Over the last decade, the wide deployment of wireless access technologies (e.g. WiFi, 3G, and LTE) and the remarkable growth in the volume of streaming video content have significantly altered the telecommunications field. These developments introduce new challenges to the research community including the need to develop new solutions (e.g. traffic models and transport protocols) to address changing traffic patterns and the characteristics of wireless links and the need for new evaluation methods that generate higher fidelity results under more realistic scenarios. Unfortunately, for the last two decades, simulation studies have been the main tool for researchers in wireless networks. In spite of the advantages of simulation studies, overall they have had a negative influence on the credibility of published results. In partial response to this simulation crisis, the research community has adopted testing and evaluation using implementation-based experiments. Implementation-based experiments include field experiments, prototypes, emulations, and testbeds. An example of an implementation-based experiment is the MANIAC Challenge, a wireless networking competition that we designed and hosted, which included creation and operation of ad hoc networks using commodity hardware. However, the lack of software tools to facilitate these sorts of experiments has created new challenges. Currently, researchers must practice kernel programming in order to implement networking experiments, and there is an urgent need to lower the barriers of entry to wireless network experimentation. With respect to the growth in video traffic over wireless networks, the main challenge is a mismatch between the design concepts of current internet protocols (e.g. the Transport Control Protocol (TCP)) and the reality of modern wireless networks and streaming video techniques. Internet protocols were designed to be deployed over wired networks and often perform poorly over wireless links; video encoding is highly loss tolerant and delay-constrained and yet, for reasons of expedience is carried using protocols that emphasize reliable delivery at the cost of potentially high delay. This dissertation addresses the lack of software tools to support implementation-based networking experiments and the need to improve the performance of video streaming over wireless access networks. We propose a new software tool that allows researchers to implement experiments without a need to become kernel programmers. The new tool, called the Flexible Internetwork Stack (FINS) Framework, is available under an open source license. With our tool, researchers can implement new network layers, protocols, and algorithms, and redesign the interconnections between the protocols. It offers logging and monitoring capabilities as well as dynamic reconfigurability of the modules' attributes and interconnections during runtime. We present details regarding the architecture, design, and implementation of the FINS Framework and provide an assessment of the framework including both qualitative and quantitative comparison with significant previous tools. We also address the problem of HTTP-based adaptive video streaming (HAVS) over WiFi access networks. We focus on the negative influence of wireless last-hop connections on network utilization and the end-user quality of experience (QoE). We use a cross-layer approach to design three controllers. The first and second controllers adopt a heuristic cross-layer design, while the third controller formulates the HAVS problem as a Markov decision process (MDP). By solving the model using reinforcement learning, we achieved 20% performance improvement (after enough training) with respect to the performance of the best heuristic controller under unstable channel conditions. Our simulation results are backed by a system prototype using the FINS Framework. Although it may seem predictable to achieve more gain in performance and in QoE by using cross-layer design, this dissertation not only presents a new technique that improves performance, but also suggests that it is time to move cross-layer and machine-learning-based approaches from the research field to actual deployment. It is time to move cognitive network techniques from the simulation environment to real world implementations. / Ph. D.

Quality-of-Service (QoS) to users is a critical requirement of resource allocation in wireless networks and has drawn significant research attention over a long time. However, the QoS requirements differ vastly based on the wireless network paradigm. At one extreme, we have a millimeter wave small-cell network for streaming data that requires very high throughput and low latency. At the other end, we have Machine-to-Machine (M2M) uplink traffic with low throughput and low latency. In this dissertation, we investigate and solve QoS-aware resource allocation problems for diverse wireless paradigms. We first study cross-layer dynamic spectrum allocation in a LTE macro-cellular network with fractional frequency reuse to improve the spectral efficiency for cell-edge users. We show that the resultant optimization problem is NP-hard and propose a low-complexity layered spectrum allocation heuristic that strikes a balance between rate maximization and fairness of allocation. Next, we develop an energy efficient downlink power control scheme in a energy harvesting small-cell base station equipped with local cache and wireless backhaul. We also study the tradeoff between the cache size and the energy harvesting capabilities. We next analyzed the file read latency in Distributed Storage Systems (DSS). We propose a heterogeneous DSS model wherein the stored data is categorized into multiple classes based on arrival rate of read requests, fault-tolerance for storage etc. Using a queuing theoretic approach, we establish bounds on the average read latency for different scheduling policies. We also show that erasure coding in DSS serves the dual purpose of reducing read latency and increasing the energy efficiency. Lastly, we investigate the problem of delay-efficient packet scheduling in M2M uplink with heterogeneous traffic characteristics. We classify the uplink traffic into multiple classes and propose a proportionally-fair delay-efficient heuristic packet scheduler. Using a queuing theoretic approach, we next develop a delay optimal multiclass packet scheduler and later extend it to joint medium access control and packet scheduling for M2M uplink. Using extensive simulations, we show that the proposed schedulers perform better than state-of-the-art schedulers in terms of average delay and packet delay jitter. / PHD

An increasing number of complex applications are being executed on resource constrained devices, such as drones and rovers. Such systems often operate in dynamic and unknown environments, and consequently have dynamic performance requirements based on their surroundings. These systems must consider the trade-off between application and platform performance in order to operate within resource means. This thesis proposes a runtime resource management system for a monolithic localization application. The proposed strategy uses gradient boosting regressors to predict localization accuracy and power consumption at runtime for a set of configurable application and platform parameters. A model-based controller selects parameters at runtime to optimize localization accuracy subject to a power constraint. The testbed used for experiments consists of maplab, a visual-inertial localization and mapping framework, executed monolithically on the Nvdia Jetson AGX platform. The results highlight the importance of incorporating dynamic parameters when identifying predictive models for localization systems. The proposed system is able to track a power reference while maintaining reasonable localization accuracy at runtime, for both platform and application parameters. The results demonstrate that runtime control can achieve better performance than alternative solutions which rely on offline profiling of the configuration space. / Ett ökande antal komplexa program körs på resursbegränsade enheter, till exempel drönare och rover. Sådana system fungerar ofta i dynamiska och okända miljöer och har därför dynamiska prestandakrav som är baserade på deras omgivningar. I dessa system måste hänsyn tas till skillnaden mellan applikations- och plattformsprestanda för att kunna fungera med hjälp av resurser. I den här tesen föreslås ett resurshanteringssystem för körning av ett monolitiskt lokaliseringsprogram. Den föreslagna strategin använder övertoningsförstärkare för att förutsäga lokaliseringens exakthet och energiförbrukning vid körning för en uppsättning konfigurerbara program- och plattformsparametrar. En modellbaserad styrenhet väljer parametrar under körning för att optimera lokaliseringsnoggrannheten under förutsättning att det finns en energibegränsning. Den testbädd som används för experiment består av maplab, en ram för visuell tröghetslokalisering och kartläggning, som utförts monolitiskt på Nvdia Jetson AGX-plattformen. Resultaten belyser vikten av att införliva dynamiska parametrar när man identifierar förutsägbara modeller för lokaliseringssystem. Det föreslagna systemet kan spåra en energireferens samtidigt som man bibehåller en rimlig lokaliseringsnoggrannhet vid körning, för både plattformsparametrar och programparametrar. Resultaten visar att körningskontroll kan ge bättre prestanda än alternativa lösningar som är beroende av offlineprofilering av konfigurationsutrymmet.

In this dissertation, we propose service-oriented sensor-actuator networks (SOSANETs) as a new paradigm for building the next generation of customizable, open, interoperable sensor-actuator networks. In SOSANETs, nodes expose their capabilities to applications in the form of service profiles. A node's service profile consists of a set of services (i.e., sensing and actuation capabilities) that it provides and the quality of service (QoS) parameters associated with those services (delay, accuracy, freshness, etc.). SOSANETs provide the benefits of both application-specific SANETs and generic SANETs. We first define a query model and an architecture for SOSANETs. The proposed query model offers a simple, uniform query interface whereby applications specify sensing and actuation queries independently from any specific deployment of the underlying SOSANET. We then present μRACER (Reliable Adaptive serviCe-driven Efficient Routing), a routing protocol suite for SOSANETs. μRACER consists of three routing protocols, namely, SARP (Service-Aware Routing Protocol), CARP (Context-Aware Routing Protocol), and TARP (Trust-Aware Routing Protocol). SARP uses an efficient service-aware routing approach that aggressively reduces downstream traffic by translating service profiles into efficient paths. CARP supports QoS by dynamically adapting each node's routing behavior and service profile according to the current context of that node, i.e. number of pending queries and number and type of messages to be routed. Finally, TARP achieves high end-to-end reliability through a scalable reputation-based approach in which each node is able to locally estimate the next hop of the most reliable path to the sink. We also propose query optimization techniques that contribute to the efficient execution of queries in SOSANETs. To evaluate the proposed service-oriented architecture, we implemented TinySOA, a prototype SOSANET built on top of TinyOS with uRACER as its routing mechansim. TinySOA is designed as a set of layers with a loose interaction model that enables several cross-layer optimization options. We conducted an evaluation of TinySOA that included a comparison with TinyDB. The obtained empirical results show that TinySOA achieves significant improvements on many aspects including energy consumption, scalability, reliability and response time. / Ph. D.

Ad hoc networks are emerging as a cost-effective, yet, powerful tool for communication. These systems, where networks can emerge and converge on-the-fly, are guided by the forward-looking goals of providing ubiquitous connectivity and constant access to information. Due to power and bandwidth constraints, the vulnerability of the wireless medium, and the multi-hop nature of ad hoc networks, these networks are becoming increasingly complex dynamic systems. Besides, modern radios are empowered to be reconfigurable, which harbors the temptation to exploit the system. To understand the implications of these issues, some of which pose significant challenges to efficient network design, we study topology control using game theory. We develop a game-theoretic framework of topology control that broadly captures the radio parameters, one or more of which can be tuned under the purview of topology control. In this dissertation, we consider two parameters, viz. transmit power and channel, and study the impact of controlling these on the emergent topologies. We first examine the impact of node selfishness on the network connectivity and energy efficiency under two levels of selfishness: (a) nodes cooperate and forward packets for one another, but selfishly minimize transmit power levels and; (b) nodes selectively forward packets and selfishly control transmit powers. In the former case, we characterize all the Nash Equilibria of the game and evaluate the energy efficiency of the induced topologies. We develop a better-response-based dynamic that guarantees convergence to the minimal maximum power topology. We extend our analysis to dynamic networks where nodes have limited knowledge about network connectivity, and examine the tradeoff between network performance and the cost of obtaining knowledge. Due to the high cost of maintaining knowledge in networks that are dynamic, mobility actually helps in information-constrained networks. In the latter case, nodes selfishly adapt their transmit powers to minimize their energy consumption, taking into account partial packet forwarding in the network. This work quantifies the energy efficiency gains obtained by cooperation and corroborates the need for incentivizing nodes to forward packets in decentralized, energy-limited networks. We then examine the impact of selfish behavior on spectral efficiency and interference minimization in multi-channel systems. We develop a distributed channel assignment algorithm to minimize the spectral footprint of a network while establishing an interference-free connected network. In spite of selfish channel selections, the network spectrum utilization is shown to be within 12% of the minimum on average. We then extend the analysis to dynamic networks where nodes have incomplete network state knowledge, and quantify the price of ignorance. Under the limitations on the number of available channels and radio interfaces, we analyze the channel assignment game with respect to interference minimization and network connectivity goals. By quantifying the interference in multi-channel networks, we illuminate the interference reduction that can be achieved by utilizing orthogonal channels and by distributing interference over multiple channels. In spite of the non-cooperative behavior of nodes, we observe that the selfish channel selection algorithm achieves load balancing. Distributing the network control to autonomous agents leaves open the possibility that nodes can act selfishly and the overall system is compromised. We advance the need for considering selfish behavior from the outset, during protocol design. To overcome the effects of selfishness, we show that the performance of a non-cooperative network can be enhanced by appropriately incentivizing selfish nodes. / Ph. D.

Le thème général du sujet tourne autour de l'optimisation inter-couche des réseaux de capteurs basés sur la technologie ultra large bande ULB (UWB, Ultra Wide Band) moyennant des solutions protocolaires permettant d'un côté de répondre au besoin de qualité de service QdS à critères multiples dans les réseaux de capteurs sans fil et d'autre côté d'assurer le partage et l'allocation efficace les ressources disponibles (spectrale et temporelle) ainsi que l'optimisation de la consommation d'énergie dans des tels réseaux. Le domaine d'application cible choisi dans le présent travail est les systèmes de suivi des patients au sein d'un réseau de capteurs déployé en hôpital intelligent (WHSN, Large-scale Wireless Hospital Sensor Network). Dans ce contexte, nous avons proposé le modèle UWBCAS pour assurer le partage des ressources spectrales entre les PANs. Puis, nous avons conçu et implémenté un protocole MAC multi-canal multi-créneau de temps avec support de qualité de service, PMCMTP, pour assurer une allocation conjointe des canaux de fréquence et des créneaux de temps au sein de chaque réseau PAN. Enfin nous avons proposé l'algorithme JSAR qui traite à la fois les problèmes d'ordonnancement des cycles d'activités des membres du réseau dans le but d'optimiser la consommation d'énergie, d'allocation efficace des canaux de fréquence et des créneaux de temps afin d'améliorer le taux d'utilisation des ressources et les performances du réseau et de routage avec support de QdS à critères multiples afin de répondre aux besoins des applications supportées / The general context of the present memory is about the cross-layer optimization of wireless sensors networks based on ultra wide band technology UWB. The proposed solutions ensure the share and the efficient allocation of spectral and temporal resources, the optimization of the energy consumption and the support of multi-constraints quality of services QoS. The most challenging issue is providing a tradeoff between the resource efficiency and the multiconstrained QoS support. For this purpose, we proposed a new Wireless Hospital Sensor Network (WHSN) three-tiered architecture in order to support large-scale deployment and to improve the network performance. Then we designed a channel allocation scheme (UWBCAS,)and a prioritized multi-channel multi-time slot MAC protocol (PMCMTP) to enhance network performance and maximize the resource utilization. Finally, we proposed a joint duty cycle scheduling, resource allocation and multi-constrained QoS routing algorithm (JSAR) which simultaneously combines, a duty cycle scheduling scheme for energy saving, a resource allocation scheme for efficient use of frequency channels and time slots, and an heuristic for multi-constrained routing protocol

The expectations in performing high-performance cyber-physical applications in resource-constrained devices are continuously increasing. The available hardware is still a main limitation in this context, both in terms of computation capability and energy limits. On the other hand, one must ensure the robust and accurate execution of the applications deployed, since their failure may entail risks for humans and the surrounding environment. The limits and risks are enhanced when multiple applications are executed on the same device. The focus of this thesis is to provide a trade-off between the required performance and power consumption. The focus is on two fundamental applications in the mobile autonomous vehicles scenario: localization and object detection. The multi-objective optimization is performed in a cross-layer manner, exploring both applications and platform configurable parameters with Design Space Exploration (DSE). The focus is on localization and detection accuracy, detection latency and power consumption. Predictive models are designed to estimate the metrics of interest and ensure robust execution, excluding potential faulty configurations from the design space. The research is approached empirically, performing tests on the Nvidia Jetson AGX and NX platforms. Results show that optimal configurations for a single application are in general sub-optimal or faulty for the concurrent execution case, while the opposite is sometimes applicable. / Resursbegränsade enheter förväntas utföra mer och mer krävande cyberfysiska program. Hårdvaran är en av de huvudsakliga begränsningarna både vad gäller beräkningshastighet och energigränser. Samtidigt måste programmen som körs vara robusta och noggranna, eftersom ett fel kan påverka människor och deras omgivning. När flera program körs på samma enhet blir både begränsningar och risker större. Den här avhandlingen fokuserar på att göra en avvägning mellan krav på prestanda och energiförbrukning för två tillämpningar inom området autonoma fordon: lokalisering och objektigenkänning. Med hjälp av Design Space Exploration (DSE) utforskas parametrar både i applikationerna och på plattformen genom att utföra tvärlageroptimering med flera mål. Lokaliserings- och detekteringsnoggrannhet, fördröjning i igenkänning och energiförbrukning är egenskaper i fokus. Prediktiva modeller designas för att estimera måtten som är av intresse och garantera robust körning genom att utesluta potentiellt felaktiga konfigurationer. Empirisk forskning görs med tester på Nvidia Jetson AGXoch NX-plattformarna. Resultaten visar att de optimala konfigurationerna för ett enda program i allmänhet är suboptimala eller felaktiga vid körning av flera program samtidigt, medan motsatsen ibland är tillämplig.

In the last decades, there has been a growing interest towards new use cases in the Internet of Things (IoT) domain, such as extended reality glasses, unmanned aerial vehicles (UAVs), and autonomous driving. The technological advancement observed in such scenarios has also been enabled by the increasing capabilities of small form factor devices. Although such devices allow to achieve remarkable computing performance with relatively low energy consumption, these are often used in contexts in which the trade-offs between power consumption and application performance play a key role (e.g., battery powered systems). Furthermore, if such trade-offs are not carefully set, the performance degradation can lead to system failure. The work proposed in this thesis aims at investigating this type of problems, and to propose a runtime model and controller pair based on the joint optimization of the platform and application parameters to reduce the likelihood of system failure. The proposed architecture is evaluated in a UAV emulated environment, in which the used platform embeds hardware features comparable to the ones of a drone, while the localization and mapping application executed on such device makes use of real-world visual-inertial datasets. The proposed runtime model-controller solution relies on the monitoring of the platform CPU peaks for identifying application failure. It has also been empirically demonstrated that the model-controller can substantially decrease the number of failures and, in specific scenarios, improve localization accuracy and power consumption even compared to the optimal static parameter configurations. Moreover, the solution has been proven to be simple and generalizable in scenarios characterized by different levels of concurrency, and in the datasets tested. / Under de senaste decennierna har det funnits ett växande intresse för nya användningsfall som Extended Reality-glasögon, obemannade flygfarkoster (UAV) och autonom körning. De tekniska framstegen som observerats i sådana scenarier har också möjliggjorts av den ökande kapaciteten hos små formfaktorenheter. Även om sådana enheter gör det möjligt att uppnå anmärkningsvärd datorprestanda med relativt låg energiförbrukning, används dessa ofta i sammanhang där kompromisserna mellan strömförbrukning och applikationsprestanda spelar en nyckelroll (t.ex. batteridrivna system). Dessutom, om sådana avvägningar inte är noggrant inställda, kan prestandaförsämringen leda till systemfel. Arbetet som föreslås i denna avhandling syftar till att undersöka denna typ av problem, och att föreslå en körtid modellstyrenhet baserad på gemensam optimering av plattformen och applikationsparametrar för att minska systemfel. Den föreslagna arkitekturen utvärderas i en UAV-emulerad miljö, där den använda plattformen har hårdvarufunktioner som är motsvarar en drönare, medan lokaliserings- och kartläggningsapplikationen som körs på en sådan enhet använder verkliga visuella tröghetsdatauppsättningar. Den föreslagna runtime-modellstyrningslösningen förlitar sig på övervakning av plattformens CPU-toppar för att identifiera programfel. Det har också visat sig empiriskt att modellstyrenheten avsevärt kan minska antalet fel och, i specifika scenarier, förbättra lokaliseringsnoggrannheten och strömförbrukningen även jämfört med de optimala statiska parameterkonfigurationerna. Dessutom har lösningen visat sig vara enkel och generaliserbar i scenarier som kännetecknas av olika nivåer av samtidighet och i de testade datamängderna.

Le développement simultané de terminaux mobiles multimédia (smartphones, tablettes) et de réseaux d’accès offrant des débits élevés conduit à une explosion du trafic liés aux contenus multimédia. Cette croissance nécessite un partage efficace des ressources radio entre fournisseurs de contenus (dans le cas de la diffusion) ou entre récepteurs (dans le cas de services de vidéo à la demande). Cette thèse propose des outils de partage équitable des ressources en termes de qualité des contenus multimédia reçu et de délai de transmission dans les deux contextes précédents. La variété des compromis débit-distorsion des contenus multimédia est exploitée à cet effet. Dans un premier temps, une solution centralisée de contrôle conjoint du débit de codage et de transmission de plusieurs programmes transmis sur un même canal est considérée. L’objectif est de fournir des flux de qualités similaires avec des variations limitées, tout en assurant des délais de transmission comparables. Ce problème est résolu en synthétisant une commande prédictive à l’aide d’outils d’optimisation sous contrainte. Dans un second temps, seule l’allocation de bande est centralisée, le contrôle des caractéristiques de compression de chaque flux est réalisé de manière distribuée. Le contrôleur centralisé ne renvoie que le niveau de remplissage des tampons associés à chaque flux aux fournisseurs de contenus distants. Une stratégie de régulation des débits de codage est alors mise en place par ces fournisseurs, de manière à réguler le niveau en bits ou en image des tampons. La stabilité de ce système de régulation couplé est étudiée en détails. Enfin, l’optimisation inter-couches d’une chaine de transmission de contenus multimédia scalable est considérée. Ce problème est formulé dans le contexte de la programmation dynamique. Lorsque des modèles de complexité raisonnable sont considérés et avec des caractéristiques du système bien connues, des solutions optimales peuvent être obtenues. Des techniques d’apprentissage sont mises en œuvre, lorsque le système n’est que partiellement connu, par exemple, lorsque l’état du canal de transmission parvient avec du retard à l’organe de commande. / Due to the emergence of new generation mobiles and media streaming services, data traffic on mobile networks is continuously exploding. Despite the emergence of standards such as LTE, resources still remain scarce and limited. Thus, efficiently sharing resources among broadcasters or between unicast receivers connected to the same base station is necessary. An efficient resources allocation, where a fair received video quality between users and an equal transmission delay are achieved, is targeted. To that end, the variety of the rate-distortion trade-off of multimedia content is exploited. First, a centralized joint encoding and transmission rate control of multiple programs sharing the same channel is considered. A satisfactory and a comparable video quality among the transmitted programs, with limited variations, as well as a comparable transmission delay are targeted. The problem is solved using constrained optimization tools. Second, only the bandwidth allocation control is centralized, the control of the encoding rate characteristics of each stream is carried in a distributed manner. By modeling the problem as a feedback control system, the centralized bandwidth allocation is required to feed back only the buffer level to its associated remote content provider. The equilibrium and stability issues are addressed for both bit and second buffer control. In the case of simple unicast connection, a cross-layer optimization of scalable video delivery over wireless channel is performed. The optimization problem is cast in the context of dynamic programming. When low complex model are considered and when the system characteristics are known, optimal solutions can be obtained. When the system is partially known, for example, when the state of the channel reaches the control process with delay, learning techniques are implemented.

We study the problem of joint congestion control, routing and MAC layer scheduling in multi-hop wireless mesh networks, where the nodes in the network are subjected to energy expenditure rate constraints. As wireless scenario does not allow all the links to be active all the time, only a subset of given links can be active simultaneously. We model the inter-link interference using the link contention graph. All the nodes in the network are power-constrained and we model this constraint using energy expenditure rate matrix. Then we formulate the problem as a network utility maximization (NUM) problem. We notice that this is a convex optimization problem with affine constraints. We apply duality theory and decompose the problem into two sub-problems namely, network layer congestion control and routing problem, and MAC layer scheduling problem. The source adjusts its rate based on the cost of the least cost path to the destination where the cost of the path includes not only the prices of the links in it but also the prices associated with the nodes on the path. The MAC layer scheduling of the links is carried out based on the prices of the links. The optimal scheduler selects that set of non-interfering links, for which the sum of link prices is maximum. We study the effects of energy expenditure rate constraints of the nodes on the maximum possible network utility. It turns out that the dominant of the two constraints namely, the link capacity constraint and the node energy expenditure rate constraint affects the network utility most. Also we notice the fact that the energy expenditure rate constraints do not affect the nature of optimal link scheduling problem. Following this fact, we study the problem of distributed link scheduling. Optimal scheduling requires selecting independent set of maximum aggregate price, but this problem is known to be NP-hard. We first show that as long as scheduling policy selects the set of non-interfering links, it can not go unboundedly away from the optimal solution of network utility maximization problem. Then we proceed and evaluate a simple greedy scheduling algorithm. Analytical bounds on performance are provided and simulations indicate that the greedy heuristic performs well in practice.