An efficient data extraction framework for mining wireless sensor networks

Behavioral patterns for sensors have received a great deal of attention recently due to their usefulness in capturing the temporal relations between sensors in wireless sensor networks. To discover these patterns, we need to collect the behavioral data that represents the sensor's activities over time from the sensor database that attached with a well-equipped central node called sink for further analysis. However, given the limited resources of sensor nodes, an effective data collection method is required for collecting the behavioral data efficiently. In this paper, we introduce a new framework for behavioral patterns called associated-correlated sensor patterns and also propose a MapReduce based new paradigm for extract data from the wireless sensor network by distributed away. Extensive performance study shows that the proposed method is capable to reduce the data size almost 50% compared to the centralized model

Wireless sensor data processing for on-site emergency response

This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders’ requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9)

Analysis of the energy latency trade-off in wireless sensor networks

Wireless Sensor Networks (WSNs) haben im letzten Jahrzehnt eine erhebliche Aufmerksamkeit erlangt. Diese Netzwerke zeichnen sich durch begrenzte Energieressourcen der Sensorknoten aus. Daher ist Energieeffizienz ein wichtiges Thema in Systemdesign und -betrieb von WSNs. Diese Arbeit konzentriert sich auf großflächige Anwendungen von WSNs wie Umwelt- oder Lebensraumüberwachung, die in der Regel den Ad-hoc-Einsatz von Knoten in großen Anzahl erfordern. Ad-hoc-Einsatz und Budgetbeschränkungen hindern Entwickler an der Programmierung der Knoten mit zusätzlichen Informationen wie beispielsweise Routingtabellen, Positionskoordinaten, oder Netzwerkgrenzen. Um diese Informationen zu beschaffen, ist es üblich verschiedene Initialisierungsschemen mit erheblichen Auswirkungen auf den Energieverbrauch und den Programmieraufwand zu implementieren. In Anbetracht dieser Beschränkungen ist ein neues Paradigma für die Initialisierung und den Betrieb von WSNs notwendig, das sich durch einfachen Einsatz und minimalen Energieaufwand auszeichnet. In dieser Arbeit nutzen wir Sink-Mobilität, um den Initialisierungsoverhead und den operativen Overhead zu reduzieren. Unser erster großer Beitrag ist ein Boundary Identification Schema für WSNs mit dem Namen "Mobile Sink based Boundary Detection" (MoSBoD). Es nutzt die Sink-Mobilität um den Kommunikationsoverhead der Sensorknoten zu reduzieren, was zu einer Erhöhung der Laufzeit des WSN führt. Außerdem entstehen durch das Schema keine Einschränkungen in Bezug auf Nodeplacement, Kommunikationsmodell, oder Ortsinformationen der Knoten. Der zweite große Beitrag ist das Congestion avoidance low Latency and Energy efficient (CaLEe) Routingprotokoll für WSNs. CaLEe basiert auf der virtuellen Partitionierung eines Sensorsbereich in Sektoren und der diskreten Mobilität der Sink im WSN. Unsere Simulationsergebnisse zeigen, dass CaLEe, im Vergleich zum derzeitigen State-of-the-art, nicht nur eine erhebliche Reduzierung der durchschnittlichen Energy Dissipation per Node erzielt, sondern auch eine geringere durchschnittliche End-to-End Data Latency in realistischen Szenarien erreicht. Darüber hinaus haben wir festgestellt, dass kein einziges Protokoll in der Lage ist, eine Best-Case-Lösung (minimale Data Latency und minimale Energy Dissipation) für variierende Netzwerkkonfigurationen, die beispielsweise mithilfe der Parameter Kommunikationsbereich der Nodes, Nodedichte, Durchsatz des Sensorfelds definiert werden können, bieten. Daher ist der dritte Hauptbeitrag dieser Arbeit die Identifikation von (auf unterschiedlichen Netzwerkkonfigurationen basierenden) „Operational Regions“, in denen einzelne Protokolle besser arbeiten als andere. Zusammenfassend kann man sagen, dass diese Dissertation das klassische Energieeffizienzproblem der WSNs (Ressource-begrenzte Knoten) aufgreift und gleichzeitig die End-to-End Data Latency auf einen annehmbaren Rahmen eingrenzt.Wireless Sensor Networks (WSN) have gained a considerable attention over the last decade. These networks are characterized by limited amount of energy supply at sensor node. Hence, energy efficiency is an important issue in system design and operation of WSN. This thesis focuses on large-scale applications of WSN, such as environment or habitat monitoring that usually requires ad-hoc deployment of the nodes in large numbers. Ad-hoc deployment and budget constraints restrict developers from programming the nodes with information like routing tables, position coordinates of the node, boundary of the network. In order to acquire this information, state-of-the-art is to program nodes with various initialization schemes that are heavy both from WSN’s (energy consumption) and programmer’s perspectives (programming effort). In view of these particular constraints, we require a new paradigm for WSN initialization and operation, which should be easy to deploy and have minimal energy demands. In this thesis, we exploit sink mobility to reduce the WSN initialization and operational overhead. Our first major contribution is a boundary identification scheme for WSN, named “Mobile Sink based Boundary detection” (MoSBoD). It exploits the sink mobility to remove the communication overhead from the sensor nodes, which leads to an increase in the lifetime of the WSN. Furthermore, it does not impose any restrictions on node placement, communication model, or location information of the nodes. The second major contribution is Congestion avoidance low Latency and Energy efficient (CaLEe) routing protocol for WSN. CaLEe is based on virtual partitioning of a sensor field into sectors and discrete mobility of the sink in the WSN. Our simulation results showed that CaLEe not only achieve considerable reduction in average energy dissipation per node compared to current state-of-the-art routing protocols but also accomplish lesser average end-to-end data latency under realistic scenarios. Furthermore, we observe that no single protocol is capable of providing best-case solution (minium data latency and minimum energy dissipation) under varying network configurations, which can be defined using communication range of the nodes, node density, throughput of the sensor field etc. Therefore, the third major contribution of this thesis is the identification of operational regions (based on varying network configurations) where one protocol performs better than the other. In summary, this thesis revisits the classic energy efficiency problem of a WSN (that have resource-limited nodes) while keeping end-to-end data latency under acceptable bounds

Wireless sensor data processing for on-site emergency response

This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders' requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9).EThOS - Electronic Theses Online ServiceGBUnited Kingdo