The problem of finite/fixed-time tracking control has garnered considerable interest, owing to the stringent requirement on convergence performance in engineering applications. However, existing finite/fixed-time control methods are not directly applicable to polynomial systems, since the introduced fractional power term disrupts the inherent polynomial structure. To overcome this problem, we propose an observer-based tracking control strategy that achieves finite/fixed-time convergence for polynomial systems with unmeasurable states. First, a polynomial state observer is designed to estimate unmeasurable states. Based on the estimation results, finite-time and fixed-time tracking controllers are developed. Then, by utilizing Lyapunov stability theory and sum of squares optimization technique, sufficient conditions are constructed for the practical finite/fixed-time stability of the closed-loop system, enabling the co-design of observer and controller gains. Moreover, settling times are explicitly derived, and the fixed-time convergence is independent of initial system states. Finally, our theoretical methods are verified by simulation results.

Information gathering focuses on designing strategies for a robot to collect data about a physical process, aiming for accurate field reconstruction. While many recent methods have been proposed to address this problem, they often assume the model of the physical process is a priori known and stationary-assumptions that rarely hold in practice. This paper presents a novel informative motion planning approach for online information gathering of a non-stationary Gaussian process. Our approach comprises two key components: an informative path planner that explores the physical field and an adaptive velocity planner that adjusts the robot's velocity profile exploiting the field's spatial variability. Additionally, we propose a path smoothing and tracking strategy to ensure continuous robot motion. Extensive simulations on a bathymetric mapping task demonstrate the effectiveness of our approach, showing superior performance in reconstructing non-stationary physical fields compared to several baseline methods.

Odometry estimation remains a critical challenge for wheeled robots, as reducing its drift directly mitigates dependency on external localization systems. This paper proposes a distributed odometry framework for steerable wheels, named ICF-DO, which is applicable to both Steerable Wheeled Mobile Robots (SWMRs) and cooperative multi-single-wheel robot systems. The proposed method features low computational complexity and reduced drift, while demonstrating strong robustness in communication-restricted scenarios. Additionally, singularity can be processed in a distributed manner in the proposed framework. Experimental validation on a real physical SWMR platform demonstrates the effectiveness and practicality of the proposed method.

Movement Primitives (MPs) are compact generators for representation and generalization of modular movements, which are usually used to implement learning from demonstration tasks in robotics. Existing works on MPs mostly utilize combinations of basis functions to represent diverse movements, whether employing probabilistic or dynamic approaches. However, applying these approaches requires manual specification of hyperparameters related to basis functions, resulting in inconvenience and a reliance on specific expertise. In this paper, we develop a Conditional Dirichlet Process Mixture Model-based Dynamic Movement Primitive (CDPMM-DMP) to achieve a non-parametric improvement for the Dynamic Movement Primitive (DMP). First, inspired by Bayesian nonparametric theory, we explore the use of the Dirichlet Process Mixture Model (DPMM) to replace the original radial basis functions in the DMP, and construct the required training set from demonstrations. Then, we study the output generation mechanism driven by the DPMM, particularly by employing conditional sampling to avoid the anomalous outputs caused by direct sampling from the DPMM. Finally, we provide analyses of the various properties brought by our nonparametric transformation of DMP. The analyses and validation results show that the proposed CDPMM-DMP can significantly reduce the parameter tuning burden in usage with its nonparametric learning property. Besides, our method still retains the inherent properties of DMP, while also incorporating some properties of probabilistic MPs, such as multi-sample learning and co-activation.

The interaction topology of multiagent systems (MASs) is crucial for effective collaboration. Recent advances in topology inference provide a better understanding of the behaviors of the systems. Nevertheless, external attackers can exploit such techniques, posing a severe privacy breach, while the challenges to the topology protection problem remain unresolved. This article proposes a distributed collaboration algorithm for MASs to defend against topology inference attacks. The novelties include: 1) Compared with traditional noise-adding methods that inject decaying random inputs, the proposed algorithm constructs a novel noise term to increase the irregularity of the agents' states in a distributed manner while satisfying the convergence requirements. 2) A weight-selecting strategy is designed to choose the subtopologies to degrade the topology inference accuracy, further improving the topology-protecting performance. Theoretically, we derive the mean-square convergence factor and the nonasymptotic error bounds of our proposed algorithm. Extensive simulations demonstrate the effectiveness of the proposed algorithm in protecting the topology.

The trade-off between computation time and path optimality is a key consideration in motion planning algorithms. While classical sampling based algorithms fall short of computational efficiency in high dimensional planning, learning based methods have shown great potential in achieving time efficient and optimal motion planning. The SOTA learning based motion planning algorithms utilize paths generated by sampling based methods as expert supervision data and train networks via regression techniques. However, these methods often overlook the important multimodal property of the optimal paths in the training set, making them incapable of finding good paths in some scenarios. In this paper, we propose a Multimodal Neuron Planner (MNP) based on the mixture density networks that explicitly takes into account the multimodality of the training data and simultaneously achieves time efficiency and path optimality. For environments represented by point clouds, MNP first efficiently compresses point clouds into a latent vector by encoding networks that are suitable for processing point clouds. We then design multimodal planning networks which enables MNP to learn and predict multiple optimal solutions. Simulation results show that our method outperforms SOTA learning based method MPNet and advanced sampling based methods IRRT* and BIT*.

We investigate a pursuit-evasion game taking place in an unbounded three-dimensional space, where a flexible pursuer with hybrid dynamics collaborates with a fast pursuer and aims to capture a flexible evader within a finite time. The key feature of this problem lies in the hybrid dynamics of the flexible pursuer, which can change its dynamics once during the game and switch to a fast pursuer with increased speed but lower maneuverability. To address this challenge, we devise a hybrid strategy based on the soft actor-critic framework, tailored specifically for the flexible pursuer, which encompasses both maneuvering and switch tactics. We introduce a switch factor to the input of the actor network and incorporate switch actions to further expand the action space. These additions enable the flexible pursuer to execute maneuvering actions and determine a moment to switch to a fast pursuer. The reward function is designed to account for related angle, altitude, speed, and sparse reward. Through extensive ablation experiments conducted in a simulated environment, we demonstrate the efficacy of our algorithm in facilitating the learning of hybrid strategies for the flexible pursuer, resulting in significantly improved capture rates compared to alternative methods.

Consensus is one of the fundamental distributed control technologies for collaboration in multi-agent systems such as collaborative handling in intelligent manufacturing. In this paper, we study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. To protect consensus value from being influenced by misbehaving nodes, we address this problem by detecting misbehaviors, mitigating the corresponding adverse impact, and achieving the resilient average consensus. General types of misbehaviors are considered, including attacks, accidental faults, and link failures. We characterize the adverse impact of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection compensation based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound. Considering scenarios wherein information sets are intermittently available due to link failures, a stochastic extension named stochastic detection compensation based consensus (S-DCC) algorithm is proposed. We prove that D-DCC and S-DCC allow nodes to asymptotically achieve resilient accurate average consensus and unbiased resilient average consensus in a statistical sense, respectively. Then, the Wasserstein distance is introduced to analyze the accuracy of S-DCC. Finally, extensive simulations are conducted to verify the effectiveness of the proposed algorithms.

The harsh industrial environment and the high exposure of wireless communication networks (WCNs) seriously degrade the control performance of edge-enabled Industrial Internet of Things systems. Recently, the codesign of control and scheduling has been studied as a promising method to improve system performance. However, due to the dynamic feature of multiple unreliable factors, the impact of communication randomness on data timeliness, and the difficulty to gather sensing data, it is challenging to jointly design the schedule and control policy to mitigate the adverse effects of WCNs. To address these issues, this article presents a trust-age of information (AoI)-aware codesign scheme (TACS). We first propose a learning-based trust model with the aid of a conditional generative adversarial network to handle the sparse industrial data and a deep-neural-network-based trust online prediction to comprehensively measure the WCNs' reliability. Then, we study the impact of AoI on control performance and design the optimal controller based on the separation principle. Moreover, we derive a trust-AoI-aware scheduling policy at the edge side to dynamically select the optimal data to participate in plant control, which maximizes the control system performance and the trust of WCNs. Simulation results reveal the effectiveness of the TACS in terms of improving the system performance significantly.

Cooperative adaptive cruise control (CACC) can optimize velocity planning by interaction between connected vehicles, and thus increase energy efficiency. It is much significant for platoon of electric vehicles (EVs) powered by hybrid energy storage system (HESS). HESS which is composed of battery and supercapacitor (SC), has been implemented to improve energy efficiency by regulating the distribution of internal energy sources. Different from most existing works on energy management for EVs, this paper is concerned with a bi-level control strategy for platoon’s velocity planning and HESS management. The upper layer focuses on the cooperative velocity planning for platoon to guarantee internal stability of individual vehicle and platoon robust string stability. The optimal velocities for each vehicle in the platoon could be determined by using distributed model predictive control (DMPC) method. The safety condition and the constraint on communication delay are considered. In the lower layer, a rolling horizon optimization method is proposed to optimize the power of HESS with the assistance of planned velocities in the upper layer. The simulation results indicate the effectiveness of the proposed strategy and methods.

Designing safety-critical control for robotic manipulators is challenging, especially in a cluttered environment. This article proposes an online control barrier function (CBF) construction method, which extracts CBF from distance samples and enforces the safety of the motion control of robotic manipulators. Specifically, the CBF guarantees the controlled invariant property for considering the system dynamics. The proposed method samples the distance function and determines the safe set. Then, the CBF is synthesized based on the safe set by a scenario-based sum-of-square program. Unlike most existing linearization-based approaches, our method preserves the volume of the feasible space for planning without approximating the signed distance function, which helps find a solution in a cluttered environment. The control law is obtained by solving a real-time CBF-based quadratic program. Moreover, our method guarantees safety with the probabilistic result validated on a 7-DOF manipulator in real and virtual environments. The experiments show that the manipulator is able to execute tasks where the potential clearance between obstacles is in millimeters.

Patrolling with multiple robots is a challenging task. While the robots collaboratively and repeatedly cover the regions of interest in the environment, their routes should satisfy two often conflicting properties: i) (efficiency) the time intervals between two consecutive visits to the regions are small; ii) (unpredictability) the patrolling trajectories are random and unpredictable. We manage to strike a balance between the two goals by i) recasting the original patrolling problem as a Graph Deep Learning problem; ii) directly solving this problem on the graph in the framework of cooperative multi-agent reinforcement learning. Treating the decisions of a team of agents as a sequence input, our model outputs the agents' actions in order by an autoregressive mechanism. Extensive simulation studies show that our approach has comparable performance with existing algorithms in terms of efficiency and outperforms them in terms of unpredictability. To our knowledge, this is the first work that successfully solves the patrolling problem with reinforcement learning on a graph.

The new generation of edge computing supported industrial cyber–physical system (ICPS) promotes the deep integration of sensing and control. The unknown model is one of the key challenges to characterize their interactions. In most existing works, many efforts have been devoted to overcoming the challenge for the single aspect of sensing and control. However, the industrial revolution puts forward the higher requirements of the overall production performance. To solve this problem, we propose a novel framework for learning-based edge sensing and control co-design. Specifically, the model learning error is first analyzed to bound the actual control performance. Then, the bound is further linked to the sensing design through the bridge of relaxed assumptions of the nonzero initial state and unknown order. Besides, the cloud-edge symphony (CES) algorithm is designed for the co-design problem solving considering the defects of the single edge computing unit (ECU). In the novel framework, the processes of sensing, control, and learning are comprehensively considered for global optimization. Finally, the proposed algorithm is applied to the personalized production of laminar cooling based on the semiphysical evaluation, and the effectiveness is verified by the results. Note to Practitioners—Edge computing supported ICPS deeply integrates the sensing and control processes. It is beneficial to realize the small-batch customized production for the individual demands in intelligent manufacturing. However, the inevitable problem of weak prior knowledge of system models motivates us to adopt appropriate learning methods to deal with the model inaccuracy and characterize the internal relationship between sensing, control, and model learning. In this article, we propose a novel framework to comprehensively consider the performance of different aspects for global optimization. Specifically, the relaxed assumptions of the nonzero initial state and unknown order are regarded as the bridge to combine edge sensing and control. The cloud-edge symphony (CES) algorithm is proposed to solve the co-design problem and applied to the laminar cooling process for evaluation. It is observed that better overall performance is achieved than previous methods. In the future, our framework can be further extended from the single edge computing unit (ECU) and collaboration with the industrial cloud platform to coordinate sensing and control between the multiple ECUs. Besides, the production requirements of specific applications can be further considered including the real-time response and the reuse of production experience.

The security issue of mobile robots has attracted considerable attention in recent years. In this article, we propose an intelligent physical attack to trap mobile robots into a preset position by learning the obstacle-avoidance mechanism from external observation. The salient novelty of our work lies in revealing the possibility that physical-based attacks with intelligent and advanced design can present real threats while without prior knowledge of the system dynamics or access to the internal system. This kind of attack cannot be handled by countermeasures in traditional cyberspace security. To practice, the cornerstone of the proposed attack is to actively explore the complex interaction characteristic of the victim robot with the environment and learn the obstacle-avoidance knowledge exhibited in the limited observations of its behaviors. Then, we propose shortest-path and hands-off attack algorithms to find efficient attack paths from the tremendous motion space, achieving the driving-to-trap goal with low costs in terms of path length and activity period, respectively. The convergence of the algorithms is proved and the attack performance bounds are further derived. Extensive simulations and real-life experiments illustrate the effectiveness of the proposed attack, beckoning future investigation for the new physical threats and defense on robotic systems.

To efficiently generate safe trajectories for an autonomous vehicle in dynamic environments, a layered motion planning method with decoupled path and speed planning is widely used. This paper studies speed planning, which mainly deals with dynamic obstacle avoidance given a planned path. The main challenges lie in the optimization in a non-convex space and the trade-off between safety, comfort, and efficiency. First, this work proposes to conduct a search in second-order derivative space for generating a comfort-optimal reference trajectory. Second, by combining abstraction and refinement, an algorithm is proposed to construct a convex feasible space for optimization. Finally, a piecewise Bézier polynomial optimization approach with trapezoidal corridors is presented, which theoretically guarantees safety and significantly enlarges the solution space compared with the existing rectangular corridors-based approach. We validate the efficiency and effectiveness of the proposed approach in simulations.

The performance of a camera network monitoring a set of targets depends crucially on the configuration of the cameras. In this paper, we investigate the reconfiguration strategy for the parameterized camera network model, with which the sensing qualities of the multiple targets can be optimized globally and simultaneously. We first propose to use the number of pixels occupied by a unit-length object in image as a metric of the sensing quality of the object, which is determined by the parameters of the camera, such as intrinsic, extrinsic, and distortional coefficients. Then, we form a single quantity that measures the sensing quality of the targets by the camera network. This quantity further serves as the objective function of our optimization problem to obtain the optimal camera configuration. We verify the effectiveness of our approach through extensive simulations and experiments, and the results reveal its improved performance on the AprilTag detection tasks. Codes and related utilities for this work are open-sourced and available at https://github.com/sszxc/MultiCam-Simulation.

This paper investigates autonomous navigation for mobile robots with a low-cost monocular camera. The main challenge lies in: i) how to accurately detect prior unseen obstacles and acquire obstacles position from monocular images without depth information, and ii) how to get the constraints for path planning to generate safe and stable path for the robot. To accurately locate surrounding obstacles using only a monocular camera, we adopt a weakly-supervised semantic segmentation network trained from LIDAR data and perform inverse perspective transformation based on ground plane constraint. Meanwhile, to reduce segmentation noise, we establish a probability occupancy map based on the distance between robot and obstacles. For path generations, we present a novel search-and-optimization based planning approach to get boundary constraints in Frenet frame and generate stable local path with consecutive image inputs. In the simulation, segmentation Intersection over Union (IoU) of the drivable area achieves more than 99% and the average mapping accuracy is less than 10cm, showing feasibility and robustness of our navigation scheme.

The optimal control for mobile agents is an important and challenging issue. Recent work shows that using randomized mechanism in agents’ control can make the state unpredictable, and thus improve the security of agents. However, the unpredictable design is only considered in single period, which can lead to intolerable control performance in long time horizon. This paper aims at the trade-off between the control performance and state unpredictability of mobile agents in long time horizon. Utilizing random perturbations consistent with uniform distributions to maximize the attackers’ prediction errors of future states, we formulate the problem as a multi-period convex stochastic optimization problem and solve it through dynamic programming. Specifically, we design the optimal control strategy considering both unconstrained and input constrained systems. The analytical iterative expressions of the control are further provided. Simulation illustrates that the algorithm increases the prediction errors under Kalman filter while achieving the control performance requirements successfully.

The new generation of the industrial cyber-physical system (ICPS) supported by the edge computing technology facilitates the deep integration of sensing and control. System observability is the key factor to characterize the internal relationship of them. In most existing works, the observability is regarded as the assumption for subsequent sensing and control. But, in fact, with the gradually expanded network scale, this assumption is more difficult to directly satisfy sensing design. For this problem, we propose the observability guaranteed method (OGM) for edge sensing and control co-design. Specifically, the nonconvex observability condition is transformed into the convex range of key parameters of the sensing strategy based on the graph signal processing (GSP) technology. Then, we establish the relationship between these parameters and control performance. In OGM, except the previous design from sensing to control, we reversely adjust the sensing design for control demands to satisfy observability. Finally, our algorithm is applied into the hot rolling laminar cooling process based on the semiphysical evaluation. The effectiveness is verified by the results.

TO BUILD a sustainable ecosystem, healthcare reinforced by the Internet of Things (IoT-Health) is a sector that makes a very useful contribution to society. With the aging of the world's population, the ability to monitor and protect people at home reduces costs and increases the quality of life. IoT healthcare has become a market with great potential, and IT giants such as IBM, Microsoft, and GE Healthcare develop products for specialized medical applications. Using IoT-Health for data collection and workflow automation is a great way to reduce waste and minimize human errors. However, the security of healthcare information is a major concern, and cybersecurity has become a significant threat for healthcare providers as well as governments to achieve sustainable city milestones. IT professionals must continually resolve health data security issues to help patients and the damage that healthcare security breaches can have on their lives.

To build a sustainable ecosystem, healthcare reinforced by the Internet of Things (IoT-Health) is a sector that makes a very useful contribution to society. With the aging of the world's population, the ability to monitor and protect people at home reduces costs and increases the quality of life. IoT healthcare has become a market with great potential, and IT giants such as IBM, Microsoft, and GE Healthcare develop products for specialized medical applications. Using IoT-Health for data collection and workflow automation is a great way to reduce waste and minimize human errors. However, the security of healthcare information is a major concern, and cybersecurity has become a significant threat for healthcare providers as well as governments to achieve sustainable city milestones. IT professionals must continually resolve health data security issues to help patients and the damage that healthcare security breaches can have on their lives.

Topology inference is a critical problem in multiagent systems. Knowledge of the topology helps to better understand the system behavior, e.g., predicting states of a multivehicle system. Different from most prior works, this paper is dedicated to inferring the directed network topology from the time-series observations of the system, which is driven by the unmeasurable latent input. Independent of the system state, the latent input is designed by the system user or injected by external excitations. To decouple its influence from the observations, an input filtering-based topology inference algorithm (IF-TIA) is proposed. Specifically, we demonstrate that the influence of the time-invariant input on the system state evolution is separable, and transform the goal into solving an optimization problem based on the separability. The novel algorithm significantly improves the inference performance by reformulating the empirical risk, where the decreasing weight setting is adopted for the cost terms in the objective function. Compared with the benchmark methods, extensive simulations illustrate the outperformance of the IF-TIA method in terms of both the topology structure and the edge weight.

Target search with multiple robots has attracted widespread attention for its numerous applications,$eg$., surveillance, reconnaissance and environmental exploration. In this paper, we propose a heuristic target search scheme for multi-robot, considering the prior information of targets is unreliable. To tactfully capture the multi-peak characteristics of the probability distribution map (PDM) of each target, we introduce the concept of high confidence area (HCA) based on the Gaussian mixture model. Then, a coordinated search method consisting of task allocation and path planning is designed to achieve efficient search performance. The main novelty of our method is twofold. First, the probability information of multi-peak is sufficiently captured by HCA and evaluated by reliability degree. Second, target allocation and path planning are designed coordinately, which dynamically update with real-time status and alleviate misleading effects even when PDM is not reliable, thereby largely reducing search time. Extensive contrastive simulations demonstrate that the HCA-based search method outperforms two other existing methods.

A mass of data generated by the widespread smart devices is transmitted through wireless communication networks for estimation and control in the Industrial-Internet-of-Things (IIoT) systems. The frequent data transmission needs to meet the high reliability and real-time demand of IIoT applications because the freshness of status updates influences the system performance. In this work, we use the Age of Information (AoI) to characterize the information freshness since it is a powerful metric to capture the randomness of state updates. Meanwhile, AoI is very useful in the control and communication co-design to improve the control performance considering communication disturbance. In order to analyze the control cost, we first derive the specific expression average AoI under the packet loss with finite retransmission times. Then, we investigate the influence of average AoI on control performance and obtain the joint cost combining communication energy consumption and control cost. According to the certainty equivalent principle, we design the optimal control law separately. Besides, we prove that the optimal joint infinite horizon cost is bounded by the linear function of average AoI. The communication policy, including the data interarrival rate and code length, is designed by optimizing the mixed-integer nonlinear programming (MINP) problem. Finally, simulation results reveal that, by employing the optimal data interarrival rate and code length, the joint control and communication cost is significantly reduced.

Collaborative computing is efficient to conduct large-scale computation tasks, especially with the surge in data volume. However, when the data contains sensitive information, privacy has to be attached significant attention during the execution of computation tasks. In this paper, based on a two-step average computation framework, we first propose three different privacy-aware schemes, where noises are carefully designed to be injected into the distributed computing process. The challenging issue is to guarantee the privacy loss in each iteration to be controllable and quantifiable, which we call the dynamic privacy-preserving collaborative computing problem. By employing Kullback-Leibler differential privacy, we obtain the privacy preserving levels in different iterations regarding the three schemes, followed by the analysis of their convergence performances. Further, we devise an approach to balance the privacy loss and the computation accuracy, whose challenge lies in how to motivate data contributors (DCs) to report more accurate data without providing them with monetized payments. This is done by allowing DCs to report data multiple times, and we obtain the optimal reporting times for each DC. Finally, extensive numerical experiments are performed to validate the obtained theoretical results.

Edge computing is emerged as a promising solution to cope with huge volumes of data generated by smart devices and low latency demand for mission-critical applications in industrial cyber-physical systems. Data processing and estimation are shifted to the edge computing side. Nevertheless, the WCN between field devices and edge computing side is exposed to malicious attackers because of its openness. Therefore, in this article, we focus on the transmission path selection strategy design to guarantee the secure state estimation on the edge side against dynamic denial-of-service attacks. First, we present a novel learning-based secure routing algorithm (LSRA) to learn the attack rule and predict the attacker's next conduct with the use of both historical and online data. With lower computational complexity, the proposed learning algorithm could track the attack rule in real time whenever new data comes. Meanwhile, we derive the analytical relationship between the probability upper bound of learning error and the learning time. Based on the predicted attacker's behavior obtained by the learning algorithm, we flexibly select the secure routing path to avoid being attacked and, thus, improve successful transmission probability. Furthermore, this secure routing path selection method improves the performance of the state estimation system. The theoretical analysis of estimator stability is given. Finally, simulation results reveal the effectiveness of LSRA and the path selection scheme.

The mixed traffic flow, which comprises both cooperative automated vehicles (CAVs) and human-driven vehicles, is becoming more common in modern urban areas. CAVs can expand the capabilities such as that can be achieved with automation and vehicular communication individually. However, the energy-saving benefits may be one of the exceptions when three or more vehicles drive in a row under the mixed traffic scenarios. The undesired driving behaviors of human-driven vehicles can introduce a high level of randomness to traffic flow, which results in energy-inefficient driving profiles for CAVs with aggressive operations or even vehicle crashing. In this paper, we investigate an eco-platooning problem for CAVs under the mixed traffic flow. This is a challenging problem due to both of the platoon-wide energy-saving and driving security requirements. To address this problem, an ecological and string stable platooning scheme, called E-CACC, is provided. First, novel spacing policies are presented to enforce that all platoon members track energy-efficient driving profiles. Then, control laws are designed to ensure the tracking performances of the proposed spacing policies. Furthermore, the theoretical proof is provided to prove the platoon string stability of the proposed strategy. Finally, performance evaluation is conducted. Results have illustrated the energy efficiency of the proposed E-CACC scheme.

Industrial Cyber-Physical Systems connect all components involved in physical processes with computing and communication abilities. Data and information from underlying devices are available for management. Industrial edge asset management is designed to cope with heterogeneous connectivity and interoperability. It enhances the autonomous feature of edge nodes to handle complex interrelationships in the field level. In this paper, an asset management node based on Asset Administration Shell is proposed. It focuses on asset management and task allocation to perform self-management and relieve the burden in the cloud. A set of new methods are proposed and applied in a simulated production system. The application shows that the asset management node facilitates to improve efficiency and reduce system complexity.

The advancements in automation and vehicular communication have enabled the platoon systems. The vehicles in a platoon are dynamically decoupled but constrained by spatial geometry. To coordinate decisions and act cooperatively, information sharing among vehicles is required, which means the platooning problem involves both communication and control issues. We consider a communication resource-aware control problem for platoon systems in this paper. To provide desirable driving performances and flexible communication scheduling opportunities for platoon systems, the distributed model predictive control (DMPC) method and communication trigger mechanism are jointly designed, which yields the communication-aware DMPC (CA-DMPC) algorithm. Simulations have been conducted to demonstrate the satisfactory control performances and significant communication savings.

This paper studies the obstacle avoidance problem of mobile vehicles. Most of the existing algorithms for obstacle avoidance are proposed for specific environments, lacking adaptability for various environments. Motivated by mimicking the obstacle-avoidance actions of humans in different environments, this paper proposes a human experience knowledge (HEK) based obstacle avoidance algorithm. The proposed algorithm provides reliable support for mobile vehicles to implement tasks in a complex environment. Specifically, we first characterize the related experience knowledge of humans by converting to categorical variables. Then, a logistic regression method is utilized to model the corresponding knowledge based on categorical variables. Finally, the HEK-based algorithm, which dynamically adapts to different situations, is designed according to the corresponding knowledge model. Extensive comparative simulation results are conducted to demonstrate that the proposed algorithm has better adaptability and flexibility for various static obstacle environments and a low computational cost.

Traffic flow prediction is one of the core technologies in Intelligent Transportation System (ITS) to improve traffic management. However, in metropolitan circumstances, the complex traffic road networks and numerous unpredictable traffic anomalies are still tough problems, which bring challenges of leveraging topological and anomalies information to accurate traffic flow prediction. In this paper, we propose a Dynamic Hidden Markov Model (DHMM) based on global PageRank algorithm to overcome these challenges. The global PageRank algorithm is more applicable than traditional algorithm for traffic scenarios, through which the PageRank metric is calculated to measure the accumulation of traffic anomalies at intersections. By incorporating the PageRank metric, DHMM leverages topological and anomalies information to dynamically model the traffic variations. Experiments on real-world dataset demonstrate that the PageRank metric can describe the degree of traffic anomalies intuitively, and the proposed model has superior traffic flow prediction performance both under normal and abnormal traffic conditions.

This paper studies adaptive task allocation for multi-agent cooperation with unknown capabilities. The tasks considered here are single-type, large-scale and not prior known initially in every implementation. This scenario is quite common in numerous latest cooperative applications, like crowdsourcing. Since the amount of tasks are fixed, it is reasonable to assume the cost is constant, and the consumed time becomes an important index. To minimize the cost time, the main challenges lie in how to allocate tasks to complete these tasks in a decentralized way and avoid solving a new optimization problem at each step. The advantages and novelty of our work are threefold: i) Leveraging the consensus and distributed method, we transform the minimum-time problem into a solvable distributed optimization problem. By consensus algorithm, the allocation method is expressed explicitly and easily. ii) We prove that the convergence of assignment process and optimal allocation is achieved geometrically. iii) The proposed algorithm is extended to the case where the efficiencies of agents are stochastic, and simulations demonstrate the effectiveness of our approach.

Reliable and scalable wireless transmissions for Internet of Vehicles (IoV) are technically challenging. Each vehicle, from driver-assisted to automated one, will generate a flood of information, up to thousands of times of that by a person. Vehicle density may change drastically over time and location. Emergency messages and real-time cooperative control messages have stringent delay constraints while infotainment applications may tolerate a certain degree of latency. On a congested road, thousands of vehicles need to exchange information badly, only to find that service is limited due to the scarcity of wireless spectrum. Considering the service requirements of heterogeneous IoV applications, service guarantee relies on an in-depth understanding of network performance and innovations in wireless resource management leveraging the mobility of vehicles, which are addressed in this article. For single-hop transmissions, we study and compare the performance of vehicle-to-vehicle (V2V) beacon broadcasting using random access-based (IEEE 802.11p) and resource allocation-based (cellular vehicle-to-everything) protocols, and the enhancement strategies using distributed congestion control. For messages propagated in IoV using multihop V2V relay transmissions, the fundamental network connectivity property of 1-D and 2-D roads is given. To have a message delivered farther away in a sparse, disconnected V2V network, vehicles can carry and forward the message, with the help of infrastructure if possible. The optimal locations to deploy different types of roadside infrastructures, including storage-only devices and roadside units with Internet connections, are analyzed.

Many third-party services and applications have integrated the login services of popular Online Social Networks, such as Facebook and Google+, and acquired user information to enrich their services by requesting user's permission. Although users can control the information disclosed to the third parties in a certain granularity, there are still serious privacy risks due to the inference attack. Even if users conceal their sensitive information, attackers can infer their secrets by exploiting the correlations among private and public information with background knowledge. To defend against such attacks, we formulate the social network data sharing problem through an optimization-based approach, which maximizes the users' self-disclosure utility while preserving their privacy. We propose two privacy-preserving social network data sharing methods to counter the inference attack. One is the efficiency-based privacy-preserving disclosure algorithm (EPPD) targeting the high utility, and the other is to convert the original problem into a multi-dimensional knapsack problem (d-KP) using greedy heuristics with a low computational complexity. We use real-world social network datasets to evaluate the performance. From the results, the proposed methods achieve a better performance when compared with the existing ones.

Cell balancing is crucial for charging supercapacitor cells to prevent cells from over-charging. Most existing cell-balancing charging methods typically adopt an output feedback control, i.e., the terminal voltages of cells are directly utilized in the controller design. One limitation of these methods is the voltage drop effect when the charging is terminated, which degrades the system capacity and results in cell imbalance. To address this challenge, in this article, we propose an observer-driven charging method for supercapacitors. The switched resistor circuit is applied and is further modeled using the switched systems theory, where the RC model of cells is considered. The communication interactions among cells is modeled using the graph theory. A switching Luenberger observer is designed to estimate the voltage of the equivalent capacitor of each cell, and a consensus-based switching control law is designed to charge and balance supercapacitors. The closed-loop system model is derived using the block diagram. A laboratory testbed has been built to verify the effectiveness of the proposed charging method. Experimental results show that the proposed method can effectively alleviate the voltage drop effect when compared with existing charging methods.

Hybrid electric storage system (HESS) is a promising power supply for electric vehicles (EVs) to prolong battery cycling life. Battery longevity is affected by the magnitude and fluctuation of the charging/discharging power profiles. While vehicle driving, unexpected high power demand can cause battery degradation and should be supplied by the supercapacitor (SC) in the HESS. However, the limited capacity of SCs restricts the HESS benefit. Thus, one of the crucial while challenging issues for an HESS is how to effectively manage the power splitting between batteries and SCs to satisfy the vehicle's driving demand as well as reducing battery degradation rate. In this paper, a proactive power management scheme is proposed to extend the EVs battery life with the HESS. First, we exploit a time-series forecasting method to predict the short-term vehicle velocity and calculate the future power demand based on prediction results. Next, due to the nonlinear dynamics of the HESS, the T-S fuzzy modeling method is adopted to approximate system nonlinearity and develop an empirical model. Finally, a model predictive control (MPC) based power management problem is formulated. Prediction errors are considered in MPC formulation to improve system robustness. Based on driving profile tests, simulation results demonstrate that the magnitude and fluctuation of battery current are both reduced and the battery life is prolonged by 17.81% compared with the existing methods.

Privacy-preserving data aggregation (DA) in network systems, e.g., Internet of Things (IoT), is a challenging problem, considering the dynamic network topology, limited computing capacity, energy supply of IoT devices, etc. The difficulty is exaggerated when there exist dishonest nodes, and how to ensure privacy, accuracy, and robustness of the DA process against dishonest nodes remains an open issue. Different from the widely investigated cryptographic approaches, in this paper, we address this challenging problem by exploiting the distributed consensus technique. To mitigate the pollution from dishonest nodes, we propose an enhanced secure consensus-based DA (E-SCDA) algorithm that allows neighbors to detect dishonest nodes, and derive the error bound when there are undetectable dishonest nodes. We prove the convergence of the E-SCDA and show that the algorithm can preserve the privacy associated to nodes' initial states. Extensive simulations have shown that the proposed algorithm has a high convergence accuracy and low complexity, even when there exist dishonest nodes in the network.

The frequent stop-and-go operations require high and fast burst driving power, which accelerates the electric vehicle batteries degradation. Hybrid electric storage system (HESS) is a promising solution, which supplements the battery with supercapacitor for rapid charging/discharging. If future power demand is available, effective power management can be done by fully exploiting the HESS benefits. Recent advances in the Internet of Things (IoT) have made the future information prediction practical, since surroundings information is obtainable. In this paper, a proactive energy management strategy is developed for the HESS with the IoT support. By analyzing the traffic data, a probabilistic graphical model, i.e., the conditional linear Gaussian (CLG), is designed for future driving information prediction. Since, the CLG prediction results are probability distributions, a scenario-tree method is developed to approximate the future power demand by sampling the possible future velocity profiles from the results. A stochastic model predictive control problem is established by incorporating the sampled trajectories. A fast dual proximal gradient method is proposed to solve the problem and facilitate real-time implementation. Simulation results demonstrate that the magnitude and fluctuation of the battery discharging power are reduced by 46.4% and 27.7%, respectively, compared with the battery only case.

Internet of Vehicles (IoV) is a promising Internet of Things application, where roadside unit (RSU) plays an important role for network service provisioning. How to select the number and locations of RSUs to deploy and allocate the traffic load to them is a critical and practical open problem. Most of the existing work focused on 1-D scenarios assuming unlimited RSU capacity, while a more practical 2-D case with limited RSU capacity has not been fully considered yet. In this paper, we investigate an RSU deployment problem for 2-D IoV networks considering the expected delivery delay requirements and task assignment. We formulate a novel utility-based maximization problem to solve the RSU deployment problem, where the utility function indicates the total benefit from the RSU deployment. We observe that each RSU has an irregular service area, which makes the problem much more difficult than the traditional facility location problem. Then, we design a utility-based RSU deployment algorithm (URDA), a linear programming-based clustering algorithm, to solve the problem. The gap between URDA and the optimal solution has been analyzed, which proved that the proposed URDA is near optimal if the deployment cost is low. Extensive simulations have been conducted to demonstrate the effectiveness and superiority of the proposed solution for IoV network service guarantee over other approaches.

Location region estimation (LRE) is a key issue for many location-based applications in the Internet of Things era. This paper explores the problem of accurate LRE (ALRE) with distance distribution methods. First, in order to capture the uncertainties during the distance ranging process, a disk error model is introduced by modeling the target as a random node inside a disk region. Then, a disk error-based ranging (DEBR) approach is designed and analyzed by proving that the parameter estimation of DEBR is unbiased. Furthermore, an ALRE algorithm is developed through taking into account both DEBR and the classical multilateration method. It is proved that the estimated region obtained by ALRE is tighter than that obtained by the traditional estimation method. In addition, extensive simulations are conducted to verify the unbiased estimation of DEBR and evaluate the performance of ALRE.

Emergency information dissemination is the most important application of Vehicular Ad-hoc Networks (VANETs). Its performance is affected by transmission mechanisms and traffic conditions. There are two typical transmission mechanisms, i.e., Internet Protocol (IP) and Information- Centric Networking (ICN). How to integrate these two mechanisms in order for timely dissemination in different traffic conditions is a critical problem. To solve this problem, in this paper, we propose a traffic-related mission-critical transmission mechanism, which can adaptively select the proper transmission mechanism according to real-time traffic conditions. First, we find the relationship between the performance of transmission mechanisms and traffic conditions, and then provide its mathematical model. Then, the traffic-related transmission algorithm is designed to achieve an adaptive switching of transmission mechanisms, in which a positive utility gain is guaranteed considering the switch benefit and cost. Lastly, we conduct extensive simulations to demonstrate the effectiveness of the proposed algorithm using SUMO and NS-3. It shows that the transmission delay is decreased by 88.2% over the IP mechanism and 37.6% over the ICN mechanism.

A typical method for privacy-preserving data publishing mechanism is to add random noise to the original data for publishing. No matter what kind of noise is added, there is a chance that the original state can be estimated in a certain accuracy. The probability of the original data inferred by the malicious receiver in a given interval is measured by (α, β) -data-privacy. With random noise added to the original data, the utility of the published data will decrease. In this paper, we investigate the tradeoff between data privacy and data utility under (α,β) -data-privacy, aiming to seek an optimal noise distribution. To maximize the weighted sum of privacy and utility we prove that when the added noise is symmetric and the data utility is measured by l1- or l2-norm function, the optimal noise follows the uniform distribution. Then we further investigate the optimal noise to maximize data utility with a certain privacy guarantee and we derive that the optimal noise is a group of impulse functions. Finally, we compare (α, β) -data-privacy with differential privacy and obtain the inequality relationship between the two privacy parameters. Simulations are conducted to validate the correctness of the obtained results.

Privacy-preserving localization in crowdsourcing has drawn much attention recently. Under the classical nonadjacent subtraction-based localization (NSL) model, existing solutions based on homomorphic encryption techniques are of high computational and communication overheads. In this paper, an adjacent subtraction-based localization (ASL) model is first proposed. Then, an efficient privacy-preserving localization (EPPL) algorithm is developed under ASL without using any homomorphic encryption technique. In terms of the correctness, privacy, and efficiency, a comprehensive analysis is presented to investigate EPPL's performance. Furthermore, the statistical equivalence between ASL and NSL is proved through the fact that the difference between their average location estimation results converges toward zero. The lower and upper bounds of the localization error are also derived for ASL under a bounded noise model. Extensive simulations are conducted to illustrate the equivalence between ASL and NSL, and the performance of EPPL regarding the correctness, privacy, and efficiency.

Network systems often rely on distributed algorithms to achieve a global computation goal with iterative local information exchanges between neighbor nodes. To preserve data privacy, a node may add a random noise to its original data for information exchange at each iteration. Nevertheless, an eavesdropping node can estimate other's original data based on the information it received. The estimation accuracy and data privacy can be measured in terms of (E, δ)-data-privacy, defined as the probability of E-accurate estimate (the difference of an estimation and the original data is within E) is no larger than δ (the disclosure probability). How to optimize the estimation and analyze data privacy is a critical and open issue. In this paper, a theoretical framework is developed to investigate how to optimize the estimation of neighbor's original data using the local information received, named optimal distributed estimation. Then, we study the disclosure probability under the optimal estimation for data privacy analysis. We further apply the developed framework to analyze the data privacy of the privacy-preserving average consensus algorithm and identify the optimal noises for the algorithm.

For vehicular networks, dropboxes are very useful for assisting the data dissemination, as they can greatly increase the contact probabilities between vehicles and reduce the data delivery delay. However, due to the costly deployment of dropboxes, it is impractical to deploy dropboxes in a dense manner. In this paper, we investigate how to deploy the dropboxes optimally by considering the tradeoff between the delivery delay and the cost of dropbox deployment. This is a very challenging issue due to the difficulty of accurate delay estimation and the complexity of solving the optimization problem. To address this issue, we first provide a theoretical framework to estimate the delivery delay accurately. Then, based on the idea of dimension enlargement and dynamic programming, we design a novel optimal dropbox deployment algorithm (ODDA) to obtain the optimal deployment strategy. We prove that ODDA has a fast convergence speed, which is less than κ (κ <; n) iterations for convergence. We also prove that the computational complexity of ODDA is O(nkm logm), i.e., ODDA has a polynomial computational complexity for a given m, the number of dropboxes for deployment. Performance evaluation by simulation demonstrates the superior performance of the proposed strategies compared with the benchmark methods.

Protecting location privacy of mobile systems is important for various location-based services in pervasive computing scenarios. How to quickly compute the target user's location without knowing each anchor user's location has drawn much attention. Under the classical nonadjacent subtraction based localization model, existing solutions based on homomophic encryption introduce much computation and communication overheads to achieve privacy-preserving localization. In this paper, an adjacent subtraction based localization model is proposed, which is suitable to efficiently protect users' privacy. Then, under such a model, an efficient privacy-preserving localization algorithm is developed without using homomophic encryption. A closed-form expression of the relationship between the localization error and the measurement noise is derived. Furthermore, a comprehensive analysis, including correctness analysis, privacy analysis, and efficiency analysis, is presented. Some simulations are conducted to show that the proposed model has equivalent accuracy and efficiency with the classical model. Some numerical results are presented to show the efficiency of the proposed privacy-preserving localization algorithm.

Data dissemination is a fundamental yet challenging issue in vehicular networks. Due to high mobility, the vehicular network topology is random and fast- changing in both the time and spatial domains. How to fully utilize limited wireless resources for supporting heterogeneous safety and multimedia services in vehicular networks is a pressing, open issue. In this article, we leverage the software- defined network architecture for hybrid vehicular networks, using both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) wireless communication technologies to ensure network performance and quality of services (QoS). The architecture of the software-defined vehicular network (SDVN) is introduced. A simulation study on how to take advantage of the SDVN framework for efficient and effective data dissemination has been given. Further research problems and opportunities are discussed.

For disconnected Vehicular Ad hoc NETworks (VANETs), the carry-and-forward mechanism is promising to ensure the delivery success ratio at the cost of a longer delay, as the vehicle travel speed is much lower than the wireless signal propagation speed. Estimating delay is critical to select the paths with low delay, and is also challenging given the random topology and high mobility, and the difficulty to let the message propagate along the selected path. In this paper, we first propose a simple yet effective propagation strategy considering bidirectional vehicle traffic for two-dimensional VANETs, so the opposite-direction vehicles can be used to accelerate the message propagation and the message can largely follow the selected path. Focusing on the propagation delay, an analytical framework is developed to quantify the expected path delay. Using the analytical model, a source node can apply the shortest-path algorithm to select the path with the lowest expected delay. Performance evaluation by simulation show that, when the vehicle density is uneven but known, the proposed Minimum Delay Routing Algorithm can achieve a substantial reduction in delay compared with the geocast-routing approach, and its performance is close to the flooding-based Epidemic algorithm, while our solution maintains only a single copy of the message.

With the increasing demand of media-rich entertainment and location-aware services from people on the road, how to disseminate the multimedia data in large-scale Vehicular Ad-Hoc Networks (VANETs) efficiently and reliably is a pressing issue. Due to the high mobility, large scale, and limited contact time between vehicles, it is quite challenging to support the multimedia data dissemination in VANETs. In this paper, we first utilize a hybrid framework to model the VANETs to address the mobility and scalability issues. Then, we formulate a utility-based maximization problem to find the best delivery strategy and select an optimal path for the multimedia data dissemination, where the utility function has taken the delivery delay, Quality of Services (QoS), and storage cost into consideration. With rigorous analysis, we obtain the closed-form of the expected utility of a path, and then obtain the optimal solution of the problem with the convex optimization theory. Finally, we conduct trace-driven simulations to evaluate the performance of the proposed algorithm with real traces collected by taxis in Shanghai. The simulation results demonstrate the rigorousness of our theoretical analysis, and the effectiveness of the proposed solution.

Research on individuals in online social networks often requires the collection of personal information such as demographics. Due to both user privacy concerns and unformatted textual information, it is quite difficult to build a completely labeled social network directly. However, both social relations and network characteristics can help attribute inference to profile online social network users. In this paper, we propose several attribute inference models based on these two factors and implement them with Naive Bayes, Decision Tree and Logistic Regression. Also, to study network characteristics and evaluate the performance of our proposed models, we use a well-labeled Google employee social network extracted from Google+ to test the proposed models on inferring the social roles of Google employees. The experiment results demonstrate that the proposed models are effective in social role inference with Dyadic Label Model performing best.

Future hybrid vehicle networks can use both Vehicle-to- Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications to provide reliable, timely, scalable, and media-rich services. In this paper, we investigate the problem that how to disseminate the data to the Road Side Unit (RSU) considering bidirectional transmissions, using vehicles to store-carry-and-forward the messages if possible, in hybrid V2I/V2V networks. We focus on the delay modeling and dissemination strategy design, aiming to minimize the delivery delay. Considering a one-dimensional vehicle network with multiple road segments, we model the process of uploading a message to an RSU either in front of or behind the source. Furthermore, based on the delay analysis, we obtain the desirable message dissemination direction, and further design the message uploading algorithm to minimize the expected deliver delay. Simulations have been conducted to verify the correctness of the analysis and illustrate the efficiency of the proposed algorithm. The analytical model can also provide important insights and guideline for the deployment of RSUs.

This paper studies the scheduling of multiple Electric Vehicles' (EVs) charging with service quality constraint at a workplace charging station, aiming to maximize the profit of the charging station under time- of-use (TOU) pricing. We develop a multi-charger framework considering both the customers' and charging station's interests. First, an admission control mechanism is proposed to guarantee that all admitted EVs' charging requirements can be satisfied before their departure time. Then, under the premise of declining the customers' requirements as little as possible, a Joint Searching (JS) scheduling algorithm is proposed to maximize the profit of the charging station. Extensive simulations based on realistic EV charging information and TOU pricing have been conducted. Simulation results exhibit that the proposed algorithm outperforms the state-of-the-art solution in terms of up to 30% profit and similar service declining probability.

Most existing works investigate consensus with noise following a certain distribution, e.g., Gaussian distribution, with fixed expectation and variance, which may not be satisfied in practical applications. This paper investigates the discrete system consensus under bounded noise, which is important and practical problem. We first provide necessary and sufficient conditions for the convergence of consensus under bounded noise. To be more general, we derive an analytical bound to show the max-min difference between the nodes' states when the general consensus algorithm converges to a stable state. Then, a novel consensus algorithm, fast consensus under bounded noise (FCBN), is proposed to eliminate the accumulative error caused by the bounded noise. It is proved that FCBN has a faster convergence speed and a higher consensus accuracy than general consensus algorithms. Extensive simulations demonstrate the effectiveness of the proposed algorithm.

Minimizing the end-to-end delay for data dissemination in a large-scale VANET with both buses of fixed schedules and taxis of random schedules is a challenging issue, due to the scalability, high-mobility, and network heterogeneity concerns. Particularly, the mix of random taxis and fixed-scheduled buses makes the delay components along a path dependent and hard to estimate. In this paper, to address the scalability and high-mobility issues, we introduce a store-and-forward framework for VANETs with extra storage using “drop boxes”, which function similar to network routers. Next, we propose an optimal link strategy which is independent of the message arrival time and can be executed in a distributed manner. Then, we derive the expected path delay, considering the dependence of the delay components along the path, and propose the optimal routing strategy to minimize the expected path delay. Trace-driven simulations have been used to validate the rigorous analysis, and demonstrate the superior performance of the proposed strategies, which result in a substantial delay reduction and a much higher delivery ratio when compared with the state-of-the-art solutions without drop boxes. The strategies can further improve the delay performance when compared with the over-simplified routing solutions which ignore the dependence of the delay components.

How to coordinate multiple Electric Vehicles' (EVs) charging demands to satisfy the requirements of the customers and also maximize the profit for the charging station is an important and challenging problem. Most of the existing works mainly focus on the interest of one side. In this paper, we develop a utility based multi-charger framework to ensure a win-win situation for both the customers and the charging station. We first propose an admission control algorithm to guarantee that the non-flexible charging requirements of all admitted EVs can be satisfied before their departure time. Then, we encourage all EVs to take more charging flexibility by setting an attractive price for the additional electricity to be taken by the EVs. To maximize the profit of the charging station, a utility based charging scheduling algorithm is proposed. Extensive simulations based on practical EV charging information have been conducted, which demonstrate the effectiveness of the proposed algorithms. The results show that the proposed approach can outperform the state-of-the-art one in terms of total utility, so that the charging station can enjoy a higher profit and the customers can enjoy more cost savings.

Considering an optimal investment problem for a retailer in electricity market, the objective is to seek the optimal investment decision that maximizes the weighted sum of the expected return and the variance of wealth. Unlike existing works, the price fluctuation of both the wholesale and retail side of electricity market is considered, and the retailer can invest its wealth in electricity market and traditional financial market simultaneously. Hence, there is a complicated wealth dynamic, which is the main challenge in our work. In this paper, by utilizing the method of Lagrange multiplier and the classical Tchebycheff inequality, we first show that the investment problem is a quadratic programming problem in terms of the decision variable, and thus has a unique optimal solution. Then, a closed-form optimal solution is derived by solving the stationary equation and comparing the feasible solution interval. Based on the optimal solution, we find the key price, which will affect the investment is the wholesale price rather than the retail price. Moreover, with a similar analysis approach, we also provide the optimal solution considering a more general model, which allows the retailer to purchase the electricity temporarily to avoid the supply shortage. Extensive simulations demonstrate the better performance of the proposed solution over the Kelly strategy widely used in the financial market.

Time synchronization is a fundamental requirement for the wide spectrum of applications with wireless sensor networks (WSNs). However, most existing time synchronization protocols are likely to deteriorate or even to be destroyed when the WSNs are attacked by malicious intruders. This paper is concerned with secure time synchronization for WSNs under message manipulation attacks. Specifically, the theoretical analysis and simulation results are first provided to demonstrate that the maximum consensus based time synchronization (MTS) protocol would be invalid under message manipulation attacks. Then, a novel secured maximum consensus based time synchronization (SMTS) protocol is proposed to detect and invalidate message manipulation attacks. Furthermore, we prove that SMTS is guaranteed to converge with simultaneous compensation of both clock skew and offset. Extensive numerical results show the effectiveness of our proposed protocol.

Recently, various consensus-based protocols have been developed for time synchronization in wireless sensor networks. However, due to the uncertainties lying in both the hardware fabrication and network communication process, it is not clear how most of the protocols will perform in real implementations. In order to reduce such gap, this paper investigates whether and how the typical consensus-based time synchronization protocols can tolerate the uncertainties in practical sensor networks through extensive testbed experiments. For two typical protocols, i.e., Average Time Synchronization (ATS) and Maximum Time Synchronization (MTS), we first analyze how the time synchronization accuracy will be affected by various uncertainties in the system. Then, we implement both protocols on our sensor network testbed consisted of Micaz nodes. We further investigate the time synchronization performance and robustness under various settings. The extensive experimental results demonstrate the advantages of MTS over ATS.