Federated learning (FL) enables collaborative model training across distributed edge devices without sharing raw data, but the large number of devices and frequent parameter exchanges create substantial communication overhead. To improve communication efficiency, event-triggered mechanisms have been introduced. However, such approaches may exclude valuable but inactive users (i.e., IoT devices with informative data but limited activity) or overlook subtle but important updates, resulting in suboptimal trade-offs between communication efficiency and training performance. To address this, we propose Stochastic Event-Triggered Lazy Aggregation with Stochastic Quantization (SET-LASQ). SET-LASQ employs a probabilistic triggering strategy that adaptively balances the participation of highly contributing users with the reactivation of long-inactive ones, while incorporating gradient quantization to further reduce transmitted bits. Under strong convexity, we theoretically prove that SET-LASQ achieves sublinear convergence with a diminishing stepsize and linear convergence to a steady-state neighborhood with a fixed stepsize. Extensive experiments on benchmark datasets verify that SET-LASQ reduces both communication rounds and transmission volume while maintaining competitive model accuracy, consistently outperforming deterministic event-triggered and lazy aggregation baselines under independent and non-independent data distributions.

Estimating the global relative position and orientation of agents is a critical task for navigation in GNSS-denied environments. This paper proposes a simple yet efficient and robust method to solve this problem in 2D space using only sparse local relative position measurements. First, we derive a complex linear model to characterize the relationship of position, orientation, and measurements of all agents. Then, for the estimation problem, we establish a broadly applicable uniqueness condition that is robust against measurement noise. Further, we develop a nonlinear least-squares method based on variable separation. and its distributed version to solve the estimation problem with guaranteed local convergence. Simulations verify that the uniqueness condition can be easily checked under noise, and both methods can accurately estimate the global relative position and orientation of agents after several iterations.

For automated vehicle, motion planning especially multi-vehicle cooperative motion planning (MVCMP), is an important and challenging problem, while the high-dimensional coupled nonlinear constraints make it become a highly non-convex nonlinear programming (NLP) problem. In this paper, we propose a parallel computing and convexifying guidance framework based on alternating direction method of multipliers (ADMM) and constraint convexification. By applying ADMM and introducing auxiliary variables, the original problem is decomposed into two types of sub-problems that can be solved in a parallel manner. The first type of sub-problem only contains kinematics constraints and can be tackled by the proposed ‘First Solve Then Regulate Time’ (FSTRT) method, which can eliminate the time coupling and divide this sub-problem into multiple parallel secondary sub-problems for each vehicle. The second type of sub-problem only considers collision avoidance constraints among multiple vehicles, and can also be solved in parallel based on copy variable. In particular, the terminal guided convex feasible set (TG-CFS) algorithm and convexifying guidance strategy are proposed to achieve constraint convexification and efficient warm-start for this sub-problem. The advantage of proposed algorithm lies at effectively resolving constraint coupling and reduce the dimension of original problem, thereby achieving parallel and fast computation of the sub-problems. Simulation and comparison results of different cases indicate that the proposed algorithm can significantly improve the computational efficiency while preserving the optimality.

With rapid electrification of transportation systems in recent years, the electric vehicle routing problem (EVRP) has received increasing attention. In order to serve a set of valuable transportation tasks using a fleet of electric vehicles (EVs), EVRP intends to jointly determine their routes and the charging schedules, with the aim of optimizing service reward and charging cost under state of charge (SOC) constraints of battery. EVRP is a difficult nonlinear integer program, and due to its intrinsic NP-hardness early EVRP studies often oversimplify the charging process and assume unlimited capacity of charging stations (CSs) and constant electricity price. The contribution of this article lies in twofold. First, to better characterize the practical charging process, this article introduces a new graph model that can take the CS capacity and dynamic electricity price into full consideration. Second, we design a series of transformation techniques, including reformulation SOC dynamics, Lagrangian relaxation, exact linear programming relaxation, etc, which eventually convert the original EVRP into an approximate linear program. Extensive simulations across 14 instances demonstrate that, the proposed approach is capable of solving large-scale instances over 500 nodes, and is 100 times faster than traditional analytic methods while achieving very close optimality. While comparing with the popular heuristic method adaptive large neighborhood search (ALNS), our method is several times faster and obtains much better optimality.

This paper proposes a very efficient algorithm for large scale Electric Vehicle Routing Problem (EVRP) with charging capacity constraint of charging facilities which has not been properly addressed yet. EVRP is to jointly optimize the sequence of serving a set of transportation tasks and the charging schedule for a fleet of electric vehicles (EVs) under the constraints on the State of Charge (SOC) of battery. EVRP has gained intensive attention in recent years, yet, it is a typical mixed integer programming (MIP), which is NP-hard thus intractable for large scale cases. Meanwhile, the maximal power capacity constraint of charging facilities is very important in practice, but has not been properly addressed in previous studies, because it will further complicate the computation. This paper proposes an efficient algorithm to solve large scale EVRP with power capacity constraint of charging stations. The algorithm approximates the original problem with three sequential subproblems that are all linear programming (LP), thus ensuring certain local optimality. Extensive simulations validate the efficiency of the proposed method and show that it is hundreds of times faster than solver and can solve large scale EVRP instances with 1000 tasks.

In recent years, sequence prediction, particularly in natural language processing tasks, has made significant progress due to advanced neural network architectures like Transformer and enhanced computing power. However, challenges persist in modeling and analyzing certain types of sequence data, such as human daily activities and competitive ball games. These segmented sequence data are characterized by short length, varying local dependencies, and coarse-grained unit states. These characteristics limit the effectiveness of conventional probabilistic graphical models and attention-based or recurrent neural networks in modeling and analyzing segmented sequence data. To address this gap, we introduce a novel generative model for segmented sequences, employing an ensemble of multiple variable-order Markov models (VOMMs) to flexibly represent state transition dependencies. Our approach integrates probabilistic graphical models with neural networks, surpassing the representation capabilities of single high-order or variable-order Markov models. Compared to end-to-end deep learning models, our method offers improved interpretability and reduces overfitting in short segments. We demonstrate the efficacy of our proposed method in two tasks: predicting tennis shot types and forecasting daily action sequences. These applications highlight the broad applicability of our segmented sequence modeling approach across diverse domains.

For autonomous driving, vehicle motion planning in complex environment is always a classic and intricate optimization problem which is highly nonlinear and nonconvex. The difficulty of problem lies in the complicated obstacle avoidance constraints and their coupling with dynamics constraints. In this article, an efficient algorithm framework is proposed based on alternating direction method of multipliers (ADMMs) and convex feasible set (CFS) algorithm. The ADMM is employed to decompose the original problem into two relatively simple subproblems containing only dynamics constraints and obstacle avoidance constraints, respectively, while the CFS is utilized to convexify the feasible region based on geometrical properties and transform the subproblem with only obstacle avoidance constraints into a series of quadratic programming (QP) problems. The combination of ADMM and CFS decouples the complicated constraints and enables the proposed algorithm to efficiently solve motion planning problem in complex environment with less dependence on the initial guess. The convergence of the proposed algorithm is proven, while the numerical evaluation results in different cases verify the feasibility and effectiveness of the proposed algorithm framework.

In this article, we study the cooperative operation problem between the fleet operator and incentive-aware customers in an on-demand delivery system. Specifically, the fleet operator offers discounts on transportation costs in exchange of customers’ delivery time flexibility. In order to capture the interaction between the fleet operator and customers, a novel bilevel optimization framework is proposed. By exploiting the strong duality, and the Karush–Kuhn–Tucker (KKT) optimality condition of customer optimization problems, we can reformulate the bilevel optimization problem as a mixed-integer nonlinear programming (MINLP) problem. Considering the inherent difficulties of MINLP, a computationally efficient algorithm, which combines the merits of Lagrangian dual decomposition and Benders decomposition, is devised to solve the resulting MINLP problem in a distributed manner. Finally, extensive numerical experiments demonstrate that the proposed cooperation scheme can decrease the delivery fees for the customers and reduce the operation cost of the fleet operator at the same time, thus leading to a win–win situation for both sides.

Crowdshipping is an emerging logistics paradigm that leverages the excess capacities of occasional couriers (OC) to transport goods. This article investigates the problem that a crowdshipping platform recruits OCs to serve the parcel pickup and delivery requests. A combinatorial reverse auction framework is introduced to model the interactions between the platform and couriers. We design an auction mechanism that implements in polynomial time, which satisfies the computational requirements of crowdshipping. It can also achieve near-optimal social cost with the approximation ratio rigidly proved. Through both rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed auction mechanism is truthful, individual rational, and has high computation efficiency.

In this article, we study the joint energy and reserve sharing problem considering renewable generation uncertainty and limited communication resources. We propose a data-driven distributionally robust energy and reserve sharing model among different agents in electricity markets. We put forward data-driven distributionally robust chance constraints (DRCC) to determine the reserve capacity, which cannot be directly solved. The inner approximation is employed to convert the DRCC into tractable linear constraints. Taking into account the agents in the Internet of Things exchange information by a resource-limited communication network, we develop a communication-censored consensus alternating direction method of multipliers (ADMMs) to utilize the limited communication resources and solve the sharing problem in a fully decentralized manner. We analyze the convergence of the proposed algorithm, and propose an adaptive penalty parameter method to speed up the convergence. Extensive simulations are conducted to verify the effectiveness of the proposed model and theoretical results.

This article investigates the online operation of distributed data centers equipped with energy battery. We aim to minimize their long-term operational cost by optimally distributing workload among data centers and operating energy battery. However, future spatio-temporally variant uncertainties in both workload and electricity prices have been the main impediment for a performance-guaranteed online data center operation strategy. To address this issue, we develop a fully distributed online algorithm that decouples workload distribution and battery operation across the network and time by introducing well-designed virtual queues for workload and batteries into the framework of Lyapunov optimization. Theoretically, an analytical gap between the long-term operational cost achieved by our algorithm and the theoretical optimum is provided to corroborate the desirable operation strategy. Extensive simulations using the real-world workload and electricity price data demonstrate the cost-delay tradeoff that our algorithm strikes and validate the theoretical results that we obtained.

This paper investigates the scheduling problem of a fleet of electric vehicles, providing mobility as a service to a set of time-specified customers, where the operator needs to solve the routing and charging problem jointly for each EV. We consider incentive-aware customers and propose that the operator offers monetary incentives to customers in exchange for time flexibility. In this way, the fleet operator can arrive at a routing and charging schedule with lower costs, whilst the customers receive monetary compensation for their flexibility. Specifically, we first propose a bi-level optimization model whereby the fleet operator optimizes the routing and charging schedule accounting for the spatio-temporal varying charging price, jointly with a monetary incentive to reimburse the delivery time flexibility experienced by the customers. Concurrently the customers choose their own time flexibility by minimizing their cost. Second, we cope with the computational burden coming from this nonlinear bi-level optimization model with an accurate reformulation approach consisting of the Karush-Kuhn-Tucker optimality conditions, a Big-M-based linearization method, and the zero duality gap of convex optimization problems. We thus convert the proposed problem into a single-level optimization problem, which can be solved by a strengthened generalized Benders decomposition method holding a faster convergence rate than the classic generalized Benders decomposition method. To evaluate the effectiveness of the proposed mathematical model, we carry out numerous simulation experiments by using the VRP-REP data of Belgium. The numerical results showcase that the proposed mathematical model can reduce the delivery fees for the customers together with the cost of operation incurred by the fleet operator.

This article studies the optimal state estimation problem for interconnected systems. Each subsystem can obtain its own measurement in real time, while, the measurements transmitted between the subsystems suffer from random delay. The optimal estimator is analytically designed for minimizing the conditional error covariance. The boundedness of the expected error covariance (EEC) is analyzed. In particular, a new condition that is easy to verify is established for the boundedness of EEC. Further, the properties of EEC with respect to the delay probability are studied. We found that there exists a critical probability such that the EEC is bounded if the delay probability is below the critical probability. Also, a lower and upper bound of the critical probability is derived. Finally, the proposed results are applied to a power system, and the effectiveness of the designed methods is illustrated by simulations.

This paper focuses on communication-efficient federated learning problem, and develops a novel distributed quantized gradient approach, which is characterized by adaptive communications of the quantized gradients. Specifically, the federated learning builds upon the server-worker infrastructure, where the workers calculate local gradients and upload them to the server; then the server obtain the global gradient by aggregating all the local gradients and utilizes it to update the model parameter. The key idea to save communications from the worker to the server is to quantize gradients as well as skip less informative quantized gradient communications by reusing previous gradients. Quantizing and skipping result in 'lazy' worker-server communications, which justifies the term Lazily Aggregated Quantized (LAQ) gradient. Theoretically, the LAQ algorithm achieves the same linear convergence as the gradient descent in the strongly convex case, while effecting major savings in the communication in terms of transmitted bits and communication rounds. Empirically, extensive experiments using realistic data corroborate a significant communication reduction compared with state-of-the-art gradient- and stochastic gradient-based algorithms.

The huge amount of energy consumed by the data centers around the world every year motivates the cloud service providers to operate data centers in a more energy efficient way. A promising solution is to turn off the idle servers, which, however, may be turned on later, incurring a significant startup cost. The problem turns to dynamically provisioning the workload, and cutting down the energy cost which includes the power to support the running of data center and the startup cost. Different from previous studies that usually consider the worst-case performance guarantee when designing online algorithms, this paper considers the average case which is more practical. We propose a simple online algorithm based on the expectation of job interval of workload, which is proven to be optimal for exponential and uniform distributions and achieves tight competitive ratio$\frac{e}{e-1}$and$\frac{4}{3}$, respectively, for them. Simulations using the synthetic data verify our theoretical analysis. Numerical results employing Google’s data center workload trace demonstrate that the proposed algorithm outperforms the worst case-based algorithm in terms of operation cost reduction.

Understanding the fine-grained temporal structure of human actions and its semantic interpretation is beneficial to many real-world tasks, such as sports movements, rehabilitation exercises, and daily-life activities analysis. Current action segmentation methods mainly rely on deep neural networks to derive feature embedding of actions from motion data, while works on analyzing human actions in fine-granularity are still lacking due to the lack of clear and generic definitions of subactions and related datasets. On the other hand, the motion representations obtained in current action segmentation methods lack semantic or mathematical interpretability that can be used to evaluate action/subaction similarity in quantitative motion analysis. Toward the goal of fine-grained, interpretable, scalable, and efficient action segmentation, we propose a novel unsupervised action segmentation and distributed representation framework based on intuitive motion primitives defined on pose data. Metrics for comprehensive evaluation of the unsupervised fine-grained action segmentation task performance are proposed, and both public and self-constructed datasets are adopted in the experiments. The results show that the proposed method has good performance and generality across different subjects, datasets, and application scenarios.

Traffic congestion is a serious social problem in modern society, while vehicle merging is one of the main causes of congestion since human driver has to decelerate to avoid collision when merging. Connected and automated vehicles (CAVs) can realize the coordinated merging which will improve the traffic efficiency, reduce the fuel consumption and improve the driving comfort. In this paper, a cooperative merging strategy for CAVs is proposed based on cooperative game theory with transferable utility (TU) and optimal control. By introducing the value of time (VOT), an economic payoff function including time benefit, fuel consumption and driving comfort is constructed. The cooperative game determines the merging sequence (MS) of vehicles and the side payment among them, and can achieve a win-win situation, while the coordinated merging trajectory of each vehicle is computed based on optimal control. The effectiveness and feasibility of the proposed framework are verified though simulation in different cases, and the comparison of simulation results indicates that the proposed framework can improve the economic benefit of vehicles and have the potential for practical application.

Logistics has gained great attentions with the prosperous development of e-commerce, which is often seen as the classic optimal vehicle routing problem. Meanwhile, electric vehicle (EV) has been widely used in logistic fleet to curb the emission of green house gases in recent years. Thus, solving the optimization problem of joint routing and charging of multiple EVs is in a urgent need, whose objective function includes charging time, charging cost, EVs travel time, usage fees of EV and revenue from serving customers. This joint problem is formulated as a mixed integer programming (MIP) problem, which, however, is NP-hard due to integer restrictions and bilinear terms from the coupling between routing and charging decisions. The main contribution of this paper lies at proposing an efficient two-stage algorithm that can decompose the original MIP problem into two linear programming (LP) problems, by exploiting the exactness of LP relaxation and eliminating the coupled term. This algorithm can achieve a near-optimal solution in polynomial time. In addition, another variant algorithm is proposed based on the two-stage one, to further improve the quality of solution. Compared with the state-of-the-art algorithm, extensive simulations are implemented to validate the effectiveness of the proposed algorithm.

Considering the penetration of numerous electric vehicles (EV) into the transportation sector, the EV routing problem that jointly optimizes the charging and routing process of EVs is becoming increasingly popular, which should be solved in online fashion for the practical requirements. First, we start with an offline EVs routing and charging (EVRP) problem which is a large-scale mixed integer nonlinear program (MINLP). Considering the NP hardness of MINLP, solving the offline EVRP directly is a time-consuming task that is not suitable for the online setting. Hence, a Benders decomposition based method is proposed to decompose the offline EVRP into a master problem and a set of sub-problems which allows for distributed implementation. To further speed up the computation, we relax the mixed integer master problem to one that can be equivalently solved as a linear program. Moreover, a novel kind of valid cut is added to the relaxed master problem to further reduce the number of iterations. In order to adapt the offline EVRP to the online setting, by introducing the virtual depot, we utilize the rolling-horizon framework to tackle the uncertainty of future information, where the offline EVRP is solved in real-time repeatedly. Finally, simulations using real road map in Belgium are performed. Besides, numerical results validate that the computation speed of proposed algorithm is faster than the state-of-the-art algorithms by several orders of magnitude, and showcase the capability of proposed algorithm to solve large size instances up to 350 nodes with 35 EVs within 100 seconds.

In this paper, we consider a scenario where each aggregator represents a group of demand-side users to participate in the reserve market. The competition among aggregators and the conventional thermal power plant (TPP) is analyzed using game theoretic methods. In consideration of the possible uncertainty about market information for aggregators, a stochastic Stackelberg game model is proposed, where aggregators act as multiple leaders and the TPP acts as the follower. Existence and uniqueness of the stochastic Stackelberg equilibrium is further derived, and a distributed algorithm is developed to approximate it. Our simulation results validate our proposed framework and demonstrate that demand-side participation in the reserve market can significantly reduce the total system cost.

Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice. The goal of the group of agents is to collaboratively learn the value function of a given policy from locally private rewards observed in a shared environment, through exchanging local estimates with neighbors. Despite their simplicity and widespread use, our theoretical understanding of such decentralized TD learning algorithms remains limited. Existing results were obtained based on i.i.d. data samples, or by imposing an ‘additional’ projection step to control the ‘gradient’ bias incurred by the Markovian observations. In this paper, we provide a finite-time analysis of the fully decentralized TD(0) learning under both i.i.d. as well as Markovian samples, and prove that all local estimates converge linearly to a small neighborhood of the optimum. The resultant error bounds are the first of its type—in the sense that they hold under the most practical assumptions—which is made possible by means of a novel multi-step Lyapunov approach.

Quantitative motion analysis to evaluate the performance of athletes has been actively studied recently. Although various methods based on wearable inertial sensors have been developed for simple and repetitive movements recognition, the understanding of continuous complex movements of in-field sports is still challenging. In this paper, we propose a new motion segmentation and recognition method based on foot swing trajectory state to achieve robust and efficient recognition of motion of interest (MOI) in the lower limbs from continuous and complex movements. In order to segment complex movements in the lower limbs, a series of foot motion states are defined based on foot-ground contact status and foot trajectory during swing. The lower body motion state sequence combining the states of both feet is matched to a prior knowledge of MOI cycle sequences obtained in advance, so as to obtain a motion type candidate set. In this case, the continuous movement is segmented based on the prescreened motion types to realize adaptive time window for feature extraction. Finally, according to the prescreened motion type candidate set, corresponding trained neural network binary classifiers are used to make the classification based on the calculated kinematic features. The proposed method is verified through experiments of football movements consisting of walking, dribbling and stepover. As the result, the motion type recognition accuracy is 95%.

The present paper develops a novel aggregated gradient approach for distributed machine learning that adaptively compresses the gradient communication. The key idea is to first quantize the computed gradients, and then skip less informative quantized gradient communications by reusing outdated gradients. Quantizing and skipping result in 'lazy' worker-server communications, which justifies the term Lazily Aggregated Quantized gradient that is henceforth abbreviated as LAQ. Our LAQ can provably attain the same linear convergence rate as the gradient descent in the strongly convex case, while effecting major savings in the communication overhead both in transmitted bits as well as in communication rounds. Empirically, experiments with real data corroborate a significant communication reduction compared to existing gradient- and stochastic gradient-based algorithms.

This paper considers a city with a large fleet of plug-in electric taxis (PETs) and studies the charging coordination problem of the fleet. The goal is to reduce charging cost for each PET, defined as the loss of service income caused by charging, by wisely choosing when and where to charge. Considering the fact that the fleet can contain thousands of autonomous PETs, this problem is approached in a distributed way. In detail, a two-stage decision process is designed for each PET in an online fashion upon receiving real-time information. In the first stage, a thresholding method is proposed to assist a PET driver in choosing a proper time slot for charging, with comprehensive consideration of state of charge of PET, time varying income, and queuing status at charging stations (CSs). In the second stage, a game-theoretical approach is devised for PETs to select CSs, so that the traveling and queuing time of each PET can be reduced with fairness. Extensive numerical simulations illustrate the following threefold benefits of the proposed approach: it can effectively reduce the charging cost for PETs, enhance the utilization ratio for CSs, and also flatten the unevenness of charging request for power grid.

Cooperative geolocation has attracted significant research interests in recent years. A large number of localization algorithms rely on the availability of statistical knowledge of measurement errors, which is often difficult to obtain in practice. Compared with the statistical knowledge of measurement errors, it can often be easier to obtain the measurement error bound. This paper investigates a localization problem assuming unknown measurement error distribution except for a bound on the error. We first formulate this localization problem as an optimization problem to minimize the worst case estimation error, which is shown to be a nonconvex optimization problem. Then, relaxation is applied to transform it into a convex one. Furthermore, we propose a distributed algorithm to solve the problem, which will converge in a few iterations. Simulation results show that the proposed algorithms are more robust to large measurement errors than existing algorithms in the literature. Geometrical analysis providing additional insights is also provided.

Non-line-of-sight (NLOS) propagation, which widely exists in wireless systems, will degrade the performance of wireless positioning system if it is not taken into consideration in the localization algorithm design. The 3rd Generation Partnership Project (3GPP) suggests that the probabilities of line-of-sight (LOS) and NLOS are related to the distance between the receiver and the transmitter. In this paper, we propose a Maximum Likelihood Estimator (MLE) for localization, which incorporates the distance dependent LOS/NLOS probabilities. Then, the position error bound is derived using Cramer-Rao Lower Bound (CRLB). Through numerical analysis, the impact of NLOS propagation on the position error bound is evaluated. The performance of our proposed algorithm is verified by real world experimental data.

We investigate a localization problem using time-difference-of-arrival measurements with unknown and bounded measurement errors. Different from most existing algorithms, we consider the minimization of the worst-case position estimation error to improve the robustness of the algorithm. The localization problem is formulated as a nonconvex optimization problem. We adopt semidefinite relaxation to relax the original problem into a convex optimization problem, which can be solved using existing semidefinite program solvers. Simulation results show that our proposed algorithm has lower worst-case position estimation error than other existing algorithms.

The size of target will induce a degradation of tracking performance, which has been neglected for simplicity in most previous studies. In multiple target tracking, occlusions will be caused by target size effect, one target can become a moving obstacle blocking the direct channel between the anchor and another target. In this paper, the data association problem in multiple target tracking is investigated. To reduce the computational complexity of traditional Joint Probabilistic Data Association (JPDA) algorithm, a modified JPDA algorithm is proposed to execute data association in multiple target tracking by utilizing the information of occlusion conditions, which is identified by a three-step algorithm. Simulation results show that the proposed algorithm is with good tracking performance and low computational complexity.

The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users' energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

The papers in this special section focus on technological and system developments in designing power grids. The power grid is a large interconnected infrastructure for delivering electricity from power plants to end users. Now, traditional grids are facing kinds of challenges, and the world is proposing to modernize legacy and make strides toward smart grid. It is widely recognized that demand response is the core feature of smart grid, which can be formally defined as “changes in electric use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized. With the support of the advanced information and communication technologies, demand response is able to improve the efficiency, reliability, economics, and sustainability of power generation, distribution, and utilization.

This brief considers the asymptotic tracking problem for a class of high-order nonaffine nonlinear dynamical systems with nonsmooth actuator nonlinearities. A novel transformation approach is proposed, which is able to systematically transfer the original nonaffine nonlinear system into an equivalent affine one. Then, to deal with the unknown dynamics and unknown control coefficient contained in the affine system, online approximator and Nussbaum gain techniques are utilized in the controller design. It is proven rigorously that asymptotic convergence of the tracking error and ultimate uniform boundedness of all the other signals can be guaranteed by the proposed control method. The control feasibility is further verified by numerical simulations.

Knowing channel sight condition is important as it has a great impact on localization performance. In this paper, a RSS-based localization algorithm, which jointly takes into consideration the effect of channel sight conditions, is investigated. In our approach, the channel sight conditions experience by a moving target to all sensors is modeled as a hidden Markov model (HMM), with the quantized measured RSSs as its observation. The parameters of HMM are obtained by an off-line training assuming that the LOS/NLOS can be identified during the training phase. With the HMM matrices, a forward-only algorithm can be utilized for real time sight conditions identification. The target is localized by extended Kalman Filter (EKF) by suitably combining with the sight conditions. Simulation results show that our proposed localization strategy can provide good identification to channel sight conditions, hence results in a better localization estimation.

In this paper, the localization accuracy of range-only sensors is investigated. Being different from most previous studies, we consider a more general measurement model with both additive and multiplicative noise other than with only additive noise. The main contributions lie in twofold. First, a CRLB(Cramer-Rao Lower Bound)-based metric is proposed to evaluate the localization accuracy. In addition, the analytical relationships between target-sensor distance and localization accuracy, and between noise and localization accuracy are derived. Second, numerical analysis is executed to evaluate the localization accuracy for three important regular patterns of sensor deployment, i.e., triangle, square and hexagon. Two aspects have been examined and discussed, including (a) the geometric distribution of localization accuracy, (b) the average localization accuracy. Both theoretical and numerical results show that the multiplicative noise will influence significantly the localization accuracy. This study also provides important guidelines for optimal sensor deployment.

Efficient demand management policies at the grid side are required for large scale charging of Plug-in Hybrid Electric Vehicles and Plug-in Electric vehicles (PHEVs/PEVs). The SoC level and Charging Cost should be optimized while the aggregate load is kept under a safety limit to avoid overloads. Conventionally, optimal managing of the charging rates requires gathering and processing data in a center. However, as the scale of the problem increases to consider thousands of charging stations distributed over a vast geographical area, the central approach suffers from vulnerability to single node/link failures as well as scalability. This paper introduces a novel decentralized network cooperative approach for controlling the PHEV/PEV charging rates. In this approach, each charging station acts as a local retailer of energy, selling the power to the plugged in vehicle while coordinating the price with its neighbors. In response to the offered price, the Smart-Charger of the vehicle adjusts the charging current to maximize the utility of the PHEV/PEV user. By iteratively repeating this process, the convergence to the global optimum is attained without the requirement for any central unit. Robustness to single link/node failures is another advantage of our method.

In this brief, a continuous tracking control law is proposed for a class of high-order multi-input-multi-output uncertain nonlinear dynamic systems with external disturbance and unknown varying control direction matrix. The proposed controller consists of high-gain feedback, Nussbaum gain matrix selector, online approximator (OLA) model and a robust term. The OLA model is represented by a two-layer neural network. The continuousness of the control signal is guaranteed to relax the requirement for the actuator bandwidth and avoid the incurred chattering effect. Asymptotic tracking performance is achieved theoretically by standard Lyapunov analysis. The control feasibility is also verified in simulation environment.