Smart cities employ technology and data to increase efficiencies, economic development, sustainability, and life quality for citizens in urban areas. Inevitably, clean technologies promote smart cities development including for energy, transportation and health. The smart city concept is ambitious and is being refined with standards. Standards are used to help with regulating how smart cities function and contributing to define a smart city. Smart cities must be officially recognized by national and international authorities and organizations in order to promote societal advancement. There are many research and review articles on smart cities. However, technical standards are seldom discussed in the current literature. This review firstly presents the study of smart city definitions and domain. The well-known smart city standards will be presented to better recognize the smart city concept. Well-defined standards allow meaningful comparisons among smart cities implementation. How smart city initiatives make a city smarter and improve the quality of life will be discussed for various countries. This review highlights that technical standards are important for smart cities implementation. This paper serves as a guide to the most recent developments of smart cities standards.

This paper proposes a novel relay algorithm to optimize the communication throughput of unmanned aerial vehicle (UAV) mobile relay formations while considering the challenges posed by obstacle avoidance, channel complexity, high dynamics of UAVs, and real-time mission requirements. To tackle the non-convex nature of this problem, we develop the unscented Kalman filter and hybrid particle swarm optimization (UKF-HPSO) algorithm. Initially, real-time prediction of the source and destination of UAV positions is accomplished using the UKF. Subsequently, these predicted coordinates serve as inputs for achieving the optimal deployment of relay UAVs under the constraints imposed by HPSO. The superiority of the UKF-HPSO algorithm compared to baseline approaches is demonstrated through extensive simulations. System throughput is effectively optimized while maintaining real-time performance by our proposed algorithm, which addresses the unique challenges of UAV communication in dynamic environments.

Wireless Body Area Network (WBANs), as a crucial technology in the field of healthcare monitoring, also plays a significant role in personal consumer electronics (CE). However, existing solutions have yet to effectively address challenges such as personalized demands, data heterogeneity, and dynamic link variations caused by diverse environmental factors. To address these issues, this study proposes a novel approach based on the IEEE 802.15.6 standard, which incorporates personalized node characteristics to cater to individual needs and adapt to node heterogeneity. Furthermore, a dynamic scheduling mechanism for node data is introduced, leveraging multi-feature environmental factors to enhance the practicality of WBAN systems. Finally, for real-time scheduling of emotional anomaly data, this study incorporates skin conductance nodes for the first time to evaluate emotional factors, ensuring the efficient transmission of urgent data. Theoretical analysis and simulation results demonstrate that the proposed approach significantly outperforms traditional methods in terms of energy efficiency, latency, throughput, and resource balance.

Wireless Body Area Networks (WBANs), as an effective technology for electronic health monitoring, have transformed traditional consumer electronics (CE) into the next generation of devices with enhanced connectivity and intelligence. The improved interconnectivity between sensor nodes, coordinators, and other consumer devices has increased data availability and enabled autonomous monitoring within CE networks. However, due to the time-sensitive nature of physiological data transmission in WBANs and the urgency of sensor node data, addressing real-time data transmission under dynamic link conditions remains a significant challenge. To tackle this issue, we propose a joint optimization scheduling strategy that considers both data urgency and freshness. Our proposed strategy consists of two key components: a Sink Channel Allocation (SCA) strategy and a Node Scheduling Selection (NSS) strategy. By integrating deep reinforcement learning (DRL), we overcome the challenges posed by the large action space in channel allocation and timeslot selection, thereby improving scheduling efficiency. Both theoretical analysis and simulation results demonstrate that our method significantly outperforms traditional approaches in terms of real-time data transmission and scheduling optimization.

The race against climate change has been a great challenge for years, and the UK government has taken serious steps towards achieving the net-zero carbon target by 2050. Technology is leading the way and innovation is believed to be a key solution. Nevertheless, tackling the issue, by attempting to limit the waste in energy, due to negative energy usage behaviour, has proven to be a successful approach that is capable of complementing other technology-based initiatives. The first step towards this is to promote energy-conscious behaviour and pinpoint where savings can be made. Thereby, this paper contributes to the existing literature, by presenting a new methodology to identify potential energy waste and negative energy usage behaviour in an NHS hospital. The paper presents an analysis of electricity consumption vs occupancy during minimal consumption periods (i.e, bank holidays and weekends) and it presents a log of equipment left switched on outside of working hours, in order to highlight the level of energy-conscious behaviour. The results revealed that the proposed technique is not only able to identify negative energy usage behaviour amongst the hospital staff but helps identify areas where immediate energy savings can be made, with potential savings of more than 30,000 pounds, if action is taken.