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There is a continuing interest in exploring the impact of operational excellence (OE) on operational performance. Several OE practices such as Lean, Agile, and Six Sigma are well recognized. However, not all OE deployments in firms have been successful, and a failure of such efforts has often been expensive. Past research reveals several critical failure factors (CFFs) that lead to OE deployment failures in firms. We present these CFFs as an integrated model, based on two dominant schools of OE that promote technical and behavioral factors independently. Further, we validate their direct and mediating relationships through an empirical examination of 663 responses collected from 153 manufacturing units across the USA, U.K., China, and India. This article contributes to the OE body of knowledge by examining the significant direct and mediating effects of behavioral and technical CFFs on the OE failures. Our findings reveal that the technical CFFs mediate the impact of behavioral CFFs on OE failures. Consequently, several managerial and theoretical implications are presented.

Driven by the increasing demand of real-time mobile application processing, Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges. In this paper, we investigate an MEC network enabled by Unmanned Aerial Vehicles (UAV), and consider both the multi-user computation offloading and edge server deployment to minimize the system-wide computation cost under dynamic environment, where users generate tasks according to time-varying probabilities. We decompose the minimization problem by formulating two stochastic games for multi-user computation offloading and edge server deployment respectively, and prove that each formulated stochastic game has at least one Nash Equilibrium (NE). Two learning algorithms are proposed to reach the NEs with polynomial-time computational complexities. We further incorporate these two algorithms into a chess-like asynchronous updating algorithm to solve the system-wide computation cost minimization problem. Finally, performance evaluations based on real-world data are conducted and analyzed, corroborating that the proposed algorithms can achieve efficient computation offloading coupled with proper server deployment under dynamic environment for multiple users and MEC servers.

The 5G ecosystem is comprised of the cellular 5G System, as well as a managed and orchestrated infrastructure providing virtualized network and service functions. The automotive industry with its stringent requirements for connected vehicles is a promising and yet challenging consumer of such 5G ecosystem. Deployment of service instances at distributed cloud resources of cellular network infrastructure edges enables localized low-latency access to these services from moving vehicles but comes along with challenges, such as the need for fast recon-figuration of the distributed deployment according to mobility pattern and associated service and resource demand. In this paper, we investigate a solution for the collaborative orchestration of services for Connected, Cooperative and Automated Mobility (CCAM) within such 5G ecosystem. A key objective is the service continuity for a highly dynamic automotive scenario, achieved by the associated management and orchestration of these services in distributed edge clouds. The proposed solution leverages a multi-tier orchestration system as well as localized management and protocol operations for collaborative edge resources. By means of both analytical and experimental evaluations, the paper draws conclusions on the gain in accelerating orchestration decisions and enforcements, while balancing associated protocol and computational load over the highly distributed and multi-layered orchestration system.

Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed -Nash equilibrium. Performance results show that our algorithm has significant superiority on average profits, utilities and execution time compared with other representative algorithms.

Internet of Things (IoT) has brought changes in many fields by joining physical space with the cyber space. The IoT devices are becoming increasingly complex. With the rapid deployment of cameras, tasks in IoT like visual information are more important, but IoT devices have limited computing resources, including power, computing ability, storage, etc. Some tasks that might be perfectly normal to perform on a computer would be rather challenging on an IoT device. Therefore, how to maintain acceptable performance while minimizing resources is becoming a more consequential part in IoT. In the paper, we aim to solve the problem of object detection and tracking in IoT while minimizing resources. The traditional algorithms need to use convolutional neural network (CNN) to identify different objects in each frame, and then determine the tracking target from identified objects, which typically requires a lot of computing resources. By incorporating spatial attention, and multi-domain network, we proposed a novel algorithm named as Spatial Attention Powered Multi-Domain Network (SA-MDNet). By adding spatial attention mechanism to the original MDNet model, and using multi class cross entropy loss, we are able to distinguish the background and the target in different video sequences effectively and efficiently. This novel algorithm achieves similar performance on the OTB 50/100/2013 datasets compared to several state-of-art models, while uses only a fraction of the memory compared to MDNet.

With the massive deployment of the Internet of things (IoT) devices, many data analysis applications emerge for the large amount of data accumulated by IoT. Federated learning (FedL) on IoT devices is an appealing mode to train a precise data analysis model. However, existing federated learning schemes either take expensive computation costs (e.g., public key cryptographic operations) or a large number of interactions among participants. Obviously, these schemes are unsuitable for IoT devices due to the limited computational and communication resources. In this work, we propose a lightweight privacy-preserving federated learning scheme for IoT devices. To protect the privacy of individual local data, we add masks to intervening parameters. An effective secret sharing scheme is adopted to ensure that masks can be eliminated accurately. Considering that FedL involves multiple iterations and mask generation for each iteration costs a large number of interactions among users for privacy guarantee, we also design a secure mask reusing mechanism for large-scale FedL tasks. We prove that our scheme is secure against the honest-but-curious model. In addition, we also expand our scheme to deal with the collusion attack. Extensive experiments on real IoT devices demonstrate the accuracy and efficiency of our work.

The seamless integration of sensors and smart communication technologies has led to the development of various supporting systems for Financial Technology (FinTech). The emergence of the Next-Generation Internet of Things (Nx-IoT) for FinTech applications enhances the customer satisfaction ratio. The main research challenge for FinTech applications is to analyse the incoming tasks at the edge of the networks with minimum delay and power consumption while increasing the prediction accuracy. Motivated by the above-mentioned challenge, in this paper, we develop a ranked-based service deployment strategy and an Artificial Intelligence technique for financial data analysis at edge networks. Initially, a risk-based task classification strategy has been developed for classifying the incoming financial tasks and providing the importance to the risk-based task for meeting users’ satisfaction ratio. Besides that, an efficient service deployment strategy is developed using Hall’s theorem to assign the ranked-based financial data to the suitable edge or cloud servers with minimum delay and power consumption. Finally, the standard support vector machines (SVM) algorithm is used at edge networks for analysing the financial data with higher accuracy. The experimental results demonstrate the effectiveness of the proposed strategy and SVM model at edge networks over the baseline algorithms and classification models, respectively.

Applications deployed on edge servers improve users experience, when compared to deployments on cloud servers. Existing works usually assume that a central scheduler helps in making decisions, but they are often inefficient, inaccurate, or time-consuming. In this paper, we present a proactive application deployment system, which consists of three modules (i.e., incentive, profit, and latency). Based on the architecture of a fully distributed edge network, our system includes SELL, a Spontaneous Edge depLoyment aLgorithm in the incentive module. SELL lets edge servers compete with each other in a two-stage Stackelberg game to win deployment rights, and the winners get paid for their deployment efforts. The other two modules help recursively adjust service prices and deployment intentions in view of their own profits. Simulations on the proactive edge application deployment system demonstrate that SELL can help an application provider find appropriate edge servers to deploy applications while maximizing the profits for both parties in a low latency.

This publication offers a comprehensive survey on the topic of Low-Power Wide Area Networks (LPWANs). We discuss the scope of LPWAN connectivity and present numerous well-established technologies that can offer such a service. From this presentation, LoRa/LoRaWAN systems emerge as one of the dominant solutions, and hence we offer details about end-to-end LoRa/LoRaWAN-enabled networking. As an area being in its infancy, LPWANs in general and LoRa/LoRaWAN in specific undergo extensive research. We shed light in key contributions, whereas we also provide our findings regarding the main research focus and needs. Simulation studies appear to be a popular choice among researchers, whereas real system implementations are constantly emerging thanks to low deployment and operational cost. For the former, we offer a comparative overview of existing simulators, whereas for the latter we discuss key considerations about the server-side part of a deployed system, which is usually supported by cloud infrastructure. Finally, LPWANs are systems that experience intense data generation, and thus we deliver a presentation of data-related operations which take place in such systems. This presentation revealed a major vertical market that is expected to be served by LPWANs: the Industiral IoT; which embodies concepts like Asset Administration Shell and Digital Twins.

Given a publicly available pool of machine learning models constructed for various tasks, when a user plans to build a model for her own machine learning application, is it possible to build upon models in the pool such that the previous efforts on these existing models can be reused rather than starting from scratch? Here, a grand challenge is how to find models that are helpful for the current application, without accessing the raw training data for the models in the pool. In this paper, we present a two-phase framework. In the upload phase, when a model is uploading into the pool, we construct a reduced kernel mean embedding (RKME) as a specification for the model. Then in the deployment phase, the relatedness of the current task and pre-trained models will be measured based on the value of the RKME specification. Theoretical results and extensive experiments validate the effectiveness of our approach.

Machine learning (ML) models are increasingly trained with distributed workers possessing heterogeneous resources. In such scenarios, model training efficiency may be negatively affected by \emph{stragglers}---workers that run much slower than others. Efficient model training requires eliminating such stragglers, yet for modern ML workloads, existing load balancing strategies are inefficient and even infeasible. In this paper, we propose a novel strategy, called \emph{semi-dynamic load balancing}, to eliminate stragglers of distributed ML workloads. The key insight is that ML workers shall be load-balanced at \emph{iteration boundaries}, being non-intrusive to intra-iteration execution. Based on it we further develop LB-BSP, an integrated worker coordination mechanism that adapts workers' load to their instantaneous processing capabilities---by right-sizing the sample batches at the synchronization barriers. We have designed distinct load tuning algorithms for ML in CPU clusters, in GPU clusters as well as in federated learning setups, based on their respective characteristics. LB-BSP has been implemented as a Python module for ML frameworks like TensorFlow and PyTorch. Our EC2 deployment confirms that LB-BSP is practical, effective and light-weight, and is able to accelerating distributed training by up to $54\%$ .

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 exibly across city regions that expand and/or shrink, accurate prediction of the e-scooter distribution given the recongured regions becomes essential for city planning. We present GCScoot, a novel ow distribution prediction approach for reconguring urban DES systems. Based on real-world datasets with reconguration, we analyze e-scooter distribution features and ow dynamics for the data-driven designs. We propose a novel spatio-temporal graph capsule neural network within GCScoot to predict future dockless e-scooter ows given the recongured regions. GCScoot pre-processes historical spatial e-scooter distributions into ow graph structures, where discretized city regions are considered as nodes and inter-region ows 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 ow graphs are captured via the multi-graph capsule convolutional neural network which accurately predicts the DES ows. 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.

Cloud deployment architectures have become a preferable computation model of Big Data (BD) operations. Their scalability, flexibility, and cost-effectiveness motivated this trend. In a such deployment model, the data are no longer physically maintained under the user’s direct control, which raises new security concerns. In this context, BD security plays a decisive role in the widespread adoption of cloud architectures. However, it is challenging to develop a comprehensive security plan unless it is based on a preliminary analysis that ensures a realistic secure assembly and addresses domain-specific vulnerabilities. This article presents a novel security-by-design framework for BD frameworks deployment over cloud computing (BigCloud). In particular, it relies on a systematic security analysis methodology and a completely automated security assessment framework. Our framework enables the mapping of BigCloud security domain knowledge to the best practices in the design phase. We validated the proposed framework by implementing an Apache Hadoop stack use case. The study findings demonstrate its effectiveness in improving awareness of security aspects and reducing security design time. It also evaluates the strengths and limitations of the proposed framework, from which it highlights the main existing and open challenges in the BigCloud-related area.

With the ubiquitous deployment and applications of internet of things (IoT), security issues pose a critical challenge to IoT devices. External attackers often utilize vulnerable IoT devices to invade the target’s internal network and then further cause a security threat to the whole network. To prevent such attacks, it is necessary to develop a security mechanism to control the access of suspicious IoT devices and manage the internal devices. In recent years, deep learning (DL) algorithm has been widely used in the field of edge device identification (EDI), and has made great achievements. However, these previous methods are essentially centralized learning-based EDI (CentEDI) that trains all data together, which can not guarantee data security and not conducive to deployment on edge devices. To address this problem, we introduce a federated learning-based EDI (FedeEDI) method via network traffic to automatically identify edge devices connected to the whole network. Experimental results show that the training efficiency of our proposed FedeEDI method is much higher than that of the CentEDI method, although its classification accuracy is slightly reduced. In contrast to the CentEDI method, the proposed FedeEDI method has two main advantages: faster training speed and safer training process.

Maintenance of military equipment plays a vital role in achieving higher war readiness. The vast number of military equipment and their deployment on distinct locations make their maintenance a challenging task. Decision-makers are always interested in knowing the latest status of equipment, develop effective maintenance strategies, and predict mission reliability as accurately as possible. To suffice this need, maintenance data of military equipment must be maintained with high granularity and accuracy, including maintenance information about every lowest maintainable unit of every military equipment. There is a profound need for a comprehensive maintenance management framework, which addresses several military-specific issues with equipment maintenance and stores the maintenance data in the utmost secure environment. In this article, we attempt to provide a novel blockchain enabled framework to make military maintenance management comprehensive and future ready. This article first provides a representation of the maintenance scenario of military equipment and later discusses the several choices made available within the blockchain technology, along with the architecture of the proposed blockchain. The proposed framework uses smart contracts to make monitoring and validation more uncompromising and with minimum human intervention. Some pragmatic analytics are presented in this article, along with the inherent blockchain advantages.

Participant selection is a fundamental research issue in Mobile Crowdsensing (MCS). Previous approaches commonly assume that adequately long periods of candidate participants' historical mobility trajectories are available to model their patterns before the selection process, which is not realistic for some new MCS applications or platforms. The sparsity or even absence of mobility traces will incur inaccurate location prediction, thus undermining the deployment of new MCS applications. To this end, this paper investigates a novel problem called From-Scratch MCS (FS-MCS for short), in which we study how to intelligently select participants to minimize such cold-start effect. Specifically, we propose a novel framework based on reinforcement learning, named RL-Recruiter+. With the gradual accumulation of mobility trajectories over time, RL-Recruiter+ is able to make a good sequence of participant selection decisions for each sensing slot. Compared to its previous version, RL-Recruiter, Re-Recruiter+ jointly considers both the previous coverage and current mobility predictability when training the participant selection decision model. We evaluate our approach experimentally based on two real-world mobility datasets. The results demonstrate that RL-Recruiter+ outperforms the baseline approaches, including RL-Recruiter under various settings.

Healthcare blockchains provide an innovative way to store healthcare information, execute healthcare transactions, and build trust for healthcare data sharing and data integration in a decentralized open healthcare network environment. Although the healthcare blockchain technology has attracted broad interests and attention in industry, government and academia, the security and privacy concerns remain the focus of debate when deploying blockchains for information sharing in the healthcare sector from business operation to research collaboration. This paper focuses on the security and privacy requirements for medical data sharing using blockchain, and provides a comprehensive analysis of the security and privacy risks and requirements, accompanied by technical solution techniques and strategies. First, we discuss the security and privacy requirements and attributes required for electronic medical data sharing by deploying the healthcare blockchain. Second, we categorize existing efforts into three reference blockchain usage scenarios for electronic medical data sharing, and discuss the technologies for implementing these security and privacy properties in the three categories of usage scenarios for healthcare blockchain, such as anonymous signatures, attribute-based encryption, zero-knowledge proofs, verification techniques for smart contract security. Finally, we discuss other potential blockchain application scenarios in healthcare sector. We conjecture that this survey will help healthcare professionals, decision makers, and healthcare service developers to gain technical and intuitive insights into the security and privacy of healthcare blockchains in terms of concepts, risks, requirements, development and deployment technologies and systems.

The deployment of monoculture software stacks can have devastating consequences because a single attack can compromise all of the vulnerable computers in cyberspace. This one-vulnerability-affects-all phenomenon will continue until after software stacks are diversified, which is well recognized by the research community. However, existing studies mainly focused on investigating the effectiveness of software diversity at the building-block level (e.g., whether two independent implementations indeed exhibit independent vulnerabilities); the effectiveness of enforcing network-wide software diversity is little understood, despite its importance in possibly helping justify investment in software diversification. As a first step towards ultimately tackling this problem, we propose a systematic framework for modeling and quantifying the cybersecurity effectiveness of network diversity, including a suite of cybersecurity metrics. We also present an agent-based simulation to empirically demonstrate the usefulness of the framework. We draw a number of insights, including the surprising result that proactive diversity is effective under very special circumstances, but reactive-adaptive diversity is much more effective in most cases.

As an emerging computing paradigm, the Internet of Vehicles (IoV) brings a lot of benefits for drivers and consumers, such as route recommendation, reducing traffic congestion, and parking difficulty. However, participants in IoV are inevitably exposed to privacy threats because their data is outsourced to a third party. This paper presents a comprehensive analysis of the root causes of privacy risks in IoV and the potential solutions. We begin by introducing the concrete privacy challenges, which are tied to the structural properties of IoV. Then, we present a unified framework of data privacy preservation in IoV, which integrates multiple privacy techniques, including encryption, anonymity, and perturbation. Next, we investigate the possible research directions for route recommendation, charging deployment, traffic prediction under different privacy requirements in detail. Finally, we discuss emerging techniques and environments (e.g., blockchain, smart grid, 5G communication) to strengthen the ability and application of IoV under strong protection. With the fast development of communication and infrastructure technologies, the Internet of Vehicles (IoV) is attracting more and more attention from academia and industry [1]. The novel computing paradigm consists of hundreds of thousands of interacted and autonomous vehicles. Each is viewed as an intelligent object capable of sensing, computing, and data storage. Intelligent entities are able to undertake multiple different roles, which brings a series of functions, including autonomous driving and platooning, information sharing and learning, traffic control, and optimization.

Despite widespread LTE deployment, coverage does not necessarily translate to usable service. Even in well-provisioned urban networks, unusually high usage (such as during a public event or after a natural disaster) can lead to congestion that makes the LTE service difficult, if not impossible, to use, even if the user is solidly within the coverage area. A typical approach to detect and quantify congestion on LTE networks is to secure the cooperation of the network provider for access to internal metrics. An alternative approach is to deploy multiple mobile devices with active subscriptions to each network operator. Both approaches are resource and time intensive. In this work, we propose a novel method to estimate congestion from overloaded LTE networks using only passive measurements, and without requiring provider cooperation. We analyze packet-level traces for four commercial LTE service providers, from several locations during both typical levels of usage and during public events that yield large, dense crowds. This study presents the first look at congestion detection through overload estimation by examining unencrypted broadcast messages. We show that an upsurge in broadcast reject and cell barring messages, leading to overload, can accurately detect an increase in network congestion.

The edge computing paradigm, spurred by the Internet-of-Things, poses new requirements and challenges for distributed application deployment. There is a need for an orchestrator design that leverages characteristics that enable this new paradigm. We present HYDRA, a decentralized and distributed orchestrator for containerized microservice applications. This orchestrator focuses on scalability and resiliency to enable the global manageability of cloud and edge environments. It can manage heterogeneous resources across geographical locations and provide robust application control. Further, HYDRA enables the location-aware deployment of microservice applications via containerization. Thus, an application's services may be deployed to separate locations according to expected needs. In this paper, the experiments show the orchestrator scaling to 20 000 nodes and simultaneously deploying 30 000 applications. Further, empirical results show that location-aware application deployment does not hinder HYDRA's performance, and the random resource search algorithm currently being employed may be used as a baseline to find resources in this decentralized orchestrator. Therefore, we conclude that HYDRA is a viable orchestrator design for the new computing paradigm.

Adverse weather has long been recognized as one of the major causes of motor vehicle crashes due to its negative impact on visibility and road surface. Providing drivers with real-time weather information is therefore extremely important to ensure safe driving in adverse weather. However, identification of road weather and surface conditions is a challenging task because it requires the deployment of expensive weather stations and often needs manual identification and/or verification. Most of the Department of Transportations (DOTs) in the U.S. have installed roadside webcams mostly for operational awareness. This study leveraged these easily accessible data sources to develop affordable automatic road weather and surface condition detection systems. The developed detection models are focused on three weather conditions; clear, light snow, and heavy snow; as well as three surface conditions: dry, snowy, wet/slushy. Several pre-trained Convolutional Neural Network (CNN) models, including AlexNet, GoogLeNet, and ResNet18, were applied with proper modification via transfer learning to achieve the classification tasks. The best performance was achieved using ResNet18 architecture with an unprecedented overall detection accuracy of 97% for weather detection and 99% for surface condition detection. The proposed study has the potential to provide more accurate and consistent weather information in real-time that can be made readily available to be used by road users and other transportation agencies. The proposed models could also be used to generate temporal and spatial variations of adverse weather for proper optimization of maintenance vehicles’ route and time.

“Digitalization of forest”, as this phrase itself suggests the sustainable implementation of cutting-edge technologies into forests for improving the current trends that are being used for forest environment monitoring, data acquisition, and analysis in the field of research and development. Technologies that can be used effectively for achieving these objectives include the Internet of Things, Wireless Sensor Networks, Internet of Trees, Deep Learning, etc. In this study, we will be exploring and assimilating the limitless possibilities for technological interventions in forests to drastically improve their ecosystem. Intelligent systems for sensing, monitoring, and methods for analysis to be used in applications such as forest fire incidents, illegal logging of trees, poaching, etc. have been discussed briefly. In addition to that, generalized architectures have been proposed which can be used directly in the future for advancements in research and development related to data collection and processing applications like flora analysis, forest fire predictions, etc. without the need for any changes in forest deployment systems. Various methods for enhancement of tribal livelihood, high yield marketing of minor forest produces, and wildlife monitoring is also discussed relating to the areas concentric on the forest environment. Effective connectivity, sustained deployment of real-time sensing systems, and energy harvesting are some of the vital recommendations included and addressed in this study for aiding the proper implementation of digital networks in the forest ecosystem.

Due to the advantage of flexible and quick movement, unmanned aerial vehicles (UAVs) have been widely utilized in assisting wireless communications. A challenging problem is how to deploy UAVs to provide communication services for more user devices for a long term while minimizing the number of UAVs. In this article, we propose a UAV network system with UAV deletion due to redundancy or energy exhaustion and UAV insertion to provide long-lasting communication services. Then, a distributed algorithm based on the virtual Coulomb force and Voronoi diagram is proposed to deploy UAVs improving the communication coverage and turning redundant UAVs off. On the one hand, in order to improve the communication coverage, we propose two moving schemes to move UAVs. On the other hand, in order to save energy, we propose a sleeping scheme to turn several redundant UAVs off. Particularly, the redundancy of a UAV is evaluated according to a proposed definition of the average local coverage rate. Simulation results demonstrate that the proposed algorithm can deploy a minimal number of UAVs to provide enough communication coverage and the presented network system can offer long-lasting service.

The Ultra HD Forum was established in 2015 to accelerate Ultra HD deployment by bringing together market leaders from all parts of the industry. Broadcasters, service providers, consumer electronics, and technology vendors collaborate on solving real-world obstacles in deploying advanced media formats and publishing these solutions for broad adoption. The Forum’s Guidelines Working Group has published guidelines that describe a uniform set of characteristics for Ultra HD content and consistent methods for creating and delivering Ultra HD content.

Unmanned Aerial Vehicles (UAVs) have received a wide range of attention for military and commercial applications. Enhanced with communication capability, UAVs are considered to play important roles in the Sixth Generation (6G) networks due to their low cost and flexible deployment. 6G is supposed to be an all-coverage network to provide ubiquitous connections for space, air, ground and underwater. UAVs are able to provide air-borne wireless coverage flexibly, serving as aerial base stations for ground users, as relays to connect isolated nodes, or as mobile users in cellular networks. However, the onboard energy of small UAVs is extremely limited. Thus, UAVs can be only deployed to establish wireless links temporarily. Prolonging the lifetime and developing green UAV communication with low power consumption becomes a critical challenge. In this article, a comprehensive survey on green UAV communications for 6G is carried out. Specifically, the typical UAVs and their energy consumption models are introduced. Then, the typical trends of green UAV communications are provided. In addition, the typical applications of UAVs and their green designs are discussed. Finally, several promising techniques and open research issues are also pointed out.

The increasing complexity of Intelligent Transportation Systems (ITS), that comprise a wide variety of applications and services, has imposed a necessity for high-performance Modern Hardware Devices (MHDs). The performance challenge has become more noticeable with the integration of Machine Learning (ML) techniques deployed in large-scale settings. ML has effectively supported the field of ITS by providing efficient and optimized solutions to problems that were otherwise tackled using traditional statistical and analytical approaches. Addressing the hardware deployment needs of ITS in the era of ML is a challenging problem that involves temporal, spatial, environmental, and economical factors. This survey reviews the recent literature of ML-driven ITS, in which MHDs were utilized, with a focus on performance indicators. A taxonomy is then synthesized, giving a complete representation of what the current capabilities of the surveyed ITS rely on in terms of ML techniques and technological infrastructure. To alleviate the difficulties faced in the non-trivial task of selecting suitable ML techniques and MHDs for an ITS with a specific complexity level, a performance evaluation framework is proposed. The presented survey sets the basis for developing suitable hardware, facilitating the integration of ML within ITS, and bridging the gap between research and real-world deployments.

Recently, rapid deployment on fifth-generation (5G) networks has brought great opportunities for the enabling data-intensive applications, and brings an extending expectation on the developments of 6G. A basic requirement to develop 6G networks is to reach data with low latency, low cost and high coverage in smart IoT. Therefore, this paper proposes a novel machine learning based approach to collect data from multiple sensor devices by cooperation between vehicle and UAV in IoT. Firstly, genetic algorithm is utilized to select vehicular collectors to collect massive data from sensor devices, which aims to maximize coverage ratio and to minimize employment cost. Secondly, we design a novel Deep Reinforcement Learning (DRL)-based route policy to plan collection routes of UAVs with constrain energy, which simplifies the network model, accelerates training speeds and realizes dynamic planning of flight paths. The optimal collection route of an UAV is a series of outputs based on the proposed DRL-based route policy. Finally, our extensive experiments demonstrate that the proposed scheme can comprehensively improve the coverage ratio of massive data collections and reduce collection costs in smart IoT for the future 6G networks.

The rapid progress of urbanization has expedited the process of urban planning, e.g., new residential, commercial areas, which in turn boosts the local travel demand. We propose a novel "off-deployment traffic estimation problem", namely, to foresee the traffic condition changes of a region prior to the deployment of a construction plan. This problem is important to city planners to evaluate and develop urban deployment plans. However, this task is challenging. Traditional traffic estimation approaches lack the ability to solve this problem, since no data about the impact can be collected before the deployment and old data fails to capture the traffic pattern changes. In this paper, we define the off-deployment traffic estimation problem as a traffic generation problem, and develop a novel deep generative model TrafficGAN that captures the shared patterns across spatial regions of how traffic conditions evolve according to travel demand changes and underlying road network structures. In particular, TrafficGAN captures the road network structures through a dynamic filter in the dynamic convolutional layer. We evaluate our TrafficGAN using a large-scale traffic data collected from Shenzhen, China. Results show that TrafficGAN can more accurately estimate the traffic conditions compared with all baselines.

With widespread deployment of GPS devices, massive spatiotemporal trajectories became more accessible. This booming trend paved the solid data ground for researchers to discover the regularities or patterns of human mobility. However, there are still three challenges in semantic pattern extraction including semantic absence, semantic bias and semantic complexity. In this paper, we invent and apply a novel data structure namely City Semantic Diagram to overcome above three challenges. First, our approach resolves semantic absence by exactly identifying semantic behaviours from raw trajectories. Second, the delicate design of semantic purification helps us to detect semantic complexity from human mobility. Third, we avoid semantic bias using objective data source such as ubiquitous GPS trajectories. Comprehensive and massive experiments have been conducted based on real taxi trajectories and points of interest in Shanghai. Compared with existing approaches, City Semantic Diagram shows its satisfied effectiveness and precision to discover fine-grained semantic patterns.

Driver Assistance Systems (DAS) have been progressively incorporated into commercial vehicles in recent years. All these systems are paving the way for the forthcoming autonomous vehicle which will become a reality in the near future. Existing systems are based on numerous electronic systems with advanced skills, high performances, and high degrees of adaptability and intelligence. As is to be expected, these cutting-edge features require, in most cases, the use of powerful computing platforms. However, the deployment of such platforms is not an easy task, since they have to be integrated in the vehicle where there exist important restrictions regarding size, power consumption and cost. In this sense, every smart proposal aimed at reducing the complexity of these systems without degrading performance, is always a valuable contribution in the field. In this work, we propose a methodology to reduce the dimensionality of a driver distraction recognition system. The methodology is based on a multi-objective genetic algorithm that looks for the minimum set of useful features collected during the driving task and also for the simplest recognition system. The recognition algorithm is an Extreme Learning Machine (ELM) whose simplicity and fast learning procedure make it especially suitable to be used by a Genetic Algorithm which needs to evaluate thousands of candidate solutions. The proposed methodology has been tested with a real-world database collected from different drivers performing an itinerary with an instrumented car. The results obtained validate the proposal as a method to reduce the complexity of a driver distraction recognition system.