22 research outputs found

    Synchronous and Concurrent Transmissions for Consensus in Low-Power Wireless

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    With the emergence of the Internet of Things, autonomous vehicles and the Industry 4.0, the need for dependable yet adaptive network protocols is arising. Many of these applications build their operations on distributed consensus. For example, UAVs agree on maneuvers to execute, and industrial systems agree on set-points for actuators.Moreover, such scenarios imply a dynamic network topology due to mobility and interference, for example. Many applications are mission- and safety-critical, too.Failures could cost lives or precipitate economic losses.In this thesis, we design, implement and evaluate network protocols as a step towards enabling a low-power, adaptive and dependable ubiquitous networking that enables consensus in the Internet of Things. We make four main contributions:- We introduce Orchestra that addresses the challenge of bringing TSCH (Time Slotted Channel Hopping) to dynamic networks as envisioned in the Internet of Things. In Orchestra, nodes autonomously compute their local schedules and update automatically as the topology evolves without signaling overhead. Besides, it does not require a central or distributed scheduler. Instead, it relies on the existing network stack information to maintain the schedules.- We present A2 : Agreement in the Air, a system that brings distributed consensus to low-power multihop networks. A2 introduces Synchrotron, a synchronous transmissions kernel that builds a robust mesh by exploiting the capture effect, frequency hopping with parallel channels, and link-layer security. A2 builds on top of this layer and enables the two- and three-phase commit protocols, and services such as group membership, hopping sequence distribution, and re-keying.- We present Wireless Paxos, a fault-tolerant, network-wide consensus primitive for low-power wireless networks. It is a new variant of Paxos, a widely used consensus protocol, and is specifically designed to tackle the challenges of low-power wireless networks. By utilizing concurrent transmissions, it provides a dependable low-latency consensus.- We present BlueFlood, a protocol that adapts concurrent transmissions to Bluetooth. The result is fast and efficient data dissemination in multihop Bluetooth networks. Moreover, BlueFlood floods can be reliably received by off-the-shelf Bluetooth devices such as smartphones, opening new applications of concurrent transmissions and seamless integration with existing technologies

    A framework for multimodal wireless sensor networks

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    Wireless Sensor Networks are a widely used solution for monitoring oriented applications (e.g., water quality on watersheds, pollution monitoring in cities). These kinds of applications are characterized by the necessity of two data-reporting modes: time-driven and event-driven. The former is used mainly for continually supervising an area and the latter for event detection and tracking. By switching between both modes, a WSN can improve its energy-efficiency and event reporting latency, compared to single data-reporting schemes. We refer to those WSNs, where both data-reporting modes are required simultaneously, as MultiModal Wireless Sensor Networks (M2WSNs). M2WSNs arise as a solution for the trade-off between energy savings and event reporting latency in those monitoring-oriented applications where regular and emergency reporting are required simultaneously. The multimodality in these M2WSNs allows sensor nodes to perform data-reporting in two possible schemes, time-driven and event-driven, according to the circumstances, providing higher energy savings and better reporting results when compared to traditional schemes. Traditionally, sophisticated power-aware wake-up schemes have been employed to achieve energy efficiency in WSNs, such as low-duty cycling protocols using a single radio architecture. These protocols achieve good results regarding energy savings, but they suffer from idle-listening and overhearing issues, that make them not reliable for most ultra-low-power demanding applications, especially, those deployed in hostile and unattended environments. Currently, Wake-up Radio Receivers based protocols, under a dual-radio architecture and always-on operation, are emerging as a solution to overcome these issues, promising higher energy consumption reduction and reliability in terms of latency and packet-delivery-ratio compared to classic wake-up protocols. By combining different transceivers and reporting protocols regarding energy efficiency and reliability, multimodality in M2WSNs is achieved. This dissertation proposes a conceptual framework for M2WSNs that integrates the goodness of both data-reporting schemes and the Wake-up Radio paradigm--data periodicity, responsiveness, and energy-efficiency--, that might be suitable for monitoring oriented applications with low bandwidth requirements, that operates under normal circumstances and emergencies. The framework follows a layered approach, where each layer aims to fulfill specific tasks based on its information, the functions provided by its adjacent layers, and the information resulted from the cross-layer interactions.Doctor en IngenierĂ­aDoctoradohttps://orcid.org/0000-0003-1346-6451https://scholar.google.com.co/citations?user=0I4kXQUAAAAJ&hl=enhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=000001365

    Towards reliable communication in low-power wireless body area networks

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    Es wird zunehmend die Ansicht vertreten, dass tragbare Computer und Sensoren neue Anwendungen in den Bereichen Gesundheitswesen, personalisierte Fitness oder erweiterte RealitĂ€t ermöglichen werden. Die am Körper getragenen GerĂ€te sind dabei mithilfe eines Wireless Body Area Network (WBAN) verbunden, d.h. es wird drahtlose Kommunikation statt eines drahtgebundenen Kanals eingesetzt. Der drahtlose Kanal ist jedoch typischerweise ein eher instabiles Kommunikationsmedium und die Einsatzbedingungen von WBANs sind besonders schwierig: Einerseits wird die KanalqualitĂ€t stark von den physischen Bewegungen der Person beeinflusst, andererseits werden WBANs hĂ€ufig in lizenzfreien FunkbĂ€ndern eingesetzt und sind daher Störungen von anderen drahtlosen GerĂ€ten ausgesetzt. Oft benötigen WBAN Anwendungen aber eine zuverlĂ€ssige DatenĂŒbertragung. Das erste Ziel dieser Arbeit ist es, ein besseres VerstĂ€ndnis dafĂŒr zu schaffen, wie sich die spezifischen Einsatzbedingungen von WBANs auf die intra-WBAN Kommunikation auswirken. So wird zum Beispiel analysiert, welchen Einfluss die Platzierung der GerĂ€te auf der OberflĂ€che des menschlichen Körpers und die MobilitĂ€t des Benutzers haben. Es wird nachgewiesen, dass wĂ€hrend regelmĂ€ĂŸiger AktivitĂ€ten wie Laufen die empfangene SignalstĂ€rke stark schwankt, gleichzeitig aber SignalstĂ€rke-Spitzen oft einem regulĂ€ren Muster folgen. Außerdem wird gezeigt, dass in urbanen Umgebungen die Effekte von 2.4 GHz Radio Frequency (RF) Interferenz im Vergleich zu den Auswirkungen von fading (Schwankungen der empfangenen SignalstĂ€rke) eher gering sind. Allerdings fĂŒhrt RF Interferenz dazu, dass hĂ€ufiger BĂŒndelfehler auftreten, d.h. Fehler zeitlich korrelieren. Dies kann insbesondere in Anwendungen, die eine geringe Übertragungslatenz benötigen, problematisch sein. Der zweite Teil dieser Arbeit beschĂ€ftigt sich mit der Analyse von Verfahren, die potentiell die ZuverlĂ€ssigkeit der Kommunikation in WBANs erhöhen, ohne dass wesentlich mehr Energie verbraucht wird. ZunĂ€chst wird der Trade-off zwischen Übertragungslatenz und der ZuverlĂ€ssigkeit der Kommunikation analysiert. Diese Analyse basiert auf einem neuen Paket-Scheduling Algorithmus, der einen Beschleunigungssensor nutzt, um die WBAN Kommunikation auf die physischen Bewegungen der Person abzustimmen. Die Analyse zeigt, dass unzuverlĂ€ssige Kommunikationsverbindungen oft zuverlĂ€ssig werden, wenn Pakete wĂ€hrend vorhergesagter SignalstĂ€rke-Spitzen gesendet werden. Ferner wird analysiert, inwiefern die Robustheit gegen 2.4 GHz RF Interferenz verbessert werden kann. Dazu werden zwei Verfahren betrachtet: Ein bereits existierendes Verfahren, das periodisch einen Wechsel der Übertragungsfrequenz durchfĂŒhrt (channel hopping) und ein neues Verfahren, das durch RF Interferenz entstandene Bitfehler reparieren kann, indem der Inhalt mehrerer fehlerhafter Pakete kombiniert wird (packet combining). Eine Schlussfolgerung ist, dass FrequenzdiversitĂ€t zwar das Auftreten von BĂŒndelfehlern reduzieren kann, dass jedoch die statische Auswahl eines Kanals am oberen Ende des 2.4 GHz Bandes hĂ€ufig schon eine akzeptable Abhilfe gegen RF Interferenz darstellt.There is a growing belief that wearable computers and sensors will enable new applications in areas such as healthcare, personal fitness or augmented reality. The devices are attached to a person and connected through a Wireless Body Area Network (WBAN), which replaces the wires of traditional monitoring systems by wireless communication. This comes, however, at the cost of turning a reliable communication channel into an unreliable one. The wireless channel is typically a rather unstable medium for communication and the conditions under which WBANs have to operate are particularly harsh: not only is the channel strongly influenced by the movements of the person, but WBANs also often operate in unlicensed frequency bands and may therefore be exposed to a significant amount of interference from other wireless devices. Yet, many envisioned WBAN applications require reliable data transmission. The goals of this thesis are twofold: first, we aim at establishing a better understanding of how the specific WBAN operating conditions, such as node placement on the human body surface and user mobility, impact intra-WBAN communication. We show that during periodic activities like walking the received signal strength on an on-body communication link fluctuates strongly, but signal strength peaks often follow a regular pattern. Furthermore, we find that in comparison to the effects of fading 2.4 GHz Radio Frequency (RF) interference causes relatively little packet loss - however, urban 2.4 GHz RF noise is bursty (correlated in time), which may be problematic for applications with low latency bounds. The second goal of this thesis is to analyze how communication reliability in WBANs can be improved without sacrificing a significant amount of additional energy. To this end, we first explore the trade-off between communication latency and communication reliability. This analysis is based on a novel packet scheduling algorithm, which makes use of an accelerometer to couple WBAN communication with the movement patterns of the user. The analysis shows that unreliable links can often be made reliable if packets are transmitted at predicted signal strength peaks. In addition, we analyze to what extent two mechanisms can improve robustness against 2.4 GHz RF interference when adopted in a WBAN context: we analyze the benefits of channel hopping, and we examine how the packet retransmission process can be made more efficient by using a novel packet combining algorithm that allows to repair packets corrupted by RF interference. One of the conclusions is that while frequency agility may decrease "burstiness" of errors the static selection of a channel at the upper end of the 2.4 GHz band often already represents a good remedy against RF interference

    Towards Spatial Queries over Phenomena in Sensor Networks

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    Today, technology developments enable inexpensive production and deployment of tiny sensing and computing nodes. Networked through wireless radio, such senor nodes form a new platform, wireless sensor networks, which provide novel ability to monitor spatiotemporally continuous phenomena. By treating a wireless sensor network as a database system, users can pose SQL-based queries over phenomena without needing to program detailed sensor node operations. DBMS-internally, intelligent and energyefficient data collection and processing algorithms have to be implemented to support spatial query processing over sensor networks. This dissertation proposes spatial query support for two views of continuous phenomena: field-based and object-based. A field-based view of continuous phenomena depicts them as a value distribution over a geographical area. However, due to the discrete and comparatively sparse distribution of sensor nodes, estimation methods are necessary to generate a field-based query result, and it has to be computed collaboratively ‘in-the-network’ due to energy constraints. This dissertation proposes SWOP, an in-network algorithm using Gaussian Kernel estimation. The key contribution is the use of a small number of Hermite coefficients to approximate the Gaussian Kernel function for sub-clustered sensor nodes, and processes the estimation result efficiently. An object-based view of continuous phenomena is interested in aspects such as the boundary of an ‘interesting region’ (e.g. toxic plume). This dissertation presents NED, which provides object boundary detection in sensor networks. NED encodes partial event estimation results based on confidence levels into optimized, variable length messages exchanged locally among neighboring sensor nodes to save communication cost. Therefore, sensor nodes detect objects and boundaries based on moving averages to eliminate noise effects and enhance detection quality. Furthermore, the dissertation proposes the SNAKE-based approach, which uses deformable curves to track the spatiotemporal changes of such objects incrementally in sensor networks. In the proposed algorithm, only neighboring nodes exchange messages to maintain the curve structures. Based on in-network tracking of deformable curves, other types of spatial and spatiotemporal properties of objects, such as area, can be provided by the sensor network. The experimental results proved that our approaches are resource friendly within the constrained sensor networks, while providing high quality query results

    Industrial Wireless Sensor Networks

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    Wireless sensor networks are penetrating our daily lives, and they are starting to be deployed even in an industrial environment. The research on such industrial wireless sensor networks (IWSNs) considers more stringent requirements of robustness, reliability, and timeliness in each network layer. This Special Issue presents the recent research result on industrial wireless sensor networks. Each paper in this Special Issue has unique contributions in the advancements of industrial wireless sensor network research and we expect each paper to promote the relevant research and the deployment of IWSNs

    Field-Deployable Concurrent-Transmitter Networks for Long Term Environmental Monitoring

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    In this work, I draw upon my experience with field deployments of low-power data collection systems to come up with new approaches to long-term environmental monitoring (LTEM). Our goal is to develop long-lived, reliable systems that can be deployed by non-experts. The specific requirements of LTEM systems lead us to pursue techniques which can take advantage of the natural heirarchies that exist in sensor deployments while avoiding the difficulties inherent in selecting efficient data transmission routes in the face of unreliable hardware and difficult-to-measure link dynamics. The approach we advocate eschews traditional single-path routing and transmission methods in favor of approaches that leverage non-destructive simultaneous packet transmissions over subsets of the network. We apply this principle to develop a medium access protocol suitable for dense networks (Flip-MAC) as well as a method for identifying the set of potentially-useful forwarders between a data source and its destination (CX). This document not only characterizes and evaluates the low-level behavior of these protocols, but also describes the design of a larger multi-tiered data collection system based on CX, a suite of hardware which is well-suited to both CX and common deployment patterns, and the design of a ``dirt-to-database'' system which gives domain scientists the tools they need to deploy and manage networks on their own

    Intelligent Sensor Networks

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    In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

    On Age-of-Information Aware Resource Allocation for Industrial Control-Communication-Codesign

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    Unter dem Überbegriff Industrie 4.0 wird in der industriellen Fertigung die zunehmende Digitalisierung und Vernetzung von industriellen Maschinen und Prozessen zusammengefasst. Die drahtlose, hoch-zuverlĂ€ssige, niedrig-latente Kommunikation (engl. ultra-reliable low-latency communication, URLLC) – als Bestandteil von 5G gewĂ€hrleistet höchste DienstgĂŒten, die mit industriellen drahtgebundenen Technologien vergleichbar sind und wird deshalb als Wegbereiter von Industrie 4.0 gesehen. Entgegen diesem Trend haben eine Reihe von Arbeiten im Forschungsbereich der vernetzten Regelungssysteme (engl. networked control systems, NCS) gezeigt, dass die hohen DienstgĂŒten von URLLC nicht notwendigerweise erforderlich sind, um eine hohe RegelgĂŒte zu erzielen. Das Co-Design von Kommunikation und Regelung ermöglicht eine gemeinsame Optimierung von RegelgĂŒte und Netzwerkparametern durch die Aufweichung der Grenze zwischen Netzwerk- und Applikationsschicht. Durch diese VerschrĂ€nkung wird jedoch eine fundamentale (gemeinsame) Neuentwicklung von Regelungssystemen und Kommunikationsnetzen nötig, was ein Hindernis fĂŒr die Verbreitung dieses Ansatzes darstellt. Stattdessen bedient sich diese Dissertation einem Co-Design-Ansatz, der beide DomĂ€nen weiterhin eindeutig voneinander abgrenzt, aber das Informationsalter (engl. age of information, AoI) als bedeutenden Schnittstellenparameter ausnutzt. Diese Dissertation trĂ€gt dazu bei, die EchtzeitanwendungszuverlĂ€ssigkeit als Folge der Überschreitung eines vorgegebenen Informationsalterschwellenwerts zu quantifizieren und fokussiert sich dabei auf den Paketverlust als Ursache. Anhand der Beispielanwendung eines fahrerlosen Transportsystems wird gezeigt, dass die zeitlich negative Korrelation von Paketfehlern, die in heutigen Systemen keine Rolle spielt, fĂŒr Echtzeitanwendungen Ă€ußerst vorteilhaft ist. Mit der Annahme von schnellem Schwund als dominanter Fehlerursache auf der Luftschnittstelle werden durch zeitdiskrete Markovmodelle, die fĂŒr die zwei Netzwerkarchitekturen Single-Hop und Dual-Hop prĂ€sentiert werden, Kommunikationsfehlerfolgen auf einen Applikationsfehler abgebildet. Diese Modellierung ermöglicht die analytische Ableitung von anwendungsbezogenen ZuverlĂ€ssigkeitsmetriken wie die durschnittliche Dauer bis zu einem Fehler (engl. mean time to failure). FĂŒr Single-Hop-Netze wird das neuartige Ressourcenallokationsschema State-Aware Resource Allocation (SARA) entwickelt, das auf dem Informationsalter beruht und die AnwendungszuverlĂ€ssigkeit im Vergleich zu statischer Multi-KonnektivitĂ€t um GrĂ¶ĂŸenordnungen erhöht, wĂ€hrend der Ressourcenverbrauch im Bereich von konventioneller EinzelkonnektivitĂ€t bleibt. Diese ZuverlĂ€ssigkeit kann auch innerhalb eines Systems von Regelanwendungen, in welchem mehrere Agenten um eine begrenzte Anzahl Ressourcen konkurrieren, statistisch garantiert werden, wenn die Anzahl der verfĂŒgbaren Ressourcen pro Agent um ca. 10 % erhöht werden. FĂŒr das Dual-Hop Szenario wird darĂŒberhinaus ein Optimierungsverfahren vorgestellt, das eine benutzerdefinierte Kostenfunktion minimiert, die niedrige AnwendungszuverlĂ€ssigkeit, hohes Informationsalter und hohen durchschnittlichen Ressourcenverbrauch bestraft und so das benutzerdefinierte optimale SARA-Schema ableitet. Diese Optimierung kann offline durchgefĂŒhrt und als Look-Up-Table in der unteren Medienzugriffsschicht zukĂŒnftiger industrieller Drahtlosnetze implementiert werden.:1. Introduction 1 1.1. The Need for an Industrial Solution . . . . . . . . . . . . . . . . . . . 3 1.2. Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. Related Work 7 2.1. Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2. Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3. Codesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1. The Need for Abstraction – Age of Information . . . . . . . . 11 2.4. Dependability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3. Deriving Proper Communications Requirements 17 3.1. Fundamentals of Control Theory . . . . . . . . . . . . . . . . . . . . 18 3.1.1. Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.2. Performance Requirements . . . . . . . . . . . . . . . . . . . 21 3.1.3. Packet Losses and Delay . . . . . . . . . . . . . . . . . . . . . 22 3.2. Joint Design of Control Loop with Packet Losses . . . . . . . . . . . . 23 3.2.1. Method 1: Reduced Sampling . . . . . . . . . . . . . . . . . . 23 3.2.2. Method 2: Markov Jump Linear System . . . . . . . . . . . . . 25 3.2.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3. Focus Application: The AGV Use Case . . . . . . . . . . . . . . . . . . 31 3.3.1. Control Loop Model . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.2. Control Performance Requirements . . . . . . . . . . . . . . . 33 3.3.3. Joint Modeling: Applying Reduced Sampling . . . . . . . . . . 34 3.3.4. Joint Modeling: Applying MJLS . . . . . . . . . . . . . . . . . 34 3.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4. Modeling Control-Communication Failures 43 4.1. Communication Assumptions . . . . . . . . . . . . . . . . . . . . . . 43 4.1.1. Small-Scale Fading as a Cause of Failure . . . . . . . . . . . . 44 4.1.2. Connectivity Models . . . . . . . . . . . . . . . . . . . . . . . 46 4.2. Failure Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2.1. Single-hop network . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2.2. Dual-hop network . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.1. Mean Time to Failure . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.2. Packet Loss Ratio . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.3.3. Average Number of Assigned Channels . . . . . . . . . . . . . 57 4.3.4. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5. Single Hop – Single Agent 61 5.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 61 5.2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5.3. Erroneous Acknowledgments . . . . . . . . . . . . . . . . . . . . . . 67 5.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6. Single Hop – Multiple Agents 71 6.1. Failure Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.1. Admission Control . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.2. Transition Probabilities . . . . . . . . . . . . . . . . . . . . . . 73 6.1.3. Computational Complexity . . . . . . . . . . . . . . . . . . . 74 6.1.4. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . 75 6.2. Illustration Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6.3.1. Verification through System-Level Simulation . . . . . . . . . 78 6.3.2. Applicability on the System Level . . . . . . . . . . . . . . . . 79 6.3.3. Comparison of Admission Control Schemes . . . . . . . . . . 80 6.3.4. Impact of the Packet Loss Tolerance . . . . . . . . . . . . . . . 82 6.3.5. Impact of the Number of Agents . . . . . . . . . . . . . . . . . 84 6.3.6. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 84 6.3.7. Channel Saturation Ratio . . . . . . . . . . . . . . . . . . . . 86 6.3.8. Enforcing Full Channel Saturation . . . . . . . . . . . . . . . 86 6.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 7. Dual Hop – Single Agent 91 7.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 91 7.2. Optimization Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 7.3.1. Extensive Simulation . . . . . . . . . . . . . . . . . . . . . . . 96 7.3.2. Non-Integer-Constrained Optimization . . . . . . . . . . . . . 98 7.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 8. Conclusions and Outlook 105 8.1. Key Results and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 105 8.2. Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 A. DC Motor Model 111 Bibliography 113 Publications of the Author 127 List of Figures 129 List of Tables 131 List of Operators and Constants 133 List of Symbols 135 List of Acronyms 137 Curriculum Vitae 139In industrial manufacturing, Industry 4.0 refers to the ongoing convergence of the real and virtual worlds, enabled through intelligently interconnecting industrial machines and processes through information and communications technology. Ultrareliable low-latency communication (URLLC) is widely regarded as the enabling technology for Industry 4.0 due to its ability to fulfill highest quality-of-service (QoS) comparable to those of industrial wireline connections. In contrast to this trend, a range of works in the research domain of networked control systems have shown that URLLC’s supreme QoS is not necessarily required to achieve high quality-ofcontrol; the co-design of control and communication enables to jointly optimize and balance both quality-of-control parameters and network parameters through blurring the boundary between application and network layer. However, through the tight interlacing, this approach requires a fundamental (joint) redesign of both control systems and communication networks and may therefore not lead to short-term widespread adoption. Therefore, this thesis instead embraces a novel co-design approach which keeps both domains distinct but leverages the combination of control and communications by yet exploiting the age of information (AoI) as a valuable interface metric. This thesis contributes to quantifying application dependability as a consequence of exceeding a given peak AoI with the particular focus on packet losses. The beneficial influence of negative temporal packet loss correlation on control performance is demonstrated by means of the automated guided vehicle use case. Assuming small-scale fading as the dominant cause of communication failure, a series of communication failures are mapped to an application failure through discrete-time Markov models for single-hop (e.g, only uplink or downlink) and dual-hop (e.g., subsequent uplink and downlink) architectures. This enables the derivation of application-related dependability metrics such as the mean time to failure in closed form. For single-hop networks, an AoI-aware resource allocation strategy termed state-aware resource allocation (SARA) is proposed that increases the application reliability by orders of magnitude compared to static multi-connectivity while keeping the resource consumption in the range of best-effort single-connectivity. This dependability can also be statistically guaranteed on a system level – where multiple agents compete for a limited number of resources – if the provided amount of resources per agent is increased by approximately 10 %. For the dual-hop scenario, an AoI-aware resource allocation optimization is developed that minimizes a user-defined penalty function that punishes low application reliability, high AoI, and high average resource consumption. This optimization may be carried out offline and each resulting optimal SARA scheme may be implemented as a look-up table in the lower medium access control layer of future wireless industrial networks.:1. Introduction 1 1.1. The Need for an Industrial Solution . . . . . . . . . . . . . . . . . . . 3 1.2. Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. Related Work 7 2.1. Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2. Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3. Codesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1. The Need for Abstraction – Age of Information . . . . . . . . 11 2.4. Dependability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3. Deriving Proper Communications Requirements 17 3.1. Fundamentals of Control Theory . . . . . . . . . . . . . . . . . . . . 18 3.1.1. Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.2. Performance Requirements . . . . . . . . . . . . . . . . . . . 21 3.1.3. Packet Losses and Delay . . . . . . . . . . . . . . . . . . . . . 22 3.2. Joint Design of Control Loop with Packet Losses . . . . . . . . . . . . 23 3.2.1. Method 1: Reduced Sampling . . . . . . . . . . . . . . . . . . 23 3.2.2. Method 2: Markov Jump Linear System . . . . . . . . . . . . . 25 3.2.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3. Focus Application: The AGV Use Case . . . . . . . . . . . . . . . . . . 31 3.3.1. Control Loop Model . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.2. Control Performance Requirements . . . . . . . . . . . . . . . 33 3.3.3. Joint Modeling: Applying Reduced Sampling . . . . . . . . . . 34 3.3.4. Joint Modeling: Applying MJLS . . . . . . . . . . . . . . . . . 34 3.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4. Modeling Control-Communication Failures 43 4.1. Communication Assumptions . . . . . . . . . . . . . . . . . . . . . . 43 4.1.1. Small-Scale Fading as a Cause of Failure . . . . . . . . . . . . 44 4.1.2. Connectivity Models . . . . . . . . . . . . . . . . . . . . . . . 46 4.2. Failure Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2.1. Single-hop network . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2.2. Dual-hop network . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.1. Mean Time to Failure . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.2. Packet Loss Ratio . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.3.3. Average Number of Assigned Channels . . . . . . . . . . . . . 57 4.3.4. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5. Single Hop – Single Agent 61 5.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 61 5.2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5.3. Erroneous Acknowledgments . . . . . . . . . . . . . . . . . . . . . . 67 5.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6. Single Hop – Multiple Agents 71 6.1. Failure Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.1. Admission Control . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.2. Transition Probabilities . . . . . . . . . . . . . . . . . . . . . . 73 6.1.3. Computational Complexity . . . . . . . . . . . . . . . . . . . 74 6.1.4. Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . 75 6.2. Illustration Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6.3.1. Verification through System-Level Simulation . . . . . . . . . 78 6.3.2. Applicability on the System Level . . . . . . . . . . . . . . . . 79 6.3.3. Comparison of Admission Control Schemes . . . . . . . . . . 80 6.3.4. Impact of the Packet Loss Tolerance . . . . . . . . . . . . . . . 82 6.3.5. Impact of the Number of Agents . . . . . . . . . . . . . . . . . 84 6.3.6. Age of Information . . . . . . . . . . . . . . . . . . . . . . . . 84 6.3.7. Channel Saturation Ratio . . . . . . . . . . . . . . . . . . . . 86 6.3.8. Enforcing Full Channel Saturation . . . . . . . . . . . . . . . 86 6.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 7. Dual Hop – Single Agent 91 7.1. State-Aware Resource Allocation . . . . . . . . . . . . . . . . . . . . 91 7.2. Optimization Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 7.3.1. Extensive Simulation . . . . . . . . . . . . . . . . . . . . . . . 96 7.3.2. Non-Integer-Constrained Optimization . . . . . . . . . . . . . 98 7.4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 8. Conclusions and Outlook 105 8.1. Key Results and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 105 8.2. Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 A. DC Motor Model 111 Bibliography 113 Publications of the Author 127 List of Figures 129 List of Tables 131 List of Operators and Constants 133 List of Symbols 135 List of Acronyms 137 Curriculum Vitae 13

    ENVIRONMENTAL MONITORING THROUGH WIRELESS SENSOR NETWORKS

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    Wireless sensor networks (WSN) deployed for the purpose of environmental monitoring are becoming increasingly popular. This research examines the use of WSNs for the application of studying the hydrologic cycle with respect to the soil-plant-atmosphere continuum. The goal was to pair WSN technology with inexpensive hydrological sensors for the purpose of affordable and reliable environmental monitoring. This work encompasses the design, construction, and calibration of sap flow sensors; an examination into the power characteristics of the environmental sensors used to study hydrology and the wireless motes used to communicate data in the WSN; the results from deploying a pilot WSN test bed; the deployment, maintenance, and findings of a main WSN test bed; and the primarily results from the processed environmental sensor data. Lab experiments were used to determine an optimal sampling rate of environmental sensors, which was a compromise between the battery life of the motes and the temporal resolution of the environmental measurements. Based on the sampling rate, it was determined that the thermal dissipation sap flow method was the most practical for WSN applications. For the purposes of large-scale deployments, lab-made sap flow sensors provide a comparable cost-effective alternative to their commercial counterparts. Environmental monitoring of soil moisture, soil water potential, and sap flow was successful and exhibited spatial distributions and temporal changes within the main test bed. Analysis of the WSN communication revealed numerous factors impact network stability. While environmental monitoring through WSNs is feasible, its practicality is dependent on numerous variables

    Routing for Wireless Sensor Networks: From Collection to Event-Triggered Applications

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    Wireless Sensor Networks (WSNs) are collections of sensing devices using wireless communication to exchange data. In the past decades, steep advancements in the areas of microelectronics and communication systems have driven an explosive growth in the deployment of WSNs. Novel WSN applications have penetrated multiple areas, from monitoring the structural stability of historic buildings, to tracking animals in order to understand their behavior, or monitoring humans' health. The need to convey data from increasingly complex applications in a reliable and cost-effective manner translates into stringent performance requirements for the underlying WSNs. In the frame of this thesis, we have focused on developing routing protocols for multi-hop WSNs, that significantly improve their reliability, energy consumption and latency. Acknowledging the need for application-specific trade-offs, we have split our contribution into two parts. Part 1 focuses on collection protocols, catering to applications with high reliability and energy efficiency constraints, while the protocols developed in part 2 are subject to an additional bounded latency constraint. The two mechanisms introduced in the first part, WiseNE and Rep, enable the use of composite metrics, and thus significantly improve the link estimation accuracy and transmission reliability, at an energy expense far lower than the one achieved in previous proposals. The novel beaconing scheme WiseNE enables the energy-efficient addition of the RSSI (Received Signal Strength Indication) and LQI (Link Quality Indication) metrics to the link quality estimate by decoupling the sampling and exploration periods of each mote. This decoupling allows the use of the Trickle Algorithm, a key driver of protocols' energy efficiency, in conjunction with composite metrics. WiseNE has been applied to the Triangle Metric and validated in an online deployment. The section continues by introducing Rep, a novel sampling mechanism that leverages the packet repetitions already present in low-power preamble-sampling MAC protocols in order to improve the WSN energy consumption by one order of magnitude. WiseNE, Rep and the novel PRSSI (Penalized RSSI, a combination of PRR and RSSI) composite metric have been validated in a real smart city deployment. Part 2 introduces two mechanisms that were developed in the frame of the WiseSkin project (an initiative aimed at designing highly sensitive artificial skin for human limb prostheses), and are generally applicable to the domain of cyber-physical systems. It starts with Glossy-W, a protocol that leverages the superior energy-latency trade-off of flooding schemes based on concurrent transmissions. Glossy-W ensures the stringent synchronization requirements necessary for robust flooding, irrespective of the number of motes simultaneously reporting an event. Part 2 also introduces SCS (Synchronized Channel Sampling), a novel mechanism capable of reducing the power required for periodic polling, while maintaining the event detection reliability, and enhancing the network coexistence. The testbed experiments performed show that SCS manages to reduce the energy consumption of the state-of-the-art protocol Back-to-Back Robust Flooding by over one third, while maintaining an equivalent reliability, and remaining compatible with simultaneous event detection. SCS' benefits can be extended to the entire family of state-of-the-art protocols relying on concurrent transmissions
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