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Once a freight train is delayed on a busy railway under a quasi-moving-block signaling system, the speed of its following trains may fluctuate, without proper adjustment. Updating the reference speed profile of the delayed freight train is an urgent task to improve the train operation performance and rail utilization ratio. To this end, this article proposes a speed profile optimization method for following delayed trains that is based on the idea of model predictive control (MPC). The state change time of the block section ahead is predicted for each following train according to the speed profile of its preceding train, based on which the distance between the following train and each speed protection curve (DTC) is predicted. The DTC is taken as a key optimization index for the speed profile optimization, and the other optimization indices are train delay and energy consumption. A two-step hierarchical optimization algorithm is proposed in this article. In the upper level, each prediction step is defined as the traveling time of the preceding train in each block section ahead, and the train target cruising speed sequence is calculated, with the dimension decided by the prediction horizon, using particle swarm optimization. In the lower level, the optimal speed profile is calculated based on the rolling optimization algorithm of the train speed curve in the prediction horizon. The proposed algorithm repeats the optimization process with updated train information after each control horizon. Three simulations are presented, which consider a downhill scenario, steep uphill scenario, and temporary speed limit, respectively. Then, the MPC parameters are analyzed and the optimized speed profiles are compared with the other algorithm. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a major concern in most cities all over the world. The proper guidance of cars with an effective route planning method has become a fundamental and smart way to alleviate congestion under existing urban road facilities. Current route planning methods mainly focus on a single car, but ignoring the dynamic effect between cars may lead to severe congestion during the actual driving guidance. In this article, we extend the study of route planning to the case of multiple cars and present a novel multicar shortest travel-time routing problem. The objective is to minimize the average travel time by considering the dynamic effect of the induced traffic congestion on travel speed, while ensuring that each car’s travel distance is within an acceptable range. We construct a time-hierarchical graph model for structuring the spatiotemporal dynamic properties of the urban road network and then develop a two-level multicar route planning optimization method for complex problem solving. The experimental results show that our path recommendations reduce the average travel time by 51.74% and 38.87% on average compared to two representative methods. Our research will become more important in the years ahead as self-driving cars become more commonplace. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are one important enabler for the evolution of sustainable transportation sector. However, one major barrier to the widespread adoption of EVs is their limited battery capacity and the excessive time required to fully charge depleted batteries. Although techniques such as charging with a rapid charger and battery swapping may partially alleviate such concerns, the corresponding high capital investment may not be profitable for areas with low EV adoption rates. Moreover, the diversified charging demands of randomly distributed EVs, both spatially and temporally, restrict high-quality services from current fixed charging stations (FCSs). To resolve such challenges, mobile charging services (MCSs) have been investigated as a supplemental charging method for EVs, wherein energy replenishment is provided by mobile charging vehicles (MCVs). Under the framework of the Internet of EVs (IoEV), this article describes the concept of MCSs and investigates their charging merits under some distinct charging scenarios, e.g., real-time response service and emergency rescue service. A case study concerning real-time response services is investigated to illustrate the influence of MCSs on the current charging system. Simulation results demonstrate the ability of MCSs to improve the quality of charging service by reducing the average service time and service acceptance ratio. Finally, technical challenges, future research issues, and possible methodologies regarding the MCSs for EVs are discussed. [ABSTRACT FROM AUTHOR]

Multicore systems were developed to provide a substantial performance increase over monocore systems. But shared hardware resources are a problem as they add unpredictable delays that affect the schedulability of multicore hard real-time systems. In recent years much effort has been put into modelling interference and proposing scheduling techniques to mitigate its negative effect. Using one of these models, we propose a scheduling technique, based on Integer Linear Programming (ILP) that, in combination with a task to core allocator, not only achieves a feasible schedule but also reduces the interference produced by shared hardware resources in the context of hard real-time multicore systems. The evaluation shows how interference is reduced ( $\approx 83.47$ %) and schedulability is increased ( $\approx 12.25$ %) compared to traditional heuristics.

Embedded computing systems are becoming increasingly relevant in the Internet of Things (IoT) and edge computing domains, where they are often employed as the control entity of a cyber-physical system. When operating in such interconnected domains, a software system is susceptible to cyber-attacks from external agents, which can compromise the correct behavior of the system. In addition, the software executing in these systems is typically characterized by stringent timing constraints, which must be satisfied during system execution. Enabling software protections to enhance the security level of the embedded software comes at the cost of increasing the computation times of the tasks, introducing the risk of deadline misses that could also jeopardize the system behavior. This paper presents a methodology to optimize the security level of real-time software while preserving system-wide schedulability by leveraging timing analysis. The proposed approach is based on a mixed-integer linear programming (MILP) formulation that maximizes the security level of the tasks and integrates a response-time analysis technique to assess the schedulability of the system whenever additional protections are activated to shield the software against cyber-attacks targeting specific classes of vulnerabilities. An experimental evaluation is presented to assess the performance of the proposed approach on a representative set of tasks included in standard benchmarking suites for embedded software.

Traffic congestion caused by the rapid growth of vehicles and unadaptable signal control has become a major constraint on traffic efficiency and travel experience. However, the existing studies have primarily focused on a particular aspect of traffic behaviors, without considering the systemic and interconnected nature of traffic congestion. Hence, a comprehensive strategy integrating monitoring, prediction, and coordinated control at regional intersections is proposed to improve traffic efficiency. First, an intelligent algorithm is designed to identify and predict traffic condition information. Then, the intersections with similar traffic behaviors and higher relevancy are divided into the same subzone, and a multiobjective optimization model is proposed to improve traffic capacity and green time utilization. Furthermore, the timing is modified according to the temporal and spatial characteristics of the oversaturation. Simulation studies in long-term and short-term traffic congestion scenarios are designed to verify the performance of the proposed strategy. Compared with the existing multitime timing method and a newly formed reinforcement learning control method, the results show that the average delay of the proposed strategy is 4.945% and 6.737% inferior to the outstanding strategies. [ABSTRACT FROM AUTHOR]

Under a connected environment, demand information such as vehicle routes in urban networks is accessible. Based on space-time trajectories, this article proposes a real-time network-wide coordinated control strategy within a rolling horizon scheme. A traffic demand framework involving route and arrival and departure times at each intersection is introduced to describe the space-time trajectories of vehicles in the network. In the coordinated control optimization algorithm, the following two steps are iterated: first, the departure time of vehicles with minimal total delay is optimized by a nonfixed intersection signal control model; then, the arrival time is updated according to the departure time and the link travel time. The intersection signal control strategy is composed of a duration division, vehicle aggregates, and an optimization model, where the signal timing plan with minimal delay can be obtained via the departure time. The simulation results indicate that the proposed approach is capable of optimizing the signal timing plan and decreasing vehicle delay given the demand information of vehicles. [ABSTRACT FROM AUTHOR]

Unmanned surface vessel (USV) autonomous navigation on the open sea involving real-time path planning and collision avoidance is still one of the essential problems to ensure the USV’s safe and efficient navigation. Especially in a congested and uncertain marine traffic environment, not only will static obstacles be taken into account but other target vessels in motion should also be considered. Also, the general requirement of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) should be satisfied. Hence, an improved A-star algorithm for USV path planning and improved dynamic window approach (IDWA) for collision avoidance were proposed. First, considering the requirement of COLREGs, the velocity search space was filtered again, and the quantity of USV trajectories was reduced. Then, the improved A-star algorithm was introduced to let the USV avoid static obstacles and reach its destination without trapping in local optimization. Moreover the Deep Q-network method was utilized to train weight coefficients of the IDWA objective function. Thereby, the improved algorithm-generated path during the process of collision avoidance was more reasonable and safer. To verify feasibility of the proposed path-planning algorithm, a comparison experiment with the traditional DWA method was carried out. The results showed that whether it was for a single USV to a single target or for multiple USVs to multiple targets, path planning, the proposed method, could work effectively to avoid obstacles safely and reach the destination quickly. The improved algorithm will be expected to provide a reference for USV path planning and collision avoidance as well as contribute to the implementation of autonomous ship navigation. [ABSTRACT FROM AUTHOR]

The electromagnetic levitation (EML) system serves as a key subsystem in maglev trains for the purpose of levitation. It is highly dynamic, open-loop unstable, and safety-critical. The expense of establishing an accurate model out of the Maglev train, in addition to the varying operating conditions, results in an imperfectly known model in engineering practice. Thus high-performance levitation control, w.r.t. an imperfectly known model, is of considerable practical interest. Motivated by such an observation, this article investigates real-time levitation performance optimization of the EML system, with an imperfectly known model. The EML system is first modeled and an equivalent demonstration benchmark is developed. Then, the structure for levitation performance optimization is presented on top of the coprime factorization technique. Furthermore, the real-time levitation performance optimization algorithm is developed, utilizing only the input and output data. In the end, the proposed methods are validated on the benchmark. [ABSTRACT FROM AUTHOR]

In recent studies, the power scheduling in more electric aircraft (MEA) has been formulated as a mixed-integer quadratic programming problem. Many model-driven methods, such as branch-and-bound algorithms, are advocated to solve it. However, these methods are often prone to considerably high complexities, which makes real-time processing a problem. In this article, a two-stage hybrid learning-based optimization approach is proposed to address this challenging issue. In the first stage, the task of optimizing integer variables is considered as a multilabel classification problem, which is solved by a data-driven method, i.e., ensemble deep neural networks (EDNNs). In the second stage, with the obtained integer solutions, the problem is transformed into a quadratic programming problem that can be quickly solved by model-driven numerical optimization methods. Compared to the state-of-the-art power scheduling algorithms, the model-driven and data-driven methods can compensate each other so that this two-stage hybrid learning-based approach can achieve orders of magnitude speedup in computational time, while guaranteeing the optimal scheduling performance. The structures of EDNNs are well designed so that the proposed approach can adapt to varying operating conditions in MEA. An offline simulation test and an online hardware-in-the-loop test validate the above advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

With the development of mobile networks and intelligent equipment, as a new intelligent data sensing paradigm in large-scale sensor applications such as the industrial Internet of Things, mobile crowd sensing (MCS) assigns industrial sensing tasks to workers for data collection and sharing, which has created a bright future for building a strong industrial system and improving industrial services. How to design an effective worker selection mechanism to maximize the utility of crowdsourcing is the research hotspot of mobile sensing technologies. This article studies the problem of least workers selection to make large MCS system perform sensing tasks more effective and achieve certain coverage with certain constraints being meeting. A many-objective worker selection method is proposed to achieve the desired tradeoff and an optimization mechanism is designed based on the enhanced differential evolution algorithm to ensure data integrity and search solution optimality. The effectiveness of the proposed method is verified through a large scale of experimental evaluation datasets collected from real world. [ABSTRACT FROM AUTHOR]

Real-time dense SLAM techniques aim to reconstruct the dense three-dimensional geometry of a scene in real time with an RGB or RGB-D sensor. An indoor scene is an important type of working environment for these techniques. The planar prior can be used in this scenario to improve the reconstruction quality, especially for large low-texture regions that commonly occur in an indoor scene. This article fully explores the planar prior in a dense SLAM pipeline. First, we propose a novel plane detection and segmentation method that runs at 200 Hz on a modern graphics processing unit. Our algorithm for constructing global plane constraints is very efficient; hence, we use it in the process of each input frame for the camera pose estimation while maintaining the real-time performance. Second, we propose herein a plane-based map representation that greatly reduces the memory footprint of plane regions while keeping the geometric details on planes. The experiments reveal that our system yields superior reconstruction results with planar information running at more than 30 fps. Aside from speed and storage improvements, our technique also handles the low-texture problem in plane regions. [ABSTRACT FROM AUTHOR]

With the rapid development of Industry 4.0, the industrial cyber-physical systems (ICPS) are expected to realize the digital sensing, automatic control, and refined management in smart factories. However, limited bandwidth resources and severe industrial interference make it difficult to meet the real-time and ultrahigh reliability in edge computing (EC)-based next-generation industrial automation networks. To tackle these challenges, in this article, we propose a real-time transmission optimization scheme to accelerate EC. First, we establish a hierarchical system model for smart manufacturing and automation scenarios. Then we present a power control optimization method based on noncooperative game to alleviate interference and reduce energy consumption. Finally, we propose a path optimization scheme based on Q-learning for low-latency and ultrahigh reliability transmission requirements. Extensive simulation results reveal that our proposals perform better in terms of transmission delay and packet-loss rate compared with traditional methods, and therefore, contributes to EC deployment in ICPS. [ABSTRACT FROM AUTHOR]

Network reconfiguration (NR) has attracted much attention due to its ability to convert conventional distribution networks (DNs) into self-healing grids. This paper proposes a new strategy for real-time voltage regulation (VR) in a reconfigurable DN, whereby optimal feedforward control of distributed generators (DGs) is achieved in coordination with the operation of line switches (SWs). This enables preemptive compensation of upcoming deviations in DN voltages resulting from NR-aided load restoration. A robust optimization problem is formulated using a dynamic analytical model of NR to design the feedforward voltage controllers (FVCs) that minimize voltage deviations with respect to the H∞ norm. Errors in the estimates of DG modeling parameters and load demands are reflected in the design of optimal FVCs via polytopic uncertainty modeling. Small-signal analysis and case studies are conducted, verifying the effectiveness and robustness of the optimal FVCs in improving real-time VR when NR is activated for load restoration. The performance of the proposed FVCs is confirmed under various conditions of a self-healing DN,characterized by network islanding and size, parameter errors, SW operations, and communication time delays. [ABSTRACT FROM AUTHOR]

This paper proposes a novel stochastic robust real-time power dispatch model considering wind uncertainty. In this dispatch model that incorporates automatic power generation control, system constraints are constructed with affinely adjustable robust optimization to enhance the system security, while the objective concerning the expected value of generation cost is optimized to improve the cost efficiency. The optimized objective is consistent with stochastic optimization, while the formulation for the objective is different. A Nataf conversion-based three-point estimate (NC-TPE) is adopted, which uses an approximating function to formulate the expected generation cost with deterministic estimate points. Compared to traditional stochastic sampling methods, the deterministic assessment method NC-TPE achieves an accurate formulation with more stable and efficient computation performance. The dispatch model is derived as a problem with nonconvex objective and bilinear constraints using the dual theory. To address the highly nonconvex problem, the objective and bilinear constraints are first converted to difference-of-convexity functions, and then a tailored difference-of-convexity optimization is employed to convexify the nonconvex functions, such that the original problem can be solved with an iterative quadratically constrained program. Numerical simulations demonstrate the economic superiority and robustness of the proposed model, as well as the effectiveness of the convexified solution. [ABSTRACT FROM AUTHOR]

This article proposes a cooperative energy transaction model for a virtual power plant (VPP) developed as a bilevel optimization program based on the Stackelberg game in the deregulated electricity markets. The VPP integrates multiple renewable energy hubs consisting of electric vehicle charging stations with photovoltaic and battery energy storage systems. The proposed model derives co-optimized strategic decisions for the VPP operator in the day-ahead (joint active power, reserve, and reactive power) markets while considering the balancing market operation. The proposed model is reformulated as a mathematical program with equilibrium constraints (MPECs) using the Karush–Kuhn–Tucker conditions and the strong duality theorem. For an efficient solution, the MPEC is approximated as a mixed-integer linear program by applying the Fortuny-Amat transform on the complementarity and slackness conditions and the binary expansion on the bilinear terms. The uncertainties of the renewables, loads, and prices are modeled using stochastic scenarios. Finally, comparative case studies of a VPP in an IEEE 33 node active distribution network show that the proposed model improves the profit by strategic cooperative energy transactions. [ABSTRACT FROM AUTHOR]

NAND flash-based high-density solid-state drives (SSDs) commonly have limited write endurance, as a single block becomes unavailable after a finite number of program/erase cycles. The (static) wear leveling algorithms migrate cold data to more worn blocks for yielding an even distribution of wears in SSDs. However, migrating cold data must delay normal I/O processing, and unnecessary wear leveling operations may damage SSD endurance as each operation causes a program/erase cycle. This article proposes an adaptive switch on wear leveling (called ASWL), to boost I/O performance and lifetime for high-density SSDs. The basic idea of ASWL is to adaptively switch the threshold function for intelligently triggering wear leveling operations at different stages of SSD lifetime. To this end, we build a mathematical model determining the wear leveling threshold condition with a dynamic manner, by considering the real-time factors of I/O intensity and the wear difference of SSD blocks. Through a series of emulation experiments on several realistic disk traces, we show that the proposed ASWL mechanism can not only greatly improve I/O performance but also noticeably extend the lifetime of high-density SSDs, in contrast to existing wear leveling methods. [ABSTRACT FROM AUTHOR]

This paper formulates an energy community’s centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio’s aggregated flexibility. First, it is emphasized in a stylized analysis that risk-based energy constraints are highly beneficial (compared to chance-constraints) in coordinating distributed assets with unknown costs of constraint violation, as they limit both violation magnitude and probability. The presented research extends state-of-the-art models by implementing a worst-case conditional value at risk (WCVaR) based constraint for the storage SoC bounds. Then, an extensive numerical comparison is conducted to analyze the trade-off between out-of-sample violations and expected objective values, revealing that the proposed WCVaR based constraint shields significantly better against extreme out-of-sample outcomes than the conditional value at risk based equivalent. To bypass the non-trivial task of capturing the underlying time and asset-dependent uncertain processes, real-life measurement data is directly leveraged for both imbalance market uncertainty and load forecast errors. For this purpose, a shape-based clustering method is implemented to capture the input scenarios’ temporal characteristics. [ABSTRACT FROM AUTHOR]

The uncertainties from the distributed energy resources (DERs) and the prosumer’s heterogeneous characteristics bring great challenges to the operation of the community energy market. In this paper, the real-time energy sharing and management in the community market are decomposed into two-layered sub-problems: the household appliance scheduling problem and the internal energy trading pricing problem. A hierarchical deep reinforcement learning (HDRL) based scheme is proposed for the community energy trading with multiple households, containing a two-stage learning process. In the inner stage, a multi-agent deep reinforcement learning (MADRL) based approach is developed to learn the real-time appliances scheduling policy based on the local observations and given internal electricity price in a decentralized way. In the outer stage, a deep reinforcement learning (DRL) based pricing approach is proposed to determine the real-time internal electricity prices based on the participants’ historical net power and external energy supplier’s electricity prices. The scheduling policy and households’ state are not required in the pricing process. Finally, the simulations with real-world datasets demonstrate that the internal energy trading can significantly reduce the prosumers’ daily cost and the control performance of the proposed scheme is superior to the existing studies. [ABSTRACT FROM AUTHOR]

In this paper, we consider a community energy storage (CES) system that is shared by various electricity consumers who want to charge and discharge the CES throughout a given time span. We study the problem facing the manager of such a CES who must schedule the charging, discharging, and capacity reservations for numerous users. Moreover, we consider the case where requests to charge/discharge the CES arrive in an online fashion and the CES manager must immediately allocate charging power and energy capacity to fulfill the request or reject the request altogether. The objective of the CES manager is to maximize the total value gained by all of the users of the CES while accounting for the operational constraints of the CES. We discuss an algorithm titled COmmunityEnergyScheduling that acts as a pricing mechanism based on online primal-dual optimization as a solution to the CES manager’s problem. The online algorithm estimates the dual variables (prices) in real-time to allow for requests to be allocated or rejected immediately as they arrive. Furthermore, the proposed method promotes charging and discharging cancellations to reduce the CES’s usage at popular times and is able to handle the inherent stochastic nature of the requests to charge/discharge stemming from randomness in users’ net load patterns and weather uncertainties. Additionally, we are able to show that the algorithm is able to handle any adversarially chosen request sequence and will always yield total welfare within a factor of $\frac {1}{\alpha }$ of the offline optimal welfare. [ABSTRACT FROM AUTHOR]

Bundle adjustment (BA) is widely used in SLAM and SfM, which are key technologies in Augmented Reality. For real-time SLAM and large-scale SfM, the efficiency of BA is of great importance. This paper proposes CoLi-BA, a novel and efficient BA solver that significantly improves the optimization speed by compact linearization and reordering. Specifically, for each reprojection function, the redundant matrix representation of Jacobian is replaced with a tiny 3D vector, by which the computational complexity, memory storage, and cache missing for Hessian matrix construction and Schur complement are significantly reduced. Besides, we also propose a novel reordering strategy to improve the cache efficiency for Schur complement. Experiments on diverse datasets show that the speed of the proposed CoLi-BA is five times that of Ceres and two times that of g2o without sacrificing accuracy. We further verify the effectiveness by porting CoLi-BA to the open-source SLAM and SfM systems. Even when running the proposed solver in a single thread, the local BA of SLAM only takes about 20ms on a desktop PC, and the reconstruction of SfM with seven thousand photos only takes half an hour. The source code is available on the webpage: https://github.com/zju3dv/CoLi-BA. [ABSTRACT FROM AUTHOR]

In practice, the operation of high-speed trains is often affected by adverse weather conditions or equipment failures, which result in delays and even cancellations of train services. In this article, a novel two-layer hierarchical model predictive control (MPC) model is proposed for on-line high-speed railway delay management and train control for minimizing train delays and cancellations. The upper layer manages the global objectives of the train operation, that is, minimizing the total train delays and providing guidance for the speed control in the lower layer. The objectives of the lower layer are to satisfy the running time requirements given by the upper layer and to save energy at the same time. The optimization problems in both levels of the hierarchical MPC framework are formulated as small-scale mixed integer linear programming problems, which can be solved efficiently by existing solvers. Particularly, the train control problem is solved in a distributed way for each train. Simulation analysis based on the real-life data of the Beijing–Shanghai high-speed railway shows that the proposed hierarchical MPC framework can meet the real-time requirements and reduce train delays effectively when compared with widely accepted strategies, for example, first-scheduled-first-serve and first-come-first-serve. Moreover, the proposed hierarchical MPC framework also provides good robustness performance for different disturbance scenarios. [ABSTRACT FROM AUTHOR]

This special issue of the IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (T-ASE) focuses on how the state-of-the-art achievements and applications in the general area of artificial intelligence in automation for autonomous unmanned systems applications. As Guest Editors, we are very pleased to present the selected 16 articles, whose topics are specifically related to artificial intelligence real-time object detection, recognition, localization, control optimization, motion planning, formation control, adaptive control, and autonomous decision-making. [ABSTRACT FROM AUTHOR]

It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices, because even the powerful modern mobile devices are considered as “resource-constrained” when executing large-scale DNNs. It necessitates the sparse model inference via weight pruning, i.e., DNN weight sparsity, and it is desirable to design a new DNN weight sparsity scheme that can facilitate real-time inference on mobile devices while preserving a high sparse model accuracy. This paper designs a novel mobile inference acceleration framework GRIM that is General to both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and that achieves Real-time execution and high accuracy, leveraging fine-grained structured sparse model Inference and compiler optimizations for Mobiles. We start by proposing a new fine-grained structured sparsity scheme through the Block-based Column-Row (BCR) pruning. Based on this new fine-grained structured sparsity, our GRIM framework consists of two parts: (a) the compiler optimization and code generation for real-time mobile inference; and (b) the BCR pruning optimizations for determining pruning hyperparameters and performing weight pruning. We compare GRIM with Alibaba MNN, TVM, TensorFlow-Lite, a sparse implementation based on CSR, PatDNN, and ESE (a representative FPGA inference acceleration framework for RNNs), and achieve up to $14.08\times$ 14. 08 × speedup. [ABSTRACT FROM AUTHOR]

An increasing trend for reducing cost, space, and weight leads to modern embedded systems that execute multiple tasks with different criticality levels on a common hardware platform while guaranteeing a safe operation. In such mixed-criticality (MC) systems, multiple worst case execution times (WCETs) are defined for each task, corresponding to the system operation mode to improve the MC system’s timing behavior at runtime. Determining the appropriate WCETs for lower criticality (LC) modes is nontrivial. On the one hand, considering a very low WCET for tasks can improve the processor utilization by scheduling more tasks in that mode, on the other hand, using a larger WCET ensures that the mode switches (which causes by task overrunning) are minimized, thereby improving the quality of service for all tasks, albeit at the cost of processor utilization. Hitherto, no analytical solutions are proposed to determine WCETs in LC modes. In this regard, we propose a scheme to determine WCETs by the Chebyshev theorem, to make a tradeoff between the number of scheduled tasks at design-time and the number of dropped low-criticality tasks at runtime as a result of frequent mode switches. To have a tight bound of execution times and mode switching probability, we also propose a distribution analytics-based scheme, in which the mode switching probability is obtained based on the cumulative distribution function. Our experimental results show that our scheme improves the utilization of state-of-the-art MC systems by up to 72.27%, while maintaining 24.28% mode switching probability in the worst case scenario. Besides, the results of running embedded real-time benchmarks on a real platform show that the distribution-based scheme can improve the utilization by 7.30% while bounding the mode switching probability by 4.85% more, compared to the Chebyshev-based scheme. [ABSTRACT FROM AUTHOR]

The increasing popularity of on-demand ride-hailing services has introduced research problems, including those related to fleet management. Many existing fleet management approaches discretize a spatiotemporal distribution to reduce the problem complexity. Although spatial discretization greatly simplifies the problems, this approximation results in a suboptimal solution. To address this limitation, we present a novel spatial discretization-free, particle-based formulation of fleet management. Our model requires only temporal discretization. The nodes in our spatiotemporal network model represent the positions of the fleet and ride requests over the time horizon. Under this representation, a taxi dispatch problem can be solved by the minimum-cost maximum-flow (MCMF) algorithm. We show some structural properties that disclose a type of monotonicity by applying the MCMF algorithm to our problem. The obtained dispatch solution maximizes the number of served requests from these structural properties at least at the most current time step, regardless of the demand prediction quality. Moreover, these properties enable the decomposition of the MCMF algorithm without sacrificing optimality, and the decomposed multistage maximum-flow algorithm is significantly faster than the MCMF algorithm. Furthermore, we propose an efficient method that alleviates the dearth of input data for service providers who lack sufficient computing power to calculate substantial travel time information. We demonstrate the performance of our method by using data collected in New York City, USA. Our simulation studies indicate that a service provider can fulfill 74–85% of the ride requests within 2 minutes. [ABSTRACT FROM AUTHOR]

This paper studies the joint problem of optimal pricing, charging scheduling and rebalancing for one-way car sharing systems. Firstly, a price driven customers travel mechanism is given which represents the customers ride demand as a linear function of the ride price. Then, a joint framework ensuring load balance and profit maximization of the system is introduced, in which the migration of vehicles and customers in the system is captured by fluid models, while the charging procedure of electric vehicles in the charging stations is described by multi-server queues. The well-posedness and equilibrium of the fluid model are analyzed, which yields the minimum fleet size needed. Next, a rebalancing method is derived to maximize the operating income while the stations operate at an equilibrium state, i.e., vehicles available and no waiting customers. To make the system better adapt to the changing customers demand in highly dynamic environments, a real-time rebalancing policy is developed by introducing a customer arrival predicting scheme based on exponential smoothing. Numerical experiments and case studies in Chengdu show that the proposed method can significantly improve the quality of service and rebalancing performance. [ABSTRACT FROM AUTHOR]

With the development of intelligent transportation technology, more and more traffic information can be obtained to enhance arterial coordination efficiency. A time-varying arterial coordination control program is proposed in this paper. By extracting Origin-Destination (OD) information from Automatic number plate recognition (ANPR) data, an OD-based Multipath-Band method (MP-BAND) is developed to achieve the function of the program. The proposed method breaks vehicle routing at intersections, and treats one link as an analysis unit. The method can automatically select link paths for coordination, and multiple paths can be synchronized simultaneously according to the actual traffic condition. The overlap bandwidth is introduced to capture the connectivity between paths of adjacent intersections. The PM-BAND is formulated as a mixed-integer linear program, which can be solved by the standard branch-and-bound technique. Numerical tests are conducted to evaluate the performance of MP-BAND under different traffic conditions. The results have demonstrated that MP-BAND outperformed MULTIBAND and AM-BAND in various aspects. MP-BAND can improve network performance significantly almost in all scenarios. Moreover, the intersection efficiency was improved significantly, especially the efficiency of left-turning vehicles. Also, MP-BAND can well recognize major routes and improve traffic efficiency. Compared with Synchro, MP-BAND also had superiority when flow rate is not too high. Thus, MP-BAND has a much wider applicability, and can be treated as an optimization core to achieve time-varying arterial coordination. [ABSTRACT FROM AUTHOR]

Optimal Volt/VAR control (VVC) in distribution networks relies on an effective coordination between the conventional utility-owned mechanical devices and the smart residential photovoltaic (PV) inverters. Typically, a central controller carries out a periodic optimization and sends setpoints to the local controller of each device. However, instead of tracking centrally dispatched setpoints, smart PV inverters can cooperate on a much faster timescale to reach optimality within a PV inverter group. To accommodate such PV inverter groups in the VVC architecture, this paper proposes a bi-level optimization framework. The upper-level determines the setpoints of the mechanical devices to minimize the network active power losses, while the lower-level represents the coordinated actions that the inverters take for their own objectives. The interactions between these two levels are captured in the bi-level optimization, which is solved using the Karush-Kuhn-Tucker (KKT) conditions. This framework fully exploits the capabilities of the different types of voltage regulation devices and enables them to cooperatively optimize their goals. Case studies on typical distribution networks with field-recorded data demonstrate the effectiveness and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

Increasing machine investments and expensive operations and maintenance (O&M) costs have made manufacturing system leasing and maintenance service outsourcing gaining a momentum. Leading original equipment manufacturers, as lessors, have focused on providing cost-effective maintenance schemes to serve their client-enterprises (lessees) all over the world. However, individual equipment degradations, complex system structures, and global network layout bring challenges for the real-time decision-making. This article comprehensively develops a service-outsourcing progressive opportunistic maintenance methodology for a global service-outsourcing network with prognostic updating and dynamical optimization. At the equipment layer, an automated prognostic model is utilized to characterize and update the individual path of each leased equipment's degradation signals. At the local layer, an opportunistic maintenance policy is developed for balancing production capacity and optimizing maintenance decisions of each system with even series-parallel structure. At the global layer, a routing optimization policy is proposed for the service-outsourcing network by integrating service time windows and multiple geographical locations to optimize the service route of required maintenance teams and the service start time of each group set. Finally, this hierarchical methodology has been verified in a multilocation service-outsourcing network. Its mechanism with real-time prognostic updating and dynamical O&M optimization can significantly ensure cost reduction, service timeliness and network robustness. [ABSTRACT FROM AUTHOR]

In this article, a parallel and gradient-free strategy is presented for autonomous navigation of a quadrotor with a limited field of view (FOV). The FOV constraint is nonlinear and noncontinuous in general and involves higher order dynamics of the vehicle. Planning problems with such constraints are difficult to solve, especially with the real-time requirement. The proposed method first constructs an environment map by integrating the measurement of a limited-FOV sensor in a parallel process. Based on the map, it then selects a feasible local goal from a global path and generates a constraint-free nominal control sequence. Finally, a stochastic optimal control problem is solved to satisfy the limited FOV constraint, the dynamic constraint, and the collision-free conditions at the same time. The objective functions are nonlinear and noncontinuous and involves dynamic of MAV. A parallel-gradient-free algorithm is used to address these problems. Both the sensing and planning problems are solved parallelly on a lightweight graphics processing unit. The effectiveness of the proposed strategy is demonstrated through both simulation and real-flight experiments. [ABSTRACT FROM AUTHOR]

Unmanned systems (USs) including unmanned aerial vehicles, unmanned underwater vehicles, and unmanned ground vehicles have great application prospects in military and civil fields, among which the process of finding feasible and optimal paths for the agents in USs is a kernel problem. Traditional path finding algorithms are hard to adequately obtain optimal paths in real-time under fast time-varying and poor communication environments. We propose an online optimal control algorithm for USs based on a one-way broadcast communication mode under the assumption of a poor communication environment, mobile targets, radars (or sonar), and missiles (or torpedoes). With the principle of receding horizon control, optimal (or suboptimal) paths are then generated by the approximation theory of neural networks and gradient optimization techniques, with low computation requirements. Also, we give a convergence analysis for our algorithm, and show that each agent can reach its target in finite time under some conditions on agents, targets and radar-missiles. Moreover, simulations demonstrate that the agents in USs can generate optimal (or suboptimal) paths in real time using our algorithm while effectively avoiding collision with other agents or detection by enemy radars. [ABSTRACT FROM AUTHOR]

Real-time monitoring tools and their optimal measurement infrastructure are expected to play a critical role in smart grid applications. These applications require greater measurement precision and speed, which is not possible with conventional measurement devices. Due to their high economic burden and superseded technology, transmission level phasor measurement units (PMUs) are inappropriate for unbalanced active distribution networks (ADNs). Micro-synchrophasor technology is faster, less expensive with tighter accuracy tolerances. However, large-scale implementation of real-time monitoring instrumentation is economically unfeasible due to the size of ADNs, and successful deployment requires a new meter placement approach. In this paper, a micro-phasor measurement unit ($\mu $ PMU or micro-PMU) placement technique is proposed considering the unique traits of ADNs, which establishes a new concept of usable zero-injection phase strings (UZIP Strings). The method is evaluated using the IEEE-13, IEEE-34, and IEEE-123 node distribution test feeders. The fidelity of the placement algorithm is further verified via a fault detection and localization study. The results indicate better performance than previous literature with minor placement variations between deterministic and meta-heuristic algorithms for the presented holistic and UZIP String reduction methods. [ABSTRACT FROM AUTHOR]

High penetration of intermittent renewable energy sources (RES) and unexpected disruptions (e.g., natural disasters) are fundamental challenges which can threaten the secure operation of microgrids, especially during the islanded condition, with no support from the upstream grid. This paper introduces a hierarchical tri-layer min-max-min joint risk- and security-constrained model predictive control (RSC-MPC) framework for real-time energy scheduling of islanded microgrids (IMGs) under the influence of uncertainty and real-time time-varying contingency conditions. While the first layer processes a pre-scheduling day-ahead optimisation, the second layer detects the worst-case contingency conditions by maximising the load curtailment and the mismatch between pre-scheduling (i.e., first layer) and real-time operation. The third layer implements the corrective security measures to minimise the negative effect of contingency conditions while accounting to the cost associated with the risk of uncertainty in the forecasted inputs. The third layer also explores the economic effects of the RES’ uncertainty on the proposed RSC-MPC, considering the risk and energy procurement cost as conflicting objectives. The computational efficiency of the proposed hierarchical control system in terms of accuracy and processing time is guaranteed through a mixed integer conic programming model. The proposed RSC-MPC is tested in different case studies and its efficiency is validated by numerical results. [ABSTRACT FROM AUTHOR]

Power systems are undergoing significant transformations due to the introduction of microgrids (MGs) and distributed energy resources (DERs). In this regard, MGs as new entities in the system facilitate the integration of independently operated DERs to the power system. In this structure, DERs would be operated by independent agents, while the microgrid's control unit (MCU) would coordinate agents’ resource scheduling to maximize social welfare. In this structure, the uncertainty of resources operated by agents could lead to real-time energy imbalance in the MG resulting in agents’ loss of profits. Consequently, a novel transactive-based-scheme is developed in this article to facilitate flexibility service exchange between the agents to minimize the real-time energy imbalance in the MG. In this context, MCU provides independent agents with bonuses as transactive signals to exploit their operational scheduling while addressing privacy concerns. The proposed framework increases the overall social welfare as well as minimizes the dependence of the MG on the upper-level system to ensure the demand–supply balance in the MG. Finally, this scheme is implemented on an MG with a multiagent structure to investigate its effectiveness in minimizing the real-time energy imbalance in the system. [ABSTRACT FROM AUTHOR]

This article presents a distributed resilient demand response program integrated with electrical energy storage systems for residential consumers to maximize their comfort level. A dynamic real-time pricing method is proposed to determine the hourly electricity prices and schedule the electricity consumption of smart home appliances and energy storage systems commitment. The algorithm is employed in normal and emergency operating conditions, taking into account the comfort level of consumers. In emergency conditions, the power outage of consumers is modeled for different hours and outage patterns. To evaluate the applicability of the proposed model, real samples of Southern California households are considered to model the smart homes and their appliances. Further, a sensitivity analysis is performed to assess the impacts of the number of households and number of persons per household on the output results. The results showed that the proposed model reduced the costs of utility in normal and emergency conditions by about 33.77% and 30.92%, respectively. The values of total payments of consumers in normal and emergency conditions were decreased by about 34.26% and 31.31%, respectively. Further, the consumers comfort level for normal and emergency conditions increased by about 146.78% and 110.2%, respectively. Finally, the social welfare for normal and emergency conditions increased by about 46% and 49.06%, respectively. [ABSTRACT FROM AUTHOR]

This article elucidates a new real-time optimization framework for advanced energy-efficient management of active radial distribution networks. The proposed energy management process leverages the benefits of simultaneous deployment of online direct load control (DLC) and conservation voltage reduction (CVR) for decreasing peak energy demand. Initially the proposed problem is designed as a time-coupled stochastic mixed integer nonconvex programming (MINCP) to accommodate long-term offline beneficial aspects in real-time optimization framework, which is later simplified using merger of Queueing theory and Lyapunov optimization. A successive mixed integer linear programming (s-MILP) solution approach is proposed for accurate and fast convergence of the revised MINCP framework. The efficacy of the developed strategy is evaluated after comparing with two-benchmark energy management models (viz. offline and online greedy algorithm) by demonstrating on modified IEEE 69-bus test system. Further scalability of the proposed approach is validated by testing on large distribution networks. [ABSTRACT FROM AUTHOR]

Programmable wireless environments (PWEs) utilize internetworked intelligent metasurfaces to transform wireless propagation into a software-controlled resource. In this article, the interplay is explored between the user devices, the metasurfaces, and the PWE control system from the theory to the end-to-end implementation. This article first discusses the metasurface hardware and software, covering the complete workflow from the user device initialization to its final service via the PWE. Furthermore, to be compatible with the 5G and 6G wireless systems, the software-defined networking (SDN) paradigm is extended to achieve scalable internetworking and central control in PWE deployments with multiple metasurfaces and multihop communication. Subsequently, the set of SDN foundations is exploited in order to abstract the physics behind PWEs and a theoretical framework is established to describe and manipulate them in an algorithmic form. This can lead to smart radio environments that are readily accessible from various engineering disciplines, facilitating their integration into existing networks, wireless systems, and applications. This article is concluded by outlining strategies for the optimal placement of metasurfaces within a PWE-controlled space, open challenges in PWE security, specialized SDN integration issues, and theoretical problems toward the graph-driven modeling of PWEs. [ABSTRACT FROM AUTHOR]

The evolution of generational networks has stimulated the growth of delay-sensitive traffic, destabilizing the already incapacitated existing network infrastructure. Due to time-constrained resource requirements of delay-sensitive traffic, managing end-to-end delays becomes very challenging with limited resources. Therefore, we introduce space-division-multiplexing-elastic-optical-networks (SDM-EONs) and point-to-multi-point (P2MP) transfer mechanisms to increase capacity transmission rates. However, managing resource allocation during SDM-EONs-P2MP transfer requires a multi-constraint optimization (MCO) approach to significantly overcome the severe impact of crosstalk (XT) during a joint optimization of multicast traffic in multi-core fibers (MCFs). To solve this problem, we propose a deadline-aware resource allocation algorithm (PRAA) based on mixed-integer linear programming (MILP) and a heuristic approach while leveraging a delay constraint to support a joint optimization of multicast traffic. During routing, modulation, spectrum, and core allocation (RMSCA), PRAA implements MCO to allocate MCFs to reduce congestion rates by improving sending rates. We simulate experimental results and compare them with two state-of-art algorithms: DaRTree-RMSCA and DST-OM-RMSCA, to evaluate PRAA’s performance during RMSCA. The output results highlight that PRAA-RMSCA outperforms DaRTree-RMSCA and DST-OM-RMSCA during peak load of fluctuating delay-sensitive traffic. [ABSTRACT FROM AUTHOR]

The paper addresses a real-time optimization-based reference calculation integrated with a control structure for Modular Multilevel Converters (MMC) operating under normal and constrained situations (where it has reached current and/or voltage limitations, as it may occur during system faults). Firstly, a nonlinear optimization problem has been developed in which it prioritizes to satisfy the AC grid current set-points imposed by the transmission System Operator (TSO). The constrained nonlinear optimization problem is formulated based on the steady-state model of the MMC, whereby the prioritization is achieved through distinct weights defined in the Objective Function’s (OF) terms. The resultant optimization problem, however, is highly nonlinear requiring high computation burden to be solved in real-time. To cope with this issue, this paper applies a Linear Time-Varying (LTV) approximation, which permits to represent the nonlinear dynamics of the system as constant parameters, while a Linear Time-Invariant (LTI) system is used to formulate the optimization constraints. The converter’s current references are determined in real-time by solving a constrained linearized optimization problem at each control time step, which considers the TSO’s demands, the current MMC operating point and its physical limitations. Theoretical analyses comparing the responses of the linear and nonlinear optimization problems are performed to validate the accuracy of the LTV approximation. Finally, the linearized-optimization problem is integrated with the MMC controllers, evaluated under different AC and DC network conditions and compared with conventional control strategies, where it is shown that the presented method can be potentially employed to obtain the MMC current references for distinct network scenarios. [ABSTRACT FROM AUTHOR]

This paper elucidates a new real time energy management framework for radial active unbalance distribution networks (UDNs) by integrating load shedding and conservation voltage reduction (CVR) techniques. In contrast with the shortsighted real time optimization strategies, the proposed technique accounts offline beneficial aspects in real time optimization platform as time coupled stochastic expressions. Those are further simplified to a mixed integer non-convex programming (MINCP) using merger of Queueing theory and Lyapunov optimization process. To solve the complex MINCP portfolio, a consecutive mixed integer linear programming (c-MILP) based solution method is proposed after adopting necessary linear approximations. After demonstrating on modified IEEE 123 bus test network, it is showed that the proposed real time strategy can provide most energy efficient, secure and reliable operation to the UDNs and can accommodate offline advantageous attributes successfully along with the real time load shedding and CVR constraints. Validating the power flow solutions at OpenDSS platform, it is proved that the proposed c-MILP approach possess fast convergence and provide near optimal power flow solutions. Further investigations certify that presence of residential consumers are more beneficial for the networks as they are more sensitive to voltage. [ABSTRACT FROM AUTHOR]

We consider a mission-driven multiple unmanned aerial vehicles (multi-UAVs) network with millimeter-wave (mmWave) transmissions, where a leader UAV (LUAV) communicates with a large number of follower UAVs (FUAVs) simultaneously via a uniform planar array with only a limited number of radio frequency chains. Since only a few orthogonal beams are available and mission-driven UAV networks are autonomous and delay-sensitive, non-orthogonal multiple access (NOMA) over these beams is considered for agility and efficiency. In particular, aiming to address these challenges of highly dynamic mobility, we propose a machine learning framework to enable agile analog beam management for mmWave-NOMA transmissions. The proposed beam management aims to achieve efficient beam tracking and NOMA-grouping-aware analog beamforming optimization to facilitate mmWave-NOMA transmissions by judiciously utilizing angular domain information (ADI). More specifically, a Gaussian process machine learning-based ADI prediction scheme is proposed to track the angular dynamics of FUAVs, which facilitates fast beam-tracking for mmWave-NOMA transmissions. Moreover, by exploiting the predicted ADI, an unsupervised-learning-based FUAV grouping scheme is proposed to facilitate mmWave-NOMA transmissions with high radio-frequency chain efficiency, while a deep learning-based NOMA-grouping-aware fast transmit beamforming optimization scheme is proposed to improve the coverage of mmWave-NOMA transmissions in highly dynamic multi-UAVs networks. Simulation results validate the performance advantages of our proposed beam management scheme against state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

The evolution of telecommunication networks toward the fifth generation of mobile services (5G), along with the increasing presence of cloud-native applications, and the development of Cloud and Mobile Edge Computing (MEC) paradigms, have opened up new opportunities for the monitoring and management of logistics and transportation. We address the case of distributed streaming platforms with multiple message brokers to develop an optimisation model for the real-time assignment and load balancing of event streaming generated data traffic among Edge Computing facilities. The performance indicator function to be optimised is derived by adopting queuing models with different granularity (packet- and flow-level) that are suitably combined. A specific use case concerning a logistics application is considered and numerical results are provided to show the effectiveness of the optimisation procedure, also in comparison to a “static” assignment proportional to the processing speed of the brokers. [ABSTRACT FROM AUTHOR]

Connected Vehicles and Autonomous Vehicles (CAVs) provide various sources of vehicular related information to intersection infrastructure by integrating on-board sensors processing, wireless communication and other Vehicle-to-Infrastructure (V2I) technologies. Thus connected vehicle technologies can potentially remedy data collection limitations of existing urban intersection managements, enhancing the performances of intersection controls such as reducing vehicle delay, reducing vehicle number of stops and improving energy efficiency. This paper reviews optimization-based signal controls for different penetrations of connected vehicles and conventional vehicles environments, autonomous intersection management specific to completely 100% AVs road states, as well as signal-trajectory joint control for different adoptions of conventional vehicles, CVs and AVs mixture environments. Real time data processing, signal timing optimizations, vehicle trajectory motion planning and evaluation frameworks are summarized to highlight the advantages and limitations of respective intersection control paradigms. It is important to recognize that realistic scenarios in comparative assessments for proposed methods need to be achieved in future works. The effectiveness of different approaches is challenging to be compared without complete evaluation frameworks, and sensitivity analysis and hypothesis tests involving variety penetration rates and flow demands should be performed in order to test the stability of methods in different scenarios. [ABSTRACT FROM AUTHOR]

The development of Connected Automated Vehicle (CAV) technology introduces vehicle connectivity and automation into urban intersection management. It offers powerful means for accurate traffic state perception and effective traffic control actuation. This enabled the development of an intersection control algorithm that included an optimal traffic signal control algorithm and a trajectory planning function. The intersection controller aimed to maximize the intersection throughput while improving the vehicle energy efficiency via implementing adaptive signal phasing and time plans and sending reference trajectories to CAVs. Its effectiveness was tested on a closed test track equipped with a real-time traffic simulation platform, a traffic signal controller, and three physical test vehicles. The test results suggest that the intersection control algorithm was able to improve the average speed of the test vehicles by 9%, although the optimal signal controller or the trajectory planning algorithm alone could only provide marginal speed benefits. This demonstrated the advantage of combining multiple advanced traffic management functions for obtaining an elevated performance increase. For the energy efficiency analysis, both traditional gasoline-engine and hybrid vehicles were used. The hybrid test vehicles obtained fuel savings mostly via the optimal traffic signal controller (e.g., 17% fuel consumption reduction). On the other hand, the gasoline-engine test vehicle’s fuel savings could be largely attributed to the trajectory planning (e.g., 21% fuel consumption reduction). Overall, the proposed intersection controller demonstrated significant vehicle mobility and energy efficiency improvements for vehicles with different powertrains in a nearly realistic traffic condition. [ABSTRACT FROM AUTHOR]

Existing multi-class cell transmission model (CTM) based methodologies for signal timing or traffic assignment may transfer prioritized transit vehicles from one cell to the next one before processing their preceding passenger cars. In addition, existing CTM-based methodologies process a proportion of a slow-moving transit vehicle in each time step. As such a portion of each transit vehicle remains in each cell and it never clears them. This paper presents constraints to project the position of transit vehicles based on the speed and cell occupancy variations between different classes of vehicles and incorporates them into the CTM. The resulting optimization program is a mixed-integer nonlinear problem. We used a distributed receding horizon control framework to solve it in real-time. The proposed formulation is executed in a simulated arterial street with four signalized intersections in Springfield, IL with different traffic volume levels and transit vehicle frequencies. The results showed that the proposed algorithm addressed the mentioned issues of the existing multi-class CTM, and yielded more efficient network performance than the conventional transit signal priority-based (CTSP) systems. The proposed formulation reduced average bus delay by 1% to 70% and car delay by 52% to 76% compared to CTSP. [ABSTRACT FROM AUTHOR]

Bike Sharing Systems (BSSs) have emerged as an economical and eco-friendly solution to alleviate the last-mile problem in intelligent transport systems. As an exclusive route selection problem in BSSs, Bike Trip Planning (BTP) has a clear goal: to select the available routes with minimum time cost for bike users, while satisfying their basic needs, e.g., the bounded longest walking distance. In this paper, we propose a real-time Lyapunov-based Bike Trip Planning (LBTP) policy that considers the users’ waiting at stations, which has not been sufficiently studied in the literature. Both the total time cost and the service rate of all users are the core criteria of a BSS, so we make use of the Lyapunov optimization theory to make a tradeoff between them. Our policy can achieve self-organizing bike redistribution without extra redistribution budget required. The evaluation results show the superiority of our policy on the both system utility and user service rate compared with the existing BTP policies, and reveal the extra travel time fairness degree among different types of users under our policy. [ABSTRACT FROM AUTHOR]

The problems of bus bunching mitigation and the energy management of groups of vehicles have traditionally been treated separately in the literature and been formulated in two different frameworks. The present work bridges this gap by formulating the optimal control problem of the bus line eco-driving and regularity control as a smooth, multi-objective nonlinear program. Since this nonlinear program has only a few coupling variables, it is shown how it can be solved in parallel aboard each bus, such that only a marginal amount of computations need to be carried out centrally. This procedure leverages the structure of the bus line by enabling parallel computations and reducing the communication loads between the buses, which makes the problem resolution scalable in terms of the number of buses. Closed-loop control is then achieved by embedding this procedure in a model predictive control. Stochastic simulations based on real passengers and travel times data are realized for several scenarios with different levels of bunching for a line of electric buses. Our method achieves fast recoveries to regular headways as well as energy savings of up to 9.3% when compared with traditional holding or speed control baselines. [ABSTRACT FROM AUTHOR]

Single-path code is a code generation technique for real-time systems that reduces execution time variability. However, doing so can incur significant execution-time overhead and does not guarantee constant execution times. In this paper, we address the performance challenges of single-path code and solve the variability issue. We present the repetition dominance relation to identify and optimize code blocks that are always executed a fixed number of times. We show that single-path code's instructions are uniquely easy to schedule, and we explore an extension to the Patmos architecture that allows additional instruction types in the second issue slot. Lastly, we present two techniques for ensuring that functions always perform the same number of accesses to memory, resulting in programs with constant execution time. We compare the performance of single-path code to that of statically analyzed traditional code. Our results show that single-path code's performance is mostly competitive while outright superior in several cases. However, pathological cases of poor performance are still observed. [ABSTRACT FROM AUTHOR]

Streaming data are continuously generated by multiple Internet of Things (IoT) sources, e.g., sensors, mobile devices, etc., and sent simultaneously to relevant applications to be processed in real time in a continuous and timely fashion. Existing research has dealt with the integration of IoT streaming data from multiple sources. Some of the earlier research introduced models to facilitate streaming data compression and the subsequent indexing. However, as IoT data come from multiple sensors and differ in terms of variety and velocity, there is still an urgent need to build an efficient indexing mechanism so that we can optimise responses to users’ queries. In this research, we identify a variety of queries from different motivating scenarios and develop a framework to optimise the way to access and retrieve IoT streaming data to respond to the users’ queries. To this end, (i) a streaming data scenario is analysed, and (ii) an optimisation framework with indexing schemes from multiple sources are introduced, and (iii) some common and illustrative queries are presented to explain optimisation. Finally, a set of experiments on five queries is performed to illustrate the ability of the optimisation framework in a wide range of different scenarios. The results prove that the proposed optimisation framework is much better than the existing IoT data integration and indexing frameworks. [ABSTRACT FROM AUTHOR]