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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.

“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.

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.

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 close-in weapon system (CIWS) is a combat system that faces a complex environment full of dynamic and unknown challenges, whose construction and planning require a systematic design method. Multi-living agent (MLA) theory is a methodology for the combat system design, which uses the livelihood degree to evaluate the multi-dimensional long-term operational effectiveness of the system; whereas, there is still no uniform quantization framework for the livelihood degree, and the adjustment methods of livelihood degree need to be further improved. In this paper, we propose the uniform quantization framework for the livelihood degree and detailed discuss the methods of livelihood adjustment. Based on the MLA theory, the multi-dimensional operational effectiveness of the missile-gun integrated weapon system (MGIWS) is analyzed, and the long-term combat effectiveness against the saturation attack is assessed. Furthermore, the planning problem of the equipment deployment and configuration is investigated. Two objectives, including the overall livelihood degree and cost-effectiveness (CE), are proposed, and the optimization method based on genetic algorithm (GA) is studied for the planning problem.

Cloud Computing is a robust model that allows users and organizations to purchase required services per their needs. The model offers many services such as storage, platforms for deployment, convenient access to web services, and so on. Load Balancing is a common issue in the cloud that makes it hard to maintain the performance of the applications adjacent to the Quality of Service (QoS) measurement and following the Service Level Agreement (SLA) document as required from the cloud providers to enterprises. Cloud providers struggle to distribute equal workload among the servers. An efficient LB technique should optimize and ensure high user satisfaction by utilizing the resources of VMs efficiently. This paper presents a comprehensive review of various Load Balancing techniques in a static, dynamic, and nature-inspired cloud environment to address the Data Center Response Time and overall performance. An analytical review of the algorithms is provided, and a research gap is concluded for the future research perspective in this domain. This research also provides a graphical representation of reviewed algorithms to highlight the operational flow. Additionally, this review presents a fault-tolerant framework and explores the other existing frameworks in the recent literature.

Among all the road accidents, speeding is the most deadly factor. To reduce speeding, it is essential to devise efficient schemes for ubiquitous speed monitoring. Traditional approaches either suffers from using special equipment or special deployment. In this paper, we propose SpeedTalker, a mobile phone-based approach to perform speed detection on automobiles. SpeedTalker estimates the automobile speed by passively sensing the acoustic and image signals. Specifically, we use the time difference of arrivals (TDOA) model based on acoustic signals to figure out the candidate trajectories of automobile, and use the pin-hole model based on image frames to figure out the vertical distance between the user's position and the automobile's trajectory, thus to estimate the unique trajectory. Besides, we propose a method to effectively mitigate the influence of the movement jitters of mobile phone. We implemented a system prototype for SpeedTalker and estimated the automobile speed with high accuracy. Experiment results show that in the scenario of single automobile, SpeedTalker can achieve an average estimation error of 6.1% compared to radar speed guns. In the scenario of multiple automobiles, SpeedTalker can achieve an average estimation error of 9.8%, which is acceptable for usage.

Hardware platforms for socially interactive robotics can be limited by cost or lack of functionality. This paper presents the overall system -- design, hardware, and software -- for Quori, a novel, affordable, socially interactive humanoid robot platform for facilitating non-contact human-robot interaction (HRI) research. The design of the system is motivated by feedback sampled from the HRI research community. The overall design maintains a balance of affordability and functionality. Initial Quori testing and a six-month deployment are presented. Ten Quori platforms have been awarded to a diverse group of researchers from across the United States to facilitate HRI research to build a community database from a common platform., 20 pages. 21 figures. This was accepted to and will be published to the IEEE Transactions on Robotics Journal

A huge variety of fields and industries depend upon cloud computing based microservice due to its high-performance capability. Also, the merit of container usage is enormous; it enable larger portability, easier and faster deployment and restricted overheads. However, the rapid evolution causes issues in terms of container automation and management, Till now, a number of research works has concentrated on solving the open issues in container automation and management. In fact, container resource allocation is the major key hole for cloud providers since it directly influences the resource consumption and system performance. In this manner, this paper introduces a new optimized container resource allocation model by proposing a new optimization concept. To make the possibility of optimal container resource allocation, a new hybridized algorithm is implanted; namely, Whale Random update assisted Lion Algorithm (WR-LA), which is the hybrid form of Lion Algorithm (LA) and Whale Optimization Algorithm (WOA) is introduced. Moreover, the solution of optimized resource allocation is made by considering objectives like Threshold Distance, Balanced Cluster Use, System Failure, and Total Network Distance, respectively. Finally, the performance of the proposed model is compared over other conventional models and proves its superiority.

Deep learning has gained tremendous success and great popularity in the past few years. However, deep learning systems are suffering several inherent weaknesses, which can threaten the security of learning models. Deep learning's wide use further magnifies the impact and consequences. To this end, lots of research has been conducted with the purpose of exhaustively identifying intrinsic weaknesses and subsequently proposing feasible mitigation. Yet few are clear about how these weaknesses are incurred and how effective these attack approaches are in assaulting deep learning. In order to unveil the security weaknesses and aid in the development of a robust deep learning system, we undertake an investigation on attacks towards deep learning, and analyze these attacks to conclude some findings in multiple views. In particular, we focus on four types of attacks associated with security threats of deep learning: model extraction attack, model inversion attack, poisoning attack and adversarial attack. For each type of attack, we construct its essential workflow as well as adversary capabilities and attack goals. Pivot metrics are devised for comparing the attack approaches, by which we perform quantitative and qualitative analyses. From the analysis, we have identified significant and indispensable factors in an attack vector, e.g., how to reduce queries to target models, what distance should be used for measuring perturbation. We shed light on 18 findings covering these approaches' merits and demerits, success probability, deployment complexity and prospects. Moreover, we discuss other potential security weaknesses and possible mitigation which can inspire relevant research in this area., 28 pages, 6 figures

Deploying Wireless Sensor Networks (WSNs) in real-world applications is still a challenging problem. Indeed, the number, locations, and orientations of directional sensors determine the topology of the WSN, which will further influence its overall cost and performance. Existing works have assumed simple sensor behavior and/or ideal environmental factors; thus, often they do not produce practical results. On the contrary, in this paper, we revisit the 3D WSNs deployment problem while considering realistic assumptions regarding the modeling of both the sensors and the environment. More precisely, we propose a Bresenham line-of-sight based realistic coverage model for 3D environments. This latter is used to re-formalize the 3D WSNs deployment problem while considering a realistic spatial model of the environment. The problem is then solved using a multi-objective genetic algorithm endowed with new adaptive and guided genetic operators. Moreover, we enhance the performance of the proposed approach by introducing two optimization techniques, namely: search space reduction and sampling-based evaluation. We show the effectiveness and efficiency of the proposed approach through extensive simulations.

The tether deployment of a tethered satellite system involves the consideration of complex dynamic properties of the tether, such as large deformation, slack, and even rebound, and therefore, the dynamic modelling of the tether is necessary for performing a dynamic analysis of the system. For a variable-length tether element, the absolute nodal coordinate formulation (ANCF) in the framework of the arbitrary Lagrange-Euler (ALE) description was used to develop a precise dynamic model of a tethered satellite. The model considered the gravitational gradient force and Coriolis force in the orbital coordinate frame, and it was validated through numerical simulation. In the presence of dynamic constraints, a deployment velocity of the tether was obtained by an optimal procedure. In the simulation, rebound behavior of the tethered satellite system was observed when the ANCF-ALE model was employed. Notably, the rebound behavior cannot be predicted by the traditional dumbbell model. Furthermore, an improved optimal deployment velocity was developed. Simulation results indicated that the rebound phenomenon was eliminated, and smooth deployment as well as a stable state of the station-keeping process were achieved. Additionally, the swing amplitude in the station-keeping phase decreased when a deployment strategy based on the improved optimal deployment velocity was used.

Sweep coverage plays an important role in many applications such as data gathering, sensing coverage and devices control. In this paper, we deal with the cooperative sweep coverage problem with mobile sensors to periodically cover all positions of interest (PoIs) in the surveillance region. Different from traditional sweep coverage scenarios, the cooperative sweep coverage (CSC) problem allows the deployment of multiple sensors on the same trajectory to reduce sweep period or detection delay. We also consider the multi-sink sweep coverage (MSSC) problem where each mobile sensor must periodically transmit its collected data to a base station due to the limited storage capacity and power supply. Correspondingly, we propose two constant-factor approximations, namely CoCycle and SinkCycle, to minimize the sweep period for these two problems. The approximation ratios of CoCycle and SinkCycle are proved to be 4 and 6 respectively. As far as we know, SinkCycle is the first approximation for the sweep coverage problem with multiple sinks. We also provide two optimal algorithms for the CSC problem in one dimensional case and a useful insight regarding the MSSC problem with only one available sink. Finally, we conduct various numerical experiments to validate the effectiveness and efficiency of our designs.

The constant evolution of transportation systems and traffic in developing countries is nowadays confronted with a problem of road safety and therefore of a high accident rate, especially in the context of inter-urban road transport. In this work, we propose a communication architecture for an Intelligent Transport System (ITS) to provide surveillance in an inter-urban transport network in the context of developing countries. We introduce two types of control points: Relay Control Points (RCP) and Treatment Control Points (TrCP). We also designed two multi-objective models for the deployment of these points. In order to ensure good coverage, to minimize the cost of installation and to put more emphasis in the areas having a high number of accidents. To ensure optimal deployment of these points, we have used the Non-Dominated Sorted Genetic Algorithm II (NSGA-II) that will allow to realize the Pareto front of non-dominated solutions. The results of the simulations show the effectiveness of our proposal.

The growing commercial interest in indoor localization-based services has stimulated the development of many indoor positioning systems. Despite extensive research on localization, system requirements, such as site survey, user intervention, or specific hardware/software, place limitations on the widespread deployment of localization. To overcome these limitations, we propose a path estimation and localization system for indoor environments, termed PYLON, that runs on a smartphone and a server without any human intervention. PYLON uses an actual floor plan and measurements from widely deployed WiFi access points (APs) and Bluetooth Low Energy (BLE) beacons to estimate the user's path. It creates virtual rooms according to received signal strength indicator (RSSI) values and matches them to actual rooms in the real-world floor plan. After room mapping, PYLON uses door passing times to precisely refine a user's estimated path. Unlike conventional path estimation and localization systems, PYLON works independently of device types. We implement PYLON on five Android smartphones and conduct evaluation with three users in an office building. Our experimental results show that PYLON achieves 97% floor plan mapping accuracy with a localization error of 1:42 m.

Edge computing pushes computation and storage resources to the network edge which is close to end users, and thus is critical for latency-sensitive applications, e.g., intelligent Vehicular Ad Hoc Networks (VANETs). To enable these services, a set of edge servers need to be deployed to the roadsides. Such deployment should offer low-latency services to end users, while keeping a low deployment or maintenance cost, which is a non-trivial task. In this paper, we study the edge server placement problem in a metropolitan area. This problem is composed of two parts: to determine 1) the locations of the servers and 2) the coverage of each server, with multiple optimization objectives. First, we study the Shanghai Taxi Trace to gain insights into the traffic pattern of taxis, especially how vehicles move between different locations. Second, we build multi-objective optimization models to characterize the trade-off among three critical performance metrics, namely the initial deployment cost, the runtime cost (i.e., number of hand-offs between different servers), and the average delay of tasks. Due to the intractability of these NP-hard problems, we propose a heuristic multi-objective optimization method to decompose the global problem into a set of local problems with tractable scale. Numerical results verify that our heuristic strategy achieves a desirable balance among the three performance metrics, e.g., a 5% compromise of the delay can reduce up to 50% of the hand-offs for small local areas, and 10%+ for the entire global area, compared with the best existing algorithms.

Network and cloud service providers are facing an unprecedented challenge to meet the demand of end-users during the COVID-19 pandemic. Currently, billions of people around the world are ordered to stay at home and use remote connection technologies to prevent the spread of the disease. The COVID-19 crisis brought a new reality to network service providers that will eventually accelerate the deployment of edge computing resources to attract the massive influx of users' traffic. The user can elect to procure its resource needs from any edge computing provider based on a variety of attributes such as price and quality. The main challenge for the user is how to choose between the price and multiple quality of service deals when such offerings are changing continually. This problem falls under multi-attribute decision-making. This paper investigates and proposes a novel auction mechanism by which network service brokers would be able to automate the selection of edge computing offers to support their end-users. We also propose a multi-attribute decision-making model that allows the broker to maximize its utility when several bids from edge-network providers are present. The evaluation and experimentation show the practicality and robustness of the proposed model.

Comments from S. Joseph Munchak and Robert Meneghini of NASA Goddard Space Flight Center, Charles (Chip) Helms of Universities Space Research Association, Liang Liao of Morgan State University are highly appreciated. Discussions with Norm Wood of University of Wisconsin-Madison on PIP data processing was very helpful. Leo Pio D’Adderio of National Research Council of Italy provided Figure 1 of this study. Thanks to the National Weather Service in Marquette, Michigan for hosting and maintaining the suite of instruments used in this work and sharing meteorological data. Thanks to the National Aeronautics and Space Administration Goddard Space Flight Center Wallops Flight Facility and the Global Precipitation Measurement (GPM) program for providing the PIP, Pluvio, and Parsivel instruments used in this work. Acknowledgments extend to three anonymous reviewers for their constructive comments. This study is partially supported by the NASA award (80NCCS19M0139) under Patrick N. Gatlin of NASA Marshall Space Flight Center, Principal Investigator. The instrument deployment and data processing for the observations from the Marquette, Michigan snowfall suite are supported by NASA grant number 80NSSC18K0701 and NOAA grant number NA15NES4320001. Claire Pettersen’s efforts on this work are supported by NASA grant number 80NSSC19K0712 and Mark Kulie’s efforts are supported by NASA PMM grant 80NSSC20K0982. Annakaisa von Lerber is funded by the Academy of Finland postdoctoral scholarship (333901). The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the authors and do not necessarily reflect those of NOAA or the Department of Commerce.

Mobile edge computing (MEC) is a key technology that enables the deployment of applications (or services) at the proximity of mobile users. However, the performance of mobile edge computing is sensitive to the quality and availability of underlying connection links. It is still unclear to what extent intermittent connectivity affects the performance of mobile edge computing. In this paper, we make the first attempt to quantify the influence of intermittent connectivity on mobile edge computing from a theoretical perspective. Specifically, we propose an analytical framework based on discrete-time Markov chain and derive a closed-form expression of the task processing time under different network conditions. Our model can be further extended to account for the case with group task arrivals. We also conduct extensive simulations to examine the accuracy of our proposed analytical models with both synthetic and real-world user mobility traces. The results show that our model can well capture the influence of intermittent connectivity on MEC. Our model sheds important insights into the impact of intermittent connectivity on task processing in MEC, which we believe should be taken into account when designing future MEC systems.

Small-scale PV generation has become popular with residential customers in several jurisdictions with high solar radiation, as an alternative to improve their carbon footprint and reduce their electricity bills. However, massive deployment of such distributed generation is creating a particular and undesirable shape in the net demand, which deepens at hours of peak solar PV injections at noon and suddenly rises towards the evening, known as the “duck curve". Hence, this paper investigates the use of pre-cooling strategies in residential households to mitigate the duck-curve effects. To this aim, appropriate thermal models and simulations of houses are first developed and carried out to demonstrate the technical feasibility of pre-cooling in a house with a typical configuration, based on the Smart Residential Load Simulator (SRLS) developed at the University of Waterloo. Then, an aggregation technique is proposed to evaluate the effects on a large grid of different penetration levels of PV, and pre-cooling approaches to manage the duck-curve in California and Texas, concluding that such techniques are capable of substantially flattening the system net demand curve.

The performance requirement of deep learning inevitably brings up with the expense of high computational complexity and memory requirements, to make it problematic for the deployment on resource-constrained devices. Edge computing, which distributed organizes the computing node close to the data source and end-device, provides a feasible way to tackle the high-efficiency demand and substantial computational load. Whereas given edge device is resource-constrained and energy-sensitive, designing effective neural network architecture for specific edge device is urgent in the sense that deploys the deep learning application by the edge computing solution. Undoubtedly manually design the high-performing neural architectures is burdensome,let alone taking account of the resource-constraint for the specific platform. Fortunately, the success of Neural Architecture Search techniques come up with hope recently. This paper dedicate todirectly employ multi-objective NAS on the resource-constrained edge devices. We first propose the framework of multi-objectiveNAS on edge device, which comprehensively considers the performance and real-world efficiency. Our improved MobileNet-V2 search space also strikes the scalability and practicality, so that a series of Pareto-optimal architectures are received. Benefits from the directness and specialization during search procedure,our experiment on JETSON NANO shows the comparable resultwith the state-of-the-art models on ImageNet.

Towards a fully connected intelligent digital world, 5G and beyond networks experience a new era of internet of intelligence with connected people and things. This new era brings challenging demands to the network, such as high spectral efficiency, low-latency, high-reliable communication, and high energy efficiency. One of the major technological breakthroughs to cope with these unprecedented demands is the cell-free (CF) massive multiple-input multiple-output (mMIMO) systems. In CF mMIMO, a large number of distributed access points are connected to a central processing unit, and serve a smaller number of users over the same time-frequency resources. The system has shown a great potential in improving the network performance in various perspectives compared to the co-located mMIMO and conventional small-cell systems. Furthermore, the system can be flexibly integrated with various emerging techniques/technologies for 5G and beyond networks to boost the network performance in different perspectives. Despite the substantial reported theoretical gains of CF mMIMO systems, the full picture of a practical scalable deployment of the system is not clear yet. In this paper, we provide a comprehensive survey of different aspects of the CF mMIMO system from the general system model, the detailed system operation, the limitations towards a practically implemented system to the potential of integrating the system with emerging techniques/technologies. Besides, we provide a number of timely open problems and future research directions to fully exploit the CF mMIMO system potential in delivering the anticipated requirements of future wireless networks.

Microservice systems are complicated due to their runtime environments and service communications. Debugging a failure involves the deployment and manipulation of microservice systems on a containerized environment and faces unique challenges due to the high complexity and dynamism of microservices. To address these challenges, we propose a debugging approach for microservice systems based on the delta debugging algorithm, which is to minimize failure-inducing deltas of circumstances (e.g., deployment, environmental configurations). Our approach includes novel techniques for defining, deploying/manipulating, and executing deltas during delta debugging. In particular, to construct a (failing) circumstance space for delta debugging to minimize, our approach defines a set of circumstance dimensions that can affect the execution of microservice systems. To automate the testing of deltas, our approach includes the design of an infrastructure layer for automating deployment and manipulation of microservice systems. To optimize the delta debugging process, our approach includes the design of parallel execution for delta testing tasks. Our evaluation shows that our approach is scalable and efficient with the provided infrastructure resources and the designed parallel execution for optimization. Our experimental study on a medium-size microservice benchmark system shows that our approach can effectively identify failure-inducing deltas that help diagnose the root causes.

This letter presents a self-contained system for robust deployment of autonomous aerial vehicles in environments without access to global navigation systems and with limited lighting conditions. The proposed system, application-tailored for documentation in dark areas of large historical monuments, uses a unique and reliable aerial platform with a multi-modal lightweight sensory setup to acquire data in human-restricted areas with adverse lighting conditions, especially in areas that are high above the ground. The introduced localization method relies on an easy-to-obtain 3-D point cloud of a historical building, while it copes with a lack of visible light by fusing active laser-based sensors. The approach does not rely on any external localization, or on a preset motion-capture system. This enables fast deployment in the interiors of investigated structures while being computationally undemanding enough to process data online, onboard an MAV equipped with ordinary processing resources. The reliability of the system is analyzed, is quantitatively evaluated on a set of aerial trajectories performed inside a real-world church, and is deployed onto the aerial platform in the position control feedback loop to demonstrate the reliability of the system in the safety-critical application of historical monuments documentation.

ZusammenfassungThe experiments taking place around connected and automated vehicles (CAVs) in global innovation networks today are largely technological in nature. This research takes a relational view of CAVs by investigating how they can be conceptualized within the larger context of strategic spatial planning. To do so, it takes Japan as a case study and explores how the current government is applying the tools of its new National Spatial Strategy (NSS) to strategically steer the development of its main economic and social sectors, including transport and the deployment of CAVs.

The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. This will be done by taking into account intrinsic conditions of nowadays deep learning models, data and software quality issues and human-centered artificial intelligence (AI) postulates, including confidentiality and ethical aspects. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment. The aim of this paper is to pinpoint research topics to explore approaches to address these challenges.

Inflatable robotics is a promising area for the deployment of low-cost structures that are easy to transport and deploy while allowing safe interactions with humans and the environment. One of the key elements in the development of inflatable robots is their actuation system. In this work, we introduce the original concept of a Simultaneous Eversion-Retraction Inflatable Actuator, used in the actuation of an inflatable joint of a long-range manipulator. Through an analytical study, simple relations about the total stroke and the effective area are obtained. These relations are compared to finite elements simulations and contrasted with experimental data obtained from tensile tests of an actuator prototype. The results show that the proposed design outperforms existing concepts in terms of total stroke and force distribution through the entire stroke, which makes it suitable for the actuation of long reach inflatable arms.

The increasing number of electric vehicles (EVs) demands management solutions to deal with the impacts of EV charging on the efficiency of distribution grids. Many suggested methods are derived from analysis on laboratory-scale systems with declared data, which cannot be implemented for real networks. In this article, a two-step scheduling model is developed that effectively guides a large-scale EV fleet in microgrids without demanding a dynamic monetary scheme. The first step corresponds to prediction-based day-ahead optimal scheduling for large scale EVs, which minimizes the costs of electricity supply and EVs’ battery degradation. To avoid dimensional problems in calculations, an improved K-means clustering algorithm is presented to divide vehicles into different clusters. In the second step, online coordination is deployed based on an effective scoring system to encourage drivers to follow the first-step provided model. The proposed model is analyzed on a grid-connected microgrid with photovoltaic system integration. The problem (real) data are derived based on an estimate of the development process on the Ontario energy network over the next ten years. Results show that the introduced model can guarantee the accurate deployment of optimal charging/discharging schedules in large-scale systems.

A rail disruption may lead to rapid degradation of the public transportation system. In response to rail disruptions, this study focuses on bus bridging service design considering a routing strategy that combines express and short-turn routes. An integrated optimization framework is proposed to jointly optimize the bus bridging routes and timetables under time-varying demand. A brute-force search method is developed to identify all candidate bus bridging routes. A mixed integer linear programming model is then formulated to solve the route selection, bus deployment, and bus timetabling problem simultaneously. To handle the arising computational challenges in solving this large-scale problem, a tabu search-based heuristic with LP rounding is developed. Numerical studies based on Cranbourne Line, Melbourne, demonstrates the applicability of the proposed approach in practice. The results indicate that the proposed framework can generate high-quality bus bridging solutions in a reasonable time, which allows quick response to rail line disruptions. The bus bridging strategy with multiple routes outperforms the standard bridging route in terms of bus line capacity and passenger travel time.

This article revisits a multiagent deployment problem where agents are restricted from requesting information from other agents as well as sending acknowledgments when information is received. These communication constraints relax the assumptions of instantaneous communication and synchronous actions by agents (request and response actions). In this article, we propose a fully asynchronous communication aware solution to the multiagent deployment problem that uses an event-triggered broadcasting strategy. Unlike all existing triggered solutions, our event-triggered broadcasting algorithm relies on agents to decide when to broadcast (push) information to others in the network without the need for a response from other agents. In addition, the proposed strategy determines how best to move when up-to-date information is unavailable and cannot be requested. The algorithm is capable of achieving similar levels of performance to that of a continuous or periodic strategy. Our solution is proven to achieve asymptotic convergence, and simulation results are provided to demonstrate that the proposed event-triggered broadcasting algorithm can achieve an adequate level of performance under the communication constraints.

Real-time traffic monitoring becomes an essential part of an intelligent city. In recent years, the adoption of surveillance cameras is rapidly growing because they are helpful to manage and control the traffic. However, it is impossible to install cameras on every road in a city due to the high costs of deployment and maintenance. Given the information from limited surveillance cameras, can we infer the citywide traffic volume accurately? This is a challenging question because we have no historical data on the roads without cameras. In this paper, we propose a framework named CityVolInf to infer citywide traffic volume based on surveillance camera records. Our framework combines a semi-supervised learning-based similarity module with a novel simulation module to address the above challenges. While the similarity module focuses on spatiotemporal correlations of traffic volume between road segments, the simulation module utilizes incomplete trajectories to model transitions of traffic volume between adjacent road segments. Our framework bridges the conventional data-driven approach and transportation domain knowledge from the simulator. Extensive experiments on a real-world dataset, containing 405,370,631 camera records collected from 1,704 surveillance cameras over a period of 31 days in Jinan, China, demonstrate the effectiveness of CityVolInf compared with existing methods.

In order to lower latency and reduce energy consumption, Edge Computing proposes offloading some computation intensive tasks usually performed in the Cloud onto nearby devices in the frontier/Edge of the access networks. However, current task offloading approaches are often quite simple. They neither consider the high diversity of hardware and software technologies present in edge network devices, nor take into account that some tasks may require some specific software and hardware infrastructure to be executed. This paper proposes a task offloading process that leans on Software Product Line technologies, which are a very good option to model the variability of software and hardware present in edge environments. Firstly, our approach automates the separation of application tasks, considering the data and operation needs and restrictions among them, and identifying the hardware and software resources required by each task. Secondly, our approach models and manages separately the infrastructure available for task offloading, as a set of nodes that provide certain hardware and software resources. This separation allows to reason about alternative offloading of tasks with different hardware and software resource requirements, in heterogeneous nodes and minimizing energy consumption. In addition, the offloading process considers alternative implementations of tasks to choose the one that best fits the hardware and software characteristics of available edge network infrastructure. The experimental results shows that our approach reduces the energy consumption in the user node by approximately 41%–62%, and the energy consumption of the devices involved in a task offloading solution by 34-48%., This work is supported by the MEDEA RTI2018-099213-B-I00 (co-funded by FEDER funds), LEIA UMA18-FEDERJA-157 (co-funded by FEDER funds) and RHEA P18-FR-1081 (MCI/AEI/FEDER, UE)

Condition assessment is an essential process to comprehend the condition of the water pipelines and facilitate the maintenance as well as the renewal plans. Nowadays, varied in-pipe inspection platforms equipped with closed-circuit cameras are employed to capture the internal condition of the water pipelines. However, the automated platform often faces the challenge to negotiate with the installed valves during the inspection. To ensure continuous inspection, the platform needs identify the valve automatically and activate the control mechanism to pass through it. Thus, the valves need to be detected to facilitate the negotiation and ensure that the control mechanism can take an action in time. This paper focuses on real-time valve detection using Jetson TX2™ and a lightweight algorithm, namely YOLOv3-tiny. The performance of the implementation is compared with state-of-the-art real-time detection models. The experimental results demonstrate that YOLOv3-tiny has a high detection speed in frame per second for valve detection and outperforms the state-of-the-art real-time algorithms. Hence, the deployment YOLOv3-tiny into the embedded system will aid the automated platform to accomplish the uninterrupted inspection and enhance the capability for the condition assessment of the water pipelines.

New regulations allow peer-to-peer energy trading worldwide, empowering users while recognizing the prosumer as a critical stakeholder. In recent years, microgrids have surfaced as a distributed and embedded energy production alternative. This literature review analyses agent-based model systems with distinct approaches that model, control, and supervise microgrids, establishing a viewpoint for the transition to renewable energy, which engulfs energy security, and technology adoption. Procurement of microgrids utilizing energy transactions between end-users and implementing agent-based models has derived a better understanding of local and micro-energy markets. This literature review explores community microgrids deployment’s social dynamics and technical performance from a micro point of view. Finally, we present a roadmap for long and short-term use of agent-based models to access energy security via local energy markets, energy storage systems and community-based microgrids in rural areas. The results of this comprehensive review give way to further research questions on the use of agent-based models to improve the adoption of energy storage systems, the use of technologies to accelerate the creation of local energy markets, and the successful deployment of community-based microgrids.

Constraints depict the dependency relationships between parameters in a software system under test. Because almost all systems are constrained in some way, techniques that adequately cater for constraints have become a crucial factor for adoption, deployment and exploitation of Combinatorial Testing (CT). Currently, despite a variety of different constraint handling techniques available, the relationship between these techniques and the generation algorithms that use them remains unknown, yielding an important gap and pressing concern in the literature of constrained combination testing. In this article, we present a comparative empirical study to investigate the impact of four common constraint handling techniques on the efficiency of six representative (greedy and search-based) test suite generation algorithms. The results reveal that the Verify technique implemented with the Minimal Forbidden Tuple (MFT) approach is the fastest, while the Replace technique is promising for producing the smallest constrained covering arrays, especially for algorithms that construct test cases one-at-a-time. The results also show that there is an interplay between efficiency of the constraint handler and the test suite generation algorithm into which it is developed.

The pursuit of economic growth leads to an increase demand for energy that, in turn, may cause negative impacts on the environment due to consumption of non-renewable resources. Thus, it is necessary to search for sustainable strategies for the electric energy's conversion, standing out the distributed generation and the renewable energy sources. In this context, this work presents an intelligent computational tool that combines the concepts of sustainable development and distributed generation to detect locations that are suitable for the installation of renewable energy sources such as wind turbines and solar energy systems. This tool incorporates an intelligent system based on fuzzy logic integrated within a geoprocessing software package, providing an indicative methodology to assist the stakeholders of the energy industry in their studies of energetic planning aiming a sustainable development.

The out-of-plane and in-plane deployment dynamics of a flexible space structure, namely, a solar sail quadrant consisting of a membrane attached to two support booms, are considered. The equations ...

In Software-Defined Networks (SDN), one of the critical research challenges for large-scale deployment of SDN is controller placement problem (CPP). However, link failures are critical security issues in networks and greatly impact SDN’s robustness. The CPP for link failures is still challenging today. To this end, we study the CPP for multi-link failures (CPP-MLF). Firstly, we investigate the real link data of today’s networks to analyze the characteristics of link failures and formalize the link failure rate. Next, we formulate minimizing the number of controllers and worst-case delay as the optimization objectives of CPP-MLF and develop a heuristic algorithm based on improved NSGA-II to solve CPP-MLF efficiently. Specifically, we improve the non-dominated set and crowding distance for the NSGA-II by dynamic competition mechanism and even distribution operator, respectively, thereby quickly obtaining the Pareto-optimal solutions of CPP-MLF. Afterwards, we introduce controller load variance to evaluate them to make a satisfactory decision on controller placement. We conduct simulations with real network topologies, and the results show that the proposed heuristic algorithm performs well on the number of controllers, worst-case delay and robustness, while producing acceptable runtime overheads.

With the rapid development of space technology and the increasing demand for space missions, the traditional spacecraft manufacturing, deployment and launch methods have been unable to meet existing needs. In-space assembly (ISA) technologies can effectively adapt to the assembly of large space structures, improve spacecraft performance, and reduce operating costs. In this paper, the development and technologies for ISA are reviewed. ISA is classified from multiple angles, and the research status of ISA is shown clearly through the visual mapping knowledge domain. Then the development status of autonomous robot assembly in the United States, Europe, Japan, Canada and China is reviewed. Furthermore, the key technologies of ISA are analyzed from three aspects: assembly structure design, robot technologies and integrated management technologies. ISA technologies are still facing major challenges and need to be further explored to promote future development. Finally, future development trends and potential applications of ISA are given, which show that ISA will play a vital role in human space exploration in the future.

To meet the needs of many IoT applications with low-latency requirement, fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. In this paper, we study the fog computing service deployment problem, where the operator allocates and deploys the required computing and network resources on the edge of the network to accommodate the requests of various applications operated by the application service providers (ASPs). The operator negotiates with the ASPs to determine serving QoS of applications and how much to pay. A queuing-based latency performance model with bulk arrival is proposed for the problem to estimate the resources needed for the fog network to achieve the QoS requirements of applications. We then model and analyze the interactions between the operator and multiple ASPs as sequential one-to-many bargaining using Nash bargaining. Next, to find the optimal bargaining sequence, we propose an improved optimal algorithm, along with fast heuristic algorithms, to find the optimal sequence with low complexity. Through extensive simulations, we show that the fog service can benefit all parties, and the proposed optimal and heuristic algorithms can improve the OP's payoff by averages of 21.24% and 14.16% respectively.

This paper provides a comprehensive yet concise overview of the potential, deployment, outlook, and barriers to renewable energy including small-scale hydropower, solar, wind, geothermal and bioenergy for the five Central Asian countries of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan. Small-scale hydropower potential estimates range from 275 MW to 30,000 MW, solar PV from 195,000 MW to 3,760,000 MW, wind from 1500 MW to 354,000 MW, geothermal from 2 MW to 54,000 MW, and bioenergy from 200 MW to 800 MW. However, deployment is minuscule: 5–225 MW in small-scale hydropower across five countries, whereas only Kazakhstan deployed large-scale solar PV (>800 MW) and wind (>300 MW). Though there are strategies and programs to increase deployment, with Kazakhstan in the lead, other countries lag behind in their efforts. A number of barriers hinder the development of renewable energy in the region, including issues with regulatory framework, infrastructure, finances, expertise, awareness and support, and data and information. Because these obstacles are interrelated, a cross-sector and integrated approach is necessary to overcome them. Renewable energy can help Central Asian countries satisfy a growing energy demand and avoid the negative environmental impacts of using fossil fuels.

International audience; In cattle farming, the artificial insemination technique is a biotechnology that brings to farmers a wide range of benefits namely health security, genetic gain and economic costs. The main condition for the success of artificial insemination within cattle is the heat (or estrus) detection. In this context, several cow heat detection systems have been recently proposed in the literature to assist the farmer in this task. Nevertheless, they are mainly based on the analysis of the physical behavior of the cow which may be affected by several factors related to its health and its environment. In this paper, we present a new vision system for cow heat detection which is based on the analysis of the genital tract of the cow. The main core of our system is a CNN model that has been designed and tailored for analyzing endoscopic images collected using an innovative insemination technology named Eye breed. The conducted experiments on two datasets namely our own dataset and a public dataset show the high accuracy of our CNN model (more than 97% for both datasets) outperforming 19 methods from the state of the art. Moreover, we propose an optimized version of our model for an Android deployment by exploiting several techniques namely quantization, GPU acceleration and video downsampling. The conducted tests on a smart-phone shows that our heat detection system has a response time of a few seconds.

International audience; Consumer Internet of Things (IoT) applications are largely built through end-user programming in the form of event-action rules. Although, end-user tools help simplify the building of IoT applications to a large extent, there are still challenges in developing expressive applications in a simple yet correct fashion. In this context, we propose a formal development framework based on the Web of Things specification. An application is defined using a composition language which allows users to compose the basic event-action rules to express complex scenarios. It is transformed into a formal specification which serves as the input for formal analysis, where the application is checked for functional and quantitative properties at design time using model checking techniques. Once the application is validated, it can be deployed and the rules are executed following the composition language semantics. We have implemented these proposals in a tool built on top of Mozilla WebThings platform. The steps from design to deployment were validated on real-world applications.

In this article, we propose a lightweight blockchain-inspired framework—Magnum—as a magazine of transfer learning models in blocks. We propose the storage of these blocks on proximal fog nodes to simplify access to pretrained base models by industrial plants to tune them before deployment. We design Magnum for B5G-enabled scenarios to reduce the block transfer time. We formulate a demand-centric distribution scheme to further reduce the search and access time by adopting a nonlinear program model and solving it using the branch-and-bound method. Through extensive experiments and comparison with state-of-the-art solutions, we show that Magnum retains the accuracy of the models and present its feasibility with a maximum CPU and memory usage of 80% and 6%, respectively. Additionally, while Magnum requires a maximum of 10 s for writing models as large as 17 Mb on the blocks, it requires 16 ms for fetching the same.

The last decade has witnessed a massive deployment of Machine Learning tools in everyday life automated tasks. Neural Networks are nowadays in use in a growing number of application areas because of their excellent performances. Unfortunately, it has been shown by many researchers that they can be attacked and fooled in several different ways, and this can dangerously impair their ability to correctly perform their tasks. In this paper we describe a watermarking algorithm that can protect and verify the integrity of (Deep) Neural Networks when deployed in safety critical systems, such as autonomous driving systems or monitoring and surveillance systems.

The emerging 5G New Radio (5G NR) stimulates the evolution of applications in many fields. Compared with 4G, 5G NR can provide much more reliable, efficient, flexible networking access services to mobile devices which can escalate the quality of services (QoS) for the users remarkably. The advantages of affordability, easy assembling and quick driving, allow the deployment of Unmanned Aircraft System (UAS) on a large scale, and provide the elevation for the industrial and the civilian implementations ubiquitously. With the enhancement of cellular networking, the control range of UAS can extend significantly which allows the controller finish their mission far away from workplace and prevents the threats derived from the hazards in the uncertain environment. The Base Stations (BSs) are vital to the cellular-connected UAS because the BSs can provide the Internet access for the UAS and the remote controllers that sends instructions to the UAS and backhauls the packets to the controllers precisely and timely. However, the malicious BSs are great threats to the cellular-connected UAS. Specifically, the malicious BSs are scattered in the legislative BSs and cannot provide formal networking access services to the UAS. What is worse, the attackers can leverage the malicious BSs to disclose the controllers’ privacy and set threats to the public property. In this paper, we propose a novel blockchain-based approach to mitigate the threats from accessing the malicious BSs. We implement the consensus construction on the authentication of networking access to enhance the efficiency of verification between BSs. To achieve the security of cellular connected UAS, we deploy blockchain to maintain the high online rate for accessing networking. Apart from the prevention of accessing malicious BSs, our approach can detect the malicious BSs and eliminate the proportion of the malicious BSs in the cellular networking. The evaluation shows that the proposed approach outperforms the conventional point-to-point authentication method. Concurrently, we enhanced the verification between BSs and the cellular-connected UAS. Our proposed blockchain enabled verification for cellular UAS networking has high potentials to rise the efficiency and the security of the UAS deployment on a large scale.

Quality of Transmission (QoT) metrics are used to predict and evaluate optical transmission performance, and have received much attention due to recent interest in real-time software defined networking (SDN) control and the challenges posed by open, disaggregated optical systems. In this letter, we address the QoT-estimation (QoT-E) issue, by focusing on real-time operations, where a dynamic control plane improves path performance estimation accuracy by interrogating optical performance monitoring (OPM) devices in the network. We also recognize the difficulty in testing such control plane procedures over large scale systems, which is a necessary step to validate control plane scalability before it is implemented in real systems. Accordingly, we have developed an optical network simulation system that incorporates physical transmission modeling into the Mininet packet-network emulator to enable the development and evaluation of optical control plane processes that depend on optical transmission performance. In this letter, we use this new tool, Mininet-Optical, to evaluate a SDN QoT-E procedure based on the deployment of emulated OPM nodes at periodic locations in an optical transmission system, which are interrogated by a SDN controller in real-time. Through this new capability, we observe behavior that manifests across a large scale system of 70 in line amplifiers and 15 ROADM nodes and are able show improvements in QoT accuracy up to 3 dB.

Context Automotive software architectures describe distributed functionality by an interaction of software components. One drawback of today’s architectures is their strong integration into the onboard communication network based on predefined dependencies at design time. The idea is to reduce this rigid integration and technological dependencies. To this end, service-oriented architecture offers a suitable methodology since network communication is dynamically established at run-time. Aim We target to provide a methodology for analysing hardware resources and synthesising automotive service-oriented architectures based on platform-independent service models. Subsequently, we focus on transforming these models into a platform-specific architecture realisation process following AUTOSAR Adaptive. Approach For the platform-independent part, we apply the concepts of design space exploration and simulation to analyse and synthesise deployment configurations, i. e., mapping services to hardware resources at an early development stage. We refine these configurations to AUTOSAR Adaptive software architecture models representing the necessary input for a subsequent implementation process for the platform-specific part. Result We present deployment configurations that are optimal for the usage of a given set of computing resources currently under consideration for our next generation of E/E architecture. We also provide simulation results that demonstrate the ability of these configurations to meet the run time requirements. Both results helped us to decide whether a particular configuration can be implemented. As a possible software toolchain for this purpose, we finally provide a prototype. Conclusion The use of models and their analysis are proper means to get there, but the quality and speed of development must also be considered.

Sending forged emails by taking advantage of domain spoofing is a common technique used by attackers. The lack of appropriate email anti-spoofing schemes or their misconfiguration may lead to successful phishing attacks or spam dissemination. In this paper, we evaluate the extent of the SPF and DMARC deployment in two large-scale campaigns measuring their global adoption rate with a scan of 236 million domains and high-profile domains of 139 countries. We propose a new algorithm for identifying defensively registered domains and enumerating the domains with misconfigured SPF rules by emulating the SPF check_function. We define for the first time new threat models involving subdomain spoofing and present a methodology for preventing domain spoofing, a combination of good practices for managing SPF and DMARC records and analyzing DNS logs. Our measurement results show that a large part of the domains do not correctly configure the SPF and DMARC rules, which enables attackers to successfully deliver forged emails to user inboxes. Finally, we report on remediation and its effects by presenting the results of notifications sent to CSIRTs responsible for affected domains in two separate campaigns.

Over the past few years, the Internet of Things (IoT) has played a crucial role in designing e-health systems. An IoT-enabled e-health system allows different entities to examine a patient's health condition anywhere and anytime. However, the deployment of e-health systems in an IoT environment faces various security and privacy challenges. The authentication of participating entities, the confidentiality of information transferred through an insecure channel, and access control are essential issues to be resolved. To settle these challenges, we found many authentication protocols for e-health systems based on the discrete logarithm and integer factorization problems. However, quantum attacks expose all these protocols. In this article, we propose a lattice-based authentication and access control (LAAC) protocol for IoT-enabled e-health systems to mitigate the quantum attacks. We prove that LAAC is robust with the hardness assumption of the inhomogeneous small integer solution problem. Moreover, we provide provable security analysis of LAAC. Besides, performance evaluation shows it is reasonable to execute LAAC in an IoT device.