Meal-Monitoring Systems Using Weight and Temperature Sensors for Elder Residents in Long-Term Care Facilities

This paper presents a few meal-monitoring systems for elder residents (especially patients) in LTCFs by using electronic weight and temperature sensors. These monitoring systems enable to convey the information of the amount of meal taken by the patients in real-time via wireless communication networks onto the mobile phones of their nurses in charge or families. Thereby, the nurses can easily spot the most patients who need immediate assistance, while the families can have relief in seeing the crucial information for the well-being of their parents at least three times a day. Meanwhile, the patients tend to suffer burns of their tongues because they can hardly recognize the temperature of hot meals served. This situation can be avoided by utilizing the meal temperature-monitoring system, which displays an alarm to the patients when the meal temperature is above the reference. These meal-monitoring systems can be easily implemented by utilizing low-cost sensor chips and Arduino NANO boards so that elder-care hospitals and nursing homes can afford to exploit them with no additional cost. Hence, we believe that the proposed monitoring systems would be a potential solution to provide a great help and relief for the professional nurses working in elder-care hospitals and nursing homes.

Reconfigurable intelligent surfaces assisted wireless communication networks: ergodic capacity and symbol error rate

By enabling reconfigurable intelligent surfaces (RIS), we can deploy intelligent reflecting signals from the base station to destinations. Different from traditional relaying system, RIS relies on programmable metasurfaces and mirrors to improve system performance of destinations. We derive the formulas of main system performance metrics such as ergodic capacity and symbol error rate (SER). Based on types of modulation, we need to demonstrate other parameters which make influence to system performance. We show analytically that the number of reflecting elements along with the transmit power at the source can improve system performance. Moreover, we check the exactness of derived expressions by matching Monte-Carlo with analytical simulations. Finally, we find the best performance can be achieved at specific parameters and results are verified by explicit simulations.

Forward-Reverse Orthogonal Matching Pursuit-Union-Subspace Pursuit-Based Multiuser Detector for Uplink Grant-Free NOMA Networks

Multiuser Detection (MUD) is quite challenging in uplink grant-free non-orthogonal multiple access wireless communication networks in which users sporadically transmit data. The reason for this is that the base station (BS) must perform detection of both multiuser activity and user signals concurrently, because knowledge of user activity status is not available at the BS. In this paper, a new multiuser detector, named the Forward-Reverse Orthogonal Matching Pursuit–Union–Subspace pursuit (FROMPUS)-based MUD, is proposed. The detector takes advantage of the concept of an initial support set. This serves as initial knowledge that is then employed in the reconstruction of active users’ signals. In addition, the detector uses the “serial-include” technique of incorporating a likely support set element candidates and a reliability testing procedure in which the most prominent elements of the support set are selected. To assess the performance of the proposed detector, computer simulations are performed. The results obtained for various parameter settings show that the FROMPUS performs better than any of the other five detectors considered in this paper. However, this excellent performance comes with a slightly higher computational complexity cost. Nonetheless, the cost is inconsequential, since the detector operates at the BS where complexity is of low priority in comparison to performance.

A Review on Cybersecurity of Cloud Computing for Supporting Connected Vehicle Applications

In an internet-of-things (IoT) environment, cloud computing is emerging as a technologically feasible and economically viable solution for supporting real-time and non-real-time connected vehicle (CV) applications due to its unlimited storage, enormous computing capabilities, and cost advantage, i.e., cloud computing costs less than owning such systems. However, maintaining cybersecurity is a major challenge in cloud-supported CV applications as it requires CVs and various transportation or non-transportation services to exchange data with the cloud via multiple wired and wireless communication networks, such as long-term evolution (LTE) and Wi-Fi. In this paper, we review the cybersecurity requirements of cloud-supported CV applications, such as confidentiality, integrity, availability, authentication, accountability, and privacy. Our review also identifies the associated cybersecurity challenges that might impact cloud-supported CV applications and corresponding solutions to these challenges. In addition, we present future research opportunities to prevent and mitigate cybersecurity issues in cloud computing for CV-related applications.

A Review on Cybersecurity of Cloud Computing for Supporting Connected Vehicle Applications

In an internet-of-things (IoT) environment, cloud computing is emerging as a technologically feasible and economically viable solution for supporting real-time and non-real-time connected vehicle (CV) applications due to its unlimited storage, enormous computing capabilities, and cost advantage, i.e., cloud computing costs less than owning such systems. However, maintaining cybersecurity is a major challenge in cloud-supported CV applications as it requires CVs and various transportation or non-transportation services to exchange data with the cloud via multiple wired and wireless communication networks, such as long-term evolution (LTE) and Wi-Fi. In this paper, we review the cybersecurity requirements of cloud-supported CV applications, such as confidentiality, integrity, availability, authentication, accountability, and privacy. Our review also identifies the associated cybersecurity challenges that might impact cloud-supported CV applications and corresponding solutions to these challenges. In addition, we present future research opportunities to prevent and mitigate cybersecurity issues in cloud computing for CV-related applications.

Review of fibreless optical communication technology: history, evolution, and emerging trends

In the present era of technology, it is quite fascinating to design a system capable of transmitting information from one end to another by using the optical spectrum. This system differs from optical fibre communication in terms of channel medium. Optical fibre technology uses the end-to-end physical connection via fibre. In contrast, Light-based wireless communication networks, also known as wireless optical communication (WOC) networks or fibreless optical communication networks, use light as a carrier and air as an unguided propagation media for transmission. Fibreless optical communication eliminates the need for Licensing and Buried Fibre Cables; also, its installation is relatively easy. This survey article describes the evolution of WOC technologies right from the old methods of communication to today’s deep-space optical satellite communication. This article briefly describes the WOC system working principle, categorization of wireless optical systems (based on the carrier frequency wavelength, distance covered, and application), different modulation techniques adopted for wireless optical communication, challenges associated with channel medium and channel modelling, performance analysis of WOC system, various application areas of WOC network, commercial developments in the field of WOC, and some other emerging trends associated with WOC.

Double Deep Recurrent Reinforcement Learning for Centralized Dynamic Multichannel Access

We consider the problem of dynamic multichannel access for transmission maximization in multiuser wireless communication networks. The objective is to find a multiuser strategy that maximizes global channel utilization with a low collision in a centralized manner without any prior knowledge. Obtaining an optimal solution for centralized dynamic multichannel access is an extremely difficult problem due to the large-state and large-action space. To tackle this problem, we develop a centralized dynamic multichannel access framework based on double deep recurrent Q-network. The centralized node first maps current state directly to channel assignment actions, which can overcome prohibitive computation compared with reinforcement learning. Then, the centralized node can be easy to select multiple channels by maximizing the sum of value functions based on a trained neural network. Finally, the proposed method avoids collisions between secondary users through centralized allocation policy.