Formal analysis techniques for gossiping protocols

We give a survey of formal verification techniques that can be used to corroborate existing experimental results for gossiping protocols in a rigorous manner. We present properties of interest for gossiping protocols and discuss how various formal evaluation techniques can be employed to predict them

Temporal pattern-based denoising and calibration for low-cost sensors in IoT monitoring platforms

The introduction of low-cost sensors (LCSs) in air quality Internet of Things (IoT) monitoring platforms presents the challenge of improving the quality of the data that these sensors provide. In this article, we propose two algorithms to perform denoising and calibration for LCSs used in IoT monitoring platforms. Sensors are first calibrated in situ using linear or nonlinear machine learning models that only take into account instantaneous measurements. The best calibration model is used to estimate the values measured by the sensor during the sensor deployment. To improve the values of the estimates produced by the in situ calibration model, we propose to take into account the temporal patterns present in signals, such as temperature or tropospheric ozone that have regular patterns, e.g., daily. The first method, which we call temporal pattern-based denoising (TPB-D), performs signal denoising by projecting the daily signals of the in situ calibrated LCS onto a subspace generated by the daily signals stored in a database taken by reference instruments. The second method, which we call temporal pattern-based calibration (TPB-C), considers that if we also have a reference instrument colocated to the LCSs over a period of time, we can correct with a linear mapping with regularization the daily LCS signals projected in the subspace produced by the reference database to be as similar as possible to the projected signals of the colocated reference instrument. The results show that the TPB-D improves the estimates made by in situ calibration by up to 10%–20%, while the TPB-C improves the estimates made by in situ calibration by up to 20%–40%.This work was supported in part by the National Spanish Funding under Grant PID2019-107910RB-I00 and in part by the Regional Project under Grant 2021 SGR 01059.Peer ReviewedPostprint (author's final draft

Geometric Algorithms for Intervals and Related Problems

In this dissertation, we study several problems related to intervals and develop efficient algorithms for them. Interval problems have many applications in reality because many objects, values, and ranges are intervals in nature, such as time intervals, distances, line segments, probabilities, etc. Problems on intervals are gaining attention also because intervals are among the most basic geometric objects, and for the same reason, computational geometry techniques find useful for attacking these problems. Specifically, the problems we study in this dissertation includes the following: balanced splitting on weighted intervals, minimizing the movements of spreading points, dispersing points on intervals, multiple barrier coverage, and separating overlapped intervals on a line. We develop efficient algorithms for these problems and our results are either first known solutions or improve the previous work. In the problem of balanced splitting on weighted intervals, we are given a set of n intervals with non-negative weights on a line and an integer k ≥ 1. The goal is to find k points to partition the line into k + 1 segments, such that the maximum sum of the interval weights in these segments is minimized. We give an algorithm that solves the problem in O(n log n) time. Our second problem is on minimizing the movements of spreading points. In this problem, we are given a set of points on a line and we want to spread the points on the line so that the minimum pairwise distance of all points is no smaller than a given value δ. The objective is to minimize the maximum moving distance of all points. We solve the problem in O(n) time. We also solve the cycle version of the problem in linear time. For the third problem, we are given a set of n non-overlapping intervals on a line and we want to place a point on each interval so that the minimum pairwise distance of all points are maximized. We present an O(n) time algorithm for the problem. We also solve its cycle version in O(n) time. The fourth problem is on multiple barrier coverage, where we are given n sensors in the plane and m barriers (represented by intervals) on a line. The goal is to move the sensors onto the line to cover all the barriers such that the maximum moving distance of all sensors is minimized. Our algorithm for the problem runs in O(n2 log n log log n + nm log m) time. In a special case where the sensors are all initially on the line, our algorithm runs in O((n + m) log(n + m)) time. Finally, for the problem of separating overlapped intervals, we have a set of n intervals (possibly overlapped) on a line and we want to move them along the line so that no two intervals properly intersect. The objective is to minimize the maximum moving distance of all intervals. We propose an O(n log n) time algorithm for the problem. The algorithms and techniques developed in this dissertation are quite basic and fundamental, so they might be useful for solving other related problems on intervals as well

Designing power aware wireless sensor networks leveraging software modeling techniques

Wireless Sensor Networks (WSNs) are typically used to monitor specific phenomena and gather the data to a gateway node, where the data is further processed. WSNs nodes have limited power resources, which require developing power efficient systems. Additionally, reaching the nodes after a deployment to correct any design flaws is very challenging due the distributed nature of the nodes. The current development of WSNs occurs at the coding layer, which prevent the design from going through a typical software design process. Designing and analyzing the software modules of a WSN system at a higher abstraction layer than at the coding level will enable the designer of a WSN to fix any design errors and improve the system for power consumption at an early design stage, before the actual deployment of the network. This thesis presents multiple Unified Modeling Language (UML) design patterns that enable the designer to capture the structure and the behavior of the design of a WSN at higher abstraction layers. The UML models are developed based on these design patterns that are capable of early validation of the functional requirements and the power consumption of the system hardware resources by leveraging animation and instrumentation of the UML diagrams. To support the analysis of power consumption of the communication components of a WSN node, the Avrora network simulator was integrated with the UML design environment such that designer is able to analyze the power consumption analysis of the communication process at the UML layer. The UML and the Avrora simulation integration is achieved through developing a code generator that produces the necessary configuration for Avrora simulator and through parsing the simulator results. The methodology presented in this thesis is evaluated by demonstrating the power analysis of a typical collector system

Adaptive Robust Self-Balancing and Steering of a Two-Wheeled Human Transportation Vehicle

This paper presents adaptive robust regulation methods for self-balancing and yaw motion of a two-wheeled human transportation vehicle (HTV) with varying payload and system uncertainties. The proposed regulators are aimed at providing consistent driving performance for the HTV with system uncertainties and parameter variations caused by different drivers. By decomposing the overall system into the yaw motion subsystems and the wheeled inverted pendulum, two proposed adaptive robust regulators are synthesized to achieve self-balancing and yaw motion control. Numerical simulations and experimental results on different terrains show that the proposed adaptive robust controllers are capable of achieving satisfactory control actions to steer the vehicle

Reliability and Efficiency of Vehicular Network Applications

The DSRC/WAVE initiative is forecast to enable a plethora of applications, classified in two broad types of safety and non-safety applications. In the former type, the reliability performance is of tremendous prominence while, in the latter case, the efficiency of information dissemination is the key driving factor. For safety applications, we adopt a systematic approach to analytically investigate the reliability of the communication system in a symbiotic relationship with the host system comprising a vehicular traffic system and radio propagation environment. To this aim, the¬ interference factor is identified as the central element of the symbiotic relationship. Our approach to the investigation of interference and its impacts on the communication reliability departs from previous studies by the degree of realism incorporated in the host system model. In one dimension, realistic traffic models are developed to describe the vehicular traffic behaviour. In a second dimension, a realistic radio propagation model is employed to capture the unique signal propagation aspects of the host system. We address the case of non-safety applications by proposing a generic framework as a capstone architecture for the development of new applications and the efficiency evaluation of existing ones. This framework, while being independent from networking technology, enables accurate characterization of the various information dissemination tasks that a node performs in cooperation with others. As the central element of the framework, we propose a game theoretic model to describe the interaction of meeting nodes aiming to exchange information of mutual or social interests. An adaptive mechanism is designed to enable a mobile node to measure the social significance of various information topics, which is then used by the node to prioritize the forwarding of information objects

Model Predictive Control of Highway Emergency Maneuvering and Collision Avoidance

Autonomous emergency maneuvering (AEM) is an active safety system that automates safe maneuvers to avoid imminent collision, particularly in highway driving situations. Uncertainty about the surrounding vehicles’ decisions and also about the road condition, which has significant effects on the vehicle’s maneuverability, makes it challenging to implement the AEM strategy in practice. With the rise of vehicular networks and connected vehicles, vehicles would be able to share their perception and also intentions with other cars. Therefore, cooperative AEM can incor- porate surrounding vehicles’ decisions and perceptions in order to improve vehicles’ predictions and estimations and thereby provide better decisions for emergency maneuvering. In this thesis, we develop an adaptive, cooperative motion planning scheme for emergency maneuvering, based on the model predictive control (MPC) approach, for vehicles within a ve- hicular network. The proposed emergency maneuver planning scheme finds the best combination of longitudinal and lateral maneuvers to avoid imminent collision with surrounding vehicles and obstacles. To implement real-time MPC for the non-convex problem of collision free motion planning, safety constraints are suggested to be convexified based on the road geometry. To take advantage of vehicular communication, the surrounding vehicles’ decisions are incorporated in the prediction model to improve the motion planning results. The MPC approach is prone to loss of feasibility due to the limited prediction horizon for decision-making. For the autonomous vehicle motion planning problem, many of detected ob- stacles, which are beyond the prediction horizon, cannot be considered in the instantaneous de- cisions, and late consideration of them may cause infeasibility. The conditions that guarantee persistent feasibility of a model predictive motion planning scheme are studied in this thesis. Maintaining the system’s states in a control invariant set of the system guarantees the persis- tent feasibility of the corresponding MPC scheme. Specifically, we present two approaches to compute control invariant sets of the motion planning problem; the linearized convexified ap- proach and the brute-force approach. The resulting computed control invariant sets of these two approaches are compared with each other to demonstrate the performance of the proposed algorithm. Time-variation of the road condition affects the vehicle dynamics and constraints. Therefore, it necessitates the on-line identification of the road friction parameter and implementation of an adaptive emergency maneuver motion planning scheme. In this thesis, we investigate coopera- tive road condition estimation in order to improve collision avoidance performance of the AEM system. Each vehicle estimates the road condition individually, and disseminates it through the vehicular network. Accordingly, a consensus estimation algorithm fuses the individual estimates to find the maximum likelihood estimate of the road condition parameter. The performance of the proposed cooperative road condition estimation has been validated through simulations

Safe Robot Planning and Control Using Uncertainty-Aware Deep Learning

In order for robots to autonomously operate in novel environments over extended periods of time, they must learn and adapt to changes in the dynamics of their motion and the environment. Neural networks have been shown to be a versatile and powerful tool for learning dynamics and semantic information. However, there is reluctance to deploy these methods on safety-critical or high-risk applications, since neural networks tend to be black-box function approximators. Therefore, there is a need for investigation into how these machine learning methods can be safely leveraged for learning-based controls, planning, and traversability. The aim of this thesis is to explore methods for both establishing safety guarantees as well as accurately quantifying risks when using deep neural networks for robot planning, especially in high-risk environments. First, we consider uncertainty-aware Bayesian Neural Networks for adaptive control, and introduce a method for guaranteeing safety under certain assumptions. Second, we investigate deep quantile regression learning methods for learning time-and-state varying uncertainties, which we use to perform trajectory optimization with Model Predictive Control. Third, we introduce a complete framework for risk-aware traversability and planning, which we use to enable safe exploration of extreme environments. Fourth, we again leverage deep quantile regression and establish a method for accurately learning the distribution of traversability risks in these environments, which can be used to create safety constraints for planning and control.Ph.D