Efficient Information Access in Data-Intensive Sensor Networks

Recent advances in wireless communications and microelectronics have enabled wide deployment of smart sensor networks. Such networks naturally apply to a broad range of applications that involve system monitoring and information tracking (e.g., fine-grained weather/environmental monitoring, structural health monitoring, urban-scale traffic or parking monitoring, gunshot detection, monitoring volcanic eruptions, measuring rate of melting glaciers, forest fire detection, emergency medical care, disaster response, airport security infrastructure, monitoring of children in metropolitan areas, product transition in warehouse networks etc.).Meanwhile, existing wireless sensor networks (WSNs) perform poorly when the applications have high bandwidth needs for data transmission and stringent delay constraints against the network communication. Such requirements are common for Data Intensive Sensor Networks (DISNs) implementing Mission-Critical Monitoring applications (MCM applications).We propose to enhance existing wireless network standards with flexible query optimization strategies that take into account network constraints and application-specific data delivery patterns in order to meet high performance requirements of MCM applications.In this respect, this dissertation has two major contributions: First, we have developed an algebraic framework called Data Transmission Algebra (DTA) for collision-aware concurrent data transmissions. Here, we have merged the serialization concept from the databases with the knowledge of wireless network characteristics. We have developed an optimizer that uses the DTA framework, and generates an optimal data transmission schedule with respect to latency, throughput, and energy usage. We have extended the DTA framework to handle location-based trust and sensor mobility. We improved DTA scalability with Whirlpool data delivery mechanism, which takes advantage of partitioning of the network. Second, we propose relaxed optimization strategy and develop an adaptive approach to deliver data in data-intensive wireless sensor networks. In particular, we have shown that local actions at nodes help network to adapt in worse network conditions and perform better. We show that local decisions at the nodes can converge towards desirable global network properties e.g.,high packet success ratio for the network. We have also developed a network monitoring tool to assess the state and dynamic convergence of the WSN, and force it towards better performance

Real Time Big Data Analytics Dependence on Network Monitoring Solutions using Tensor Networks and its Decomposition

Organizations dealing with huge volumes of data must have a big data infrastructure in place that can accommodate the load of storing, analysing and transporting the data. Suboptimal network performance represents a potential point of failure. Therefore, it is essential to implement redundancy and/or a fail over strategy in order to minimize downtime. With network monitoring, we come to know the status of everything on the network without having to watch it personally and be able to take the timely action to correct problems. But to the extent that companies increase their reliance on real-time streams of marketing and performance big data, the network will become a central part of big data application performance. This is why incorporating network monitoring should be on the company's big data road map if we anticipate using live streaming and analytics of big data in business applications. Keywords: Big Data analytics, suboptimal network performance, network monitoring, live streaming, WAN Management, Network Application Performance Management, Tensor Network

VII data use analysis and processing (DUAP): final project report (phase II)

This report covers several key subjects related to the generation of IntelliDriveSM probe vehicle data and use of this data in application of interest to state departments of transportation and local public transportation agencies. The evaluations conducted as part of this project are primarily based on the probe vehicle data collection system that was deployed by the U.S. Department of Transportation (USDOT) around Novi, Michigan, in 2008 for its Vehicle‐Infrastructure Integration (VII) Proof‐of‐Concept (POC) test program. This system was designed around the use of the 5.9‐GHz Dedicated Short Range Communication (DSRC) wireless protocol to enable vehicles to communicate with Roadside Equipment (RSE). The generation of snapshots further followed the protocols defined within the SAE J2735 DSRC Message Set standard. Following a general introduction in Chapter 1, Chapter 2 briefly reviews the protocols that were used to generate and retrieve probe vehicle snapshots, while Chapter 3 presents a general evaluation of the POC test data that were accumulated during the 2008 test program. This is followed by a presentation in Chapter 4 of the evaluation framework of the current project. This presentation includes an overview of the envisioned DUAP system and descriptions of project stakeholders, potential data sources, supporting technologies, applications of interests, and potential operational constraints. Chapter 5 then presents a general description of the Paramics IntelliDriveSM virtual simulator that is used to conduct some of the subsequent evaluations. While the initial POC test program aimed to evaluate data collection capabilities across a range of application, this program was significantly shortened due to various technical issues. This resulted in incomplete data collection and partial application designs that were insufficient to complete the initial project deliverables associated without rely on simulation. Chapter 6 then examines the effects of snapshot generation protocols and privacy policies on data latency, data quality, and the ability to track vehicles over short distances. Chapter 7 follows with a mapping of application data needs and general descriptions of processes required to convert raw probe data into useful information, while Chapter 8 evaluates how basic traffic flow performance measures (flow rates, flow density, travel times, speed profiles, queue parameters) can be estimated from probe data in systems featuring full and partial proportions of probe vehicles. Chapter 9 further develops a concept of operations for an enhanced traffic monitoring system incorporating probe vehicle and other data sources, while Chapter 10 investigates various issues that must be considered when developing application deployment plans. Chapters 11, 12 and 13 finally present a summary of primary findings, lessons learned and recommendations for future work.Michigan Department of Transportation, Lansing, MIhttp://deepblue.lib.umich.edu/bitstream/2027.42/78569/1/102726.pd

Improving the Routing Layer of Ad Hoc Networks Through Prediction Techniques

Cada dia és més evident el paper clau que juguen la informàtica/computació mòbil i les tecnologies sense fils a les nostres activitats diàries. Estar sempre connectat, en qualsevol moment i lloc, és actualment més una necessitat que un luxe. Els escenaris de computació ubics creats en base a aquests avenços tecnològics, permeten a les persones proporcionar i consumir informació compartida. En aquests escenaris, les xarxes que donen suport a aquestes comunicacions són típicament sense fils i ad hoc. Les característiques dinàmiques i canviants de les xarxes ad hoc, fan que el treball realitzat per la capa d'enrutament tingui un gran impacte en el rendiment d'aquestes xarxes. És molt important que la capa d'enrutament reaccioni ràpidament als canvis que es produeixen, i fins i tot s'avanci als que es produiran en un futur proper, mitjançant l'aplicació de tècniques de predicció. Aquesta tesi investiga si les tècniques de predicció poden millorar la capa d'enrutament de les xarxes ad hoc. Com a primer pas en aquesta direcció, explorem la potencialitat d'una estratègia de Predictor-Basat-en-Història (HBP) per predir la Informació de Control Topològic (TCI) generada pels protocols d'enrutament. Demostrem que hi ha una gran oportunitat per predir TCI, i aquesta predicció pot centrar-se en un petit subconjunt de missatges. En base a les nostres troballes, implementem el predictor OLSR-HBP i l'avaluem respecte al protocol Optimized Link State Routing (OLSR). OLSR-HBP aconsegueix disminucions importants de TCI (sobrecàrrega de senyalització), sense afectar el funcionament de la xarxa i necessita una quantitat de recursos petita i assequible. Finalment, en referència a l'impacte de la predicció en les dades d'enrutament tant de la informació de Qualitat d'Enllaç como de Ruta (o Extrem-a-Extrem), demostrem que l'Anàlisi de Sèries Temporals és un enfocament prometedor per predir amb precisió, tant la Qualitat d'Enllaç como la Qualitat d'Extrem a Extrem en Xarxes Comunitàries.Cada día es más evidente el papel clave que juegan la informática/computación móvil y las tecnologías inalámbricas en nuestras actividades diarias. Estar siempre conectado, en cualquier momento y lugar, es actualmente más una necesidad que un lujo. Los escenarios de computación ubicuos creados en base a estos avances tecnológicos, permiten a las personas proporcionar y consumir información compartida. En estos escenarios, las redes que dan soporte a estas comunicaciones son típicamente inalámbricas y ad hoc. Las características dinámicas y cambiantes de las redes ad hoc, hacen que el trabajo realizado por la capa de enrutamiento tenga un gran impacto en el rendimiento de estas redes. Es muy importante que la capa de enrutamiento reaccione rápidamente a los cambios que se producen, e incluso se adelante a los que sucederán en un futuro cercano, mediante la aplicación de técnicas de predicción. Esta tesis investiga si las técnicas de predicción pueden mejorar la capa de enrutamiento de las redes ad hoc. Como primer paso en esta dirección, exploramos la potencialidad de una estrategia de Predictor-Basado-en-Historia (HBP) para predecir la Información de Control Topológico (TCI) generada por los protocolos de enrutamiento. Demostramos que hay una gran oportunidad para predecir TCI, y esta predicción puede centrarse en un pequeño subconjunto de mensajes. En base a nuestros hallazgos, implementamos el predictor OLSR-HBP y lo evaluamos con respecto al protocolo Optimized Link State Routing (OLSR). OLSR-HBP consigue disminuciones importantes de TCI (sobrecarga de señalización), sin afectar al funcionamiento de la red, y necesita una cantidad de recursos pequeña y asequible. Finalmente, en referencia al impacto de la predicción en los datos de enrutamiento tanto de la información de Calidad de Enlace como de Ruta (o Extremo-a-Extremo), demostramos que el Análisis de Series Temporales es un enfoque prometedor para predecir con precisión, tanto la Calidad de Enlace como la Calidad de Extremo a Extremo en Redes Comunitarias.Everyday becomes more evident the key role that mobile computing and wireless technologies play in our daily activities. Being always connected, anytime, and anywhere is today more a necessity than a luxury. The ubiquitous computing scenarios created based on these technology advances allow people to provide and consume shared information. In these scenarios, the supporting communication networks are typically wireless and ad hoc. The dynamic and changing characteristics of the ad hoc networks, makes the work done by the routing layer to have a high impact on the performance of these networks. It is very important for the routing layer to quickly react to changes that happen, and even be advanced to what will happen in the near future, by applying prediction techniques. This thesis investigates whether prediction techniques can improve the routing layer of ad hoc networks. As a first step in this direction, in this thesis we explored the potentiality of a History-Based Predictor (HBP) strategy to predict the Topology Control Information (TCI) generated by routing protocols. We demonstrated that there is a high opportunity for predicting theTCI, and this prediction can be just focused on a small subset of messages. Based on our findings we implemented the OLSR-HBP predictor and evaluated it with regard to the Optimized Link State Routing (OLSR) protocol. OLSR History-Based Predictor (OLSR-HBP) achieved important decreases of TCI (signaling overhead), without disturbing the network operation, and requiring a small and affordable amount of resources. Finally, regarding the impact of Prediction on the routing data for both Link and Path (or End-to-End) Quality information, we demonstrated that Time-series analysis is a promising approach to accurately predict both Link and End-to-End Quality in Community Networks

Information dissemination in mobile networks

This thesis proposes some solutions to relieve, using Wi-Fi wireless networks, the data consumption of cellular networks using cooperation between nodes, studies how to make a good deployment of access points to optimize the dissemination of contents, analyzes some mechanisms to reduce the nodes' power consumption during data dissemination in opportunistic networks, as well as explores some of the risks that arise in these networks. Among the applications that are being discussed for data off-loading from cellular networks, we can find Information Dissemination in Mobile Networks. In particular, for this thesis, the Mobile Networks will consist of Vehicular Ad-hoc Networks and Pedestrian Ad-Hoc Networks. In both scenarios we will find applications with the purpose of vehicle-to-vehicle or pedestrian-to-pedestrian Information dissemination, as well as vehicle-to-infrastructure or pedestrian-to-infrastructure Information dissemination. We will see how both scenarios (vehicular and pedestrian) share many characteristics, while on the other hand some differences make them unique, and therefore requiring of specific solutions. For example, large car batteries relegate power saving techniques to a second place, while power-saving techniques and its effects to network performance is a really relevant issue in Pedestrian networks. While Cellular Networks offer geographically full-coverage, in opportunistic Wi-Fi wireless solutions the short-range non-fullcoverage paradigm as well as the high mobility of the nodes requires different network abstractions like opportunistic networking, Disruptive/Delay Tolerant Networks (DTN) and Network Coding to analyze them. And as a particular application of Dissemination in Mobile Networks, we will study the malware spread in Mobile Networks. Even though it relies on similar spreading mechanisms, we will see how it entails a different perspective on Dissemination