Minimization of IEEE 802.11p Packet Collision Interference through Transmission Time Shifting

V2I communications are characterized by the presence of network nodes in vehicles and in the infrastructures that these vehicles use, as well as by the wireless interactions among them. Safety-related applications demand stringent requirements in terms of latency and packet delivery probability, especially when safety messages have to be delivered to vehicles by the infrastructure. Interference issues stem from the typical characteristics of wireless communications, i.e., the noise of the wireless medium, the limited communication range of the wireless entities, and the receiver passivity of all the conventional wireless transceivers during transmissions. This paper presents a synchronization mechanism to artificially replicate at a host premises destructive interference due to hidden terminals, together with an application-level technique to minimize that interference by shifting the packet transmission time, similarly to the MAC TDMA channel access method. As both have been field-tested, the paper also analyzes the results of these tests, all performed with real hardware on IEEE 802.11p over different frequencies and transmission powers, and with repeatability in mind. The resulting figures attest that interference effects due to hidden terminals may indeed take place on real IEEE 802.11p networks, and that carefully designed time-shifting mechanisms can actively mitigate them

Review of Recent Trends

This work was partially supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework, through projects SOCA (CENTRO-01-0145-FEDER-000010) and ORCIP (CENTRO-01-0145-FEDER-022141). Fernando P. Guiomar acknowledges a fellowship from “la Caixa” Foundation (ID100010434), code LCF/BQ/PR20/11770015. Houda Harkat acknowledges the financial support of the Programmatic Financing of the CTS R&D Unit (UIDP/00066/2020).MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.publishersversionpublishe

PSON: A serialization format for IoT sensor networks

In Many Internet Of Things (Iot) Environments, The Lifetime Of A Sensor Is Linked To Its Power Supply. Sensor Devices Capture External Information And Transmit It. They Also Receive Messages With Control Commands, Which Means That One Of The Largest Computational Overheads Of Sensor Devices Is Spent On Data Serialization And Deserialization Tasks, As Well As Data Transmission. The Simpler The Serialization/Deserialization And The Smaller The Size Of The Information To Be Transmitted, The Longer The Lifetime Of The Sensor Device And, Consequently, The Longer The Service Life. This Paper Presents A New Serialization Format (Pson) For These Environments, Which Simplifies The Serialization/Deserialization Tasks And Minimizes The Messages To Be Sent/Received. The Paper Presents Evaluation Results With The Most Popular Serialization Formats, Demonstrating The Improvement Obtained With The New Pson Format.This work was funded by public research projects of the Spanish Ministry of Economy and Competitivity (MINECO) (MINECO), references TEC2017-88048-C2-2-R, RTC-2016-595-2, RTC-2016-5191-8, and RTC-2016-5059-8, and the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M17) in the context of the V PRICIT (Regional Programme of Research and Technological Innovation) and the CDTI (Centro para el Desarrollo Tecnológico Industrial E.P.E.), CNU/1308/2018, 28 November

Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking into account several concepts, including data acquisition, data processing, data fusion, and pattern recognition. These concepts can be mapped onto different modules of the framework. The proposed framework should perform the identification of ADL without Internet connection, performing these tasks locally on the mobile device, taking in account the hardware and software limitations of these devices. The main purpose of this paper is to present a new approach for the creation of a framework for the recognition of ADL, analyzing the allowed sensors available in the mobile devices, and the existing methods available in the literature.This work was supported by FCT project UID/EEA/50008/2013. The authors would also like to acknowledge the contribution of the COST Action IC1303–AAPELE–Architectures, Algorithms and Protocols for Enhanced Living Environments

A review of deep learning applications for the next generation of cognitive networks

Intelligence capabilities will be the cornerstone in the development of next-generation cognitive networks. These capabilities allow them to observe network conditions, learn from them, and then, using prior knowledge gained, respond to its operating environment to optimize network performance. This study aims to offer an overview of the current state of the art related to the use of deep learning in applications for intelligent cognitive networks that can serve as a reference for future initiatives in this field. For this, a systematic literature review was carried out in three databases, and eligible articles were selected that focused on using deep learning to solve challenges presented by current cognitive networks. As a result, 14 articles were analyzed. The results showed that applying algorithms based on deep learning to optimize cognitive data networks has been approached from different perspectives in recent years and in an experimental way to test its technological feasibility. In addition, its implications for solving fundamental challenges in current wireless networks are discussed

Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study

Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring

Positioning in 5G and 6G Networks—A Survey

Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios

Digitally-Compensated Wideband 60 GHz Test-Bed for Power Amplifier Predistortion Experiments

Millimeter waves will play an important role in communication systems in the near future. On the one hand, the bandwidths available at millimeter-wave frequencies allow for elevated data rates, but on the other hand, the wide bandwidth accentuates the effects of wireless front-end impairments on transmitted waveforms and makes their compensation more difficult. Research into front-end impairment compensation in millimeter-wave frequency bands is currently being carried out, mainly using expensive laboratory setups consisting of universal signal generators, spectral analyzers and high-speed oscilloscopes. This paper presents a detailed description of an in-house built MATLAB-controlled 60 GHz measurement test-bed developed using relatively inexpensive hardware components that are available on the market and equipped with digital compensation for the most critical front-end impairments, including the digital predistortion of the power amplifier. It also demonstrates the potential of digital predistortion linearization on two distinct 60 GHz power amplifiers: one integrated in a direct-conversion transceiver and an external one with 24 dBm output power