, this tag design features a long read range and covers most of UHF RFID frequency bands. The evaluation of the read range associated with fabricated prototype of proposed tag configuration is done through turn on power measurements using Tagformance Pro setup. A variety of subjects with different physical constitutions such as metal, wood and glass are considered in order to demonstrate the robustness of our solution. The proposed tag offers a relatively longer reader range of 12.5 m after mounting on piece of metal, wood and glass materials. Therefore, this tag design is suitable candidate for supply chain visibility and cargo monitoring.

The rise of antenna technology, smartphones, and the Internet-of-things (IoT) has enabled wearable antennas for wireless communication between implantable devices such as pacemakers, infusion pumps, etc., and external devices for health monitoring. This work describes the key challenges that need to be addressed for such wireless body area network (WBAN) technologies to be integrated into large-scale health monitoring programs. These include the miniaturization of antennas, fabrication techniques to enable mass production, and methods to protect patients from data infringement and hackers. Furthermore, the role of wearable and implantable antennas is pivotal to realize devices for continuous healthcare monitoring especially during Pandemic situations such as Coronavirus Disease-2019 (COVID-19).

Beamforming is a cellular base station traffic-signaling system that determines the most effective data-delivery path for a specific user while reducing interference for neighboring users, and it has now become an integral technology in modern wireless communication systems. Contextual beamforming has evolved as an intelligent method in which the location information of a mobile user is predicted based on prior knowledge, and subsequently reconfigure the antenna arrays. Such a system relies heavily on a machine and deep learning applied on the data gathered to infer the context. This paper presents an antenna beamforming approach that exploits the location information of a moving mobile user in a mobile network. Our results show that network parameters such as the received power and propagation path information can assist to construct a machine learning-based adaptive beamforming framework.

With the advent of Coronavirus Disease 2019 (COVID-19), the world encountered an unprecedented health crisis due to the severe acute respiratory syndrome (SARS) pathogen. This impacted all of the sectors but more critically the transportation sector which required a strategy in the light of mobility trends using transportation modes and regions. We analyse a mobility prediction model for smart transportation by considering key indicators including data selection, processing and, integration of transportation modes, and data point normalisation in regional mobility. A Machine Learning (ML) driven classification has been performed to predict transportation modes efficiency and variations using driving, walking and transit. Additionally, regional mobility by considering Asia, Europe, Africa, Australasia, Middle-East, and America has also been analysed. In this regard, six ML algorithms have been applied for the precise assessment of transportation modes and regions. The initial experimental results demonstrate that the majority of the world's travelling dynamics have been contrastively shaped with the accuracy of 91.21% and 84.5% using Support Vector Machine (SVM) and Random Forest (RT) for different transportation modes and regions. This study will pave a new direction for the assessment of transportation modes affected by the pandemic to optimize economic benefits for smart transportation.

Falls are a major health concern for the elderly as it threatens their livelihood and independence. Nearly 50% of the older adults, aged over 65 years old, fall in a span of 5 years, with 62% sustaining injuries and 28% extensive protracting injuries. This paper presents a high accuracy contactless falls detection framework based on channel state information extracted from software-defined radios. The aim is to develop a system capable of detecting whether an individual subject is present within the sensing area, or if the subject is falling, and, finally, if the subject is performing one of three other activities, including sitting, standing, and walking. The results showed a promising detection accuracy of 95.6% and 98%, using the 10-fold cross-validation and train-test split methods, based on the Random Forest classifier, respectively. Furthermore, we present a real-time analysis of the system to highlight its capability to detect, analyze, and report falls in real-time.

New technological advancements in wireless networks have enlarged the number of connected devices. The unprecedented surge of data volume in wireless systems empowered by artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloudcentric machine learning (ML)-based services are implemented by centrally collecting datasets and training models. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost and (ii) threatened data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of federated learning (FL), a newly emerging technique coined to bring ML to the edge of wireless networks. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth-generation (B5G) and sixth-generation (6G) communication systems. This survey aims to provide an overview of the state-ofthe-art FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.

As we move into the third decade of the 21st century, the 2020s, the unprecedented rate of technological disruption and the short-lived nature of the specifics of engineering state-of-the-art require us to carefully evaluate what it takes to be an effective engineer and what this entails for engineering education and their lifelong learning. While it is true that certain basics of engineering will not change, there will be an increased premium for some skills (such as lifelong learning, meta-learning, collaboration, creativity, critical thinking, communication skills, and cultural/global literacy). 21st-century skills are, as such, timeless skills: it is paradoxically the volatile nature of the modern world that has forced us from ephemeral vocational fads back to these permanently valuable skills. In this full research-to-practice paper, after reporting on the skills that policy think tanks and thought leaders deem necessary for the 21st century, we provide a synthesis in which we describe the pulls and pushes that learners and educators will face in the turbulent times of 2020 and beyond, and how they can thrive in the uncertain future through holistic well-rounded engineering education.

COVID-19 disease, caused by SARS-CoV-2, has resulted in a global pandemic recently. With no approved vaccination or treatment, governments around the world have issued guidance to their citizens to remain at home in efforts to control the spread of the disease. The goal of controlling the spread of the virus is to prevent strain on hospital. In this paper, we have focus on how non-invasive methods are being used to detect the COVID-19 and assist healthcare workers in caring for COVID-19 patients. Early detection of the COVID-19 virus can allow for early isolation to prevent further spread. This study outlines the advantages and disadvantages and a breakdown of the methods applied in the current state-of-the-art approaches. In addition, the paper highlights some future research directions, which are required to be explored further to come up with innovative technologies to control this pandemic., Comment: 10 pages, 6 figures. Submitted as journal paper

Distributed systems are crucial to the full realization of the Internet of Thing (IoT) ecosystem as it mitigates the challenges of trust, security, and scalability associated with the traditional centralized approach. In this paper, we present an analytical modeling framework for Practical Byzantine Fault Tolerance (PBFT)-a consensus method for blockchain in IoT networks. We define the viable area for the wireless PBFT networks which guarantees the minimum number of replica nodes required for achieving the protocol's safety and liveliness. We also present an analytical framework for obtaining the viable area which we later utilize for power optimization. Results show that significant energy saving can be achieved with the utilization of the viable area concept in wireless PBFT networks. The proposed framework can serve as a theoretical guidance for practical PBFT based wireless blockchain network deployment.

With the huge advancement of nanotechnology over the past years, the devices are shrinking into micro-scale, even nano-scale. Additionally, the Internet of nano-things (IoNTs) are generally regarded as the ultimate formation of the current sensor networks and the development of nanonetworks would be of great help to its fulfilment, which would be ubiquitous with numerous applications in all domains of life. However, the communication between the devices in such nanonetworks is still an open problem. Body-centric nanonetworks are believed to play an essential role in the practical application of IoNTs. BCNNs are also considered as domain specific like wireless sensor networks and always deployed on purpose to support a particular application. In these networks, electromagnetic and molecular communications are widely considered as two main promising paradigms and both follow their own development process. In this survey, the recent developments of these two paradigms are first illustrated in the aspects of applications, network structures, modulation techniques, coding techniques and security to then investigate the potential of hybrid communication paradigms. Meanwhile, the enabling technologies have been presented to apprehend the state-of-art with the discussion on the possibility of the hybrid technologies. Additionally, the inter-connectivity of electromagnetic and molecular body-centric nanonetworks is discussed. Afterwards, the related security issues of the proposed networks are discussed. Finally, the challenges and open research directions are presented.

The need for ubiquitous coverage and the increasing demand for high data rate services, keeps constant pressure on the cellular network infrastructure. There has been intense research to improve the system spectral efficiency and coverage, and a significant part of this effort focused on developing and optimizing the multiple access techniques. One such technique that has been recently proposed is the low density spreading (LDS), which manages the multiple access interference to offer efficient and low complexity multiuser detection. The LDS technique has shown a promising performance as a multiple access technique for cellular systems. This chapter will give an overview on the LDS multiple access technique. The motivations for the LDS design will be highlighted by comparing it to conventional spreading techniques, including brief history of the early work on LDS. Furthermore, a background on the design of LDS in multicarrier communications, such as signatures design, a belief propagation multiuser detection, etc., will be presented along with the challenges and opportunities associated with the multicarrier LDS multiple access.

In this paper, using the stochastic geometry, we develop a tractable uplink modeling framework for the outage probability of both the single and multi-tier millimeter wave (mmWave) cellular networks. Each tier's mmWave base stations (BSs) are randomly located and they have particular spatial density, antenna gain, receiver sensitivity, blockage parameter and pathloss exponents. Our model takes account of the maximum power limitation and the per-user power control. More specifically, each user, which could be in line-of-sight (LOS) or non-LOS to its serving mmWave BS, controls its transmit power such that the received signal power at its serving BS is equal to a predefined threshold. Hence, a truncated channel inversion power control scheme is implemented for the uplink of mmWave cellular networks. We derive closed-form expressions for the signal-to-interference-and-noise-ratio (SINR) outage probability for the uplink of both the single and multi-tier mmWave cellular networks. Furthermore, we analyze the case with a dense network by utilizing the simplified model, where the LOS region is approximated as a fixed LOS disc. The results show that imposing a maximum power constraint on the user significantly affects the SINR outage probability in the uplink of mmWave cellular networks., Comment: 11 Figures

Index modulation (IM) has recently emerged as a promising concept for spectrum and energy-efficient next generation wireless communications systems since it strikes a good balance among error performance, complexity, and spectral efficiency. IM technique, when applied to multicarrier waveforms, yields the ability to convey the information not only by M-ary signal constellations as in conventional multicarrier systems but also by the indexes of the subcarriers, which are activated according to the incoming bit stream. Although IM is well studied for OFDM based systems, FBMC with index modulation has not been thoroughly investigated. In this paper, we shed light on the potential and implementation of IM technique for QAM based FBMC system. We start with a mathematical model of the IM based QAM-FBMC system (FBMC/QAM-IM) along with the derivation of interference terms at the receiver due to channel distortions and noise. The interference terms including the ones introduced by the multipath channel are analyzed in terms of MSE and output SINR. It is shown with analytical and simulation results that the interference power in FBMC/QAM-IM is smaller compared to that of the conventional FBMC/QAM system as some of the subcarriers are inactive. The performance of FBMC/QAM with IM is investigated by comparing the SIR and output SINR with that of the conventional FBMC/QAM system along with the BER performance which shows that the FBMC/QAM-IM is a promising transmission technique for future wireless networks.

2014 Muhammad Ali Imran (B111 10 438), Tinjauan Psikologi Hukum Terhadap Tindak Pidana Yang Dilakukan Oleh Anak Di Kota Makassar dibawah bimbingan Musakkir dan Wiwie Heryani. Penelitian ini bertujuan untuk mengetahui jenis dan terjadinya tindak pidana yang dilakukan oleh anak dalam perspektif psikologi hukum dan upaya penanganan oleh aparat penegak hukum terhadap anak dibawah umur yang melakukan tindak pidana di kota Makassar. Penelitian ini dilaksanakan di Kota Makassar yang mengambil lokasi penelitian di kantor Kepolisian Resort Kota Besar (Polrestabes) Makassar dan Pengadilan Negeri (PN) Makassar dengan melakukan wawancara terhadap pihak-pihak terkait serta melakukan pengumpulan data berkenaan dengan objek penelitian. Temuan yang diperoleh dari penelitian ini, adalah bentuk dan jenis tindak pidana yang dilakukakan oleh anak di kota Makassar selama 4 tahun terakhir ini mengalami peningkatan hal ini dipengaruhi oleh faktor rendahnya tingkat pendidikan anak, kurangnya kedekatan dengan keluarga, lingkungan tempat tinggal yang buruk, dan pergaulan anak yang kurang baik sehingga membuat anak terpaksa berkonflik dengan hukum. Mengingat pelaku tindak pidana masih dibawah umur sehingga perlu penanganan dan pendekatan khusus oleh aparat penegak hukum dalam melaksanakan proses penyelidikan dan penyidikan baik di tingkat kepolisian maupaun dalam proses pemeriksaan dan persidangan di pengadilan, selama proses penanganan, aparat penegak hukum telah berusah sebaik mungkin sesuai dengan peraturan perundangan-undangan yang berlaku yaitu Undang Undang No. 3 Tahun 1997 Tentang Peradilan Anak dan memperhatikan hak-hak anak selama proses pemeriksaan sesuai dengan Undang-Undang No. 23 Tahun 2002 Tentang Perlindungan Anak. Namun ada beberapa kendala yang dialami oleh aparat penegak hukum dalam menangani anak yang berkonflik dengan hukum, diantaranya kuranganya sarana dan prasarana yang diperlukan selama dalam proses penanganan oleh aparat penegak hukum, fasilitas pembinaan terhadap anak yang berkonflik dengan hukum belum tersedia di kota Makassar. Adapun pada tanggal 30 Juli 2014 akan berlaku secara efektif Undang-Undang No. 11 Tahun 2012 Tentang Sistem Peradilan Pidana Anak (SPPA) yang akan menggantikan Undang-Undang No. 3 Tahun 1997 Tentang Pengadialan Anak, diharapkan dengan berlakunya UU SPPA ini maka penanganan terhadap anak yang berkonflik dengan hukum di Indonesia menjadi lebih baik mengingat anak merupakan generasi penerus dan masa depan bangsa.

This chapter proposes the combination of temporal processing through Rake combining based on direct sequence-spread spectrum (DS-SS), and multiple antenna beamforming or antenna spatial diversity as a possible physical layer access technique for fifth generation (5G) small cell base stations (SBS) operating in the millimetre wave (mmWave) frequencies. Unlike earlier works in the literature aimed at previous generation wireless, the use of the beamforming is presented as operating in the radio frequency (RF) domain, rather than the baseband domain, to minimise power expenditure as a more suitable method for 5G small cells. Some potential limitations associated with massive multiple input-multiple output (MIMO) for small cells are discussed relating to the likely limitation on available antennas and resultant beamwidth. Rather than relying, solely, on expensive and potentially power hungry massive MIMO (which in the case of a SBS for indoor use will be limited by a physically small form factor) the use of a limited number of antennas, complimented with Rake combining, or antenna diversity is given consideration for short distance indoor communications for both the SBS) and user equipment (UE). The proposal’s aim is twofold: to solve eroded path loss due to the effective antenna aperture reduction and to satisfy sensitivity to blockages and multipath dispersion in indoor, small coverage area base stations. Two candidate architectures are proposed. With higher data rates, more rigorous analysis of circuit power and its effect on energy efficiency (EE) is provided. A detailed investigation is provided into the likely design and signal processing requirements. Finally, the proposed architectures are compared to current fourth generation long term evolution (LTE) MIMO technologies for their anticipated power consumption and EE.

This paper presents the performance evaluation of Direction of Arrival (DOA) estimation techniques such as Root-MUSIC, Root-WSF and Beamspace-ESPRIT (BS-ESPRIT) algorithms under common Wi-Fi conditions such as operating in the IEEE radio frequency spectrum of 5 GHz band, the presence of noise and correlated, closely sourced signals. In addition, we will consider the computational time required for these algorithms to complete the estimation process. Results have shown that the 3 techniques provide a mean of 98.8% accuracy in detecting closely-sourced signals. Although Root-WSF has the longest average computational time of 36.7 ms as opposed to BS-ESPRIT and Root-MUSIC at 29.4 ms and 22.5 ms respectively, Root-WSF technique performed best overall especially in detecting coherent signals with 99.78% accuracy as compared to BS-ESPRIT at 95.27% and Root-MUSIC at 64.77%.

In cognitive radio network, secondary (unlicensed) users (SUs) are allowed to utilize the licensed spectrum when it is not used by the primary (licensed) users (PUs). Because of the dynamic nature of cognitive radio network, the activities of SUs such as "how long to sense" and "how long to transmit" significantly affect both the service quality of the cognitive radio networks and protection to PUs. In this work, we formulate and analyze spectrum utilization efficiency problem in the cognitive radio network with various periodic frame structure of SU, which consists of sensing and data transmission slots. Energy detection is considered for spectrum sensing algorithm. To achieve higher spectrum utilization efficiency, the optimal sensing and data transmission length are investigated and found numerically. The simulation results are presented to verify the our analysis and to evaluate the interference to the PU which should be controlled into tolerable level.

13th European Conference on Antennas and Propagation (EuCAP) -- MAR 31-APR 05, 2019 -- Krakow, POLAND Ilyas, Muhammad/0000-0002-3207-451X; Imran, Muhammad/0000-0003-4743-9136; Abbasi, Qammer H./0000-0002-7097-9969 WOS:000480384700008 This article presents mathematical channel modeling for in-vivo communication in terms of the change in the position of the ex-vivo antenna at ultra wideband frequencies. It is shown that the location dependent characteristics are not only dependent on the in-vivo devices placed inside the human body but also the position of ex-vivo devices can impact the channel. Results are calculated using the mathematical model and curve fitting technique by calculation of the Root Mean Square Error (RMSE). The statistics of error prediction among the measured data and the proposed model is RMSE = 14.46 for the right lateral and RMSE = 11.63 for the left lateral respectively. These results will help system designer in accurate link budget calculation for designing enhance implantable devices.

This chapter focuses on the 5G key concepts and how they can be extremely beneficial in supporting the advanced smart grid services. It introduces the smart grid environment and discusses some of the future services that will be supported in the future smart grids. These services are broadly classified into two categories, namely data collection and management services that target enhanced grid monitoring capabilities, and control and operation services that deal with demand side management and electric vehicle charging and discharging coordination. The chapter illustrates how the 5G novel concepts such as software‐defined networking, network virtualization, and cloud computing offer enhanced services for grid monitoring, data processing, demand‐side management, and electric vehicle charging and discharging coordination. It also illustrates a summary of the application of these concepts in supporting the smart grid services. Future research directions are discussed to deal with the open challenging issues.