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In this Article, we will introduce several interrelated problems present in municipal solid waste recycling efforts, both globally and locally. The introduction serves to demonstrate how the lack of adequate global waste tracking and community-level waste contamination are related issues. This article elaborates on how these issues could be addressed with the Internet of Things (IoT), artificial intelligence (AI), and open data technology deployment. We will investigate the existing and possible applicability of this solution in resource-constrained environments, as opposed to exclusive use in the typical “smart city” context. Finally, we will discuss the risks and limitations of this approach. [ABSTRACT FROM AUTHOR]

Until Recently, The citizen science approach to knowledge production had been confined to scientific research, applied to a limited range of questions on which scientists and citizens could collaborate and trust each other. More recently, this approach has gained increasing attention from various quarters outside the traditional remit of professionalized science, which include local governments, civil society organizations (CSOs), professional networks, and cooperatives. These entities recognize citizen science as a practical method that harnesses the collective resources of a community to address pressing local concerns and promote a robust participatory democracy. Emerging initiatives include citizen observatories, data cooperatives, and collaboration platforms. Despite the growing popularity, managing citizen science initiatives (involving numerous participants over an extended period of time) poses considerable challenges. Leaders of citizen science initiatives grapple with difficulties that appear formidable to overcome. At present, the democratic potential of citizen science necessitates further development and scrutiny from social and political actors. Critics frequently underscore a disparity between the democratic ideals associated with citizen science and its current practical implementation. This article outlines the primary benefits and challenges associated with the democratization of citizen science. It builds from the empirical research of the ISEED project as well as the theoretical contribution of the Right to the Smart City project and formulates 10 key recommendations to citizen science project leaders. [ABSTRACT FROM AUTHOR]

We study the benefits and challenges of using Linked Open Data in smart city applications and propose a set of open source, highly scalable tools within the case of a public-rental bicycle system, which can act as a reference guide for other smart city applications. [ABSTRACT FROM AUTHOR]

This article introduces a business model for big and open linked data in smart and circular cities, laying the foundation of a new approach that generates societal, business, and public value. [ABSTRACT FROM AUTHOR]

Cities around the world are populated with more than 50% of the increasing worldwide population. In light of climate change, cities' growth poses significant challenges at all levels. Their sustainability requires the participation of all stakeholders. [ABSTRACT FROM AUTHOR]

While attack detection is key to realize trustworthy smart cities, the use of large amounts of network traffic data by machine learning techniques can lead to privacy issues for citizens. To face this issue, we propose a federated learning approach in the context of Internet of Things-enabled smart cities integrating the Threat and Manufacturer Usage Description files as a prevention/mitigation approach. [ABSTRACT FROM AUTHOR]

This article explores city intersections as intelligence nodes using high-bandwidth, low-latency services for providing privacy-preserving smart city applications. COSMOS testbed experiments using edge-computing-based artificial-intelligence techniques are reported, for monitoring of pedestrians, cloud-connected vehicles, and traffic management. [ABSTRACT FROM AUTHOR]

Dockless E-scooter Sharing (DES) has become a popular means of last-mile commute for many smart cities. As e-scooters are getting deployed dynamically and flexibly across city regions that expand and/or shrink, accurate prediction of the e-scooter distribution given the reconfigured regions becomes essential for city planning. We present GCScoot, a novel flow distribution prediction approach for reconfiguring urban DES systems. Based on real-world datasets with reconfiguration, we analyze e-scooter distribution features and flow dynamics for the data-driven designs. We propose a novel spatio-temporal graph capsule neural network within GCScoot to predict future dockless e-scooter flows given the reconfigured regions. GCScoot pre-processes historical spatial e-scooter distributions into flow graph structures, where discretized city regions are considered as nodes and inter-region flows as edges. To facilitate initial training, we cluster the regions and generate virtual data for new deployment regions based on their peers in the same cluster. Given above designs, the region-to-region correlations embedded within the temporal flow graphs are captured via the multi-graph capsule convolutional neural network which accurately predicts the DES flows. Extensive studies upon four e-scooter datasets (total $>$ > 3.4 million rides) in four populous US cities have corroborated accuracy and effectiveness of GCScoot in predicting the e-scooter distributions. [ABSTRACT FROM AUTHOR]

Online streaming feature selection (OSFS) has attracted extensive attention during the past decades. Current approaches commonly assume that the feature space of fixed data instances dynamically increases without any missing data. However, this assumption does not always hold in many real applications. Motivated by this observation, this study aims to implement online feature selection from sparse streaming features, i.e., features flow in one by one with missing data as instance count remains fixed. To do so, this study proposes a latent-factor-analysis-based online sparse-streaming-feature selection algorithm (LOSSA). Its main idea is to apply latent factor analysis to pre-estimate missing data in sparse streaming features before conducting feature selection, thereby addressing the missing data issue effectively and efficiently. Theoretical and empirical studies indicate that LOSSA can significantly improve the quality of OSFS when missing data are encountered in target instances. [ABSTRACT FROM AUTHOR]

Drone-based vehicle detection aims at detecting vehicle locations and categories in aerial images. It empowers smart city traffic management and disaster relief. Researchers have made a great deal of effort in this area and achieved considerable progress. However, because of the paucity of data under extreme conditions, drone-based vehicle detection remains a challenge when objects are difficult to distinguish, particularly in low-light conditions. To fill this gap, we constructed a large-scale drone-based RGB-infrared vehicle detection dataset called DroneVehicle, which contains 28, 439 RGB-infrared image pairs covering urban roads, residential areas, parking lots, and other scenarios from day to night. Cross-modal images provide complementary information for vehicle detection, but also introduce redundant information. To handle this dilemma, we further propose an uncertainty-aware cross-modality vehicle detection (UA-CMDet) framework to improve detection performance in complex environments. Specifically, we design an uncertainty-aware module using cross-modal intersection over union and illumination estimation to quantify the uncertainty of each object. Our method takes uncertainty as a weight to boost model learning more effectively while reducing bias caused by high-uncertainty objects. For more robust cross-modal integration, we further perform illumination-aware non-maximum suppression during inference. Extensive experiments on our DroneVehicle and two challenging RGB-infrared object detection datasets demonstrated the advanced flexibility and superior performance of UA-CMDet over competing methods. Our code and DroneVehicle will be available: https://github.com/VisDrone/DroneVehicle. [ABSTRACT FROM AUTHOR]

Future communication networks will require increased flexibility, scalability, and data computation capabilities to adequately respond to the growing number of service demands. Advanced mixed-integer network resource optimization models and algorithms are required to meet these requirements. The purpose of this article is to introduce a hybrid quantum-classical computing framework for addressing future network resource optimization issues. We begin by discussing the fundamentals of quantum computing and its parallelism. Following that, we discuss in detail the hybrid quantum-classical computing paradigm. Then, we discuss the potential applications of the proposed paradigm for network resource optimization, including network function virtualization (NFV), multi-access edge computing/fog/cloud computing, and cloud radio access networks (C-RANs). Finally, we discuss the difficulties associated with the design and implementation of hybrid quantum-classical algorithms. [ABSTRACT FROM AUTHOR]

Device-free localization (DFL) enables several new applications in various sectors including smart cities, intelligent transportation, and public safety. DFL relies on a network of sensor radars that transmit, receive, and process reflected signals propagating in a monitored environment. The accuracy of DFL degrades in cluttered environments, due to the presence of undesired objects that reflect the signal. Indeed, the multiple reflections of the signal overlap at the receiver and make the inference of targets' positions challenging. This article presents a theoretical foundation of DFL in cluttered environments by deriving the fundamental limits on DFL accuracy. In particular, we propose a system model that takes into account multiple reflections, nonline-of-sight conditions, and the presence of multiple targets. Building on such a model, we derive the Cramér-Rao bound on the inference accuracy of targets' positions by applying equivalent Fisher information analysis. The proposed bound provides guidelines for the design and analysis of DFL systems operating in cluttered environments. Then, the article presents a case study compliant with the 5G New Radio numerology and channel modeling. Results show how the minimum achievable error is affected by multiple reflections and multiple targets and to which extent the employment of a signal with larger bandwidth and a network with a higher number of receivers can lower the achievable error toward submeter accuracy. [ABSTRACT FROM AUTHOR]

Real-time traffic monitoring is a fundamental mission in a smart city to understand traffic conditions and avoid dangerous accidents. In this article, we propose a reliable and efficient traffic monitoring system that integrates blockchain and the Internet of Vehicles technologies effectively. It can crowdsource its tasks of traffic information collection to vehicles that run on the road instead of installing cameras in every corner. First, we design a lightweight blockchain-based information trading framework to model the interactions between traffic administration and vehicles. It guarantees reliability, efficiency, and security during executing trading. Second, we define the utility functions for the entities in this system and come up with a budgeted auction mechanism that motivates vehicles to undertake the collection tasks actively. In our algorithm, it not only ensures that the total payment to the selected vehicles does not exceed a given budget but also maintains the truthfulness of the auction process that prevents some vehicles from offering unreal bids for getting greater utilities. Finally, we conduct a group of numerical simulations to evaluate the reliability of our trading framework and performance of our algorithms, whose results demonstrate their correctness and efficiency perfectly. [ABSTRACT FROM AUTHOR]

This work proposes a novel deep reinforcement learning (DRL)-based demand response algorithm for smart facilities energy management to minimize electricity costs while maintaining a satisfaction index. Specifically, to accommodate the characteristics of the decision-making problem, long short-term memory (LSTM) units are adopted to extract discriminative features from past electricity price sequences and fed into fully connected multi-layer perceptrons (MLPs) with the measured energy and time information, then a deep Q-network is developed to approximate the optimal policy. After that, an experimental setup is constructed to investigate the effectiveness of the proposed DRL-based demand response algorithm to bridge the gap between theoretical studies and practical implementations. Numerical results demonstrate that the proposed algorithm can handle energy management well for multiple smart facilities. Moreover, the proposed algorithm outperforms the model predictive control (MPC) strategy and uncontrolled solution and is close to the theoretical optimal control method. [ABSTRACT FROM AUTHOR]

Crowd counting has important applications in the environments of smart cities, such as intelligent surveillance. In this paper, we propose a novel convolutional neural network (CNN) framework for crowd counting with mixed ground-truth, called top- $k$ relation-based network (TKRNet). Specifically, the estimated density maps generated in a coarse-to-fine manner are treated as coarse locations for crowds so as to assist our TKRNet to regress the scattered point-annotated ground truth. Moreover, an adaptive top- $k$ relation module (ATRM) is proposed to enhance feature representations by leveraging the top- $k$ dependencies between the pixels with an adaptive filtering mechanism. Specifically, we first compute the similarity between two pixels so as to select the top- $k$ relations for each position. Then, a weight normalization operation with an adaptive filtering mechanism is proposed to make the ATRM adaptively eliminate the influence from the low correlation positions in the top- $k$ relations. Finally, a weight attention mechanism is introduced to make the ATRM pay more attention to the positions with high weights in the top- $k$ relations. Extensive experimental results demonstrate the effectiveness of our proposed TKRNet on several public datasets in comparison to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Traffic congestion alleviation around intersections has been a growing challenge, and a competent traffic signal control scheme plays a pivotal role in intelligent transportation systems. Recent studies using deep reinforcement learning techniques have shown promising results for traffic signal control, but they only focus on extracting features from traffic conditions of isolated or adjacent intersections. In this work, we employed navigation information for traffic signal control, greatly enriching the features for traffic signal control with deep reinforcement learning. In addition, we are the first to propose a novel scheme DeepNavi to exploit the temporal-spatial relations from numerous navigation routes and extract dynamic real-time and future traffic features. We tested our scheme on a challenging real-world traffic dataset with 16 intersections in a residential district of Hangzhou, China. Extensive experiments were conducted and the results demonstrated that our DeepNavi scheme achieves superior performance over five popular and state-of-the-art baseline methods on different metrics, including queue length, speed, travel time and accumulative waiting time. In addition, with our method, vehicles suffer the least red lights and enjoy the most green waves, which further validates that our scheme greatly relieves the congestion and provides excellent experience for drivers. Simulations with different penetration levels of navigation routes showed that even with only part of navigation routes available in the traffic network, our scheme can obtain superior performance, further demonstrating the effectiveness and feasibility of DeepNavi. [ABSTRACT FROM AUTHOR]

In this paper, we design an Anomaly Detection (AD) framework for mobile data traffic, capable of identifying different types of anomalous events generated by flash crowds in metropolitan areas. We state the problem using a semi-supervised approach and exploit the great performance of different Recurrent Neural Network (RNN) models to learn the temporal context of input sequences. Our proposal processes real traffic traces from the unencrypted LTE Physical Downlink Control Channel (PDCCH) of an operative network, gathered during an extensive measurement campaign in two major cities in Spain. The AD framework is designed to perform: i) a-posteriori analysis to understand users’ behavior and urban environment variations; ii) real-time analysis to automatically and on-the-fly alert urban anomalies; and iii) estimation of the duration of the periods identified as anomalous. Numerical results show the higher performance of our AD framework compared to classic AD algorithms and confirm that the proposed framework predicts anomalous behaviours with high accuracy and regardless of their cause. [ABSTRACT FROM AUTHOR]

There is insufficient current understanding of how to apply fully decentralized control to networks of sparsely coupled nonlinear dynamical subsystems subject to noise to track a desired state. As exemplars, this class of problem is motivated by practical requirements of creating decentralized power grids robust to cascade failures, the digital transformation of Industry 4.0 managing IoT connectivity reliably, and controlling transport flow in smart cities by computing at the edge. We demonstrate that an approach utilizing probability theory to characterize and exploit the uncertainty in locally received information, and locally optimized messages passed between neighboring subsystems is sufficient to implicitly infer global knowledge. Thus, control of a global state could be realized through decentralized control signals applied only to local subsystems using only local information without any reference to a global current state. Given a global system that can be decomposed into a set of locally coupled subsystems, we develop a theoretical method of probabilistic message passing and probabilistic control signals all interacting only at the subsystem level, but which promotes a system-wide convergence to a desired state. Our theoretical results are corroborated using computational experiments on a network of a 10-node partially coupled system decomposed into four separated subsystems with control inputs applied and determined at the subsystem level. Comparing the results with a centralized control method utilizing information from all the nodes to achieve global state convergence validates our hypothesis that local decentralized probabilistic control can be affected by the mechanism of local probabilistic message passing without needing access to global centralized information. We also provide a set of numerical experiments increasing the network size showing that the decentralized algorithm is independent of the global system size. [ABSTRACT FROM AUTHOR]

Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the performance of a network and analyze its health status. In recent years, neural networks have been proved suitable to predict time series data, represented by the model of a long short-term memory (LSTM) neural network and a temporal convolutional network (TCN). This article proposes a novel hybrid prediction method named SG and TCN-based LSTM (ST-LSTM) for such network traffic prediction, which synergistically combines the power of the Savitzky–Golay (SG) filter, the TCN, as well as the LSTM. ST-LSTM employs a three-phase end-to-end methodology serving time series prediction. It first eliminates noise in raw data using the SG filter, then extracts short-term features from sequences applying the TCN, and then captures the long-term dependence in the data exploiting the LSTM. Experimental results over real-world datasets demonstrate that the proposed ST-LSTM outperforms state-of-the-art algorithms in terms of prediction accuracy. Note to Practitioners—This work considers real-time and high-accuracy prediction of network traffic. It is highly important to well predict network traffic by capturing long-term dependence and effectively extracting high- and low-frequency information from time series data. Yet, it is a big challenge to achieve it because there are unstable characteristics and strong nonlinear features in the network traffic due to continuous expansion of network scale and fast emergence of new services. Current prediction methods usually have oversimplified theoretical assumptions, need significant time and memory, or suffer problems of gradient disappearance or early convergence. Thus, they fail to effectively capture the nonlinear characteristics of large-scale network sequences. This work proposes a hybrid prediction method named SG and TCN-based LSTM (ST-LSTM), which integrates the merits of the Savitzky–Golay filter, the temporal convolutional network (TCN), and the long short-term memory (LSTM), serving as smoothing time series, capturing short-term local features, and capturing long-term dependence, respectively. Experimental results based on the real-life dataset demonstrate that it achieves better prediction accuracy than its state-of-the-art peers, including the TCN and the LSTM. It can be readily implemented and deployed in many real-life industrial areas including smart city, edge computing, cloud computing, and data centers. [ABSTRACT FROM AUTHOR]

With the advances of the fifth generation (5G) mobile communication technology, smart applications enhance the quality of daily life, the urban management by the government and the effective allocation of resources. Smart applications collect data through the Internet of Things, store massive data in the cloud server, and use cloud computing and deep learning to analyze the data, according to the data analysis results to guide human production and life. However, with the breakthrough of 6G technology, the amount of data applied is increasing, and the demand for privacy protection is becoming more prominent. Encrypting data using traditional cryptographic algorithms can solve the problem of privacy leakage, but it hampers the availability of the data. Searchable encryption is a special encryption structure with keyword search, which balances the availability and privacy of massive data. In this article, we analyze some typical security and privacy issues in 6G-based applications, discuss the solutions to these problems, and present a framework of 6G-based smart cities with searchable encryption, which provides a guarantee for the privacy and availability of smart city data (including ciphertext search, access control, etc.). We also propose a searchable encryption solution based on ciphertext-policy attribute-based encryption to solve the conflict between security and data availability of the smart cities as a specific scenario in order to demonstrate the contribution of cryptographic technologies such as public-key searchable encryption to the 6th generation mobile communication technology. [ABSTRACT FROM AUTHOR]

The explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communication technologies. Among others, dynamic spectrum access (DSA) is one of the most widely accepted approaches. In this paper, as an enhancement of existing works, we take into consideration of inter-node collaborations in a dynamic spectrum environment. Typically, in such distributed circumstances, intelligent dynamic spectrum access almost invariably relies on self-learning to achieve dynamic spectrum access improvement. Whereas, this paper proposes a DSA scheme based on deep reinforcement learning to enhance spectrum and access efficiency. Unlike traditional Q-learning-based DSA, we introduce the following to enhance the spectrum efficiency in dynamic IoT spectrum environments. First, deep double Q-learning is adopted to perform local self-spectrum-learning for IoT terminals in order to achieve better dynamic access accuracy. Second, to accelerate learning convergence, federated learning (FL) in edge nodes is used to improve the self-learning. Third, multiple secondary users, who do not interfere with each other and have similar operation condition, are clustered for federated learning to enhance the efficiency of deep reinforcement learning. Comparing with the traditional distributed DSA with deep learning, the proposed scheme has faster access convergence speed due to the characteristic of global optimization for federated learning. Based on this, a framework of federated deep reinforcement learning (FDRL) for DSA is proposed. Furthermore, this scheme preserves privacy of IoT users in that FDRL only requires model parameters to be uploaded to edge servers. Simulations are performed to show the effectiveness of theproposed FDRL-based DSA framework. [ABSTRACT FROM AUTHOR]

Spatially fine-grained urban flow data is critical for smart city efforts. Though fine-grained information is desirable for applications, it demands much more resources for the underlying storage system compared to coarse-grained data. To bridge the gap between storage efficiency and data utility, in this paper, we aim to infer fine-grained flows throughout a city from their coarse-grained counterparts. This task exhibits two challenges: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a model entitled UrbanFM which consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs that uses a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influence of different external factors. This structure provides outstanding effectiveness and efficiency for small scale upsampling. However, the single-pass upsampling used by UrbanFM is insufficient at higher upscaling rates. Therefore, we further present UrbanPy, a cascading model for progressive inference of fine-grained urban flows by decomposing the original tasks into multiple subtasks. Compared to UrbanFM, such an enhanced structure demonstrates favorable performance for larger-scale inference tasks. [ABSTRACT FROM AUTHOR]

The growing popularity and penetration of electric vehicles (EVs) bring in challenges and opportunities to the power grid. They represent a huge aggregate electricity consumption from the city power grid, while it is possible to make use of their batteries for supporting vehicle-to-grid (V2G) regulation service. Besides, there is a need to coordinate a collection of EVs in an intelligent manner through the smart transportation system in a smart city. In this paper, we propose a novel smart cross-system framework to support V2G regulation service in a smart city. Specifically, by considering electric buses (EBs) as a particular group of EVs with large battery packs installed and predictable operation schedules, we jointly solve the problems of EB allocation and scheduling with V2G regulation service. First of all, by using the deep learning approach, the short-term city traffic conditions can be predicted via the gated recurrent units (GRUs) neural network. With the predicted traffic conditions, we then formulate the EB allocation and V2G scheduling as a mixed-integer quadratic programming (MIQP) problem. In order to solve this problem effectively, we utilize the Lagrangian dual decomposition method to decouple the main problem into subproblems for EBs and devise a distributed algorithm to solve each subproblem. The simulation results show that our proposed approach is effective in EB allocation and V2G scheduling for the regulation service. Through efficient EB routing strategies under accurate predicted traffic conditions, the power fluctuations of the city power grid can be well flatten by providing the V2G regulation service. [ABSTRACT FROM AUTHOR]

Vehicle counting is important for smart city applications such as logistics management, traffic estimation, and financial analysis. To perform vehicle counting using aerial images, researchers have proposed many algorithms, including detection-, regression-, and density-based methods. However, most of these algorithms are only applicable to high-resolution (HR) images, which require clear vehicle outlines. For the reasons of acquisition difficulty, frequency, and cost, it is necessary to explore methods for vehicle counting using low-resolution (LR) or even very LR images. We build a cross-resolution vehicle counting (CRVC) dataset, including 192 very LR images and eight HR images of a port from 2016 to 2019. For this task, we propose a novel vehicle counting via cross-resolution spatial consistency and intraresolution time continuity constraints. The segmentation map is first obtained by semantic segmentation with the prior information above. The vehicle coverage rate relative to the located parking lot is calculated and then converted to the vehicle area. Finally, the relationship between the area and the number of vehicles is established by regression. Experiments show that the vehicle counting results obtained by our method are highly consistent with the annotations and outperform other state-of-the-art methods. Our method is also applicable for images with a lower resolution of 10 m and other locations. Code, data, and pretrained models are available online at https://github.com/hbsszq/Vehicle-Counting-in-Very-Low-Resolution-Aerial-Images. [ABSTRACT FROM AUTHOR]

Cosimulation techniques are gaining popularity amongst the power system research community to analyze future scalable smart grid solutions. However, complications such as multiple communication protocols, uncertainty in latencies are holding up the widespread usage of these techniques for the power system analysis. These issues are even further exacerbated when applied to digital real-time simulators (DRTSs) with strict real-time constraints for power hardware-in-the-loop (PHIL) tests. In this article, we present an innovative digital real-time cosimulation infrastructure that allows interconnecting different DRTS through the Aurora 8B/10B protocol to reduce the effects of communication latency and respect real-time constraints. The proposed solution synchronizes the DRTS interconnection by means of the IEEE 1588 precision time protocol (PTP) standard to align executions and results obtained by the cosimulated scenario. The ideal transformer method interface algorithm, commonly used in PHIL applications, is used to interface the DRTS. Finally, we present time-domain and frequency-domain accuracy analyzes on the obtained experimental results to demonstrate the potential of the proposed infrastructure. With the presented setup, a time step duration down to 50 $\mu$ s is shown to be stable and accurate in running an electromagnetic transients cosimulated power grid scenario by interconnecting two commercial DRTS (i.e., RTDS NovaCor), extending the scalability of future smart grid real-time simulations. [ABSTRACT FROM AUTHOR]

Energy transition toward smart grids with deep impact of renewables, energy storage systems, and electric vehicle charging stations will increasingly promote the establishment of energy communities that own portions of the electricity grid. The energy communities will consist of clusters of multiunit buildings and or single residential units aggregated sharing a common or multiple medium and low voltage (MV/LV) electrical substations. The size and impact of the location of these MV/LV substations can constitute a barrier especially for highly urbanized contexts where it is very complicated to provide technical spaces inside buildings for large technical systems like transformers, MV switchgear, etc. The idea of this work consists of developing a compact outdoor MV/LV substation to reduce the overall dimensions and to make the execution modularized to facilitate management and maintenance. Also an investigation of energy exchange between multiunit buildings, which are considered as the real energy community case study. The main objective for this case study is to minimize the operation cost of the system by maximizing the self-consumption. [ABSTRACT FROM AUTHOR]

The Industry 4.0 (I4.0) aims to develop a framework where the new technologies interoperate with each other and with employees, creating a smart and efficient environment. Although there are many public and private initiatives focused on boosting the deployment of I4.0 in all sectors worldwide, the adoption is slower than expected. One of the main reasons is the lack of training in those technologies involved in I4.0, the so-called key-enabling technologies (KET). In this article, the current status of I4.0 adoption from the industry, employees, and training point of view is analyzed. The lack of I4.0 competences in the curricula of vocational education training (VET) and higher education (HE) is also highlighted. Finally, the European innovative training action IN4WOOD is presented as a successful open and free training tool developed to offer students, employees, and managers an easy way to learn, use, and deploy KET of I4.0. Although the main target users of the training action are those in the furniture and woodworking sector, it has been designed to be useful also for users in other business sectors. The training tool is composed of more than 300 video learning pills, practical use cases, gamification, and evaluation test for measuring the level of knowledge acquired. The training tool has been tested in a pilot launched in four European countries. The results from the pilot prove that the IN4WOOD training helps to fill the skill gaps identified in the current VET/HE students and improves the competitiveness of employees, managers, and enterprises. [ABSTRACT FROM AUTHOR]

Since complete surveillance is essential to provide safe daily life to citizen in smart cities, the issue of how to achieve secure surveillance has been driven by various research communities. Also, due to recent epidemic spread such as COVID-19, it is obvious that we should focus on how to manage a cooperative framework for possible future pandemic fights and allied medical services continuously. To support those purposes, it is anticipated that we can utilize AI-assisted communications and technologies using a variety of devices and equipment, including UAVs, mobile robots, and smart devices on the aerial and ground sides. In this article, an aerial-ground cooperative infrastructure is designed to study surveillance and epidemic prevention with managing energy recharge and AI-supported communications through collected or pre-knowledge information for public and private areas. Also, in the proposed architecture, we specify system settings, promising scenarios, and strategies in order to satisfy several objectives and tasks. Then possible research challenges and issues are addressed for successful realization and management of intelligent surveillance and efficient epidemic prevention. [ABSTRACT FROM AUTHOR]

Today's blockchains suffer from low throughput and high latency, which impedes their widespread adoption of more complex applications like smart contracts. In this article, we propose a novel paradigm for smart contract execution. It distinguishes between consensus nodes and execution nodes: different groups of execution nodes can execute transactions in parallel; meanwhile, consensus nodes can asynchronously order transactions and process execution results. Moreover, it requires no coordination among execution nodes and can effectively prevent livelocks. We show two ways of applying this paradigm to blockchains. First, we show how we can make Ethereum support parallel and asynchronous contract execution without hard-forks. Then, we propose a new public, permissionless blockchain. Our benchmark shows that, with a fast consensus layer, it can provide a high throughput even for complex transactions like Cryptokitties gene mixing. It can also protect simple transactions from being starved by complex transactions. [ABSTRACT FROM AUTHOR]

The enormous energy consumed by clouds becomes a significant challenge for cloud providers and smart grid operators. Due to performance concerns, applications typically run in different clouds located in multiple sites. In different clouds, many factors, including electricity prices, available servers, and task service rates, exhibit spatial variations. Therefore, it is important to manage and schedule tasks among multiple clouds in a high-quality-of-service and low-energy-cost manner. This work proposes a task scheduling method to jointly minimize energy cost and average task loss possibility (ATLP) of clouds. A problem is formulated and tackled with an adaptive biobjective differential evolution based on simulated annealing to determine a real-time and near-optimal set of solutions. A final knee solution is further chosen to specify suitable servers in clouds and task allocation among web portals. Simulation results based on realistic data prove that less average loss possibility of tasks, and smaller energy cost is obtained with it than its widely used peers. Note to Practitioners—This work considers joint optimization of both ATLP and average energy cost of all clouds. It is of great significance to execute tasks among multiple clouds by jointly allocating all tasks among multiple web portals and specifying suitable servers in different clouds. Yet, it is challenging to achieve joint optimization in a market where factors, including prices of electricity and available servers, show spatial variations. Current studies are coarse-grained and fail to jointly achieve average energy cost minimization and quality-of-service optimization of tasks. In this work, a novel algorithm named adaptive simulated-annealing-based biobjective differential evolution is proposed for an energy cost and quality-of-service-optimized task scheduling strategy in a real-time manner. Experiments prove that it realizes lower energy cost and ATLP compared with its typical widely used peers. It can also be applied to other industrial areas, including smart manufacturing, Internet of Things, and smart city. [ABSTRACT FROM AUTHOR]

This article presents a method for evaluating smart grid functions that are implemented to operate residential loads. The proposed assessment method is developed based on the energy not-served (ENS) determined at a point-of-supply feeding residential loads. Smart grid functions can operate energy storage appliances (household water heaters, air conditioners, and heating units) to store thermal energy during the daily off-peak-demand hours. The stored thermal energy is discharged during the daily peak-demand hours, thus reducing the power demands of residential loads. The differences in daily energy demands created by smart grid functions can provide an accurate assessment of the effectiveness of smart grid functions. The ENS-based method is tested for 200 residential households fed from four distribution transformers, and are operated by smart grid functions. In these tests, smart grid functions are implemented by the peak-demand management, direct load control, and demand response. Test results demonstrate the accuracy and simplicity of the ENS-based method to assess smart grid functions in terms of the ability to reduce the power demands of residential loads during peak-demand hours. [ABSTRACT FROM AUTHOR]

Hydrogen-integrated transportation and power systems (HTPS) will become an important way to achieve the goal of carbon neutrality. As an important coupling unit of HTPS, the business mechanism of hydrogen fueling stations (HFS) is an important starting point for improving system economy and promotion value. This article proposed an interaction mechanism between HFS and HTPS and hydrogen and fuel cell EVs (HFEVs) in smart cities for HFS pricing strategies. First, we construct the framework of the HTPS interaction mechanism based on the basic combination of HTPS. Then, the interactive model of HTPS is constructed, including the scheduling model of HTPS, the pricing model of HFS and the response model of HFEVs. Finally, an HTPS system is constructed based on the improved IEEE 33-node power distribution system for simulation and analysis. The results show that the interaction mechanism and pricing strategy can improve the economics of HTPS and HFS, the operating cost of HTPS has been reduced by approximately 5.57%, the operating income of HFS has increased by approximately 4.17%. [ABSTRACT FROM AUTHOR]

Under the pressure of climate change, hydrogen has been advocated as a promising energy carrier to achieve low-carbon integration of urban transportation network (UTN) and power distribution network (PDN). In this article, the control strategy based on hydrogen refueling service fee (HRSF) is proposed for the smart city department to guide hydrogen fuel cell electric vehicles (HFCEVs) in selecting the hydrogen refueling stations. Correspondingly, the HRSF-based coordinated operation model is established to minimize the total of UTN travel cost, PDN operation cost, and the environmental cost, while the nodal carbon intensity restriction, the uncertainties of renewable distributed generators output, and origin-destination traffic demand are also taken into account. Afterward, the proposed model is solved by the decentralized alternative direction method of multipliers algorithm, and verified on the hydrogen-integrated UTN and PDN in Sioux Falls. Numerical results demonstrate that popularizing HFCEVs contributes to emission reduction, and the HRSF-based coordinated operation strategy is effective in reducing the overall emissions, promoting renewable energy accommodation, and improving the holistic operation efficiency of the hydrogen-integrated UTN and PDN. [ABSTRACT FROM AUTHOR]

Optical access networks (OANs) with passive optical network technology have been globally deployed on a massive scale, serving as the fundamental information infrastructure of teleCommunication networks. Some telecom service providers are re-architecting their networks based on cloud central office with software-defined networking and network functions virtualization capabilities to provide service agility, intelligence, and scalability. In this article, we present a synthesized analysis of the inherent value and practical use cases of access network (AN) slicing in the context of software-defined OANs. A novel hierarchical abstraction model and a workflow of end-to-end network slicing are presented to illustrate the support of multi-service and multi-tenancy applications over a converged OAN platform by enabling automatic life cycle management of AN slicing. Two successful trials utilizing the model and the workflow are also demonstrated. [ABSTRACT FROM AUTHOR]

The need to increase mobility and remove cables in industrial environments is pushing 5G as a valuable communication system to connect traditional deterministic Ethernet-based devices. One alternative is the adoption of Time Sensitive Networking (TSN) standards over 5G Non-Public Networks (5G NPN) deployed in the company premises. This scenario presents several challenges, the most relevant being the configuration of the 5G part to provide latency, reliability and throughput balance suitable to ensure that all the TSN traffic can be delivered on time. Our research work addresses this problem from the perspective of automata learning. Our aim is to learn from the live network to build a smart controller that can dynamically predict and apply a suitable configuration of the 5G NPN to satisfy the requirements of the current TSN traffic. The article presents the main ideas of this novel approach. [ABSTRACT FROM AUTHOR]

With increasing traffic congestion and accidents, the Internet of Vehicles (IoV) has become a focus of fifth generation (5G) heterogeneous networks (HetNets) and intelligent transportation systems (ITS). IoV has been studied extensively to improve vehicles, road safety, and efficiency. However, the limited spectrum efficiency and interference caused in the 5G HetNets may require robust technology and architecture. The enabling requirements of IoV in 5G HetNets are elaborated and conceptualized in the article. The deep learning algorithms proposed and used in IoV in 5G HetNets can be achieved to reduce delay and improve transmission reliability. The research progress, advantages, and challenges of deep learning in 5G HetNets IoV are illustrated and explored in detail. Moreover, the overall framework of IoV in 5G HetNets is outlined and compared with the related research, which can become the Foundation for future development of intelligent IoV. [ABSTRACT FROM AUTHOR]

RDF is being increasingly considered in a broad range of information management scenarios. Governments, large corporations, startups, and other organizations around the world are using RDF as a data model to represent and share knowledge. However, there is still a long evolutionary track with multiple challenges for RDF reaching the scale of the most recent Big Data intensive applications (e.g., Smart Cities, Sensor Networks, eHealth, Internet of Things). In this survey, we review the usage of NoSQL databases to the storage of large RDF graphs by rehearsing the latest surveys and expanding their findings by updating proposals and bringing light to aspects such as model mapping between RDF and NoSQL, triple indexing and partitioning, graph fragmentation and data caching. Moreover, we explain how the surveyed works extended the RDF capabilities so the datasets can benefit of the characteristics of scalability, schemaless data, and better overall performance of NoSQL databases. The survey summarizes the current state of art, discusses open problems, and proposes a Reference Architecture (RA). For the best of our knowledge, this is the first survey where the focus is solely on papers that use one or more NoSQL systems for the RDF persistence. [ABSTRACT FROM AUTHOR]

This work focuses on the impact of finite-blocklength (FBL) regimes on performance of hybrid automatic repeat request (HARQ) protocols under the proposed conditions of outdated channel state information (oCSI) severity, generalised-Rician fading and maximal-ratio-combining deployment. The outage probability (OP)|defined as the probability of erroneous HARQ short-packet transmissions and the OP is a key performance indicator in this work|will be derived in compact form under the proposed conditions. Compact expressions are obtained, which depict the OP under these practical conditions and present certain trade-off among latency, blocklength and the OP. A brief review of existing results is performed. Penalty of increasing information capacity and the dominance of system coherence are shown, which justifies this work. [ABSTRACT FROM AUTHOR]

The system-by-design (SbD) is an emerging engineering framework for the optimization-driven design of complex electromagnetic (EM) devices and systems. More specifically, the computational complexity of the design problem at hand is addressed by means of a suitable selection and integration of functional blocks comprising problem-dependent and computationally efficient modeling and analysis tools as well as reliable prediction and optimization strategies. Due to the suitable reformulation of the problem at hand as an optimization one, the profitable minimum-size coding of the degrees of freedom (DoFs), and the “smart” replacement of expensive full-wave (FW) simulators with proper surrogate models (SMs), which yield fast yet accurate predictions starting from minimum size/reduced CPU-costs training sets, a favorable “environment” for optimal exploitation of the features of global optimization tools in sampling wide/complex/nonlinear solution spaces is built. This research summary is then aimed at: 1) providing a comprehensive description of the SbD framework and of its pillar concepts and strategies; 2) giving useful guidelines for its successful customization and application to different EM design problems characterized by different levels of computational complexity; and 3) envisaging future trends and advances in this fascinating and high-interest (because of its relevant and topical industrial and commercial implications) topic. Representative benchmarks concerned with the synthesis of complex EM systems are presented to highlight advantages and potentialities as well as current limitations of the SbD paradigm. [ABSTRACT FROM AUTHOR]

The fields of health care, education, culture, and shopping can all be integrated into the core of a smart city to create an infrastructure that allows people to live more conveniently. We must think ahead about cybersecurity, as cyberattacks can threaten the lives of citizens. [ABSTRACT FROM AUTHOR]

Bike sharing service (BSS) networks have been proliferating all over the globe thanks to their success as the first/last-mile connectivity inside a smart city. Their (re)configuration — i.e., station (re)placement and dock resizing — has thus become increasingly important for BSS providers and smart city planners. Instead of using conventional labor-intensive manual surveys, we propose a novel information fusion framework called CBikes that (re)configures the BSS network by jointly fusing crowdsourced station suggestions from online websites and the usage history of bike stations. Using comprehensive real data analyses, we identify and exploit important global trip patterns to (re)configure the BSS network while mitigating the local biases of individual feedbacks. Specifically, crowdsourced feedbacks, station usage, cost and other constraints are fused into a joint optimization of BSS network configuration. We also model the spatial distributions of station usage to account for and estimate the unexplored regions without historical usage information. We further design a semidefinite programming transformation to solve the bike station (re)placement problem efficiently and effectively. Our extensive data analytics and evaluation have shown CBikes’ effectiveness and accuracy in (re)placing stations and resizing docks based on three large BSS systems (with $>$ > 900 stations) in Chicago, Twin Cities (Minneapolis–Saint Paul), and Los Angeles. [ABSTRACT FROM AUTHOR]

In this paper, a switched multibeam multi-sector array antenna is proposed to provide 3-D coverage for vehicle to infrastructure (V2I) under 5G-IoT application framework. The multi-sector design is optimized using a quality-conscious communication area synthesis process to achieve $\mathbf {360}^\circ$ coverage in azimuth. Each sector of the design has two microstrip array antenna (MAA) to obtain coverage in the upper and lower halves of the elevation region. The proposed antenna is designed to enhance frequency-dependent beam squint phenomena exploited to achieve multibeam coverage in the elevation region. The simulation results of the proposed antenna having $\mathbf {3\times 2}$ size of MAA indicates the achieved desired radiation characteristics, and a prototype of the same operating in WLAN band ($\mathbf {5.15-5.825}$ GHz) is fabricated for verification. The experimental results show the maximum realized gain is $\mathbf {7.85}$ dBi and the beam tilt of $\mathbf {10.8}^\circ$ to $\mathbf {46.8}^\circ$ in elevation is achieved at central frequencies $\mathbf {5.15}$ GHz and $\mathbf {5.825}$ GHz, respectively. The coverage obtained is $\mathbf {360}^\circ$ in azimuth and $\mathbf {93.6}^\circ$ in elevation. The signal to interference ratio is measured around the antenna, which shows interference suppression of more than $\mathbf {10}$ dB achieved between the MAAs of alternate sectors. Moreover, the port isolation is better than $\mathbf {26}$ dB. Hence, sixteen simultaneous beams utilizing frequency re-use can be operated in the 3-D space around the access point to communicate with multiple drones/vehicles and IoT nodes to realize a smart vehicular application. [ABSTRACT FROM AUTHOR]

With the proliferation of electric vehicles (EVs), private charging pile (PCP) sharing networks are likely to be an integral part of future smart cities, especially in places with limited public charging infrastructure. However, there are a number of operational challenges associated with the deployment of PCPs in such a shared and untrusted environment. For example, how do we formulate efficient PCP sharing strategies in PCP sharing networks, while also taking into consideration the dynamic charging behaviors of EVs? Therefore, in this paper, we propose an energy blockchain-based secure PCP sharing scheme (BBC) for PCP sharing networks. First, an energy blockchain-based framework is designed for PCP sharing networks to facilitate energy sharing services for EVs and PCPs, using both distributed ledgers and cryptocurrency. Then, we devise a reputation-based secure PCP sharing algorithm to improve consensus efficiency with smaller signature sizes. In addition, a distributed reputation mechanism is constructed to assess the trustworthiness of consensus nodes in blockchain, based on ratings, behaviors, and fading. We also model the interactions among EVs and cooperative PCPs as a joint coalition-matching game, and obtain the optimal strategies of PCPs and EVs by analyzing the Nash-stable coalitional structure and stable many-to-one matching pairs. Extensive simulations and the real-world implementation demonstrate that the proposed approach improves the utility of EV users and renewable energy efficiency in PCP sharing networks. [ABSTRACT FROM AUTHOR]

Smart agriculture (SA) incorporates low-cost and low-energy-consuming sensors and devices to enhance quantitative and qualitative agricultural production. However, this device uses an open communication channel, i.e., Internet, and generates large amount of data in real time and, thus, has the potential to be misused. As a consequence, the major concern in the implementation of SA is minimizing the risk of security and data privacy violation (e.g., adversaries performing inference attacks). To address these challenges, we propose PEFL, a deep privacy-encoding-based federated learning (FL) framework that adopts a perturbation-based encoding and long short-term memory-autoencoder technique to achieve the target of privacy. Then, an FL-based gated recurrent unit neural network algorithm (FedGRU) is designed using the encoded data for intrusion detection. The experimental results based on the ToN-IoT data set reveal that the PEFL can efficiently identify normal and attack patterns after transformation over other non-FL and FL methods. [ABSTRACT FROM AUTHOR]

To achieve net-zero emissions economy, the transition to online entertainment and retail, aging populations, urban population growth, and pressures on public finance have created huge interests for human to run cities differently and smartly. A term titled smart city is created which is considered as an idealistic city, where the quality of life for citizens is greatly improved by utilizing information and communication technology (ICT), new services, and new city infrastructures to efficiently achieve the value, such as sustainable and resilient development. The eco-sustainable method has to be used in several aspects, such as energy, mobility, environment, and social services. Research and development in smart cities is expanding exponentially. SMC is one of the core sponsors of the IEEE Smart Cities. [ABSTRACT FROM AUTHOR]

In smart grids, the large-scale integration of distributed renewable energy resources has enabled the provisioning of alternative sources of supply. Peer-to-peer (P2P) energy trading among local households is becoming an emerging technique that benefits both energy prosumers and operators. Since conventional energy supply is still needed to help fill the gap between local demand and supply when the local solar generation is not sufficient, demand–response management will keep playing an important role in the future P2P energy market. Blockchain and smart contract technology has gained increasing attention in P2P trading for its secure operation. The performance of blockchain-based P2P energy trading still remains to be improved, in terms of latency and cost of computation resources. This article studies the challenges of demand–response management in P2P energy trading and proposes a blockchain-empowered energy trading system for a community-based P2P market. The proposed demand–response mechanism is developed using two noncooperative games, in which dynamic pricing is applied for suppliers. The proposed energy trading system is prototyped on a cluster network, with a coordinator running as a smart contract in a Hyperledger blockchain. We implemented both on-chain and off-chain processing modes to study the system performance. The results from experiments with our prototype indicate that our proposed demand–response games have a great effect on reducing the net peak load, and at the same time, the off-chain processing mode provides lower latency and overhead compared to the on-chain mode while still keeping the same system integrity as the on-chain mode. [ABSTRACT FROM AUTHOR]

Sharing private parking spaces during their idle time periods has shown great potential for addressing urban traffic congestion and illegitimate parking problems in smart cities. In this article, aiming to address the online parking-space sharing issue while ensuring the privacy of customer parking destination locations, we propose a novel destination privacy-preserving online parking sharing (DPOPS) incentive scheme. In particular, the online parking-space sharing problem is formalized as a social welfare maximization problem in a two-sided market, where parking-space providers (PSPs) and customers are regarded as sellers and buyers. Then, novel threshold value-based rules are designed to determine winners, payments, and reimbursement. Finally, winners are matched by solving a mixed-integer nonlinear programming problem, aiming to minimize the distance between customer’s destination and allocated parking space. In addition, the location privacy of the customers’ destinations is protected by the Laplace mechanism. We prove that DPOPS achieves several economically effective properties and approximate differential privacy. We analyze the upper bound of the efficiency loss of our scheme. Extensive evaluation results demonstrate that our scheme can not only achieve good performance regarding social welfare, PSP satisfaction ratio, privacy preservation, and computation overhead but also leads to shorter travel distances for customers comparing to the baseline scheme. Note to Practitioners—In this article, we address the online parking-space sharing issue with considering the parking-space providers (PSPs) and customers’ individual utility while preserving the location privacy of customers’ destinations. Most of the previous works focused on designing a centralized mechanism for allocating parking spaces without considering the protection of the customers’ location privacy. In particular, we propose an online parking-space sharing scheme called DPOPS, including a novel threshold value-based winner determination rule and a parking-space allocation rule. The proposed scheme DPOPS allows the PSPs and customers submit their bids and asks according to their own willingness and is able to improve the utilization of private parking spaces during their idle time periods. Moreover, the location privacy of customers’ destinations is protected by the Laplace mechanism. The experiments demonstrate that the proposed approach outperforms the exponential-based scheme in terms of PSP satisfaction ratio and the travel distance for parking-space customer. The proposed scheme is helpful in managing the vacant parking space in a competitive market and can be readily implemented in the real-world online parking-space sharing systems. [ABSTRACT FROM AUTHOR]

In the emerging era of transportation, the Internet of Vehicles envisions a large ecosystem where vehicles, traffic infrastructure, smart city sensors, and citizens will interoperate, providing safe and fast transportation services. How close are we to realizing this concept? [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) have emerged as one of the most promising solutions for reducing carbon emissions in smart cities. However, due to the limited battery life of EVs and the scarcity of charging stations, EV drivers are not willing to travel long distances. Thus, blockchain-enabled energy trading (BET) has lately been used to securely share energy among EVs via wireless power transfer (WPT) technology. Blockchain is used to ensure the security and privacy of transactions between untrustworthy EVs in the WPT process. Nevertheless, previous works on BET have relied on existing consensus mechanisms built on the requirements of the cryptocurrency systems. These consensus mechanisms have faced significant challenges in maintaining high reliability, throughput, low latency, and network scalability in V2V energy trading that requires real-time services. To address these issues, we propose a new consensus mechanism that leverages the benefits of Practical Byzantine Fault Tolerance (PBFT) and Proof of Reputation (PoR) called PBFT-based PoR (PPoR). The energy trading process runs in a clustered vehicular network, where validator selection, block generation, and consensus processes are performed in each cluster. We adopt an incentive mechanism based on a Stackelberg game model to optimize the utility of sellers, buyers, and validator nodes, which motivates honest and cooperative nodes. The simulation results show that the proposed scheme reduces buyers’ costs by 21.1% while increasing the utility of sellers by 18%. Moreover, compared to benchmarks, the proposed scheme reduces the transaction processing delay and increases the throughput by more than 47.1% and 15.7%, respectively. [ABSTRACT FROM AUTHOR]