Routing, Localization And Positioning Protocols For Wireless Sensor And Actor Networks

Wireless sensor and actor networks (WSANs) are distributed systems of sensor nodes and actors that are interconnected over the wireless medium. Sensor nodes collect information about the physical world and transmit the data to actors by using one-hop or multi-hop communications. Actors collect information from the sensor nodes, process the information, take decisions and react to the events. This dissertation presents contributions to the methods of routing, localization and positioning in WSANs for practical applications. We first propose a routing protocol with service differentiation for WSANs with stationary nodes. In this setting, we also adapt a sports ranking algorithm to dynamically prioritize the events in the environment depending on the collected data. We extend this routing protocol for an application, in which sensor nodes float in a river to gather observations and actors are deployed at accessible points on the coastline. We develop a method with locally acting adaptive overlay network formation to organize the network with actor areas and to collect data by using locality-preserving communication. We also present a multi-hop localization approach for enriching the information collected from the river with the estimated locations of mobile sensor nodes without using positioning adapters. As an extension to this application, we model the movements of sensor nodes by a subsurface meandering current mobility model with random surface motion. Then we adapt the introduced routing and network organization methods to model a complete primate monitoring system. A novel spatial cut-off preferential attachment model and iii center of mass concept are developed according to the characteristics of the primate groups. We also present a role determination algorithm for primates, which uses the collection of spatial-temporal relationships. We apply a similar approach to human social networks to tackle the problem of automatic generation and organization of social networks by analyzing and assessing interaction data. The introduced routing and localization protocols in this dissertation are also extended with a novel three dimensional actor positioning strategy inspired by the molecular geometry. Extensive simulations are conducted in OPNET simulation tool for the performance evaluation of the proposed protocol

Validation Framework for Autonomous Aerial Vehicles

Autonomous aerial vehicles (AAV) have the potential to have market disruptions for various industries such as ground delivery and aerial transportation. Hence, the USAF has called for increased level of autonomy and rapid progress is being made in artificial intelligence (AI) engines, complex and non-deterministic system components, which are at the core of the autonomous aerial platforms. Traditional testing and validation methods fall short of satisfying the requirement of testing such complex systems. Therefore, to achieve highly or fully autonomous capabilities, a major leap forward in the validation is required. Otherwise, the full capabilities of autonomy will not be realized. The key challenges are the localization of problems, development of object models for perception and the creation of a safety measure. A similar challenge exists in ground autonomous vehicles (AVs), where there is a significant investment in recent years. However, there are important differences in the environmental and regulatory conditions between these two domains. In this paper, we present a validation framework that uses modeling and simulation and formal methods for solving the issues in the validation of AAVs. We define an abstraction stack using techniques such as separation of concerns, constrained pseudo-random test generation, coverage driven testing and functional assertions. We focus on the identification of edge cases and using test cases build a coverage matrix to determine the criteria for success. The system aims to assess the creation of an evolving safety measure and a licensing structure

Testing and Validation Framework for Autonomous Aerial Vehicles

Autonomous aerial vehicles (AAV) have the potential to have market disruptions for various industries such as ground delivery and aerial transportation. Hence, the USAF has called for increased level of autonomy. There has been a significant progress in artificial intelligence engines, complex and non-deterministic system components, which are at the core of the autonomous aerial platforms. Traditional testing and validation methods fall short of satisfying the requirement of testing such complex systems. Therefore, to achieve highly or fully autonomous capabilities, a major leap forward in the validation is required. The key challenges are the localization of problems, development of object models for perception and the creation of a safety measure. A similar challenge exists in ground autonomous vehicles (AVs), where there is a significant investment in recent years. However, there are important differences in the environmental and regulatory conditions between these two domains. In this paper, we present a validation framework that uses modeling and simulation and formal methods for solving the issues in the validation of AAVs. We define a novel abstraction stack using separation of concerns and create a testing plan using techniques such as constrained pseudo-random test generation, random walks and functional assertions. The system aims to assess the creation of an evolving safety measure and a licensing structure

A Nature-Inspired Approach for Scenario-Based Validation of Autonomous Systems

Scenario-based approaches are cost and time effective solutions to autonomous cyber-physical system testing to identify bugs before costly methods such as physical testing in a controlled or uncontrolled environment. Every bug in an autonomous cyber-physical system is a potential safety risk. This paper presents a scenario-based method for finding bugs and estimating boundaries of the bug profile. The method utilizes a nature-inspired approach adapting low discrepancy sampling with local search. Extensive simulations demonstrate the performance of the approach with various adaptations

Real-Time Urban Weather Observations for Urban Air Mobility

Cities of the future will have to overcome congestion, air pollution and increasing infrastructure cost while moving more people and goods smoothly, efficiently and in an eco-friendly manner. Urban air mobility (UAM) is expected to be an integral component of achieving this new type of city. This is a new environment for sustained aviation operations. The heterogeneity of the urban fabric and the roughness elements within it create a unique environment where flight conditions can change frequently across very short distances. UAM vehicles with their lower mass, more limited thrust and slower speeds are especially sensitive to these conditions. Since traditional aviation weather products for observations and forecasts at an airport on the outskirts of a metropolitan area do not translate well to the urban environment, weather data for low-altitude urban airspace is needed and will be particularly critical for unlocking the full potential of UAM. To help address this need, crowdsourced weather data from sources prevalent in urban areas offer the opportunity to create dense meteorological observation networks in support of UAM. This paper considers a variety of potential observational sources and proposes a cyber-physical system architecture, including an incentive-based crowdsensing application, which empowers UAM weather forecasting and operations

Generation of Modular and Measurable Validation Scenarios for Autonomous Vehicles Using Accident Data

Autonomous vehicle (AV) technology is positioned to have a significant impact on various industries. Hence, artificial intelligence powered AVs and modern vehicles with advanced driver-assistance systems have been operated in street networks for real-life testing. As these tests become more frequent, accidents have been inevitable and there have been reported crashes. The data from these accidents are invaluable for generating edge case test scenarios and understanding accident-time behavior. In this paper, we use the existing AV accident data and provide a methodology to identify the atomic blocks within each accident, which are modular and measurable scenario units. Our approach formulates each accident scenario using these atomic blocks and defines them in the Measurable Scenario Description Language (M-SDL). This approach produces modular scenario units with coverage analysis, provides a method to assist in the measurable analysis of accident-time AV behavior, and generates accident scenarios and their cousin scenarios

SUMO Traffic Simulator Interface for Scenic

SCENIC is a scenario description language for autonomous systems with interfaces for simulators such as Grand Theft Auto V, and CARLA. SUMO traffic simulator is a powerful tool for performing AV simulations with realistic traffic and the opportunity for scene micromanagement through a python interface. Linking SUMO with Scenic, opens many possibilities for AV scenes generation for fast and lightweight SUMO simulations. This paper introduces the first interface between SUMO traffic simulator and Scenic

EPIdemiology of Surgery-Associated Acute Kidney Injury (EPIS-AKI) : Study protocol for a multicentre, observational trial

More than 300 million surgical procedures are performed each year. Acute kidney injury (AKI) is a common complication after major surgery and is associated with adverse short-term and long-term outcomes. However, there is a large variation in the incidence of reported AKI rates. The establishment of an accurate epidemiology of surgery-associated AKI is important for healthcare policy, quality initiatives, clinical trials, as well as for improving guidelines. The objective of the Epidemiology of Surgery-associated Acute Kidney Injury (EPIS-AKI) trial is to prospectively evaluate the epidemiology of AKI after major surgery using the latest Kidney Disease: Improving Global Outcomes (KDIGO) consensus definition of AKI. EPIS-AKI is an international prospective, observational, multicentre cohort study including 10 000 patients undergoing major surgery who are subsequently admitted to the ICU or a similar high dependency unit. The primary endpoint is the incidence of AKI within 72 hours after surgery according to the KDIGO criteria. Secondary endpoints include use of renal replacement therapy (RRT), mortality during ICU and hospital stay, length of ICU and hospital stay and major adverse kidney events (combined endpoint consisting of persistent renal dysfunction, RRT and mortality) at day 90. Further, we will evaluate preoperative and intraoperative risk factors affecting the incidence of postoperative AKI. In an add-on analysis, we will assess urinary biomarkers for early detection of AKI. EPIS-AKI has been approved by the leading Ethics Committee of the Medical Council North Rhine-Westphalia, of the Westphalian Wilhelms-University Münster and the corresponding Ethics Committee at each participating site. Results will be disseminated widely and published in peer-reviewed journals, presented at conferences and used to design further AKI-related trials. Trial registration number NCT04165369