Urban Air Mobility, including electric vertical takeoff and landing vehicles (eVTOL), offer a promising solution to alleviate road traffic congestion and enhance transportation efficiency in cities. However, to ensure its sustainability and operational safety, there is a need for the integrated optimization of eVTOLs and power systems which power these vehicles. Sensors play an important role in data acquisition for the model optimization especially for an environment with high uncertainty. Meanwhile, a quantitative assessment of the eVTOL’s safety level is essential for effective and intuitive supervision. This paper addresses the challenge of achieving both green and safe eVTOLs by proposing an integrated optimization framework. The framework minimizes the costs of eVTOLs and power system operation, and maximizes passenger capacity, by considering the energy stored in the eVTOL as a safety measure. IEEE 2668, a global standard that uses IDex to evaluate application maturity, is incorporated to assess the safety level during the optimization process. A case study for three Chinese cities showed that eVTOLs can utilize inexpensive surplus energy.

Low-latency data sensing and transmission is critical for many city-level applications like traffic incident management to mitigate congestion and enhance road safety. Vehicular crowdsensing (VCS) emerges as a powerful paradigm to provide real-time traffic sensing services from points-of-interest (PoIs) by leveraging the collaboration of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). In this paper, we first introduce two novel metrics: sensing capability-aware age-of-information ($s$AoI) and latency-weighted data collection ratio, to measure the data freshness and amount under the condition of non-uniform status packet size, respectively. We propose an auto-regressive sequential multi-agent deep reinforcement learning framework called “A2G-MADRL”, which consists of an interaction-aware heterogeneous vehicular graph convolution network (HVGCN) for feature extractions, and a dynamically ordered masked policy generator (DOMPG) for coordinating UAVs and UGVs. Extensive experiments on two real-world datasets in KAIST and Roma demonstrate that A2G-MADRL significantly reduces the attained $s$AoI and improves latency-weighted data collection ratio, outperforming seven baselines when varying the number of UAV-UGV pairs, data generation speed in a timeslot, and the number of communication channels.

Integrated sensing and communication (ISAC) has emerged as a transformative paradigm, merging the capabilities of sensing and communication to enhance efficiency and enable advanced applications. Mobile crowdsensing (MCS), as a important example of ISAC, leverages unmanned vehicles such as UAVs to continuously gather and transmit environmental data, supporting critical applications like traffic monitoring, urban congestion management, and accident investigation. In this paper, we focus on multi-task-oriented UAV crowdsensing (UCS), where diverse tasks—such as surveillance and emergency response—each have distinct age-of-information (AoI) requirements. We introduce a novel metric, the “valid task handling index,” to evaluate the performance of handling multiple tasks effectively. Our proposed hierarchical multi-agent deep reinforcement learning (MADRL) framework, DRL-MTUCS, integrates seamlessly with multi-agent actor-critic reinforcement learning methods. It features dynamically weighted queues for UAV goal assignment, enabling efficient management of multiple emergency tasks, and a low-level UAV execution module with a self-balancing intrinsic reward mechanism. This ensures all tasks are completed within their individual AoI constraints. Extensive experiments and trajectory visualizations validate the superior performance and robustness of DRL-MTUCS compared to six baselines across varying conditions, including the number of UAVs, surveillance task AoI thresholds, and emergency task image blur requirements.

Unmanned aerial vehicles (UAVs) offer flexible deployment and cooperative capabilities for mobile crowdsensing (MCS), but their performance is limited by energy and communication resources. Meanwhile, ground users often suffer rapid battery depletion under poor channels or hardware faults. These challenges affect user selection, UAV trajectories, bandwidth and power allocation, energy-harvesting strategies, and task-time assignment, thereby determining sensing and data-delivery efficiency. Accordingly, we focus on balancing the sensing task completion time and total utility of sensed data among multiple resource-limited ground users and UAVs, by jointly designing the sensing data size, ground users' selection, UAV trajectories, and transmission strategies. We formulate this problem as a multi-objective stochastic optimization problem with the aim of obtaining a set of high-performance solutions subject to predefined latency and ground users' energy constraints. Due to the non-convexity, multi-timescale dynamics, stochastic uncertainty, and a high-dimensional, coupled decision space, conventional optimization algorithms are inadequate to solve it tractably. We then propose a lyapunov-guided transformer-enhanced multi-agent deep reinforcement learning (LyaTrans-MADRL) algorithm, which leverages historical observation sequences to capture temporal dependencies and enhance decision-making capabilities. The proposed algorithm enables UAV agents to collaborate during training and allocate network resources in a distributed manner, thereby improving cooperative decision-making and convergence efficiency. Simulation results demonstrate that our proposed algorithm outperforms existing benchmarks by approximately 30%, demonstrating superior performance in balancing sensing task completion time and total sensed data utility.

The diagnosis (i.e., risk analysis) of the elevator system is a significant challenge with the rapid growth of the elevator market in consumer electronics. The traditional elevator diagnosis strategies focus on detecting faults or predicting potential malfunctions. However, these solutions largely hinge on qualitative risk assessments, delivering risky or risk-free outcomes. As a result, the task of comprehending the level of risk and subsequently developing countermeasures that are appropriately adaptable presents a formidable challenge. To address this challenge, an IEEE 2668-compliant Adaptive Quantitative Risk Analysis strategy (AQRAS) using system theoretical process analysis (STPA) is proposed in this paper. The AQRAS analyzes the risk of the elevator system with a safety index (SDex) ranging from 0 to 5, using measurements derived from the Internet-of-Things (IoT) network. The SDex was inspired by the IoT maturity index (IDex) created by the IEEE 2668 global standard for evaluating IoT-related applications. Consequently, the AQRAS is an IEEE 2668-compliant development for elevator diagnosis. Specifically, incorporating STPA into the AQRAS improves the key assessment feature identification compared to the original framework. The feature recognition in the original IDex framework lacks an instructive technique. To complete this, the STPA finds out the key features by analyzing the interactions among intra-system components, which makes it more exhaustive and methodical. Moreover, the complex network is utilized to investigate the relationships between the features and therefore identify the riskiest components that could be impacted by the features. This allows for the adaptive design of countermeasures to mitigate the risks. Leveraging both the experimental and simulation data, two case studies are presented to demonstrate the application of AQRAS in two different scenarios.

Smart lighting system is a crucial part of the smart city, which covers all corners of human life and industrial production. The salient feature of a smart lighting system is the involvement of AI and IoT techniques, which makes it more intelligent. However, the employment of AI and IoT algorithms will also raise new challenges, such as security. As such, a universal evaluation criterion is lacking to explore the performance of smart lighting systems. The maturity IoT index (IDex), developed by the IEEE 2668 working group, aims to comprehensively evaluate smart applications in terms of performance and requirements and provides the IDex level for reference. Furthermore, IDex could give suggestions for developers to improve the application. This paper discusses an IDex case study in the smart lighting system.

The Internet of Things (IoT) has been attracting people to its capability to deal with smart applications. However, with the development of IoT, there are more attacks to threaten IoT systems. Especially, the distributed denial of service (DDoS) attack can lead to mighty destruction to IoT servers, causing the whole IoT network to be out-of-service. Hitherto, given the lack of a common standard for defining the IoT-driven DDoS (IoT-DDoS) attack, the DDoS defense system for IoT is developed without accurate guidance. Additionally, defense against multi-layer IoT-DDoS attacks is rarely covered by previous works. To address these issues, a deep reinforcement learning-based multi-layer IoT-DDoS defense system (DRL-MLDS) is proposed with the reward metrics in compliance with IEEE P2668 – the first of its kind. In addition, to provide a resilient blocking time configuration for false-positive samples, a new power-law-based blocking time mechanism is developed to cooperate with the DRL-MLDS. The outcome reveals that the DRL-MLDS can reach the same accuracy level (i.e., more than 96%) as previous works under single protocol-based IoT DDoS attack, as well as providing around 97% defense accuracy on multi-layer IoT-DDoS attack, which was rarely discussed in previous works. Additionally, by applying the IEEE P2668-compliant reward metrics, the applicability index (ADex) of DRL-MLDS can be improved from 3.2 to 4.4, fulfilling the recommendation of ADex (e.g., $\mathbf {>}3.5$ ) toward IoT best practices. The DRL-MLDS can be extended to Metaverse design and applications.

The enormous increase of intelligent devices for smart city development brings convenient services to consumers. The continuous radio waves emission raises the public's concern about the safety level of nonionizing radiation (NIR). Global NIR safety guidelines have been devised to limit the radiation level in response to public concerns. However, there is no consensus on radiation safety restrictions among various standards, causing confusion and rendering the public difficult to adapt. To address this issue, in this article, a novel index, namely Non-ionizing Radiation Safety Index (NRSDex) is proposed. NRSDex is a unified quantitative evaluation of the NIR safety which mitigates the conflicting multiple relative standards and provides a comprehensive quantified index for decision making. The development of NRSDex is motivated by the IEEE P2668 global standard, which aims to evaluate the Internet of Things (IoT) maturity by constructing an IoT index (i.e., IDex). A special form of IDex, referred as NRSDex, has been developed to provide a unified and quantified view of NIR safety to provide guidance for the public to understand the safety level. Besides, NRSDex provides insights to the specific NIR safety difference between numerous health hazard standards. To highlight the significance of NRSDex, the effects of NIR on the human body are revisited and the NIR safety standards adopted by numerous countries with top rank healthcare device markets are examined and benchmarked. The NRSDex is detailed with its framework, methodology, as well as case studies.

The conventional LoRa system faces the challenges of security and privacy due to centralized architecture and transparent forwarding mechanism. To address these challenges, a Directed Acyclic Graph (DAG)based LoRaWAN system is proposed in this paper. The proposed system is designed with distributed LoRaWAN architecture. Distributed LoRa gateways and network servers record data transmissions in tangle network to make LoRa data traceable and avoid single point of failure. In addition, a secure LoRa data ledger (SLDL) using digital signature algorithm and symmetric-key encryption is designed to defend against malicious gateway attacks, eavesdropping, etc. The proposed system is implemented and validated using DAG-based IOTA platform. The results demonstrate that the prototype system can achieve nearly up to 100 transaction per second (TPS) of throughput and about 1.3 second latency, which is about 90% reduction than existing blockchain-based solutions. As a result, the proposed DAG-based LoRaWAN system is an effective solution to ensure the security and efficiency of large-scale LoRa networks.

The concept of Internet of Things (ioT) has incubated a whole generation of smart applications to resolve problems in society. Despite cloud-based IoT systems inheriting the robustness and scalability of cloud computing, its high latency limits the implementation of time-sensitive applications. To encounter this, Edge-computing-based IoT systems make computing services closer to users which entails lower latencies. New technologies bring new attacks, and Distributed Denial of Service (DDoS) attack has been regarded as one of the major threats to Edge Internet of Things (EIoT) systems. Previous works often focus on the state-of-the-art and the defense mechanism of edge computing. However, there lacks a general standardized method for the security impact analysis, dedicated to EIoT. This paper proposes the DDoS Impact Analysis Index, or DIADex, which is compatible with the IEEE P2668 standard. From the aspects of performance and resources, this method quantifies the impact of DDoS attacks on EIoT systems with a scoring system, which can be used for evaluation in future research and penetration test on EIoT.

LoRa Wide Area Network (LoRaWAN) is one the of the most popular Internet of Things (IoT) technologies for long-range and low-cost communication. At present, LoRaWAN has been applied in a variety of applications, including localization, smart metering, etc. However, the increasing number of LoRaWAN devices would degrade their quality of service (QoS). There are two main reasons. The first reason is the competition of bandwidth and channel resources between large number of connected end devices. Another one is the redundant channel resources allocation configurations of most end devices to achieve better transmission reliability. To address these challenges, this work proposes an enhanced resource allocation scheme based on both k-means and k-prototype classification algorithms to mitigate the affection of ALOHA scheme and the multi-gateway interference problem. In this proposed scheme, intense network resources under dense end device scenario and the redundant claim of network resources configuration in end devices are considered. An outlier improved spreading factor distribution method is also proposed to reduce the negative effect of the problems. By evaluating and comparing packet loss rate and the relative distribution of spreading factors, an average of 22% increment in transmission performance of LoRaWAN networks is achieved.

COVID-19 is a highly contagious coronavirus that has caused traumatic global havoc. By September 14, 2021, there were 224+ million confirmed cases and 4.6+ million fatalities world-wide [1]. Internet of Things (IoT)-based quarantine strategies effectively slow down and prevent COVID-19 transmission [2]. However, the unstan-dardized quarantine strategy may cause negative consequences. Typically, quarantine deactivates normal economic interactions, thus causing huge economic loss [3]. Moreover, the lack of versatility and resiliency also brings safety challenges on some occasions. In this investigation, a performance scoring quarantine index, referred to as QDex, is developed to provide guidance for a new concept of dynamic geofencing quarantine directive. QDex evaluation features adaptive dynamic geofencing (QEDG) for quarantine. In QEDG, QDex is newly defined to evaluate the dynamic geofencing in relation to epidemic control and economic loss. They are represented by two formulated indicators, namely the transmission risk (TR) and the active profit (AP), which are related to the isolator bio status (e.g., body temperature). Based on the evaluation results of TR and AP, QEDG provides a proper geofencing indication for quarantine, which decreases epidemic transmission and restores economic recovery. The requirement-based applicable communication technologies for QEDG are discussed and analyzed, and two typical use cases are included. Finally, the limitations and challenges of QEDG are discussed.

Water distribution infrastructure (WDI) is well-established and significantly improves living quality. Nonetheless, aging WDI has posed an awkward worldwide problem, wasting natural resources and leading to direct and indirect economic losses. The total losses due to leaks are valued at USD 7 billion per year. In this paper, a multi-classification multi-leak identification (MC-MLI) scheme is developed to combat the captioned problem. In the MC-MLI, a novel adaptive kernel (AK) scheme is developed to adapt to different WDI scenarios. The AK improves the overall identification capability by customizing a weighting vector into the extracted feature vector. Afterwards, a multi-classification (MC) scheme is designed to facilitate efficient adaptation to potentially hostile inhomogeneous WDI scenarios. The MC comprises multiple classifiers for customizing to different pipelines. Each classifier is characterized by the feature vector and corresponding weighting vector and weighting vector pertinent to system requirements, thus rendering the developed scheme strongly adaptive to ever-changing operating environments. Hence, the MC scheme facilitates low-cost, efficient, and accurate water leak detection and provides high practical value to the commercial market. Additionally, graph theory is utilized to model the realistic WDIs, and the experimental results verify that the developed MC-MLI achieves 96% accuracy, 96% sensitivity, and 95% specificity. The average detection time is about 5 s.

LoRa wide area network (LoRaWAN), an emerging IoT protocol, has been popularized in large-scale applications, given its long-range and low-power properties. Hitherto, there is no appropriate traffic model for LoRaWAN to estimate the heterogeneous arriving traffic at the network server cluster (NSC). Inefficient computation power planning or even processing failure might be further caused. Radio replication, commonly existed in the arriving traffic at NSC in LoRaWAN, also causes difficulty estimating the makespan (i.e., mean processing time in NSC). To overcome the abovementioned limitations, a heterogeneous radio-replication-aware traffic aggregation model is proposed to estimate the arriving traffic for LoRaWAN. In addition, a radio-replication-combined supermarket model (RRC-SM), on top of HTAM, is proposed to achieve load balancing among servers in LoRaWAN. Furthermore, a nondominated sorting genetic algorithm based on multiobjective optimization is developed to simultaneously minimize cost and latency on NSC. Experiments reveal that the proposed HTAM and RRC-SM agree well with the simulation outcome. Under the arriving traffic estimated as 6.16 erlangs with four radio replications of each arriving packet on average, the proposed RRC-SM provides more than 50% reduction on the total processing latency and 75% reduction on the number of servers in NSC than other existing models.

In smart cities and smart industry, a Battery Management System (BMS) focuses on the intelligent supervision of the status (e.g., state of charge, temperature) of batteries (e.g., lithium battery, lead battery). Internet of Things (IoT) integration enhances the system's intelligence and convenience, making it a Smart BMS (SBMS). However, this also raises concerns regarding evaluating the SBMS in the wireless context in which these systems are installed. Considering the battery application, in particular, the SBMS will depend on several wireless communication characteristics, such as mobility, latency, fading, etc., necessitating a tailored evaluation strategy. This study proposes an IEEE P2668-Compatible SBMS Evaluation Strategy (SBMS-ES) to overcome this issue. The SBMS-ES is based on the IEEE P2668 worldwide standard, which aims to assess IoT solutions' maturity. It evaluates the characteristics of the wireless environment for SBMS while considering battery factors. The SBMS-ES scores the candidates under numerous scenarios with various characteristics. A final score between 0 and 5 is given to indicate the performance of the SBMS regarding the application demands. The disadvantages of the SBMS solution and the most desired candidate can be found with the evaluated score. SBMS-ES provides guidance to avoid potential risks and mitigates the issues posed by an inadequate or unsatisfactory SBMS solution. A case study is depicted for illustration.

Low-power wide area networks (LPWANs) are emerging Internet of Things (IoT) technologies that support smart city development. An individual application inherits distinct features that demand different characteristics of LPWANs. For instance, various applications demand different data rates or tolerate different packet drop rates. As such, quantified performance of the LPWAN will help developers to make decisions on classes and types of LPWAN to be used. Previous works only provided qualitative comparisons on LPWANs. Thus, smart application developers are still facing the challenge of inappropriately adopting LPWAN technologies. Currently, the IEEE Standard Working Group P2668 is working on developing a standard to evaluate, grade, and rank the performance of IoT objects by using indicator values referred to as IoT Index (IDex). To achieve smart selection on LPWANs, the IEEE P2668 IDex Working Group has first focused on the evaluation of LPWANs and developed a novel LPWAN-I. The proposed LPWAN-I is a sub-index of the IDex that quantitatively evaluates the applicability of LPWANs and selects the most suitable LPWAN candidate for a specific application. This work is the first article to report IDex, which helps to deliver the best practice. Finally, the challenges and future development of LPWAN-I are also presented.

The received signal strength (RSS) finger-print-based approaches are widely used for indoor location-based services (LBSs). The emerging long range wide area network (LoRaWAN) is a cost-effective solution for indoor latency-tolerant LBSs attributed to its long-range property. In general, there are serious RSS fluctuations due to fadings along the communication path, thus significantly jeopardizing the localization accuracy. To overcome the challenge, in this article we propose the extreme RSS (ERSS) to stabilize the fingerprint database and formulate boundary autocorrelation to downsize tremendously the searching complexity and thus proliferating localization accuracy. In essence, the RSS fluctuations are modeled as a Bernoulli random process so that the RSS stability can be estimated by a newly defined fluctuation analytic function. To mitigate the impact of the perturbative fluctuation, the ERSS is further defined to cultivate a highly stable and robust fingerprint database which withstands environmental dynamics. In addition, boundary autocorrelation is developed to measure and compare the similarity between the measured RSS values versus the prestored fingerprint database. RSS values with low autocorrelation coefficients are eradicated from the typically lengthy searching. The downsized complexity significantly improves the localization accuracy. Experiments were carried out and the results revealed that the proposed method achieved sub-10-m localization accuracy in indoor environments. Such accuracy is encouraging and superior in contemporary LoRaWAN measurements.

The population of pets in the developed countries was increased significantly in the past ten years. The owners are concerned about the safety of pet products. However, no special pet product safety standards or certification schemes are available. The industry applied the human product safety standards on the pet products, which may be detrimental to pets because some human-safe food could be toxic for pets. This paper develops a pet food safety evaluation (PFSE) scheme to distinguish the harmful human food for pets based on artificial intelligence (AI) schemes. The paper firstly introduced the current market of pet products in the developed countries. Secondly, the major differences between toxic substances to humans and pets were reviewed. The available pet food standards were then discussed. Then, the feasibility of applying human food certification schemes to pet food products is studied. Finally, the PFSE is described, and an illustrative simulation is done.

Internet of Things (IoT) has become one of the most popular technologies in recent years, covering from citywide services to industrial applications, which enlarges the smart life for human beings. Through IoT, billions of IoT end devices can be interconnected to support various applications. The emergence of low-power wide-area network (LPWAN) technologies provides a great opportunity to support such an enormous network with their kilometer-level coverage and uA-level power consumption. To improve the efficiency of network resources of LPWANs, the cooperated IoT is proposed by researchers. However, the current LPWAN consists of diverse protocols, equipment, and design standards, rendering the increasing development effort on designing a compliance network by developers. To address this issue, the IEEE 1451, developed by Instrumentation and Measurement Society, is proposed. The IEEE 1451 standardized the wireless IoT systems with wireless transducer interface module (WTIM), network capable application processor server (NCAP Server) and NCAP Client. Besides, the application programming interfaces (APIs) and transducer electronic data sheet (TEDS) are also standardized. Based on the IEEE 1451, a standardized structure for LPWANs, namely IEEE1451-LPWAN is introduced. In addition, an M/M/1/N based queueing model is built to analyze the queueing performance of IEEE1451-LPWAN, which provides guidance for adopters in the future.