Full-duplex technology is likely to be adopted in various legacy communications standards. The IEEE 802.11ax Working Group has been considering a simultaneous transmit and receive (STR) mode for the ...next generation WLANs. Enabling STR mode (FD communication mode) in 802.11 networks creates bidirectional FD (BFD) and unidirectional FD (UFD) links. The key challenge is to integrate STR mode with minimal protocol modifications, while considering the coexistence of FD and legacy half-duplex STAs and backward compatibility. This article proposes a simple and practical approach to enable STR mode in 802.11 networks with coexisting FD and HD STAs. The protocol explicitly accounts for the peculiarities of FD environments and backward compatibility. Key aspects of the proposed solution include FD capability discovery, a handshake mechanism for channel access, node selection for UFD transmission, adaptive ACK timeout for STAs engaged in BFD or UFD transmission, and mitigation of contention unfairness. Performance evaluation demonstrates the effectiveness of the proposed solution in realizing the gains of FD technology for next generation WLANs.
Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, ...transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.
Autonomous vehicles (AVs) are predicted to change transportation; however, it is still difficult to maintain robust situation awareness in a variety of driving situations. To enhance AV perception, ...methods to integrate sensor data from the camera, radar, and LiDAR sensors have been proposed. However, due to rigidity in their fusion implementations, current techniques are not sufficiently robust in challenging driving scenarios (such as inclement weather, poor light, and sensor obstruction). These techniques can be divided into two main groups: (i) early fusion, which is ineffective when sensor data are distorted or noisy, and (ii) late fusion, which is unable to take advantage of characteristics from numerous sensors and hence yields sub-optimal estimates. In this paper, we suggest a flexible selective sensor fusion framework that learns to recognize the present driving environment and fuses the optimum sensor combinations to enhance robustness without sacrificing efficiency to overcome the above-mentioned limitations. The proposed framework dynamically simulates early fusion, late fusion, and mixtures of both, allowing for a quick decision on the best fusion approach. The framework includes versatile modules for pre-processing heterogeneous data such as numeric, alphanumeric, image, and audio data, selecting appropriate features, and efficiently fusing the selected features. Further, versatile object detection and classification models are proposed to detect and categorize objects accurately. Advanced ensembling, gating, and filtering techniques are introduced to select the optimal object detection and classification model. Further, innovative methodologies are proposed to create an accurate context and decision rules. Widely used datasets like KITTI, nuScenes, and RADIATE are used in experimental analysis to evaluate the proposed models. The proposed model performed well in both data-level and decision-level fusion activities and also outperformed other fusion models in terms of accuracy and efficiency.
In a first study, this paper argues and demonstrates that spiking neural networks (SNN) can be successfully used for predictive and explainable modelling of multimodal streaming data. The paper ...proposes a new method, where both time series and on-line news are integrated as numerical streaming data in the same time domain and then used to train incrementally a SNN model. The connectivity and the spiking activity of the SNN are then analyzed through clustering and dynamic graph extraction to reveal on-line interaction between all input variables in regard to the predicted one. The paper answers the main research question of how to understand the dynamic interaction of time series and on-line news through their integrative modelling. It offers a new method to evaluate the efficiency of using on-line news on the predictive modelling of time series. Results on financial stock time series and online news are presented. In contrast to traditional machine learning techniques, the method reveals the dynamic interaction between stock variables and news and their dynamic impact on model accuracy when compared to models that do not use news information. Along with the used financial data, the method is applicable to a wide range of other multimodal time series and news data, such as economic, medical, environmental and social. The proposed method, being based on SNN, promotes the use of massively parallel and low energy neuromorphic hardware for multivariate on-line data modelling.
It is well known that buildings have a sizeable energy and environmental footprint. In particular, in environments like university campuses, the occupants as well as occupancy in shared spaces varies ...over time. Systems for cooling in such environments that are centrally controlled are typically threshold driven and do not account for occupant feedback and thus are often relying on a reactive approach (fix after identifying problems). Therefore, having a fixed thermal operating set point may not be optimal in such cases-both from an occupant comfort and well-being as well as an energy efficiency perspective. To address this issue, a study was conducted which involved development and deployment of an experimental Internet of Things (IoT) prototype system and an Android application that facilitated people engagement on a university campus located in the UAE which typically exhibits hot climatic conditions. This paper showcases data driven insights obtained from this study, and in particular, how to achieve a balance between the conflicting goals of improving occupant comfort and energy efficiency. Findings from this study underscore the need for regular reassessments and adaptation. The proposed solution is low cost and easy to deploy and has the potential to reap significant savings through a reduction in energy consumption with estimates indicating around 50-100 kWh/day of savings per building and the resulting environmental impact. These findings would appeal to stakeholders who are keen to improve energy efficiency and reduce their operating expenses and environmental footprint in such climatic conditions. Furthermore, collective action from a large number of entities could result in significant impact through this cumulative effect.
According to estimates from the United Nations, while the world population is likely to reach 9 billion by 2050 (from around 7 billion today), the water resources available to cater to this ...population are likely to remain similar to what they are today. Therefore, there is a growing concern to reduce water wastage and improve the efficiency of water distribution systems, in particular, urban water systems given the proliferation in both the number of cities that are springing up and the number of people moving to live in cities. Motivated by this, the European Commission under the aegis of the Framework Program (FP7) funded the Information and Communications Technology (ICT) Solutions for the Efficient Water Resources Management project. This Smart Water project is aimed at investigating the role of ICT in monitoring and efficiently managing urban water systems, in particular, exploring the deployment of sensors, communication technologies, and associated decision support systems in utility providers water networks geared toward addressing problems, such as leakage management, demand management, asset management, and so on. This article elaborates on the wireless connectivity considerations, proposes a total cost of ownership framework for evaluating candidate solutions, and highlights experiences from Smart Water case studies involving two utilities in Europe.
Full-duplex (FD) technology is under consideration for adoption in various legacy communications standards. The IEEE 802.11ax working group has been considering a simultaneous transmit and receive ...(STR) mode for the next-generation wireless local area networks (WLANs). Enabling STR mode (FD communication mode) in 802.11 networks creates bidirectional FD (BFD) and unidirectional FD (UFD) links. The key challenge is to integrate STR mode with minimal protocol modifications, while accounting for the coexistence of FD and legacy half-duplex (HD) stations (STAs) and backward compatibility. This paper proposes a simple and practical approach to enable STR mode in 802.11 networks while explicitly accounting for the peculiarities of BFD and UFD links along with the coexistence of FD and HD STAs. Key aspects of the proposed solution include FD capability discovery, handshake mechanism for channel access, node selection for UFD, adaptive acknowledgment timeout, and contention unfairness mitigation. A comprehensive analytical model has been developed to evaluate the gain of STR. Performance evaluation demonstrates the effectiveness of the proposed solution in realizing the gains of FD technology for next-generation WLANs.
Cellular vehicle-to-everything (C-V2X) is one of the enabling vehicular communication technologies gaining momentum from the standardization bodies, industry, and researchers aiming to realize fully ...autonomous driving and intelligent transportation systems. The 3rd Generation Partnership Project (3GPP) standardization body has actively been developing the standards evolving from 4G-V2X to 5G-V2X providing ultra-reliable low-latency communications and higher throughput to deliver the solutions for advanced C-V2X services. In this survey, we analyze the 3GPP standard documents relevant to V2X communication to present the complete vision of 3GPP-enabled C-V2X. To better equip the readers with knowledge of the topic, we describe the underlying concepts and an overview of the evolution of 3GPP C-V2X standardization. Furthermore, we provide the details of the enabling concepts for V2X support by 3GPP. In this connection, we carry out an exhaustive study of the 3GPP standard documents and provide a logical taxonomy of C-V2X related 3GPP standard documents divided into three categories: 4G, 4G & 5G, and 5G based V2X services. We provide a detailed analysis of these categories discussing the system architecture, network support, key issues, and potential solution approaches supported by the 3GPP. We also highlight the gap and the need for intelligence in the execution of different operations to enable the use-case scenarios of Level-5 autonomous driving. We believe, the paper will equip readers to comprehend the technological standards for the delivery of different ITS services of the higher level of autonomous driving.
Low Power Wide Area Networks: An Overview Raza, Usman; Kulkarni, Parag; Sooriyabandara, Mahesh
IEEE Communications surveys and tutorials,
01/2017, Letnik:
19, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Low power wide area (LPWA) networks are attracting a lot of attention primarily because of their ability to offer affordable connectivity to the low-power devices distributed over very large ...geographical areas. In realizing the vision of the Internet of Things, LPWA technologies complement and sometimes supersede the conventional cellular and short range wireless technologies in performance for various emerging smart city and machine-to-machine applications. This review paper presents the design goals and the techniques, which different LPWA technologies exploit to offer wide-area coverage to low-power devices at the expense of low data rates. We survey several emerging LPWA technologies and the standardization activities carried out by different standards development organizations (e.g., IEEE, IETF, 3GPP, ETSI) as well as the industrial consortia built around individual LPWA technologies (e.g., LoRa Alliance, Weightless-SIG, and Dash7 alliance). We further note that LPWA technologies adopt similar approaches, thus sharing similar limitations and challenges. This paper expands on these research challenges and identifies potential directions to address them. While the proprietary LPWA technologies are already hitting the market with large nationwide roll-outs, this paper encourages an active engagement of the research community in solving problems that will shape the connectivity of tens of billions of devices in the next decade.
Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents in recent years caused by the AVs launched by leading ...automobile manufacturers are due to inadequate decision-making, which is a result of their poor perceivance of environmental information. In today’s technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context, leading to inadequate decision-making, which may then lead to fatal accidents. To overcome this drawback, effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks, namely data cleaning and data fusion. Since the raw sensory data are complex and exhibit multimodal characteristics, more emphasis is given to data preprocessing. Since more innovative models have been proposed for data cleaning, this study focused on data fusion. In particular, this study proposed a generic data fusion engine, which classifies different formats of sensory data and fuses them accordingly to improve accuracy. This study proposed a generic framework to fuse the text, image, and audio data. In the first stage of this research, an innovative hybrid model was proposed to fuse multispectral image and video data. Simple and efficient models to extract the salient image features were also proposed. The hybrid image fusion model that was proposed did not yield satisfactory outcomes when combining 3D point cloud data, and its performance declined when evaluating large datasets. To address this issue, the study expanded by introducing an advanced generative adversarial network (GAN) to transform the hybrid image fusion model into a machine learning model capable of handling substantial datasets. Additionally, customized kernel functions were suggested to fuse 3D point cloud data effectively. The performance of the proposed models was assessed using standard metrics and datasets, comparing them with existing popular models. The results revealed that the proposed image fusion model outperformed the other models.
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