Short-range Communication Technology Research Articles

Interference mitigation using optimised angle diversity receiver in LiFi cellular network

Light-fidelity (LiFi) is an emerging technology for high-speed short-range mobile communications. Inter-cell interference (ICI) is an important issue that limits the system performance in an optical attocell network. Angle diversity receivers (ADRs) have been proposed to mitigate ICI. In this paper, the structure of pyramid receivers (PRs) and truncated pyramid receivers (TPRs) are studied. The coverage problems of PRs and TPRs are defined and investigated, and the lower bound of field of view (FOV) for each PD is given analytically. It is shown that the lower bound of FOV for TPR and PR are 20° and 30°, respectively. The impact of random device orientation and diffuse link signal propagation are taken into consideration. The performances of PRs and TPRs are compared, and optimised ADR structures are proposed by jointly considering the impact of tilt angle, FOV, and the number of PDs. For a transmitter-bandwidth limited system, the optimal PD values are 6 for PR and 9 for TPR, whereas, for a receiver-bandwidth limited system, the optimal PD value is 15. In addition, the double source (DS) cell system, where each LiFi AP consists of two sources transmitting the same information signals but with opposite polarity, is proved to outperform the single source cell (SS) system in interference limited or noise-plus-interference limited scenario. However, the SS cell system outperforms the DS cell system in a noise-limited scenario.

Exploring the Potential and Challenges of Vehicular Ad Hoc Networks: A Comprehensive Review

The paper discusses the architecture of vehicular ad hoc networks (VANETs) and their applications, simulation tools, communication domains, and challenges. VANETs enable wireless communication between moving vehicles using dedicated short-range communication (DSRC) technology, and vehicle-to-infrastructure communication with roadside units. The paper describes the three main components of VANETs: the roadside unit (RSU), application unit (AU), and on-board unit (OBU). The purpose of the paper is to provide researchers and developers with detailed information about VANETs and help them understand their aspects and challenges. The information provided in the paper could facilitate the development of VANETs for enhancing road safety and providing comfortable driving experiences.

An efficient ultra-wideband digital transceiver for wireless applications on the field-programmable gate array platform

<p>The ultra-wideband (UWB) technology is a promising short-range communication technology for most wireless applications. The UWB works at higher frequencies and is affected by interferences with the same frequency standards. This manuscript has designed an efficient and low-cost implementation of IEEE 802.15.4a-based UWB-digital transceiver (DTR). The design module contains UWB transmitter (TX), channel, and UWB-receiver (RX) units. Convolutional encoding and modulation units like burst position modulation and binary phase-shift keying modulation are used to construct the UWB-TX. The synchronization and Viterbi decoder units are used to recover the original data bits and are affected by noise in UWB-RX. The UWB-DTR is synthesized using Xilinx ISE<sup>®</sup> environment with Verilog hardware description language (HDL) and implemented on Artix-7 field-programmable gate array (FPGA). The UWB-DTR utilizes less than 2% (slices and look-up table/LUTs), operates at 268 MHz, and consumes 91 mW of total power on FPGA. The transceiver achieves a 6.86 Mbps data rate, which meets the IEEE 802.15.4a standard. The UWB-DTR module obtains the bit error rate (BER) of 2×10<sup>-4</sup> by transmitting 105 data bits. The UWB-DTR module is compared with similar physical layer (PHY) transceivers with improvements in chip area (slices), power, data rate, and BER. </p>

Overview of Food Preservation and Traceability Technology in the Smart Cold Chain System.

According to estimates by the Food and Agriculture Organization of the United Nations (FAO), about a third of all food produced for human consumption in the world is lost or wasted-approximately 1.3 billion tons. Among this, the amount lost during the storage stage is about 15-20% for vegetables and 10-15% for fruits. It is 5-10% for vegetables and fruits during the distribution stage, resulting in a large amount of resource waste and economic losses. At the same time, the global population affected by hunger has reached 828 million, exceeding one-tenth of the total global population. The improvement of the cold chain system will effectively reduce the amount of waste and loss of food during the storage and transportation stages. Firstly, this paper summarizes the concept and development status of traditional preservation technology; environmental parameter sensor components related to fruit and vegetable spoilage in the intelligent cold chain system; the data transmission and processing technology of the intelligent cold chain system, including wireless network communication technology (WI-FI) and cellular mobile communication; short-range communication technology, and the low-power, wide-area network (LPWAN). The smart cold chain system is regulated and optimized through the Internet of Things, blockchain, and digital twin technology to achieve the sustainable development of smart agriculture. The deep integration of artificial intelligence and traditional preservation technology provides new ideas and solutions for the problem of food waste in the world. However, the lack of general standards and the high cost of the intelligent cold chain system are obstacles to the development of the intelligent cold chain system. Governments and researchers at all levels should strive to highly integrate cold chain systems with artificial intelligence technology, establish relevant regulations and standards for cold chain technology, and actively promote development toward intelligence, standardization, and technology.

Digital Contact Tracing for COVID-19

The spread of the COVID-19 virus across the globe has permanently changed life for billions of people. Manual contact tracing has been utilized to assist in limiting the spread of contagious diseases for many years. The ubiquitous use of smartphones and similar wireless devices has allowed this process to become digital in nature through contact tracing applications installed on these devices. Various countries, and even various regional units within those countries, developed contact tracing applications. Such applications relied on location and short-range communication technologies associated with wireless devices and found varying degrees of success. This work reviews research conducted by universities, governmental organizations, and other entities with respect to the adoption, use, and ultimate success of, digital contact tracing applications across various countries and points to their rather limited success in fighting the spread of the disease. The authors also briefly discuss some implications regarding privacy and security that affected their use in certain countries.

Adaptive Information Hiding Method Based on Feature Extraction for Visible Light Communication

Visible Light Communication (VLC) is an emerging short-range optical communication technology that can alleviate spectrum congestion. However, VLC faces security problems with man-in-the-middle hijacking and bypass listening. Since the covert transmission of visible light information has the characteristics of strong concealment and difficult detection, VLC based on information hiding becomes a new paradigm to solve these security problems. This paper presents a method in which Red, Green, Blue (RGB) Light-emitting Diode (LED) is used to secure the VLC by using the information hiding technique. Specifically, a gammatone filter bank-based feature extraction method is suggested. This method extracts robust vector for information hiding and extraction. To hide and extract the information, a dither modulation information hiding scheme based on the adaptive optimization is proposed. The presented method uses dither modulation to hide information in the extracted feature vector, which can effectively improve the success rate of information extraction. On the basis of performance analysis, the superiority of the presented method is illustrated by comparing with the existing methods.

A Modular In-Vehicle C-ITS Architecture for Sensor Data Collection, Vehicular Communications and Cloud Connectivity.

The growth of the automobile industry in recent decades and the overuse of personal vehicles have amplified problems directly related to road safety, such as the increase in traffic congestion and number of accidents, as well as the degradation of the quality of roads. At the same time, and with the contribution of climate change effects, dangerous weather events have become more common on road infrastructure. In this context, Cooperative Intelligent Transport Systems (C-ITS) and Internet of Things (IoT) solutions emerge to overcome the limitations of human and local sensory systems, through the collection and distribution of relevant data to Connected and Automated Vehicles (CAVs). In this paper, an intra- and inter-vehicle sensory data collection system is presented, starting with the acquisition of relevant data present on the Controller Area Network (CAN) bus, collected through the vehicle's On-Board-Diagnostics II (OBD-II) port, as well as on an on-board smartphone device and possibly other additional sensors. Short-range communication technologies, such as Bluetooth Low Energy (BLE), Wi-Fi, and ITS-G5, are employed in conjunction with long-range cellular networks for data dissemination and remote cloud monitoring. The results of the experimental tests allow the analysis of the road environment, as well as the notification in near real-time of adverse road conditions to drivers. The developed data collection system reveals itself as a potentially valuable tool for improving road safety and to iterate on the current Road Weather Models (RWMs).

RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks

Bluetooth Low Energy (BLE) is a wireless technology for low-power, low-cost and lowcomplexity short-range communications. On top of the BLE stack, the Bluetooth Mesh profile can be adopted to handle large networks with mesh topologies. BLE is a promising candidate for the implemention of Industrial Wireless Sensor Networks (IWSNs), thanks to its wide diffusion (e.g., on smartphones and tablets) and the lower cost of the devices compared to other wireless industrial communication technologies. However, neither the BLE nor the Bluetooth Mesh specifications can provide real-time messages with bounded delays. To overcome this limitation, this work proposes RESEMBLE, a real-time stack developed on top of BLE that is able to realize low-cost IWSNs over mesh topologies. RESEMBLE offers support to both real-time and non-real-time communications on the same network. Moreover, RESEMBLE provides clock synchronization, thus allowing for Time Division Multiple Access (TDMA) transmissions. The clock synchronization provided by RESEMBLE can be also exploited by the upper layers’ industrial applications to implement timecoordinated actions.

Machine Learning-Assisted OFDM-Based DSRC Communication Systems

The automotive industry has developed Dedicated Short-Range Communication (DSRC) technology for specific Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication applications. However, the effectiveness of DSRC communication is highly dependent on other integrated standards for interoperability, which is still room for research, especially under high mobility. Deep learning has recently played a crucial role in boosting the system performance in 5G-and-beyond networks. This paper utilizes deep learning to improve the channel estimation quality of DSRC systems under time-variant and frequency-selective channels and applying a post filtering process to enhance the quality of reconstructed images. We consider an OFDM-based system where the propagation channels are roughly estimated at the receiver by a low-cost least squares method. Then, the channel estimation quality is enhanced by a data-driven approach exploiting supervised learning. Numerical results manifest the added benefits of deep learning for improving the channel estimation quality and boosting the Bit Error Ratio (BER) compared to the traditional estimation methods. Besides, a post-filter is necessary to remove artifacts and residual errors in recovered image data. Quantitatively, the support of deep learning improves the channel estimation quality by about 30%. At the same time, the post-filtering process enhances the reconstruction quality in terms of the Peak-Signal-to-Noise Ratio (PSNR) up to 4dB.

Application of Internet of Things (IoT) in Sustainable Supply Chain Management

The traditional supply chain system included smart objects to enhance intelligence, automation capabilities, and intelligent decision-making. Internet of Things (IoT) technologies are providing unprecedented opportunities to enhance efficiency and reduce the cost of the existing system of the supply chain. This article aims to study the prevailing supply chain system and explore the benefits obtained after smart objects and embedded networks of IoT are implanted. Short-range communication technologies, radio frequency identification (RFID), middleware, and cloud computing are extensively comprehended to conceptualize the smart supply chain management system. Moreover, manufacturers are achieving maximum benefits in terms of safety, cost, intelligent management of inventory, and decision-making. This study also offers concepts of smart carriage, loading/unloading, transportation, warehousing, and packaging for the secure distribution of products. Furthermore, the tracking of customers to convince them to make more purchases and the modification of shops with the assistance of the Internet of Things are thoroughly idealized.

Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System.

Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97-99.5% based on the F1 scores.

Benchmarking 4G and 5G-Based Cellular-V2X for Vehicle-to-Infrastructure Communication and Urban Scenarios in Cooperative Intelligent Transportation Systems

Vehicle-to-Infrastructure (V2I) communication is expected to bring tremendous benefits in terms of increased road safety, improved traffic efficiency and decreased environmental impact. In 2017, The 3rd Generation Partnership Project (3GPP) released 3GPP Release 14, which introduced Cellular Vehicle-to-Everything communication (C-V2X), bringing Vehicle-to-Everything (V2X) communication capabilities to cellular networks, hence creating an alternative to Dedicated Short-Range Communications (DSRC) technology. Since then, every new 3GPP Release including Release 15, a first full set of 5G standards, offered V2X capabilities. In this paper, we present a complex simulation study, which benchmarks the performance of LTE-based and 5G-based C-V2X technologies deployed for V2I communication in an urban setting. The study compares LTE and 5G deployed both in the Device-to-Device in mode 3 and in infrastructural mode. Target performance indicators used for comparison are average end-to-end (E2E) latency and Packet Delivery Ratio (PDR). The performance of those technologies is studied under varying communication conditions realized by a variation of vehicle traffic intensity, communication perimeter and message generation frequency. Furthermore, the effects of infrastructure deployment density on the performance of selected C-V2X communication technologies are explored by comparing the performance of the investigated technologies for three infrastructure density scenarios, i.e., involving two, four and eight base stations (BSs). The performance results are put into a context of the connectivity requirements of the most popular V2I communication services. The results indicate that both C-V2X technologies can support all the considered V2I services without any limitations in terms of the communication perimeter, traffic intensity and message generation frequency. When it comes to the infrastructure density deployment, the results show that increasing the density of the infrastructure deployment from two BSs to four BSs offers a remarkable performance improvement for all the considered V2I services as well as investigated technologies and their modes. Further infrastructure density increase (from four BSs to eight BSs) does not yield any practical benefits in the investigated urban scenario.

Resonant Tunneling Diodes High-Speed Terahertz Wireless Communications - A Review

Resonant tunneling diode (RTD) technology is emerging as one of the promising semiconductor-based solid-state technologies for terahertz (THz) wireless communications. This article provides a review of the state of the art, with a focus on the THz RTD oscillator, which is the key component of RTD-based THz transmitters and coherent receivers. A brief summary on the device principle of operation, technology, modeling, as well as an overview of oscillator design and implementation approaches for THz emitters, is provided. A new insight to device evaluation and to the reported oscillator performance levels is also given, together with brief remarks on RTD-based THz detectors. Thereafter, an overview of the reported wireless links which utilize an RTD in either transmission or reception, or in both roles, is given. Highlight results include the record single-channel wireless data rate of 56 Gb/s employing an all RTD-based transceiver, which demonstrates the potential of the technology for future short-range communications. The article concludes with a discussion of the current technical challenges and possible strategies for future progress.

ОСОБЛИВОСТІ ПОБУДОВИ ПІДСИСТЕМИ ЗВ’ЯЗКУ ТА АВТОМАТИЗАЦІЇ ТИЛОВИХ НАЗЕМНИХ РОБОТИЗОВАНИХ КОМПЛЕКСІВ В УМОВАХ ГРУПОВОГО ЗАСТОСУВАННЯ

The article discusses the problems associated with the need to use logistic ground robotic systems for the needs of the Ground Forces of the Armed Forces of Ukraine during operations as part of functionally oriented groups. Nowadays the delivery of ammunition and other military equipment to the combat zone is difficult and extremely dangerous for personnel. In the near future, the mass use of ground-based robotic means on the battlefield is expected. At the same time, their effective implementation in the daily activities of troops will have to be carried out through the use of rear ground-based robotic systems during the implementation of transport measures for troops, especially those in the area of fire of the enemy. The group control technologies of robotic vehicles require the use of «additional» communication devices as part of communication subsystems and automation of control of ground-based robotic complexes – the so-called «short-range» communication, to directly solve group problems arising in the process of joint use for the intended purpose. The main direction of research in this area is the use of the latest communication and sensor technologies of group wireless access as part of integrated transport communication and automation subsystems, in which radio network nodes (group members) can communicate directly with each other. Methods for solving the assigned tasks based on the use of «peer-to-peer networks» using communication protocols of the 802.11p standard (DSRC, Dedicated Short Range Communication) – short-range communication technologies are proposed. Keywords: logistic ground robotic complex, logistics support, communication and control automation subsystem, group wireless access technologies, peer-to-peer network, dedicated short range communication.

Features and differences between Desay SV and Ford in the solution of C-V2X Technology

C-V2X (means cellular vehicle to everything) is a vehicle wireless communication technology based on cellular network. In the early days of V2X, it was mainly based on DSRC. Its full name is dedicated short range communication, a dedicated short-range communication technology. C-V2X technology supports various vehicle networking applications including vehicle, vehicle road, vehicle person and vehicle network through device to device and cellular communication. C-V2X is a global standard formulated by 3GPP organization, including lte-V2X and 5g-V2X. Among them, LTE-V2X mainly carries basic traffic safety business. The standard has been formulated since 2015 and R14 version has been released in 2017; 5g-V2X, based on 5g NR technology, is mainly aimed at carrying automatic driving business, and will be released in 3GPP R16 at the end of 2019. This paper will compare the characteristics of C-V2X technology solutions of Ford and Desay SV, and explore the differences between Desay SV and Ford in C-V2X technology solutions [1].

A Comparative Study of Bluetooth SPP, PAN and GOEP for Efficient Exchange of Healthcare Data

Objectives: Current research aims to address the challenges of exchanging healthcare information, since when this information has to be shared, this happens by specifically designed medical applications or even by the patients themselves. Among the problems that the Health Information Exchange (HIE) initiative is facing are that (i) third party health data cannot be accessed without internet, (ii) there exist crucial delays in accessing citizens’ data, (iii) the direct HIE can only happen among Healthcare Institutions. Methods: Towards the solution of these issues, a Device-to-Device (D2D) protocol has been specified, running on top of the Bluetooth protocol for efficient data exchange. This research is focused on this D2D protocol, by comparing the different Bluetooth profiles that can be used for transmitting this data, based on specific metrics considering the probabilities of transferring erroneous data. Findings: An evaluation of three Bluetooth profiles takes place, concluding that two of the three profiles must be used to respect the D2D protocol nature and be fully supported by the main market vendors’ operating systems. Novelty:Based on this evaluation, the specified D2D protocol has been built on top of state-of-the-art short-range distance communication technologies, fully supporting the healthcare ecosystem towards the HIE paradigm. Doi: 10.28991/esj-2021-01276 Full Text: PDF

Performance evaluation of vertical handover in Internet of Vehicles

Abstract Internet of Vehicles (IoV) is developed by integrating the intelligent transportation system (ITS) and the Internet of Things (IoT). The goal of IoV is to allow vehicles to communicate with other vehicles, humans, pedestrians, roadside units, and other infrastructures. Two potential technologies of V2X communication are dedicated short-range communication (DSRC) and cellular network technologies. Each of these has its benefits and limitations. DSRC has low latency but it limits coverage area and lacks spectrum availability. Whereas 4G LTE offers high bandwidth, wider cell coverage range, but the drawback is its high transmission time intervals. 5G offers enormous benefits to the present wireless communication technology by providing higher data rates and very low latencies for transmissions but is prone to blockages because of its inability to penetrate through the objects. Hence, considering the above issues, single technology will not fully accommodate the V2X requirements which subsequently jeopardize the effectiveness of safety applications. Therefore, for efficient V2X communication, it is required to interwork with DSRC and cellular network technologies. One open research challenge that has gained the attention of the research community over the past few years is the appropriate selection of networks for handover in a heterogeneous IoV environment. Existing solutions have addressed the issues related to handover and network selection but they have failed to address the need for handover while selecting the network. Previous studies have only mentioned that the network is being selected directly for handover or it was connected to the available radio access. Due to this, the occurrence of handover had to take place frequently. Hence, in this research, the integration of DSRC, LTE, and mmWave 5G is incorporated with handover decision, network selection, and routing algorithms. The handover decision is to ensure whether there is a need for vertical handover by using a dynamic Q-learning algorithm. Then, the network selection is based on a fuzzy-convolution neural network that creates fuzzy rules from signal strength, distance, vehicle density, data type, and line of sight. V2V chain routing is proposed to select V2V pairs using a jellyfish optimization algorithm that takes into account the channel, vehicle characteristics, and transmission metrics. This system is developed in an OMNeT++ simulator and the performances are evaluated in terms of mean handover, handover failure, mean throughput, delay, and packet loss.

State-of-the-art approach to clustering protocols in VANET: a survey

Vehicular ad hoc network (VANET) assists in improving road safety, traveller comfort, and intelligent transportation systems to a great extent. Dedicated short-range communications technology is specially designed for VANET to form communication among vehicles and vehicles to infrastructure. The lack of router devices in the flat vehicle-to-vehicle network structure of VANET may raise complicated issues like scalability, resource scarcity, reliability, and hidden terminal problem inside the network. The concept of vehicle clustering is introduced in VANET to improve network performance by handling all such issues. This paper provides an in-depth classification of clustering protocols in VANET based on their design objectives. Two broad categories, generalized and application dependent, are considered to review on clustering protocols in VANET. In generalized clustering protocol, cluster design aims to achieve their primary objective, i.e., the formation of a robust cluster having a long sustainable life, While in application dependent clustering protocols, cluster design aims to improve the performance of specific applications (i.e., target tracking, traffic estimation, misbehaviour detection, privacy preservation, certificate revocation, etc.) on various performance metrics. After this, the paper explores essential research contributions of each category comprehensively. Detailed analysis of existing research contributions is also provided after comparing them on many important metrics. Finally, the merits and demerits of existing protocols are also listed. Our attempt to encourage researchers of the concerned field by providing an extensive analysis of clustering in VANET.

Field Evaluation of Connected Vehicle-Based Transit Signal Priority Control under Two Different Signal Plans

In 2017, a connected vehicle (CV) corridor utilizing dedicated short-range communication (DSRC) technology was built along Redwood Road, Salt Lake City, Utah. One main goal of this CV corridor is to implement transit signal priority (TSP) when the bus is behind its published schedule by a certain threshold. With the data generated by the transit vehicles, transmitted through the DSRC system, logged by traffic signal controller, and coupled with the Utah Transit Authority (UTA) data from transit operation system, some performance data of the TSP can be analyzed including TSP requested, TSP served, bus reliability, bus travel time, and bus running time. For providing better signal coordination to buses, the signal plan for this CV corridor underwent retiming in October 2018. This research aims to compare the TSP performance before and after the signal retiming. The field data of August, September, November, and December in 2018 were selected to perform this evaluation. Results show that the TSP served rate after signal retiming is 35.29%, which is higher than that of 33.12% before signal retiming. In addition, compared with the signal plan before October, bus reliability northbound and southbound on the CV corridor was improved by 2.4% and 1.47%, respectively; bus travel time and bus running time were reduced as well.

Using Reinforcement Learning With Partial Vehicle Detection for Intelligent Traffic Signal Control

Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as `Partially Detected Intelligent Transportation Systems'. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.