Abstract Smart cities are aimed to efficiently manage growing urbanization, energy consumption, maintain a green environment, improve the economic and living standards of their citizens, and raise the people’s capabilities to efficiently use and adopt the modern information and communication technology (ICT). In the smart cities concept, ICT is playing a vital role in policy design, decision, implementation, and ultimate productive services. The primary objective of this review is to explore the role of artificial intelligence (AI), machine learning (ML), and deep reinforcement learning (DRL) in the evolution of smart cities. The preceding techniques are efficiently used to design optimal policy regarding various smart city-oriented complex problems. In this survey, we present in-depth details of the applications of the prior techniques in intelligent transportation systems (ITSs), cyber-security, energy-efficient utilization of smart grids (SGs), effective use of unmanned aerial vehicles (UAVs) to assure the best services of 5G and beyond 5G (B5G) communications, and smart health care system in a smart city. Finally, we present various research challenges and future research directions where the aforementioned techniques can play an outstanding role to realize the concept of a smart city.
The advancement of the Internet of Things/5G infrastructure requires a low‐cost ubiquitous sensory network to realize an autonomous system for information collection and processing, aiming at diversified applications ranging from healthcare, smart home, industry 4.0 to environmental monitoring. The triboelectric nanogenerator (TENG) is considered the most promising technology due to its self‐powered, cost‐effective, and highly customizable advantages. Through the use of wearable electronic devices, advanced TENG technology is developed as a core technology enabling self‐powered sensors, power supplies, and data communications for the aforementioned applications. In this review, the advancements of TENG‐based electronics regarding materials, material/device hybridization, systems integration, technology convergence, and applications in healthcare, environment monitoring, transportation, and smart homes toward the future green earth are reported.
Electric vehicle (EV) charging infrastructure will play a critical role in decarbonization during the next decades, energizing a large share of the transportation sector. This will further increase the enabling role of power electronics converters as an energy transition technology in the widespread adoption of clean energy sources and their efficient use. However, this deep transformation comes with challenges, some of which are already unfolding, such as the slow deployment of charging infrastructure and competing charging standards, and others that will have a long-term impact if not addressed timely, such as the reliability of power converters and power system stability due to loss of system inertia, just to name a few. Nevertheless, the inherent transition toward power systems with higher penetration of power electronics and batteries, together with a layer of communications and information technologies, will also bring opportunities for more flexible and intelligent grid integration and services, which could increase the share of renewable energy in the power grid. This work provides an overview of the existing charging infrastructure ecosystem, covering the different charging technologies for different EV classes, their structure, and configurations, including how they can impact the grid in the future.
Over the past 20 years, the extensive expansions of informal settlements in built-up districts of developing countries have emerged as a major topic of discussion in the field of urbanization. However, peri-urban land use conflicts have resulted from the regional government's lateness in improving informal settlements. Moreover, formal and informal actors in urban fringe land use conflicts have not been clearly identified by a number of authors, despite their exploration of the link between the two. The objective of this study is to identify formal and informal actors in peri-urban land use disputes and to present a comprehensive, cross-sectoral analysis of peri-urban land use conflicts. Key land use actors in the urban–rural outskirts of Mekelle City were participated in 37 semi-structured interviews, which were followed by 12 focus group discussions. Besides, stakeholders' analysis and snowball methods were also used to answer research questions. The findings showed that friends, family, neighbors, community organizations and their immediate networks are informal actors engaged in peri-urban land disputes. In addition, government institutions, officials and authorities that set laws and regulations were identified as formal actors.
The integration of service-oriented architectures (SOA) with function offloading for distributed, intelligent transportation systems (ITS) offers the opportunity for connected autonomous vehicles (CAVs) to extend their locally available services. One major goal of offloading a subset of functions in the processing chain of a CAV to remote devices is to reduce the overall computational complexity on the CAV. The extension of using remote services, however, requires careful safety analysis, since the remotely created data are corrupted more easily, e.g., through an attacker on the remote device or by intercepting the wireless transmission. To tackle this problem, we first analyze the concept of SOA for distributed environments. From this, we derive a safety framework that validates the reliability of remote services and the data received locally. Since it is possible for the autonomous driving task to offload multiple different services, we propose a specific multi-staged framework for safety analysis dependent on the service composition of local and remote services. For efficiency reasons, we directly include the multi-staged framework for safety analysis in our service-oriented function offloading framework (SOFOF) that we have proposed in earlier work. The evaluation compares the performance of the extended framework considering computational complexity, with energy savings being a major motivation for function offloading, and its capability to detect data from corrupted remote services.
Vehicular edge intelligence (VEI) is vital for future intelligent transportation systems. However, traditional centralized learning in dynamic vehicular networks faces significant communication overhead and privacy risks. Split federated learning (SFL) offers a distributed solution but is often hindered by substantial communication bottlenecks from transmitting high-dimensional intermediate features and can present label privacy concerns. Semantic communication offers a transformative approach to alleviate these communication challenges in SFL by focusing on transmitting only task-relevant information. This paper leverages the advantages of semantic communication in the design of SFL, and presents a case study the semantic communication-enhanced U-Shaped split federated learning (SC-USFL) framework that inherently enhances label privacy by localizing sensitive computations with reduced overhead. It features a dedicated semantic communication module (SCM), with pre-trained and parameter-frozen encoding/decoding units, to efficiently compress and transmit only the task-relevant semantic information over the critical uplink path from vehicular users to the edge server (ES). Furthermore, a network status monitor (NSM) module enables adaptive adjustment of the semantic compression rate in real-time response to fluctuating wireless channel conditions. The SC-USFL framework demonstrates a promising approach for efficiently balancing communication load, preserving privacy, and maintaining learning performance in resource-constrained vehicular environments. Finally, this paper highlights key open research directions to further advance the synergy between semantic communication and SFL in the vehicular network.
As both the generation resources and load types have changed and grown over the past few decades, there is a growing need for analysis that spans traditional simulation boundaries; for example, evaluating the impact of distribution-level assets (e.g. rooftop solar, EV chargers) on bulk-power system operation. Co-simulation is a technique that allows simulators to trade information during run-time, effectively creating larger and more complex models. HELICS is a co-simulation platform that has been developed to enable these kinds of power system analysis, incorporating tools from a variety of domains including the electrical power grid, natural gas, transportation, and communications. This paper summarizes the technical design of HELICS, describes how tools can be integrated into the platform, and reviews a number of analyses that have been performed using HELICS. A short video summary of this paper can be found at https://youtu.be/BIUiR_K87Wc.
The percentage of people who work from home (WFH) skyrocketed with the onset of Covid-19. Today, many workers continue to WFH, either completely or a few days a week. One reason for the popularity of WFH is a desire to minimize the commute and its associated costs in time, money, discomfort, and danger. In fact, workers with longer commutes should theoretically receive “compensating differentials” of benefits from their employers that offset their high commute costs. That said, working at a workplace may have advantages, such as stronger connections to coworkers and supervisors, better chances to learn on the job, and improved opportunities for career advancement. Using European Social Survey data, we examine whether commute durations are associated with workers’ perceptions of their job characteristics and desirability, as well as their happiness, or “subjective well-being.” We find that, among those who commute, commute duration is unrelated to wages, job satisfaction, and overall subjective well-being. Workers who WFH report more freedom in setting hours, but face greater stress at work, more work at night, and longer hours, the latter of which may exceed the time they save by not having to commute. Importantly, people who WFH tend to report being more satisfied with their jobs, as well as being well-connected to coworkers and supervisors and having a good chance for professional advancement. Employers and society should work to accommodate WFH with such steps as developing team-building strategies for WFH workers, addressing the “digital divide” where some workers may lack at-home information and communications technology, and adapting cities to WFH, for example by facilitating the conversion of office space to other uses and accommodating the need for more spacious homes.
This study examined underground roads to evaluate the effects of traffic congestion prevention strategies. A specific framework, called the traffic congestion judgment criteria and process (TJCAP), was developed for underground road application. Using this framework, the study analyzed congestion relief effects by applying traffic strategies commonly used on surface roads. A real underground road in Seoul was used as a testbed. Microscopic traffic simulation was conducted using the VISSIM to create a realistic simulation network. The model was calibrated using observed traffic volume and speed data, both on the underground and adjacent surface roads. This approach enabled the analysis of traffic strategies aimed at reducing congestion. Results showed that the effectiveness of the strategies depends on the type of surface road (interrupted or uninterrupted flow) and its traffic conditions. In particular, the strategies were effective when the connected surface road had a level of service (LOS) of D or better.
Transportation engineering, Transportation and communications
Wi-Fi HaLow (IEEE 802.11ah) has emerged as a promising solution which can support Internet of Things (IoT) applications where energy efficiency and extended coverage are important. A key feature of Wi-Fi HaLow is the Target Wake Time (TWT) mechanism, which allows devices to schedule wake-up times, significantly reducing IDLE listening and energy consumption. However, there is currently no energy consumption model, leaving a gap in calculating how much energy a device actually consumes in a real network. This study aims to bridge this gap by developing a forecast model to accurately predict the energy consumption of devices with TWT enabled. The proposed model is then validated through experimental measurements using real Wi-Fi HaLow-compatible devices, ensuring an accurate representation of practical energy consumption. This research provides empirical insights and recommendations for optimizing network configurations in battery-constrained environments. In particular, the proposed energy consumption model can assist businesses in accurately estimating and managing energy usage, which is essential for cost-effective planning and improving operational efficiency in real-world IoT deployments.
Telecommunication, Transportation and communications
Abdulrahmon Ghazal, Santhanakrishnan Narayanan, Ibraheem Oluwatosin Adeniran
et al.
Abstract The e-commerce sector’s rapid expansion has led to an increase in delivery activities both within and across cities, fuelling the growth of the courier, express, and parcel (CEP) services. CEP service providers are crucial for the distribution of goods across all types of cities, especially for last-mile delivery. However, CEP service providers need innovative approaches for their last-mile distribution in small- and medium-sized cities to reduce transport costs and negative environmental impacts. For this reason, this paper analyses the quantitative impacts of logistics measures of CEP service providers for last-mile delivery in small- and medium-sized cities, especially the resulting transport costs and environmental impacts, in the framework of a case study for the investigation area of the Aachen city region. A simulation-based analysis was conducted using the agent-based transport simulation MATSim and the linked route optimisation Jsprit. The results revealed that electric trucks are not cost-effective as a stand-alone logistics measure for last-mile delivery in small- and medium-sized cities. However, combining electric trucks with other sustainable logistics measures, such as parcel shops and parcel lockers, results in a viable logistics measure for last-mile delivery. It is possible to reduce total transport costs by at least 5.4% and CO2 emissions by at least 61.1%. Hence, CEP service providers should replace diesel trucks with a mix of sustainable logistics measures for last-mile delivery in small- and medium-sized cities to achieve better operational efficiency and lesser environmental impacts.
Transportation engineering, Transportation and communications
Score-based diffusion models represent a significant variant within the diffusion model family and have seen extensive application in the increasingly popular domain of generative tasks. Recent investigations have explored the denoising potential of diffusion models in semantic communications. However, in previous paradigms, noise distortion in the diffusion process does not match precisely with digital channel noise characteristics. In this work, we introduce the Score-Based Channel Denoising Model (SCDM) for Digital Semantic Communications (DSC). SCDM views the distortion of constellation symbol sequences in digital transmission as a score-based forward diffusion process. We design a tailored forward noise corruption to align digital channel noise properties in the training phase. During the inference stage, the well-trained SCDM can effectively denoise received semantic symbols under various SNR conditions, reducing the difficulty for the semantic decoder in extracting semantic information from the received noisy symbols and thereby enhancing the robustness of the reconstructed semantic information. Experimental results show that SCDM outperforms the baseline model in PSNR, SSIM, and MSE metrics, particularly at low SNR levels. Moreover, SCDM reduces storage requirements by a factor of 7.8. This efficiency in storage, combined with its robust denoising capability, makes SCDM a practical solution for DSC across diverse channel conditions.
Semantic communication represents a promising technique towards reducing communication costs, especially when dealing with image segmentation, but it still lacks a balance between computational efficiency and bandwidth requirements while maintaining high image segmentation accuracy, particularly in resource-limited environments and changing channel conditions. On the other hand, the more complex and larger semantic image segmentation models become, the more stressed the devices are when processing data. This paper proposes a novel approach to implementing semantic communication based on splitting the semantic image segmentation process between a resource constrained transmitter and the receiver. This allows saving bandwidth by reducing the transmitted data while maintaining the accuracy of the semantic image segmentation. Additionally, it reduces the computational requirements at the resource constrained transmitter compared to doing all the semantic image segmentation in the transmitter. The proposed approach is evaluated by means of simulation-based experiments in terms of different metrics such as computational resource usage, required bit rate and segmentation accuracy. The results when comparing the proposal with the full semantic image segmentation in the transmitter show that up to 72% of the bit rate was reduced in the transmission process. In addition, the computational load of the transmitter is reduced by more than 19%. This reflects the interest of this technique for its application in communication systems, particularly in the upcoming 6G systems.