Recent research has shown that the Macroscopic Fundamental Diagram (MFD) is an effective way to manage traffic flow and mitigate congestion, which regulates bidirectional transfer flows at regional boundaries via perimeter control. In practice, perimeter control may fail to achieve desired control targets due to adverse conditions such as inclement weather or accidents, which may result in loss of actuator effectiveness faults. To address this challenge, this paper proposes two adaptive fault-tolerant perimeter control schemes to handle both time-invariant and time-varying actuator faults. To cope with time-invariant actuator faults, a data-driven fault-tolerant perimeter control approach is presented, which employs adaptive dynamic programming to approximate the solution of the Hamilton-Jacobi-Bellman equation. For dealing with time-varying faults, an adaptive fault-tolerant perimeter controller is designed by linearizing the MFD function and ensuring tracking of a given reference model. Theoretical analysis proves that the tracking error converges to zero asymptotically. Finally, simulation studies validate the effectiveness of the proposed schemes in improving traffic management under actuator fault conditions.

Distributed learning is the standard for training large-scale models across private data silos, offering privacy and efficiency but facing challenges in Byzantine robustness and communication efficiency. Existing Byzantine-robust and communication-efficient methods rely on full gradient information, and they only converge to an unnecessarily large neighborhood around the solution. Motivated by these issues, we propose a novel Byzantine-robust and communication-efficient stochastic distributed learning method that imposes no requirements on batch size and converges to a smaller neighborhood, aligning with the theoretical lower bound. Our key innovation is leveraging Polyak Momentum to mitigate the noise caused by both biased compressors and stochastic gradients, thus defending against Byzantine workers under information compression. We provide proof of tight complexity bounds for nonconvex smooth loss functions. Finally, we validate the practical significance of our algorithm through an extensive series of experiments, benchmarking its performance on both binary classification and image classification tasks.

Object-centric (OC) representations, which model visual scenes as compositions of discrete objects, have the potential to be used in various downstream tasks to achieve systematic compositional generalization and facilitate reasoning. However, these claims have yet to be thoroughly validated empirically. Recently, foundation models have demonstrated unparalleled capabilities across diverse domains, from language to computer vision, positioning them as a potential cornerstone of future research for a wide range of computational tasks. In this paper, we conduct an extensive empirical study on representation learning for downstream Visual Question Answering (VQA), which requires an accurate compositional understanding of the scene. We thoroughly investigate the benefits and trade-offs of OC models and alternative approaches including large pre-trained foundation models on both synthetic and real-world data, ultimately identifying a promising path to leverage the strengths of both paradigms. The extensiveness of our study, encompassing over 600 downstream VQA models and 15 different types of upstream representations, also provides several additional insights that we believe will be of interest to the community at large.

Modeling and forecasting nonlinear dynamics under distribution shifts is essential for robust decision-making in real-world systems. In this work, we propose **MetaKoopman**, a Bayesian meta-learning framework for modeling nonlinear dynamics through linear latent representations. MetaKoopman learns a Matrix Normal-Inverse Wishart (*MNIW*) prior over the Koopman operator, enabling closed-form Bayesian updates conditioned on recent trajectory segments. Moreover, it provides a closed-form posterior predictive distribution over future state trajectories, capturing both epistemic and aleatoric uncertainty in the learned dynamics. We evaluate MetaKoopman on a full-scale autonomous truck and trailer system across a wide range of adverse winter scenarios—including snow, ice, and mixed-friction conditions—as well as in simulated control tasks with diverse distribution shifts. MetaKoopman consistently outperforms prior approaches in multi-step prediction accuracy, uncertainty calibration and robustness to distributional shifts. Field experiments further demonstrate its effectiveness in dynamically feasible motion planning, particularly during evasive maneuvers and operation at the limits of traction. Project website: [https://mahmoud-selim.github.io/MetaKoopman/](https://mahmoud-selim.github.io/MetaKoopman/)

For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns arise from disclosing sensitive measurements to a cloud estimator. To solve this issue, we propose a differentially private set-based estimation protocol that guarantees true state containment in the estimated set and differential privacy for the sensitive measurements throughout the set-based state estimation process within the central and local differential privacy models. Zonotopes are employed in the proposed differentially private set-based estimator, offering computational advantages in set operations. We consider a plant of a non-linear discrete-time dynamical system with bounded modeling uncertainties, sensors that provide sensitive measurements with bounded measurement uncertainties, and a cloud estimator that predicts the system’s state. The privacy-preserving noise perturbs the centers of measurement zonotopes, thereby concealing the precise position of these zonotopes, i.e., ensuring privacy preservation for the sets containing sensitive measurements. Compared to existing research, our approach achieves less privacy loss and utility loss through the central and local differential privacy models by leveraging a numerically optimized truncated noise distribution. The proposed estimator is perturbed by weaker noise than the analytical approaches in the literature to guarantee the same level of privacy, therefore improving the estimation utility. Numerical and comparison experiments with truncated Laplace noise are presented to support our approach.

Guest Editorial: Co-Design of Communication, Computing, and Control in Industrial Cyber-Physical Systems—Part I

Autonomous Industrial Cyber-Physical Systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog Automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of Automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term “3C co-design”) is required. In this paper, we position Cloud-Fog Automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog Automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security.

Guest Editorial: Co-Design of Communication, Computing, and Control in Industrial Cyber-Physical Systems—Part II

In this work, we address the problem of charging coordination between electric trucks and charging stations. The problem arises from the tension between the trucks’ nontrivial charging times and the stations’ limited charging facilities. Our goal is to reduce the trucks’ waiting times at the stations while minimizing individual trucks’ operational costs. We propose a distributed coordination framework that relies on computation and communication between the stations and the trucks, and handles uncertainties in travel times and energy consumption. Within the framework, the stations assign a limited number of charging ports to trucks according to the first-come, first-served rule. In addition, each station constructs a waiting time forecast model based on its historical data and provides its estimated waiting times to trucks upon request. When approaching a station, a truck sends its arrival time and estimated arrival-time windows to the nearby station and the distant stations, respectively. The truck then receives the estimated waiting times from these stations in response, and updates its charging plan accordingly while accounting for travel uncertainties. We performed simulation studies for$1,000$trucks traversing the Swedish road network for 40 days, using realistic traffic data with travel uncertainties. The results show that our method reduces the average waiting time of the trucks by 46.1% compared to offline charging plans computed by the trucks without coordination and update, and by 33.8% compared to the coordination scheme assuming zero waiting times at distant stations.

A differential dynamic programming (DDP)-based framework for inverse reinforcement learning (IRL) is introduced to recover the parameters in the cost function, system dynamics, and constraints from demonstrations. Different from existing work, where DDP was usually used for the inner forward problem, our proposed framework uses it to efficiently compute the gradient required in the outer inverse problem with equality and inequality constraints. The equivalence between the proposed and existing methods based on Pontryagin's Maximum Principle (PMP) is established. More importantly, using this DDP-based IRL with an open-loop loss function, a closed-loop IRL framework is presented. In this framework, a loss function is proposed to capture the closed-loop nature of demonstrations. It is shown to be better than the commonly used open-loop loss function. We show that the closed-loop IRL framework reduces to a constrained inverse optimal control problem under certain assumptions. Under these assumptions and a rank condition, it is proven that the learning parameters can be recovered from the demonstration data. The proposed framework is extensively evaluated through four numerical robot examples and one real-world quadrotor system. The experiments validate the theoretical results and illustrate the practical relevance of the approach.

This work studies computation tree logic (CTL) model checking for finite-state Markov decision processes (MDPs) over the space of their distributions. Instead of investigating properties over states of the MDP, as encoded by formulae in standard probabilistic CTL (PCTL), the focus of this work is on the associated transition system, which is induced by the MDP, and on its dynamics over the (transient) MDP distributions. CTL is thus used to specify properties over the space of distributions, and is shown to provide an alternative way to express probabilistic specifications or requirements over the given MDP. We discuss the distinctive semantics of CTL formulae over distribution spaces, compare them to existing non-branching logics that reason on probability distributions, and juxtapose them to traditional PCTL specifications. We then propose reachability-based CTL model checking algorithms over distribution spaces, as well as computationally tractable, sampling-based procedures for computing the relevant reachable sets: it is in particular shown that the satisfaction set of the CTL specification can be soundly under-approximated by the union of convex polytopes. Case studies display the scalability of these procedures to large MDPs.

Representing uncertainty in causal discovery is a crucial component for experimental design, and more broadly, for safe and reliable causal decision making. Bayesian Causal Discovery (BCD) offers a principled approach to encapsulating this uncertainty. Unlike non-Bayesian causal discovery, which relies on a single estimated causal graph and model parameters for assessment, evaluating BCD presents challenges due to the nature of its inferred quantity – the posterior distribution. As a result, the research community has proposed various metrics to assess the quality of the approximate posterior. However, there is, to date, no consensus on the most suitable metric(s) for evaluation. In this work, we reexamine this question by dissecting various metrics and understanding their limitations. Through extensive empirical evaluation, we find that many existing metrics fail to exhibit a strong correlation with the quality of approximation to the true posterior, especially in scenarios with low sample sizes where BCD is most desirable. We highlight the suitability (or lack thereof) of these metrics under two distinct factors: the identifiability of the underlying causal model and the quantity of available data. Both factors affect the entropy of the true posterior, indicating that the current metrics are less fitting in settings of higher entropy. Our findings underline the importance of a more nuanced evaluation of new methods by taking into account the nature of the true posterior, as well as guide and motivate the development of new evaluation procedures for this challenge.

In this work, we present a small-scale testbed for evaluating the real-life performance of cellular V2X (C-V2X) applications on 5G cellular networks. Despite the growing interest and rapid technology development for V2X applications, researchers still struggle to prototype V2X applications with real wireless networks, hardware, and software in the loop in a controlled environment. To help alleviate this challenge, we present a testbed designed to accelerate development and evaluation of C-V2X applications on 5G cellular networks. By including a small-scale vehicle platform into the testbed design, we significantly reduce the time and effort required to test new C-V2X applications on 5G cellular networks. With a focus around the integration of small-scale vehicle platforms, we detail the design decisions behind the full software and hardware setup of commonly needed intelligent transport system agents (e.g. sensors, servers, vehicles). Moreover, to showcase the testbed’s capability to produce industrially-relevant, real world performance evaluations, we present an evaluation of a simple test case inspired from shared situational awareness. Finally, we discuss the upcoming use of the testbed for evaluating 5G cellular network-based shared situational awareness and other C-V2X applications.

In this work, we propose an approach for ensuring the safety of vehicles passing through an intelligent intersection. There are many proposals for the design of intelligent intersections that introduce central decision-makers to intersections for enhancing the efficiency and safety of the vehicles. To guarantee the safety of such designs, we develop a safety framework for intersections based on temporal logic and reachability analysis. We start by specifying the required behavior for all the vehicles that need to pass through the intersection as linear temporal logic formula. Then, using temporal logic trees, we break down the linear temporal logic specification into a series of Hamilton-Jacobi reachability analyses in an automated fashion. By successfully constructing the temporal logic tree through reachability analysis, we verify the feasibility of the intersection specification. By taking this approach, we enable a safety framework that is able to automatically provide safety guarantees on new intersection behavior specifications. To evaluate our approach, we implement the framework on a simulated T-intersection, where we show that we can check and guarantee the safety of vehicles with potentially conflicting paths.

Distributionally Robust Optimization (DRO) accounts for uncertainty in data distributions by optimizing the model performance against the worst possible distribution within an ambiguity set. In this paper, we propose a DRO framework that relies on a new distance inspired by Unbalanced Optimal Transport (UOT). The proposed UOT distance employs a soft penalization term instead of hard constraints, enabling the construction of an ambiguity set that is more resilient to outliers. Under smoothness conditions, we establish strong duality of the proposed DRO problem. Moreover, we introduce a computationally efficient Lagrangian penalty formulation for which we show that strong duality also holds. Finally, we provide empirical results that demonstrate that our method offers improved robustness to outliers and is computationally less demanding for regression and classification tasks.

This paper studies the optimal hub-based platoon formation at hubs along a highway under decentralized, distributed, and centralized policies. Hubs are locations along highways where trucks can wait for other trucks to form platoons. A coordinator at each hub decides the departure time of trucks, and the released trucks from the hub will form platoons. The problem is cast as an optimization problem where the objective is to maximize the platooning reward. We first show that the optimal release policy in the decentralized case, where the hubs do not exchange information, is to release all trucks at the hub when the number of trucks exceeds a threshold computed by dynamic programming. We develop efficient approximate release policies for the dependent arrival case using this result. To study the value of information exchange among hubs on platoon formation, we next study the distributed and centralized platoon formation policies which require information exchange among hubs. To this end, we develop receding horizon solutions for the distributed and centralized platoon formation at hubs using the dynamic programming technique. Finally, we perform a simulation study over three hubs in northern Sweden. The profits of the decentralized policies are shown to be approximately$3.5\%$lower than the distributed policy and$8\%$lower than the centralized release policy. This observation suggests that decentralized policies are prominent solutions for hub-based platooning as they do not require information exchange among hubs and can achieve a similar performance compared with distributed and centralized policies.

Truck platooning is a promising technology that enables trucks to travel in formations with small inter-vehicle distances for improved aerodynamics and fuel economy. The real-world transportation system includes a vast number of trucks owned by different fleet owners, for example, carriers. To fully exploit the benefits of platooning, efficient dispatching strategies that facilitate the platoon formations across fleets are required. This paper presents a distributed framework for addressing multi-fleet platoon coordination in large transportation networks, where each truck has a fixed route and aims to maximize its own fleet’s platooning profit by scheduling its waiting times at hubs. The waiting time scheduling problem of individual trucks is formulated as a distributed optimal control problem with continuous decision space and a reward function that takes non-zero values only at discrete points. By suitably discretizing the decision and state spaces, we show that the problem can be solved exactly by dynamic programming, without loss of optimality. Finally, a realistic simulation study is conducted over the Swedish road network with$5,000$trucks to evaluate the profit and efficiency of the approach. The simulation study shows that, compared to single-fleet platooning, multi-fleet platooning provided by our method achieves around$15$times higher monetary profit and increases the CO$_2$emission reductions from$0.4\%$to$5.5\%$. In addition, it shows that the developed approach can be carried out in real-time and thus is suitable for platoon coordination in large transportation systems.

This paper presents algorithms for performing data-driven reachability analysis under temporal logic side information. In certain scenarios, the data-driven reachable sets of a robot can be prohibitively conservative due to the inherent noise in the robot's historical measurement data. In the same scenarios, we often have side information about the robot's expected motion (e.g., limits on how much a robot can move in a one-time step) that could be useful for further specifying the reachability analysis. In this work, we show that if we can model this side information using a signal temporal logic (STL) fragment, we can constrain the data-driven reachability analysis and safely limit the conservatism of the computed reachable sets. Moreover, we provide formal guarantees that, even after incorporating side information, the computed reachable sets still properly over-approximate the robot's future states. Lastly, we empirically validate the prac-ticality of the over-approximation by computing constrained, data-driven reachable sets for the Small- Vehicles-for-Autonomy (SVEA) hardware platform in two driving scenarios.

In this paper, we develop a framework against inference attacks aimed at inferring the values of the controller gains of an active steering control system (ASCS). We first show that an adversary with access to the shared information by a vehicle, via a vehicular ad hoc network (VANET), can reliably infer the values of the controller gains of an ASCS. This vulnerability may expose the driver as well as the manufacturer of the ASCS to severe financial and safety risks. To protect controller gains of an ASCS against inference attacks, we propose a randomized filtering framework wherein the lateral velocity and yaw rate states of a vehicle are processed by a filter consisting of two components: a nonlinear mapping and a randomizer. The randomizer randomly generates a pair of pseudo gains which are different from the true gains of the ASCS. The nonlinear mapping performs a nonlinear transformation on the lateral velocity and yaw rate states. The nonlinear transformation is in the form of a dynamical system with a feedforward-feedback structure which allows real-time and causal implementation of the proposed privacy filter. The output of the filter is then shared via the VANET. The optimal design of randomizer is studied under a privacy constraint that determines the protection level of controller gains against inference attacks, and is in terms of mutual information. It is shown that the optimal randomizer is the solution of a convex optimization problem. By characterizing the distribution of the output of the filter, it is shown that the statistical distribution of the filter’s output depends on the pseudo gains rather than the true gains. Using information-theoretic inequalities, we analyze the inference ability of an adversary in estimating the control gains based on the output of the filter. Our analysis shows that the performance of any estimator in recovering the controller gains of an ASCS based on the output of the filter is limited by the privacy constraint. The performance of the proposed privacy filter is compared with that of an additive noise privacy mechanism. Our numerical results show that the proposed privacy filter significantly outperforms the additive noise mechanism, especially in the low distortion regime.

Truck platooning is a technology that is expected to become widespread in the coming years. Apart from the numerous benefits that it brings, its potential effects on the overall traffic situation need to be studied further, especially at bottlenecks and ramps. Assuming we can control the platoons from the infrastructure, they can be used as controlled moving bottlenecks, actuating control actions on the rest of the traffic, and potentially improving the throughput of the whole system. In this work, we use a tandem queueing model with moving bottlenecks as a prediction model to calculate control actions for the platoons. We use platoon speeds and formations as control inputs, and design a control law for throughput improvement of a highway section with a stationary bottleneck. By postponing and shaping the inflow to the bottleneck, we are able to avoid capacity drop, which significantly reduces the total time spent of all vehicles. We derived the estimated improvement in throughput that is achieved by applying the proposed control law, and tested it in a simulation study, with multi-class cell transmission model with platoons used as the simulation model, finding that the median delay of all vehicles is reduced by 75.6% compared to the uncontrolled case. Notably, although they are slowed down while actuating control actions, platooned vehicles experience less delay compared to the uncontrolled case, since they avoid going through congestion at the bottleneck.

We study the strategic interaction among vehicles in a non-cooperative platoon coordination game. Vehicles have predefined routes in a transportation network with a set of hubs where vehicles can wait for other vehicles to form platoons. Vehicles decide on their waiting times at hubs and the utility function of each vehicle includes both the benefit from platooning and the cost of waiting. We show that the platoon coordination game is a potential game when the travel times are either deterministic or stochastic, and the vehicles decide on their waiting times at the beginning of their journeys. We also propose two feedback solutions for the coordination problem when the travel times are stochastic and vehicles are allowed to update their strategies along their routes. The solutions are evaluated in a simulation study over the Swedish road network. It is shown that uncertainty in travel times affects the total benefit of platooning drastically and the benefit from platooning in the system increases significantly when utilizing feedback solutions.

This paper presents a comprehensive study on the impact of information flow topologies on the resilience of distributed algorithms that are widely used for estimation and control in vehicle platoons. In the state of the art, the influence of information flow topology on both internal and string stability of vehicle platoons has been well studied. However, understanding the impact of information flow topology on cyber-security tasks, e.g., attack detection, resilient estimation and formation algorithms, is largely open. By means of a general graph theory framework, we study connectivity measures of several platoon topologies and we reveal how these measures affect the ability of distributed algorithms to reject communication disturbances, to detect cyber-attacks, and to be resilient against them. We show that the traditional platoon topologies relying on interaction with the nearest neighbor are very fragile with respect to performance and security criteria. On the other hand, appropriate platoon topologies, namely$k$-nearest neighbor topologies, are shown to fulfill desired security and performance levels. The framework we study covers undirected and directed topologies, ungrounded and grounded topologies, or topologies on a line and on a ring. We show that there is a trade-off in the network design between the robustness to disturbances and the resilience to adversarial actions. Theoretical results are validated via simulations.

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an $\mathcal{O}(T^{\max\{c,1-c\}})$ bound for static regret and an $\mathcal{O}(T^{(1-c)/2})$ bound for cumulative constraint violation, where $c\in(0,1)$ is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to $\mathcal{O}(\log(T))$ when the loss functions are strongly convex, which also improves existing results. %In order to bound the regret with respect to any comparator sequence, In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal $\mathcal{O}(\sqrt{T(1+P_T)})$ regret and an $\mathcal{O}(\sqrt{T})$ cumulative constraint violation, where $P_T$ is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.

In this paper, we propose a leader-follower hierarchical strategy for two robots collaboratively transporting an object in a partially known environment with obstacles. Both robots sense the local surrounding environment and react to obstacles in their proximity. We consider no explicit communication, so the local environment information and the control actions are not shared between the robots. At any given time step, the leader solves a model predictive control (MPC) problem with its known set of obstacles and plans a feasible trajectory to complete the task. The follower estimates the inputs of the leader and uses a policy to assist the leader while reacting to obstacles in its proximity. The leader infers obstacles in the follower’s vicinity by using the difference between the predicted and the real-time estimated follower control action. A method to switch the leader-follower roles is used to improve the control performance in tight environments. The efficacy of our approach is demonstrated with detailed comparisons to two alternative strategies, where it achieves the highest success rate, while completing the task fastest.

Overtaking the lead vehicle on two-way roads in the presence of several oncoming vehicles is a complex task for autonomous vehicles. In this paper, we formulate the overtaking behavior of an ego vehicle based on a deep reinforcement learning (DRL) method. First, a two-way urban road is created, wherein the ego vehicle aims to reach the destination safely and efficiently while considering multiple traffic participants. We use different intelligent driver model (IDM) parameters to account for different drivers' habits. Furthermore, we introduce different responses of other vehicles when the ego vehicle takes overtaking maneuver. Then, a hierarchical control framework is proposed to manage vehicles on the road, which supervises vehicle behaviors at the high layer and controls the motion at the lower layer. The DRL method named Proximal Policy Optimization is applied to derive the high-level decision-making policies. A self-attention mechanism is further introduced to improve the performance of our algorithm. Finally, the overtaking maneuvers of the ego vehicle in different training timesteps are analyzed and how the responses of other vehicles affect the ego one's overtaking behavior is investigated. Simulation results show that our approach can achieve good performance to deal with the two-way road autonomous overtaking task. Supplementary video is available at https://youtu.be/jPEGjM7cBuk.

One of the main challenges in developing autonomous transport systems based on connected and automated vehicles is the comprehension and understanding of the environment around each vehicle. In many situations, the understanding is limited to the information gathered by the sensors mounted on the ego-vehicle, and it might be severely affected by occlusion caused by other vehicles or fixed obstacles along the road. Situational awareness is the ability to perceive and comprehend a traffic situation and to predict the intent of vehicles and road users in the surrounding of the ego-vehicle. The main objective of this paper is to propose a framework for how to automatically increase the situational awareness for an automatic bus in a realistic scenario when a pedestrian behind a parked truck might decide to walk across the road. Depending on the ego-vehicle's ability to fuse information from sensors in other vehicles or in the infrastructure, shared situational awareness is developed using a set-based estimation technique that provides robust guarantees for the location of the pedestrian. A two-level information fusion architecture is adopted, where sensor measurements are fused locally, and then the corresponding estimates are shared between vehicles and units in the infrastructure. Thanks to the provided safety guarantees, it is possible to adjust the ego-vehicle speed appropriately to maintain a proper safety margin. Three scenarios of growing information complexity are considered throughout the study. Simulations show how the increased situational awareness allows the ego-vehicle to maintain a reasonable speed without sacrificing safety.

The event-triggered model predictive control (MPC) reduces energy consumption for updating control sequences while maintaining the originality of the MPC, which copes with hard constraints on dynamical systems. In the presence of large uncertainties, however, the standard event-triggered MPC may generate too frequent event occurrences. To compensate for unknown uncertainties, this paper applies a statistical learning to event-triggered MPC. The stability and the feasibility of the proposed control system are analyzed in regard to the statistical learning influences, such as the number of training samples, model complexity, and learning parameters. Accordingly, the event-triggering policy is established to guarantee the stability. We evaluate the proposed algorithm for the tracking problem of a nonholonomic model perturbed by uncertainties. In comparison with the standard event-triggered control scheme, the simulation results of the proposed method show better tracking performance with less frequent event triggers.

This article describes a system to facilitate dynamic en route formation of heavy-duty vehicle platoons with the goal of reducing fuel consumption. Safe vehicle platooning is a maturing technology that leverages modern sensor, control, and communication technology to automatically regulate the inter-vehicle distances. Truck platooning has been shown to reduce fuel consumption through slipstreaming by up to 10%; under realistic highway-driving conditions. To further benefit from this technology, a platoon coordinator is proposed, which interfaces with fleet management systems and suggests how platoons can be formed in a fuel-efficient manner over a large region. The coordinator frequently updates the plans to react to newly available information. This way, it requires a minimum of customization with respect to the logistic operations. We discuss the system architecture in detail and introduce important underlying methodological foundations. Plans are derived in computationally tractable stages optimizing fuel savings from platooning. The effectiveness of this approach is verified in a simulation study. It shows that the coordinated platooning system can improve over spontaneously occurring platooning even under the presence of disturbances. A real demonstrator has also been developed. We present data from an experiment in which three vehicles were coordinated to form a platoon on public highways under normal traffic conditions. It demonstrates the feasibility of coordinated en route platoon formation with current communication and on-board technology. Simulations and experiments support that the proposed system is technically feasible and a potential solution to the problem of using vehicle platooning in an operational context.

This study addresses an operation of unmanned aerial vehicles (UAVs) in a network environment where there is time-varying network delay. The network delay entails undesirable effects on the stability of the UAV control system due to delayed state feedback and outdated control input. Although several networked control algorithms have been proposed to deal with the network delay, most existing studies have assumed that the plant dynamics is known and simple, or the network delay is constant. These assumptions are improper to multirotor-type UAVs because of their nonlinearity and time-sensitive characteristics. To deal with these problems, we propose a networked control system using model predictive control (MPC) designed under the consideration of multirotor characteristics. We also apply a Gaussian process (GP) to learn an unknown nonlinear model, which increases the accuracy of path planning and state estimation. Flight experiments show that the proposed algorithm successfully compensates the network delay and Gaussian process learning improves the UAV's path tracking performance.

Harmful algal blooms occur frequently and deteriorate water quality. A reliable method is proposed in this paper to track algal blooms using a set of autonomous surface robots. A satellite image indicates the existence and initial location of the algal bloom for the deployment of the robot system. The algal bloom area is approximated by a circle with time varying location and size. This circle is estimated and circumnavigated by the robots which are able to locally sense its boundary. A multi-agent control algorithm is proposed for the continuous monitoring of the dynamic evolution of the algal bloom. Such algorithm comprises of a decentralised least squares estimation of the target and a controller for circumnavigation. We prove the convergence of the robots to the circle and in equally spaced positions around it. Simulation results with data provided by the SINMOD ocean model are used to illustrate the theoretical results.

In this paper, we obtain generic bounds on the variances of estimation and prediction errors in time series analysis via an information-theoretic approach. It is seen in general that the error bounds are determined by the conditional entropy of the data point to be estimated or predicted given the side information or past observations. Additionally, we discover that in order to achieve the prediction error bounds asymptotically, the necessary and sufficient condition is that the “innovation” is asymptotically white Gaussian. When restricted to Gaussian processes and 1-step prediction, our bounds are shown to reduce to the Kolmogorov-Szegö formula and Wiener-Masani formula known from linear prediction theory.

Future mobile networks will provide support for real-time control applications. The tight real-time and reliability constraints of these applications introduce novel challenges for mobility management. Legacy individual handover schemes do not sufficiently address these issues, as they do not consider physical interactions between mobile nodes. A novel group handover scheme is proposed which allows for the simultaneous handover of a group of nodes. Both the individual and the group handover are modeled as discrete-time Markov chains. Based on these models expressions for the stochastic handover delay are derived. The results are numerically evaluated in a vehicle platooning scenario. The group handover is shown to significantly reduce the handover delay in comparison to the individual handover. Furthermore, the group handover is shown to scale well when the number of vehicles increases. These improvements are shown to come at the cost of an increased messaging overhead.

Vehicle platooning is a promising technology that can lead to significant fuel savings and emission reduction. However, the macroscopic impact of vehicle platoons on highway traffic is not yet well understood. In this article, we propose a new fluid queuing model to study the macroscopic interaction between randomly arriving vehicle platoons and the background traffic at highway bottlenecks. This model, viewed as a stochastic switched system, is analyzed for two practically relevant priority rules: proportional (or mixed) and segmented priority. We provide intuitive stability conditions, and obtain bounds on the long-run average length and variance of queues for both priority rules. We use these results to study how platoon-induced congestion varies with the fraction of platooned vehicles, and their characteristics such as intra-platoon spacing and arrival rate. Our analysis reveals a basic tradeoff between congestion induced by the randomness of platoon arrivals, and efficiency gain due to a tighter intra-platoon spacing. This naturally leads to conditions under which the proportional priority is preferred over segmented priority. Somewhat surprisingly, our analytical results are in agreement with the simulation results based on a more sophisticated two-class cell transmission model.

Queue overflow of a dynamic queue system gives rise to the information loss (or packet loss) in the communication buffer or the decrease of throughput in the transportation network. This paper investigates a stochastic optimal control problem for dynamic queue systems when imposing probability constraints on queue overflows. We reformulate this problem as a Markov decision process (MDP) with safety constraints. We prove that both finite-horizon and infinite-horizon stochastic optimal control for MDP with such constraints can be transformed as a linear program (LP), respectively. Feasibility conditions are provided for the finite-horizon constrained control problem. Two implementation algorithms are designed under the assumption that only the state (not the state distribution) can be observed at each time instant. Simulation results compare optimal cost and state distribution among different scenarios, and show the probability constraint satisfaction by the proposed algorithms.

The development of sustainable transportation infrastructure for people and goods, using new technology and business models, can prove beneficial or detrimental for mobility, depending on its design and use. The focus of this paper is on the increasing impact new mobility services have on traffic patterns and transportation efficiency in general. Over the last decade, the rise of the mobile internet and the usage of mobile devices have enabled ubiquitous traffic information. With the increased adoption of specific smartphone applications, the number of users of routing applications has become large enough to disrupt traffic flow patterns in a significant manner. Similarly, but at a slightly slower pace, novel services for freight transportation and city logistics improve the efficiency of goods transportation and change the use of road infrastructure. This paper provides a general four-layer framework for modeling these new trends. The main motivation behind the development is to provide a unifying formal system description that can at the same time encompass system physics (flow and motion of vehicles) as well as coordination strategies under various information and cooperation structures. To showcase the framework, we apply it to the specific challenge of modeling and analyzing the integration of routing applications in today's transportation systems. In this framework, at the lowest layer (flow dynamics), we distinguish routed users from nonrouted users. A distributed parameter model based on a nonlocal partial differential equation is introduced and analyzed. The second layer incorporates connected services (e.g., routing) and other applications used to optimize the local performance of the system. As inputs to those applications, we propose a third layer introducing the incentive design and global objectives, which are typically varying over the day depending on road and weather conditions, external events, etc. The high-level planning is handled on the fourth layer taking social longterm objectives into account. We illustrate the framework by considering its ability to model at two different levels. Specific to vehicular traffic, numerical examples enable us to demonstrate the links between the traffic network layer and the routing decision layer. With a second example on optimized freight transport, we then discuss the links between the cooperative control layer and the lower layers. The congestion pricing in Stockholm is used to illustrate how also the social planning layer can be incorporated in future mobility services.

In this paper, we investigate probabilistic stability of Kalman filtering over fading channels modeled by *-mixing random processes, where channel fading is allowed to generate non-stationary packet dropouts with temporal and/or spatial correlations. Upper/lower almost sure (a.s.) stabilities and absolutely upper/lower a.s. stabilities are defined for characterizing the sample-path behaviors of the Kalman filtering. We prove that both upper and lower a.s. stabilities follow a zero-one law, i.e., these stabilities must happen with a probability either zero or one, and when the filtering system is one-step observable, the absolutely upper and lower a.s. stabilities can also be interpreted using a zero-one law. We establish general stability conditions for (absolute) upper and lower a.s. stabilities. In particular, with one-step observability, we show the equivalence between absolutely a.s. stabilities and a.s. ones, and necessary and sufficient conditions in terms of packet arrival rate are derived; for the so-called non-degenerate systems, we also manage to give a necessary and sufficient condition for upper a.s. stability.

The problem of how to coordinate a large fleet of trucks with a given itinerary to enable fuel-efficient platooning is considered. Platooning is a promising technology that enables trucks to save significant amounts of fuel by driving close together and thus reducing air drag. A setting is considered in which each truck in a fleet is provided with a start location, a destination, a departure time, and an arrival deadline from a higher planning level. Fuel-efficient plans should be computed. The plans consist of routes and speed profiles that allow trucks to arrive by their arrival deadlines. Hereby, trucks can meet on common parts of their routes and form platoons, resulting in decreased fuel consumption. We formulate a combinatorial optimization problem that combines plans involving only two vehicles. We show that this problem is difficult to solve for large problem instances. Hence, a heuristic algorithm is proposed. The resulting plans are further optimized using convex optimization techniques. The method is evaluated with Monte Carlo simulations in a realistic setting. We demonstrate that the proposed algorithm can compute plans for thousands of trucks and that significant fuel savings can be achieved.

This paper proposes a new semi-supervised machine learning for localization. It improves localization efficiency by reducing efforts needed to calibrate labeled training data by using unlabeled data, where training data come from received signal strengths of a wireless communication link. The main idea is to treat training data as spatio-temporal data. We compare the proposed algorithm with the state-of-art semi-supervised learning methods. The algorithms are evaluated for estimating the unknown location of a smartphone mobile robot. The experimental results show that the developed learning algorithm is the most accurate and robust to the varying amount of training data, without sacrificing the computation speed.

In this article, we propose a planning algorithm for coverage of complex structures with a network of robotic sensing agents, with multi-robot surveillance missions as our main motivating application. The sensors are deployed to monitor the external surface of a 3D structure. The algorithm controls the motion of each sensor so that a measure of the collective coverage attained by the network is nondecreasing, while the sensors converge to an equilibrium configuration. A modified version of the algorithm is also provided to introduce collision avoidance properties. The effectiveness of the algorithm is demonstrated in a simulation and validated experimentally by executing the planned paths on an aerial robot.

In this paper, we consider the recovery of piecewise sparse signals from incomplete noisy measurements via a greedy algorithm. Here piecewise sparse means that the signal can be approximated in certain domain with known number of nonzero entries in each piece/segment. This paper makes a two-fold contribution to this problem: 1) formulating a piecewise sparse model in the framework of compressed sensing and providing the theoretical analysis of corresponding sensing matrices; 2) developing a greedy algorithm called piecewise orthogonal matching pursuit (POMP) for the recovery of piecewise sparse signals. Experimental simulations verify the effectiveness of the proposed algorithms.

This paper suggests a mapless indoor localization using wifi received signal strength (RSS) of a smartphone, collected by multiple people. A new trajectory learning algorithm by combining a dynamic time warping and a machine learning technique is proposed in order to generate an alternative map. Moreover, we combine particle filter and Gaussian process (GP) for the position estimation, because it can use the alternative map as the probabilistic function (the prior), and can use probabilistic relationship (the likelihood) between wifi RSSs and location. Field experimental results confirm the usefulness of our algorithm when the map is not available and robustness against outliers, in that the accuracy of the proposed localization is similar to that using the true map information.

In this paper, we consider a discrete time linear quadratic Gaussian (LQG) control problem in which state information of the plant is encoded in a variable-length binary codeword at every time step, and a control input is determined based on the codewords generated in the past. We derive a lower bound of the rate achievable by the class of prefix-free codes attaining the required LQG control performance. This lower bound coincides with the infimum of a certain directed information expression, and is computable by semidefinite programming (SDP). Based on a technique by Silva et al., we also provide an upper bound of the best achievable rate by constructing a controller equipped with a uniform quantizer with subtractive dither and Shannon-Fano coding. The gap between the obtained lower and upper bounds is less than 0:754r + 1 bits per time step regardless of the required LQG control performance, where r is the rank of a signal-to-noise ratio matrix obtained by SDP, which is no greater than the dimension of the state.

The zero-error secrecy capacity of uncertain wiretap channels is defined. If the sensor-estimator channel is perfect, it is also calculated. Further properties are discussed. The problem of estimating a dynamical system with nonstochastic disturbances is studied where the sensor is connected to the estimator and an eavesdropper via an uncertain wiretap channel. The estimator should obtain a uniformly bounded estimation error whereas the eavesdropper's error should tend to infinity. It is proved that the system can be estimated securely if the zero-error capacity of the sensor-estimator channel is strictly larger than the logarithm of the system's unstable pole and the zero-error secrecy capacity of the uncertain wiretap channel is positive.

Freight transportation is of outmost importance in our society and is continuously increasing. At the same time, transporting goods on roads accounts for about 26% of the total energy consumption and 18% of all greenhouse gas emissions in the European Union. Despite the influence the transportation system has on our energy consumption and the environment, road transportation is mainly done by individual long-haulage trucks with no real-time coordination or global optimization. In this paper, we review how modern information and communication technology supports a cyber-physical transportation system architecture with an integrated logistic system coordinating fleets of trucks traveling together in vehicle platoons. From the reduced air drag, platooning trucks traveling close together can save about 10% of their fuel consumption. Utilizing road grade information and vehicle-to-vehicle communication, a safe and fuel-optimized cooperative look-ahead control strategy is implemented on top of the existing cruise controller. By optimizing the interaction between vehicles and platoons of vehicles, it is shown that significant improvements can be achieved. An integrated transport planning and vehicle routing in the fleet management system allows both small and large fleet owners to benefit from the collaboration. A realistic case study with 200 heavy-duty vehicles performing transportation tasks in Sweden is described. Simulations show overall fuel savings at more than 5% thanks to coordinated platoon planning. It is also illustrated how well the proposed cooperative look-ahead controller for heavy-duty vehicle platoons manages to optimize the velocity profiles of the vehicles over a hilly segment of the considered road network.

In this paper, we propose a robust approach for scheduling of smart appliances and electrical energy storages (EESs) in active apartments with the aim of reducing both the electricity bill and the CO2emissions. The proposed robust formulation takes the user behavior uncertainty into account so that the optimal appliances schedule is less sensitive to unpredictable changes in user preferences. The user behavior uncertainty is modeled as uncertainty in the cost function coefficients. In order to reduce the level of conservativeness of the robust solution, we introduce a parameter allowing to achieve a trade-off between the price of robustness and the protection against uncertainty. Mathematically, the robust scheduling problem is posed as a multi-objective Mixed Integer Linear Programming (MILP), which is solved by using standard algorithms. The numerical results show effectiveness of the proposed approach to increase both the electricity bill and CO2emissions savings, in the presence of user behavior uncertainties. Mathematical insights into the robust formulation are illustrated and the sensitivity of the optimum cost in the presence of uncertainties is investigated. Although home appliances and EESs are considered in this work, we point out that the proposed scheduling framework is generally applicable to many use cases, e.g., charging and discharging of electrical vehicles in an effective way. In addition, it is applicable to various scenarios considering different uncertainty sources, different storage technologies and generic programmable electrical loads, as well as different optimization criteria.

Heavy-duty vehicles driving close behind each other, also known as platooning, experience a reduced aerodynamic drag, which reduces the overall fuel consumption up to 20% for the trailing vehicle. However, due to each vehicle being assigned with different transport missions (with different origins, destinations, and delivery times), platoons should be formed, split, and merged along the highways, and vehicles have to drive solo sometimes. In this paper, we study how two or more scattered vehicles can cooperate to form platoons in a fuel-efficient manner. We show that when forming platoons on the fly on the same route and not considering rerouting, the road topography has a negligible effect on the coordination decision. With this, we then formulate an optimization problem when coordinating two vehicles to form a platoon. We propose a coordination algorithm to form platoons of several vehicles that coordinates neighboring vehicles pairwise. Through a simulation study with detailed vehicle models and real road topography, it is shown that our approach yields significant fuel savings.

Gossip algorithms are widely used in modern distributed systems, with applications ranging from sensor networks and peer-to-peer networks to mobile vehicle networks and social networks. A tremendous research effort has been devoted to analyzing and improving the asymptotic rate of convergence for gossip algorithms. In this work we study finite-time convergence of deterministic gossiping. We show that there exists a symmetric gossip algorithm that converges in finite time if and only if the number of network nodes is a power of two, while there always exists an asymmetric gossip algorithm with finite-time convergence, independent of the number of nodes. For n=2mnodes, we prove that a fastest convergence can be reached in nm=nlog2 n node updates via symmetric gossiping. On the other hand, under asymmetric gossip among n=2m+r nodes with , it takes at least mn+2r node updates for achieving finite-time convergence. It is also shown that the existence of finite-time convergent gossiping often imposes strong structural requirements on the underlying interaction graph. Finally, we apply our results to gossip algorithms in quantum networks, where the goal is to control the state of a quantum system via pairwise interactions. We show that finite-time convergence is never possible for such systems.

In this paper, we study the impact of adversarial actions on voltage control schemes in interconnected microgrids. Each microgrid is abstracted as a power inverter that can be controlled to regulate its voltage magnitude and phase-angle independently. Moreover, each power inverter is modeled as a single integrator, whose input is given by a voltage droop-control policy that is computed based on voltage magnitude and reactive power injection measurements. Under mild assumptions, we then establish important properties of the nominal linearized closed-loop system, such as stability, positivity, and diagonal dominance. These properties play an important role when characterizing the potential impact of different attack scenarios. In particular, we discuss two attack scenarios where the adversary corrupts measurement data and reference signals received by the voltage droop controllers. The potential impact of instances of each scenario is analyzed using control-theoretic tools, which may be used to develop methodologies for identifying high-risk attack scenarios, as is illustrated by numerical examples.

Heavy-duty vehicle (HDV) platooning is a mean to significantly reduce the fuel consumption for the trailing vehicle. By driving close to the vehicle in front, the air drag is reduced tremendously. Due to each HDV being assigned with different transport missions, platoons will need to be frequently formed, merged, and split. Driving on the road requires interaction with surrounding traffic and road users, which will influence how well a platoon can be formed. In this paper, we study how traffic may affect a merging maneuver of two HDVs trying to form a platoon. We simulate this for different traffic densities and for different HDV speeds. Even on moderate traffic density, a platoon merge could be delayed with 20 % compared to the ideal case with no traffic.

We address the problem of estimating the number of people in a room using information available in standard HVAC systems. We propose an estimation scheme based on two phases. In the first phase, we assume the availability of pilot data and identify a model for the dynamic relations occurring between occupancy levels, CO2concentration and room temperature. In the second phase, we make use of the identified model to formulate the occupancy estimation task as a deconvolution problem. In particular, we aim at obtaining an estimated occupancy pattern by trading off between adherence to the current measurements and regularity of the pattern. To achieve this goal, we employ a special instance of the so-called fused lasso estimator, which promotes piecewise constant estimates by including an ℓ1norm-dependent term in the associated cost function. We extend the proposed estimator to include different sources of information, such as actuation of the ventilation system and door opening/closing events. We also provide conditions under which the occupancy estimator provides correct estimates within a guaranteed probability. We test the estimator running experiments on a real testbed, in order to compare it with other occupancy estimation techniques and assess the value of having additional information sources. Note to Practitioners - Home automation systems benefit from automatic recognition of human presence in the built environment. Since dedicated hardware is costly, it may be preferable to detect occupancy with software-based systems which do not require the installation of additional devices. The object of this study is the reconstruction of occupancy patterns in a room using measurements of concentration, temperature, fresh air inflow, and door opening/closing events. All these signals are information sources often available in HVAC systems of modern buildings and homes. We assess the value of such information sources in terms of their relevance in detecting occupancy in small and medium-sized rooms. The proposed estimation scheme is composed of two distinct phases. The first is a training phase where the goal is to derive a mathematical model relating the number of occupants with the concentration. It is required to record the actual occupants in the room for a time period spanning few days, a task that can be performed either with manual logging or with temporary dedicated hardware counting systems. In a second phase, we use the derived model to design an online software which collects measurements of the environmental signals and provides the number of people currently in the room. The estimated occupancy levels can then be employed to enhance the efficiency of the HVAC system of the building. We notice that, in modern residential buildings composed by structurally equal flats, the training phase can be run in one flat only, since the obtained model will be reasonably valid for the other flats.

This paper is about fully-distributed support vector machine (SVM) learning over wireless sensor networks. With the concept of the geometric SVM, we propose to gossip the set of extreme points of the convex hull of local data set with neighboring nodes. It has the advantages of a simple communication mechanism and finite-time convergence to a common global solution. Furthermore, we analyze the scalability with respect to the amount of exchanged information and convergence time, with a specific emphasis on the small-world phenomenon. First, with the proposed naive convex hull algorithm, the message length remains bounded as the number of nodes increases. Second, by utilizing a small-world network, we have an opportunity to drastically improve the convergence performance with only a small increase in power consumption. These properties offer a great advantage when dealing with a large-scale network. Simulation and experimental results support the feasibility and effectiveness of the proposed gossip-based process and the analysis.

Distributed consensus computation over random graph processes is considered. The random graph process is defined as a sequence of random variables which take values from the set of all possible digraphs over the node set. At each time step, every node updates its state based on a Bernoulli trial, independent in time and among different nodes: either averaging among the neighbor set generated by the random graph, or sticking with its current state. The connectivity-independence and arc-independence are introduced to capture the fundamental influence of the random graphs on the consensus convergence. Necessary and/or sufficient conditions are presented on the success probabilities of the Bernoulli trials for the network to reach a global almost sure consensus, with some sharp threshold established revealing a consensus zero-one law. Convergence rates are established by the lower and upper bounds of the ϵ-computation time. We also generalize the concepts of connectivity/arc independence to their analogues from the *-mixing point of view, so that our results apply to a very wide class of graphical models, including the majority of random graph models in the literature, e.g., Erdos-Rényi, gossiping, and Markovian random graphs. We show that under *-mixing, our convergence analysis continues to hold and the corresponding almost sure consensus conditions are established. Finally, we further investigate almost sure finite-time convergence of random gossiping algorithms, and prove that the Bernoulli trials play a key role in ensuring finite-time convergence. These results add to the understanding of the interplay between random graphs, random computations, and convergence probability for distributed information processing.

We introduce an atomic congestion game with two types of agents, namely, cars and trucks, to model the traffic flow on a road over various time intervals of the day. Cars maximize their utility by finding a tradeoff between the time they choose to use the road, the average velocity of the flow at that time, and the dynamic congestion tax that they pay for using the road. In addition to these terms, the trucks have an incentive for using the road at the same time as their peers because they have platooning capabilities, which allow them to save fuel. The dynamics and equilibria of this game-theoretic model for the interaction between car traffic and truck platooning incentives are investigated. We use traffic data from Stockholm, Sweden, to validate parts of the modeling assumptions and extract reasonable parameters for the simulations. We use joint strategy fictitious play and average strategy fictitious play to learn a pure strategy Nash equilibrium of this game. We perform a comprehensive simulation study to understand the influence of various factors, such as the drivers' value of time and the percentage of the trucks that are equipped with platooning devices, on the properties of the Nash equilibrium.

Heavy-duty vehicles (HDVs) traveling in single file with small intervehicle distances experience reduced aerodynamic drag and, therefore, have improved fuel economy. In this paper, we attempt to maximize the amount of fuel saved by coordinating platoon formation using a distributed network of controllers. These virtual controllers, placed at major intersections in a road network, help coordinate the velocity of approaching vehicles so they arrive at the junction simultaneously and can therefore platoon. This control is initiated only if the cost of forming the platoon is smaller than the savings incurred from platooning. In a large-scale simulation of the German Autobahn network, we observe that savings surpassing 5% when only a few thousand vehicles participate in the system. These results are corroborated by an analysis of real-world HDV data that show significant platooning opportunities currently exist, suggesting that a slightly invasive network of distributed controllers, such as the one proposed in this paper, can yield considerable savings.

We derive a modular fluid-flow network congestion control model based on a law of fundamental nature in networks: the conservation of information. Network elements such as queues, users, and transmission channels and network performance indicators like sending/acknowledgment rates and delays are mathematically modeled by applying this law locally. Our contributions are twofold. First, we introduce a modular metamodel that is sufficiently generic to represent any network topology. The proposed model is composed of building blocks that implement mechanisms ignored by the existing ones, which can be recovered from exact reduction or approximation of this new model. Second, we provide a novel classification of previously proposed models in the literature and show that they are often not capable of capturing the transient behavior of the network precisely. Numerical results obtained from packet-level simulations demonstrate the accuracy of the proposed model.

In this paper consensus in second-order multi-agent systems with a non-periodic sampled-data exchange among agents is investigated. The sampling is random with bounded inter-sampling intervals. It is assumed that each agent has exact knowledge of its own state at any time instant. The considered local interaction rule is PD-type. Sufficient conditions for stability of the consensus protocol to a time-invariant value are derived based on LMIs. Such conditions only require the knowledge of the connectivity of the graph modeling the network topology. Numerical simulations are presented to corroborate the theoretical results.

In this paper consensus in second-order multi-agent systems with a non-periodic sampled-data exchange among agents is investigated. The sampling is random with bounded inter-sampling intervals. It is assumed that each agent has exact knowledge of its own state at all times. The considered local interaction rule is PD-type. The characterization of the convergence properties exploits a Lyapunov-Krasovskii functional method, sufficient conditions for stability of the consensus protocol to a time-invariant value are derived. Numerical simulations are presented to corroborate the theoretical results.

Vehicle platooning is important for heavy-duty vehicle manufacturers, due to the reduced aerodynamic drag for the follower vehicles, which gives an overall lower fuel consumption. Heavy-duty vehicle drivers are aware this fact and sometimes drive close to other heavy-duty vehicles. However, it is not currently well known how many vehicles are actually driving in such spontaneous platoons today. This paper studies the platooning rate of 1,800 heavy-duty vehicles by analyzing sparse vehicle position data from a region in Europe during one day. Map-matching and path-inference algorithms are used to determine which paths the vehicles took. The spontaneous platooning rate is found to be 1.2 %, which corresponds to a total fuel saving of 0.07% compared to if none of the vehicles were platooning. Furthermore, we introduce several virtual coordination schemes. We show that coordinations can increase the platooning rate and fuel saving with a factor of ten with minor adjustments from the current travel schedule. The platooning rate and fuel savings can be significantly greater if higher flexibility is allowed.

The ability to maintain state awareness in the face of unexpected and unmodeled errors and threats is a defining feature of a resilient control system. Therefore, in this paper, we study the problem of distributed fault detection and isolation (FDI) in large networked systems with uncertain system models. The linear networked system is composed of interconnected subsystems and may be represented as a graph. The subsystems are represented by nodes, while the edges correspond to the interconnections between subsystems. Considering faults that may occur on the interconnections and subsystems, as our first contribution, we propose a distributed scheme to jointly detect and isolate faults occurring in nodes and edges of the system. As our second contribution, we analyze the behavior of the proposed scheme under model uncertainties caused by the addition or removal of edges. Additionally, we propose a novel distributed FDI scheme based on local models and measurements that is resilient to changes outside of the local subsystem and achieves FDI. Our third contribution addresses the complexity reduction of the distributed FDI method, by characterizing the minimum amount of model information and measurements needed to achieve FDI and by reducing the number of monitoring nodes. The proposed methods can be fused to design a scalable and resilient distributed FDI architecture that achieves local FDI despite unknown changes outside the local subsystem. The proposed approach is illustrated by numerical experiments on the IEEE 118-bus power network benchmark.

Wide deployment of wireless sensor and actuator networks in cyber-physical systems requires systematic design tools to enable dynamic tradeoff of network resources and control performance. In this paper, we consider three recently proposed aperiodic control algorithms which have the potential to address this problem. By showing how these controllers can be implemented over the IEEE 802.15.4 standard, a practical wireless control system architecture with guaranteed closed-loop performance is detailed. Event-based predictive and hybrid sensor and actuator communication schemes are compared with respect to their capabilities and implementation complexity. A two double-tank laboratory experimental setup, mimicking some typical industrial process control loops, is used to demonstrate the applicability of the proposed approach. Experimental results show how the sensor communication adapts to the changing demands of the control loops and the network resources, allowing for lower energy consumption and efficient bandwidth utilization.

In this paper, we study the coordinated tracking problem of multiple heterogeneous Lagrange systems with a dynamic leader. Only nominal parameters of Lagrange dynamics are assumed to be available. Under the local interaction constraints, i.e., the followers only have access to their neighbors' information and the leader being a neighbor of only a subset of the followers, continuous coordinated tracking algorithms with adaptive coupling gains are proposed. Except for the benefit of the chattering-free control achieved, the proposed algorithm also has the attribute that it does not require the neighbors' generalized coordinate derivatives. Global asymptotic coordinated tracking is guaranteed, and the tracking errors between the followers and the leader are shown to converge to zero. Examples are given to validate the effectiveness of the proposed algorithms.

Although the performance of the medium access control (MAC) of the IEEE 802.15.4 has been investigated under the assumption of ideal wireless channel, the understanding of the cross-layer dynamics between MAC and physical layer is an open problem when the wireless channel exhibits path loss, multi-path fading, and shadowing. The analysis of MAC and wireless channel interaction is essential for consistent performance prediction, correct design and optimization of the protocols. In this paper, a novel approach to analytical modeling of these interactions is proposed. The analysis considers simultaneously a composite channel fading, interference generated by multiple terminals, the effects induced by hidden terminals, and the MAC reduced carrier sensing capabilities. Depending on the MAC parameters and physical layer thresholds, it is shown that the MAC performance indicators over fading channels can be far from those derived under ideal channel assumptions. As novel results, we show to what extent the presence of fading may be beneficial for the overall network performance by reducing the multiple access interference, and how this information can be used for joint selection of MAC and physical layer parameters.

The IEEE 802.15.4 communication protocol is a de-facto standard for wireless applications in industrial and home automation. Although the performance of the medium access control (MAC) of the IEEE 802.15.4 has been thoroughly investigated under the assumption of ideal wireless channel, there is still a lack of understanding of the cross-layer interactions between MAC and physical layer in the presence of realistic wireless channel models that include path loss, multi-path fading and shadowing. In this paper, an analytical model of these dynamics is proposed. The analysis considers simultaneously a composite Rayleigh-lognormal channel fading, interference generated by multiple terminals, the effects induced by hidden terminals, and the MAC reduced carrier sensing capabilities. It is shown that the reliability of the contention-based MAC over fading channels is often far from that derived under ideal channel assumptions. Moreover, it is established to what extent fading may be beneficial for the overall network performance.

In this paper we propose a generic framework for real-time motion planning based on model-checking and revision. The task specification is given as a Linear Temporal Logic formula over a finite abstraction of the robot motion. A preliminary motion plan is first generated based on the initial knowledge of the system model. Then real-time information obtained during the runtime is used to update the system model, verify and further revise the motion plan. The implementation and revision of the motion plan are performed in real-time. This framework can be applied to partially-known workspaces and workspaces with large uncertainties. Computer simulations are presented to demonstrate the efficiency of the framework.

The increasing demand for wireless cyber-physical systems requires correct design, implementation and validation of computation, communication and control methods. Traditional simulation tools, which focus on either computation, communication or control, are insufficient when the three aspects interact. Efforts to extend the traditional tools to cover multiple domains, e.g., from simulating only control aspects to simulating both control and communication, often rely on simplistic models of a small subset of possible communication solutions. We introduce GISOO, a virtual testbed for simulation of wireless cyber-physical systems that integrates two state-of-the art simulators, Simulink and COOJA. GISOO enables users to evaluate actual embedded code for the wireless nodes in realistic cyber-physical experiments, observing the effects of both the control and communication components. In this way, a wide range of communication solutions can be evaluated without developing abstract models of their control-relevant aspects, and changes made to the networking code in simulations is guaranteed to be translated into production code without errors. A double-tank laboratory experimental setup controlled over a multi-hop relay wireless network is used to validate GISOO and demonstrate its features.

We propose a novel framework to combine model-checking-based motion planning with action planning using action description languages, aiming to tackle task specifications given as Linear Temporal Logic (LTL) formulas. The specifications implicitly require both sequential regions to visit and the desired actions to perform at these regions. The robot's motion is abstracted based on sphere regions of interest in the workspace and the structure of navigation function(NF)-based controllers, while the robot's action map is constructed based on precondition and effect functions associated with the actions. An optimal planner is designed that generates the discrete motion-and-action plan fulfilling the specification, as well as the low-level hybrid controllers that implement this plan. The whole framework is demonstrated by a case study.

This paper investigates the role persistent relations play for a social network to reach a global belief agreement under discrete-time or continuous-time evolution. Each directed arc in the underlying communication graph is assumed to be associated with a time-dependent weight function, which describes the strength of the information flow from one node to another. An arc is said to be persistent if its weight function has infinite L1or l1norm for continuous or discrete belief evolutions, respectively. The graph that consists of all persistent arcs is called the persistent graph of the underlying network. Three necessary and sufficient conditions on agreement or ε-agreement are established. We prove that the persistent graph fully determines the convergence to a common opinion in a social network. It is shown how the convergence rate explicitly depends on the diameter of the persistent graph. For a social networking service like Facebook, our results indicate how permanent friendships need to be and what network topology they should form for the network to be an efficient platform for opinion diffusion.

The dynamics of an agreement protocol interacting with a disagreement process over a common random network is considered. The model can represent the spreading of true and false information over a communication network, the propagation of faults in a large-scale control system, or the development of trust and mistrust in a society. At each time instance and with a given probability, a pair of network nodes interact. At random each of the nodes then updates its state towards the state of the other node (attraction), away from the other node (repulsion), or sticks to its current state (neglect). Agreement convergence and disagreement divergence results are obtained for various strengths of the updates for both symmetric and asymmetric update rules. Impossibility theorems show that a specific level of attraction is required for almost sure asymptotic agreement and a specific level of repulsion is required for almost sure asymptotic disagreement. A series of sufficient and/or necessary conditions are then established for agreement convergence or disagreement divergence. In particular, under symmetric updates, a critical convergence measure in the attraction and repulsion update strength is found, in the sense that the asymptotic property of the network state evolution transits from agreement convergence to disagreement divergence when this measure goes from negative to positive. The result can be interpreted as a tight bound on how much bad action needs to be injected in a dynamic network in order to consistently steer its overall behavior away from consensus.

To offer flexible quality of service to several classes of applications, the medium access control (MAC) protocol of IEEE 802.15.4 wireless sensor networks (WSNs) combines the advantages of a random access with contention with a time division multiple access (TDMA) without contention. Understanding reliability, delay, and throughput is essential to characterizing the fundamental limitations of the MAC and optimizing its parameters. Nevertheless, there is not yet a clear investigation of the achievable performance of hybrid MAC. In this article, an analytical framework for modeling the behavior of the hybrid MAC protocol of the IEEE 802.15.4 standard is proposed. The main challenge for an accurate analysis is the coexistence of the stochastic behavior of the random access and the deterministic behavior of the TDMA scheme. The analysis is done in three steps. First, the contention access scheme of the IEEE 802.15.4 exponential back-off process is modeled through an extended Markov chain that takes into account channel, retry limits, acknowledgements, unsaturated traffic, and superframe period. Second, the behavior of the TDMA access scheme is modeled by another Markov chain. Finally, the two chains are coupled to obtain a complete model of the hybrid MAC. By using this model, the network performance in terms of reliability, average packet delay, average queuing delay, and throughput is evaluated through both theoretical analysis and experiments. The protocol has been implemented and evaluated on a testbed with off-the-shelf wireless sensor devices to demonstrate the utility of the analysis in a practical setup. It is established that the probability density function of the number of received packets per superframe follows a Poisson distribution. It is determined under which conditions the guaranteed time slot allocation mechanism of IEEE 802.15.4 is stable. It is shown that the mutual effect between throughput of the random access and the TDMA scheme for a fixed superframe length is critical to maximizing the overall throughput of the hybrid MAC. In high traffic load, the throughput of the random access mechanism dominates over TDMA due to the constrained use of TDMA in the standard. Furthermore, it is shown that the effect of imperfect channels and carrier sensing on system performance heavily depends on the traffic load and limited range of the protocol parameters. Finally, it is argued that the traffic generation model established in this article may be used to design an activation timer mechanism in a modified version of the CSMA/CA algorithm that guarantees a stable network performance.

Distributed processing through ad hoc and sensor networks is having a major impact on scale and applications of computing. The creation of new cyber-physical services based on wireless sensor devices relies heavily on how well communication protocols can be adapted and optimized to meet quality constraints under limited energy resources. The IEEE 802.15.4 medium access control protocol for wireless sensor networks can support energy efficient, reliable, and timely packet transmission by a parallel and distributed tuning of the medium access control parameters. Such a tuning is difficult, because simple and accurate models of the influence of these parameters on the probability of successful packet transmission, packet delay, and energy consumption are not available. Moreover, it is not clear how to adapt the parameters to the changes of the network and traffic regimes by algorithms that can run on resource-constrained devices. In this paper, a Markov chain is proposed to model these relations by simple expressions without giving up the accuracy. In contrast to previous work, the presence of limited number of retransmissions, acknowledgments, unsaturated traffic, packet size, and packet copying delay due to hardware limitations is accounted for. The model is then used to derive a distributed adaptive algorithm for minimizing the power consumption while guaranteeing a given successful packet reception probability and delay constraints in the packet transmission. The algorithm does not require any modification of the IEEE 802.15.4 medium access control and can be easily implemented on network devices. The algorithm has been experimentally implemented and evaluated on a testbed with off-the-shelf wireless sensor devices. Experimental results show that the analysis is accurate, that the proposed algorithm satisfies reliability and delay constraints, and that the approach reduces the energy consumption of the network under both stationary and transient conditions. Specifically, even if the number of devices and traffic configuration change sharply, the proposed parallel and distributed algorithm allows the system to operate close to its optimal state by estimating the busy channel and channel access probabilities. Furthermore, results indicate that the protocol reacts promptly to errors in the estimation of the number of devices and in the traffic load that can appear due to device mobility. It is also shown that the effect of imperfect channel and carrier sensing on system performance heavily depends on the traffic load and limited range of the protocol parameters.

In this letter, we study the problem of fault detection and mitigation in networks where the measurements satisfy Kirchhoff's voltage law. First, we characterise the class of faults appearing as an additive fault vector (injected by a malicious adversary or due to equipment failures) that can be detected by taking into account the topology of the network. Second, we consider the problem of estimating the fault vector via tools from compressive sensing. Moreover, we comment on the applicability of the developed methods to the case where the measurements satisfy Kirchhoff's current law. The proposed methods are validated via numerical examples with application to time synchronization networks.

An energy-efficient, reliable and timely data transmission is essential for Wireless Sensor Networks (WSNs) employed in scenarios where plant information must be available for control applications. To reach a maximum efficiency, cross-layer interaction is a major design paradigm to exploit the complex interaction among the layers of the protocol stack. This is challenging because latency, reliability, and energy are at odds, and resource-constrained nodes support only simple algorithms. In this paper, the novel protocol Breath is proposed for control applications. Breath is designed for WSNs where nodes attached to plants must transmit information via multihop routing to a sink. Breath ensures a desired packet delivery and delay probabilities while minimizing the energy consumption of the network. The protocol is based on randomized routing, medium access control, and duty-cycling jointly optimized for energy efficiency. The design approach relies on a constrained optimization problem, whereby the objective function is the energy consumption and the constraints are the packet reliability and delay. The challenging part is the modeling of the interactions among the layers by simple expressions of adequate accuracy, which are then used for the optimization by in-network processing. The optimal working point of the protocol is achieved by a simple algorithm, which adapts to traffic variations and channel conditions with negligible overhead. The protocol has been implemented and experimentally evaluated on a testbed with off-the-shelf wireless sensor nodes, and it has been compared with a standard IEEE 802.15.4 solution. Analytical and experimental results show that Breath is tunable and meets reliability and delay requirements. Breath exhibits a good distribution of the working load, thus ensuring a long lifetime of the network. Therefore, Breath is a good candidate for efficient, reliable, and timely data gathering for control applications.

IEEE 802.15.4 multi-hop wireless networks are an important communication infrastructure for many applications, including industrial control, home automation, and smart grids. Existing analysis of the IEEE 802.15.4 medium access control (MAC) protocol are often based on assumptions of homogeneous traffic and ideal carrier sensing, which are far from the reality when predicting performance for multi-hop networks. In this paper, a generalized analysis of the unslotted IEEE 802.15.4 MAC is presented. The model considers heterogeneous traffic and hidden terminals due to limited carrier sensing capabilities, and allows us to investigate jointly IEEE 802.15.4 MAC and routing algorithms. The analysis is validated via Monte Carlo simulations, which show that routing over multi-hop networks is significantly influenced by the IEEE 802.15.4 MAC performance. Routing decisions based on packet loss probability may lead to an unbalanced distribution of the traffic load across paths, thus motivating the need of a joint optimization of routing and MAC.

Control applications over wireless sensor networks (WSNs) require timely, reliable, and energy efficient communications. Cross-layer interaction is an essential design paradigm to exploit the complex interaction among the layers of the protocol stack and reach a maximum efficiency. Such a design approach is challenging because reliability and latency of delivered packets and energy are at odds, and resource constrained nodes support only simple algorithms. In this paper, the TREnD protocol is introduced for control applications over WSNs in industrial environments. It is a cross-layer protocol that embraces efficiently routing algorithm, MAC, data aggregation, duty cycling, and radio power control. The protocol parameters are adapted by an optimization problem, whose objective function is the network energy consumption, and the constraints are the reliability and latency of the packets. TREnD uses a simple algorithm that allows the network to meet the reliability and latency required by the control application while minimizing for energy consumption. TREnD is implemented on a test-bed and compared to some existing protocols. Experimental results show good performance in terms of reliability, latency, low duty cycle, and load balancing for both static and time-varying scenarios.

Mobile wireless sensors in indoor environments will experience multipath fading, causing rapid variations in the capacity of the radio link. We present a strategy that increases the throughput by modifying the trajectory of the sensor so it spends more time at positions where the capacity is high. While doing so, it still maintains some desired average velocity. Our approach includes closed-loop estimation of the parameters of the fading, which may change when moving between rooms. We prove stability of the feedback system and illustrate its behavior through simulations. Finally, we demonstrate robustness to errors in the channel model.

The IEEE 802.15.4 standard for wireless sensor networks can support energy efficient, reliable, and timely packet transmission by tuning the medium access control parameters macMinBE, macMax-CSMABackoffs, and macMaxFrameRetries. Such a tuning is difficult, because simple and accurate models of the influence of these parameters on the probability of successful packet transmission, packet delay and energy consumption are not available. Moreover, it is not clear how to adapt the parameters to the changes of the network and traffic regimes by algorithms that can run on resource-constrained nodes. In this paper, an effective analytical model is used to derive an adaptive algorithm at the medium access control layer for minimizing the power consumption while guaranteeing reliability and delay constraints in the packet transmission. The algorithm does not require any modifications of the IEEE 802.15.4 standard and can be easily implemented on existing network nodes. Numerical results show that the analysis is accurate, that the proposed algorithm satisfies reliability and delay constraints, and ensures a longer lifetime of the network under both stationary and transient network conditions.

This paper develops a new model that describes the queueing process of a communication network when data sources use window flow control. The model takes into account the burstiness in sub-round-trip time (RTT) timescales and the instantaneous rate differences of a flow at different links. It is generic and independent of actual source flow control algorithms. Basic properties of the model and its relation to existing work are discussed. In particular, for a general network with multiple links, it is demonstrated that spatial interaction of oscillations allows queue instability to occur even when all flows have the same RTTs and maintain constant windows. The model is used to study the dynamics of delay-based congestion control algorithms. It is found that the ratios of RTTs are critical to the stability of such systems, and previously unknown modes of instability are identified. Packet-level simulations and testbed measurements are provided to verify the model and its predictions.

Ensuring a seamless connection when users are moving across radio cells is essential to guarantee a high communication quality. In this paper, performance of TCP during the handover in a Long Term Evolution (LTE) network is investigated. Specifically, mobile users with high bit rates TCP services are considered, and the impacts of the intra LTE handover over their perceived throughput are studied. Due to the mobility of the users across radio cells, the high bandwidth required, and possible network congestions, it is shown that the handover may cause sudden degradation of the quality of the communication if the process is not correctly controlled. To alleviate these problems, three solutions are proposed: fast path switch, handover prediction, and active queue management. The first two solutions avoids excessive delay in the packet delivery during the handover, whereas the second solution acts at the transport network with an active queue management. Simulation results, obtained by an extension of the ns-2 simulator, show that the proposed solutions present advantages, and that the handover prediction used with the active queue management increases TCP performance significantly.

Optimal rate allocation in a networked control system with limited communication resources is instrumental to achieve satisfactory overall performance. In this paper, a practical rate allocation technique for state estimation in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall distortion is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved by using Lagrange duality. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations.

A generalized analysis of the IEEE 802.15.4 medium access control (MAC) protocol in terms of reliability, delay and energy consumption is presented. The IEEE 802.15.4 exponential backoff process is modeled through a Markov chain taking into account retry limits, acknowledgements, and unsaturated traffic. Simple and effective approximations of the reliability, delay and energy consumption under low traffic regime are proposed. It is demonstrated that the delay distribution of IEEE 802.15.4 depends mainly on MAC parameters and collision probability. In addition, the impact of MAC parameters on the performance metrics is analyzed. The analysis is more general and gives more accurate results than existing methods in the literature. Monte Carlo simulations confirm that the proposed approximations offer a satisfactory accuracy.

We propose a mathematical framework, inspired by the Wireless HART specification, for modeling and analyzing multi-hop communication networks. The framework is designed for systems consisting of multiple control loops closed over a multi-hop communication network. We separate control, topology, routing, and scheduling and propose formal syntax and semantics for the dynamics of the composed system. The main technical contribution of the paper is an explicit translation of multi-hop control networks to switched systems. We describe a Mathematica notebook that automates the translation of multihop control networks to switched systems, and use this tool to show how techniques for analysis of switched systems can be used to address control and networking co-design challenges.

Accurate analytical expressions of delay and packet reception probabilities, and energy consumption of duty-cycled wireless sensor networks with random medium access control (MAC) are instrumental for the efficient design and optimization of these resource-constrained networks. Given a clustered network topology with unslotted IEEE 802.15.4 and preamble sampling MAC, a novel approach to the modeling of the delay, reliability, and energy consumption is proposed. The challenging part in such a modeling is the random MAC and sleep policy of the receivers, which prevents to establish the exact time of data packet transmission. The analysis gives expressions as function of sleep time, listening time, traffic rate and MAC parameters. The analytical results are then used to optimize the duty cycle of the nodes and MAC protocol parameters. The approach provides a significant reduction of the energy consumption compared to existing solutions in the literature. Monte Carlo simulations by ns2 assess the validity of the analysis.

Time-critical applications for wireless sensor networks (WSNs) are an important class of services supported by the standard IEEE 802.15.4. Control, actuation, and monitoring are all examples of applications where information must be delivered within some deadline. Understanding the delay in the packet delivery is fundamental to assess performance limitation for the standard. In this paper we analyze the guaranteed time slot (GTS) allocation mechanism used in IEEE 802.15.4 networks for time- critical applications. Specifically, we propose a Markov chain to model the stability, delay, and throughput of GTS allocation. We analyze the impact of the protocol parameters on these performance indexes. Monte Carlo simulations show that the theoretical analysis is quite accurate. Thus, our analysis can be used to design efficient GTS allocation for IEEE 802.15.4.

To achieve satisfactory overall performance, optimal rate allocation in a networked control system with highly limited communication resources is instrumental. In this paper, a rate allocation technique for state feedback control in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall cost is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved. It is shown that a non-uniform quantization is in general the best strategy for state feedback control over noisy channels. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations.

A radio power control strategy to achieve maximum throughput for the up-link of CDMA wireless systems with variable spreading factor is investigated. The system model includes slow and fast fading, rake receiver, and multi-access interference caused by users with heterogeneous data sources. The quality of the communication is expressed in terms of outage probability, while the throughput is defined as the sum of the users' transmit rates. The outage probability is accounted for by resorting to a lognormal approximation. A mixed integer-real optimization problem P1, where the objective function is the throughput under outage probability constraints, is investigated. Problem P1is solved in two steps: firstly, we propose a modified problem P2to provide feasible solutions, and then the optimal solution is obtained with an efficient branch-and-bound search. Numerical results are presented and discussed to assess the validity of our approach.

A theoretical framework is proposed for accurate comparison of minimum energy coding in Coded Division Multiple Access (CDMA) Wireless Sensor Networks (WSNs). Energy consumption and reliability are analyzed for two coding schemes: Minimum Energy coding (ME), and Modified Minimum Energy coding (MME). A detailed model of consumed energy is described as function of the coding, radio transmit power, the characteristics of the transceivers, and the dynamics of the wireless channel. Since CDMA is strongly limited by multi-access interference, the system model includes all the relevant characteristics of wireless propagation. A distributed and asynchronous algorithm, which minimizes the total energy consumption by controlling the radio power, is developed. Numerical results are presented to validate the theoretical analysis and show under which conditions MME outperforms ME with respect to energy consumption and bit error rate. It is concluded that MME is more energy efficient than ME only for short codewords.

A distributed control law that guarantees connectivity maintenance in a network of multiple mobile agents is presented. The control law respects the limited sensing capabilities of the agents by allowing each agent to take into account only agents within its sensing radius in the controller implementation. In contrast to previous approaches to the problem, the proposed control law does not attain infinite values whenever an edge tends to be lost, and is therefore a bounded distributed control law. This is achieved via the use of decentralized navigation functions which are potential fields with guaranteed boundedness and are redefined in this paper in order to take into account the connectivity maintenance objective. We first treat the case of connectivity maintenance in a static initial position based communication graph and extend the results to the case of dynamic edge addition. The results are then applied to a formation control problem.

This paper investigates the scenario of a robot making a tradeoff between tracking a time-varying reference trajectory and stopping to communicate at points where the radio signal strength is high. Under the assumption that the signal is subject to multipath fading, we formulate this as a hybrid optimal control problem with penalties on tracking error, communication buffer length and control power. The problem is then solved using relaxed dynamic programming, resulting in control laws for the discrete switching sequence and the continuous control. We finally illustrate the results through simulations under non-ideal conditions, confirming that the system maintains a bounded buffer size and zero-mean tracking error.

A novel continuous time fluid flow model of the dynamics of the interaction between ACK-clocking and the link buffer is presented. A fundamental integral equation relating the instantaneous flow rate and the window dynamics is derived. Properties of the model, such as well-posedness and stability, are investigated. Packet level experiments verify that this new model is more accurate than existing models, correctly predicting qualitatively different behaviors, for example when round trip delays are heterogeneous.

This paper studies window flow control focusing on bridging the gap between microscopic factors such as burstiness in sub-RTT timescales, and observable macroscopic properties such as steady state bandwidth sharing and flow level stability. Using new models, we analytically capture notable effects of microscopic behavior on macroscopic quantities. For loss-based protocols, we calculate the loss synchronization rate for different flows and use it to quantitatively explain the unfair bandwidth sharing between paced and unpaced TCP flows. For delay-based protocols, we show that the ratios of round trip delays are critical to the stability of the system. These results deepen the fundamental understanding of congestion control systems. Packet level simulations are used to verify our theoretical claims.

The novel cross-layer protocol Breath for wireless sensor networks is designed, implemented, and experimentally evaluated. The Breath protocol is based on randomized routing, MAC and duty-cycling, which allow it to minimize the energy consumption of the network while ensuring a desired packet delivery end-to-end reliability and delay. The system model includes a set of source nodes that transmit packets via multi-hop communication to the destination. A constrained optimization problem, for which the objective function is the network energy consumption and the constraints are the packet latency and reliability, is posed and solved. It is shown that the communication layers can be jointly optimized for energy efficiency. The optimal working point of the network is achieved with a simple algorithm, which adapts to traffic variations with negligible overhead. The protocol was implemented on a test-bed with off-the-shelf wireless sensor nodes. It is compared with a standard IEEE 802.15.4 solution. Experimental results show that Breath meets the latency and reliability requirements, and that it exhibits a good distribution of the working load, thus ensuring a long lifetime of the network.

A distributed estimation algorithm for sensor networks is proposed. A noisy time-varying signal is jointly tracked by a network of sensor nodes, in which each node computes its estimate as a weighted sum of its own and its neighbors' measurements and estimates. The weights are adaptively updated to minimize the variance of the estimation error. Both estimation and the parameter optimization is distributed; no central coordination of the nodes is required. An upper bound of the error variance in each node is derived. This bound decreases with the number of neighboring nodes. The estimation properties of the algorithm are illustrated via computer simulations, which are intended to compare our estimator performance with distributed schemes that were proposed previously in the literature. The results of the paper allow to trading-off communication constraints, computing efforts and estimation quality for a class of distributed filtering problems.

A comprehensive performance evaluation of a cross- layer solution to increase users' downlink data rates over HSDPA is provided. The solution consists of a proxy entity between a server and the radio network controller, and cross-layer signalling from the base station to the proxy. The performance of the solution is evaluated though a detailed ns-2 simulator environment, which includes all HSDPA features, as well as some existing TCP enhancing protocols widely adopted for internet traffic over wireless links. Numerical results show that the proxy significantly increases the users' throughput, while also improving the utilization of the radio resources.

A new distributed estimation algorithm for tracking using a wireless sensor network is presented. We investigate how to track a time varying signal, noisily sensed by the nodes of the network. The algorithm is distributed, meaning that it does not require a central coordination among the nodes. Moreover, the proposed approach is scalable with respect to the network size, which means that its complexity does not grow with respect to the total number of nodes. The algorithm designed turns out to be composed by a cascade structure. Local constraints are determined to guarantee the global asymptotic stability of the estimation error. The algorithm can be applied e.g., for the position estimation, temporal synchronization, as well as tracking of signals. Performance is illustrated by simulations, where our filter is shown to behave better than other distributed schemes proposed in the literature.

We study a closed-loop multivariable control system with sensor feedback transmitted over a discrete noisy channel. For this problem, we propose a joint design of the state measurement quantization, protection against channel errors, and control. The proposed algorithm leads to a practically feasible design of time-varying non-uniform encoding and control. Numerical results demonstrate the performance obtained by employing the proposed iterative optimization algorithm.

An adaptive handover algorithm for wireless communication systems is addressed in this paper. Moving from the Generalized Extended Least Square handover algorithm proposed in N. Benvenuto et al. (2002), we model the handover mechanism as a hybrid system, and we include it in a dynamic optimization problem which is solved through the use of a trellis diagram. The objective function takes into account the parameters that influence the handover mechanism. The optimal solution of the program allows to evaluate performance of the handover in terms of the probability of handover and probability of outage.

A new control structure is proposed to improve user experience of wireless Internet. Information on radio bandwidth and queue length available in the radio network, close to the base station, is used in a proxy that resides between the Internet and the cellular system. The control algorithm in the proxy sets the window size according to event-triggered information on radio bandwidth changes and time-triggered information on the queue length at the wireless link. The mechanism is compared to TCP Reno in two simulation scenarios. The first scenario models a dedicated channel with stepwise changes in the bandwidth, while the second scenario models the High-speed Downlink Shared Channel recently introduced by 3GPP. The proposed mechanism significantly reduces the amount of buffer space needed in the radio network, and it also gives modest improvements to user response time and link utilization. Reduced buffering is particularly beneficial for third-party end-to-end real-time services such as voice, video, and online gaming.

Abstract — Spatio-Temporal processing is a control technique to increase the quality of the received signals in wireless networks. Outage events have a strong influence not only on the performance of the physical layer, but also on routing, MAC, and application layer. In this paper, we propose an outage based performance analysis of collaborative STP for WSNs. After an accurate characterization of the wireless channel, we derive the outage statistics as function of the STP coefficients, and investigate the effects of STP on the probability, average duration and rate of the outage events. Furthermore, we show that a proper control policy of the STP coefficients can be derived according to the requirements from the applications and WSNs communication layers.

Encoder-decoder design is considered for a closed-loop scalar control system with feedback transmitted over a binary symmetric channel. We propose an iterative procedure which can jointly optimize adaptive encoder-decoder pairs for a certainty equivalence controller. The goal is to minimize a design criterion, in particular, the linear quadratic (LQ) cost function over a finite horizon. The algorithm leads to a practically feasible design of time-varying non-uniform encoding and decoding. Numerical results demonstrate the promising performance obtained by employing the proposed iterative optimization algorithm

An overview of some recent advances in distributed information processing for control over wireless sensor networks is presented in this paper. Firstly, a taxonomy of fundamental control and communication schemes for these systems is introduced. Next, specific research issues are proposed and discussed with three prominent examples on distributed source coding with packet aggregation, distributed cooperative diversity and distributed cooperative localization. In regard to these examples, it is argued about some open research problems and suggestions for further investigations on joint control and communication design for distributed processing and control over wireless sensor networks.

This paper proposes an analytical framework for performance evaluation of TCP (transport control protocol) over adaptive wireless links. Specifically, we include adaptation of power, modulation format and error recovery strategy, and incorporate some features of wireless fading channels. This framework is then used to pursue joint optimization through maximization of an objective functional, that expresses a trade-off between achievable throughput and energy costs. A set of numerical results is reported, and it is seen that hybrid ARQ schemes may provide significant benefits in the optimization framework

We derive a power control policy for a group of sensor nodes that are monitoring a real-time application sensitive to disconnections (outages) of the communication. Specifically, we suggest that the sensor nodes perform cooperative diversity while running a sleep discipline. After the description of a detailed model of the wireless links, we propose a power minimization algorithm with a constraint expressed in terms of outage probability. Suboptimal solutions are also discussed. Numerical examples are provided for various number of nodes, wireless scenarios and nodes activities. It is argued that nodes with reduced activity show better performance

The problem of coordinating a team of robots with heterogeneous sensing capabilities is studied in this paper. A control structure is proposed that combines terrain servoing for the leading robot with on-line formation planning and path following for the followers, while enabling obstacle avoidance for all the robots.

A new distributed coordination algorithm for multi-vehicle systems is presented in this paper. The algorithm combines a particular choice of navigation function with Voronoi partitions. This results not only in obstacle avoidance and motion to the goal, but also in a desirable geographical distribution of the vehicles. Our algorithm is decentralized in that each vehicle needs only to know the position of neigh boring vehicles, but no other inter-vehicle communication or centralized control are required. The algorithm gives a natural priority to safety, goal convergence, and formation keeping, in that (1) collision avoidance is guaranteed under all circumstances, (2) the vehicles will move toward the goal as long as a given optimization problem is feasible, and (3) if prior criteria admit, the vehicles tend to a desirable lattice formation. These theoretical properties are discussed in the paper and the performance of the algorithm is illustrated in simulations with realistic models of twenty all-terrain vehicles. Planned experimental evaluation using customized miniature cars is also briefly described.

The problem of estimating and tracking the motion of a moving target by a team of mobile robots is studied in this paper. Each robot is assumed to have a directional sensor with limited range, thus more than one robot (sensor) is needed for solving the problem. A sensor fusion scheme based on inter-robot communication is proposed in order to obtain accurate real-time information of the target's position and motion. Accordingly a hierarchical control scheme is applied, in which a consecutive set of desired formations is planned through a discrete model and low-level continuous-time controls are executed to track the resulting references. The algorithm is illustrated through simulations and on an experimental platform.