Applying named data networking in mobile ad hoc networks

This thesis presents the Name-based Mobile Ad-hoc Network (nMANET) approach to content distribution that ensure and enables responsible research on applying named data networking protocol in mobile ad-hoc networks. The test framework of the nMANET approach allows reproducibility of experiments and validation of expected results based on analysis of experimental data. The area of application for nMANETs is the distribution of humanitarian information in emergency scenarios. Named-Data Networking (NDN) and ad-hoc mobile communication allow exchange of emergency information in situations where central services such as cellular towers and electric systems are disrupted. The implemented prototype enables researchers to reproduce experiments on content distribution that consider constraints on mobile resources, such as the remaining power of mobile devices and available network bandwidth. The nMANET framework validates a set of experiments by measuring network traffic and energy consumption from both real mobile devices and those in a simulated environment. Additionally, this thesis presents results from experiments in which the nMANET forwarding strategies and traditional wireless services, such as hotpost, are analysed and compared. This experimental data represents the evidence that supports and validates the methodology presented in this thesis. The design and implementation of an nMANET prototype, the Java NDN Forwarder Daemon (JNFD) is presented as a testing framework, which follows the principles of continuous integration, continuous testing and continuous deployment. This testing framework is used to validate JNFD and IP-based technologies, such as HTTP in a MANET using the OLSR routing protocol, as well as traditional wireless infrastructure mode wireless. The set of experiments executed, in a small network of Android smart-phones connected in ad-hoc mode and in a virtual ad-hoc network simulator show the advantages of reproducibility using nMANET features. JNFD is open source, all experiments are scripted, they are repeatable and scalable. Additionally, JNFD utilises real GPS traces to simulate mobility of nodes during experiments. This thesis provides experimental evidence to show that nMANET allows reproducibility and validation of a wide range of future experiments applying NDN on MANETs

An informatics based approach to respiratory healthcare.

By 2005 one person in every five UK households suffered with asthma. Research has shown that episodes of poor air quality can have a negative effect on respiratory health and is a growing concern for the asthmatic. To better inform clinical staff and patients to the contribution of poor air quality on patient health, this thesis defines an IT architecture that can be used by systems to identify environmental predictors leading to a decline in respiratory health of an individual patient. Personal environmental predictors of asthma exacerbation are identified by validating the delay between environmental predictors and decline in respiratory health. The concept is demonstrated using prototype software, and indicates that the analytical methods provide a mechanism to produce an early warning of impending asthma exacerbation due to poor air quality. The author has introduced the term enviromedics to describe this new field of research. Pattern recognition techniques are used to analyse patient-specific environments, and extract meaningful health predictors from the large quantities of data involved (often in the region of '/o million data points). This research proposes a suitable architecture that defines processes and techniques that enable the validation of patient-specific environmental predictors of respiratory decline. The design of the architecture was validated by implementing prototype applications that demonstrate, through hospital admissions data and personal lung function monitoring, that air quality can be used as a predictor of patient-specific health. The refined techniques developed during the research (such as Feature Detection Analysis) were also validated by the application prototypes. This thesis makes several contributions to knowledge, including: the process architecture; Feature Detection Analysis (FDA) that automates the detection of trend reversals within time series data; validation of the delay characteristic using a Self-organising Map (SOM) that is used as an unsupervised method of pattern recognition; Frequency, Boundary and Cluster Analysis (FBCA), an additional technique developed by this research to refine the SOM

An informatics based approach to respiratory healthcare

By 2005 one person in every five UK households suffered with asthma. Research has shown that episodes of poor air quality can have a negative effect on respiratory health and is a growing concern for the asthmatic. To better inform clinical staff and patients to the contribution of poor air quality on patient health, this thesis defines an IT architecture that can be used by systems to identify environmental predictors leading to a decline in respiratory health of an individual patient. Personal environmental predictors of asthma exacerbation are identified by validating the delay between environmental predictors and decline in respiratory health. The concept is demonstrated using prototype software, and indicates that the analytical methods provide a mechanism to produce an early warning of impending asthma exacerbation due to poor air quality. The author has introduced the term enviromedics to describe this new field of research. Pattern recognition techniques are used to analyse patient-specific environments, and extract meaningful health predictors from the large quantities of data involved (often in the region of '/o million data points). This research proposes a suitable architecture that defines processes and techniques that enable the validation of patient-specific environmental predictors of respiratory decline. The design of the architecture was validated by implementing prototype applications that demonstrate, through hospital admissions data and personal lung function monitoring, that air quality can be used as a predictor of patient-specific health. The refined techniques developed during the research (such as Feature Detection Analysis) were also validated by the application prototypes. This thesis makes several contributions to knowledge, including: the process architecture; Feature Detection Analysis (FDA) that automates the detection of trend reversals within time series data; validation of the delay characteristic using a Self-organising Map (SOM) that is used as an unsupervised method of pattern recognition; Frequency, Boundary and Cluster Analysis (FBCA), an additional technique developed by this research to refine the SOM.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Correctness-aware high-level functional matching approaches for semantic web services

Existing service matching approaches trade precision for recall, creating the need for humans to choose the correct services, which is a major obstacle for automating the service matching and the service aggregation processes. To overcome this problem, the matchmaker must automatically determine the correctness of the matching results according to the defined users' goals. That is, only service(s)-achieving users' goals are considered correct. This requires the high-level functional semantics of services, users, and application domains to be captured in a machine-understandable format. Also this requires the matchmaker to determine the achievement of users' goals without invoking the services. We propose the G+ model to capture the high-level functional specifications of services and users (namely goals, achievement contexts and external behaviors) providing the basis for automated goal achievement determination; also we propose the concepts substitutability graph to capture the application domains' semantics. To avoid the false negatives resulting from adopting existing constraint and behavior matching approaches during service matching, we also propose new constraint and behavior matching approaches to match constraints with different scopes, and behavior models with different number of state transitions. Finally, we propose two correctness-aware matching approaches (direct and aggregate) that semantically match and aggregate semantic web services according to their G+ models, providing the required theoretical proofs and the corresponding verifying simulation experiments

Algorithms for Energy Efficiency in Wireless Sensor Networks

The recent advances in microsensor and semiconductor technology have opened a new field within computer science: the networking of small-sized sensors which are capable of sensing, processing, and communicating. Such wireless sensor networks offer new applications in the areas of habitat and environment monitoring, disaster control and operation, military and intelligence control, object tracking, video surveillance, traffic control, as well as in health care and home automation. It is likely that the deployed sensors will be battery-powered, which will limit the energy capacity significantly. Thus, energy efficiency becomes one of the main challenges that need to be taken into account, and the design of energy-efficient algorithms is a major contribution of this thesis. As the wireless communication in the network is one of the main energy consumers, we first consider in detail the characteristics of wireless communication. By using the embedded sensor board (ESB) platform recently developed by the Free University of Berlin, we analyze the means of forward error correction and propose an appropriate resync mechanism, which improves the communication between two ESB nodes substantially. Afterwards, we focus on the forwarding of data packets through the network. We present the algorithms energy-efficient forwarding (EEF), lifetime-efficient forwarding (LEF), and energy-efficient aggregation forwarding (EEAF). While EEF is designed to maximize the number of data bytes delivered per energy unit, LEF additionally takes into account the residual energy of forwarding nodes. In so doing, LEF further prolongs the lifetime of the network. Energy savings due to data aggregation and in-network processing are exploited by EEAF. Besides single-link forwarding, in which data packets are sent to only one forwarding node, we also study the impact of multi-link forwarding, which exploits the broadcast characteristics of the wireless medium by sending packets to several (potential) forwarding nodes. By actively selecting a forwarder among all nodes that received a packet successfully, retransmissions can often be avoided. In the majority of cases, multi-link forwarding is thus more efficient and able to save energy. In the last part of this thesis, we present a topology and energy control algorithm (TECA) to turn off the nodes' radio transceivers completely in order to avoid idle listening. By means of TECA, a connected backbone of active nodes is established, while all other nodes may sleep and save energy by turning off their radios. All algorithms presented in this thesis have been fully analyzed, simulated, and implemented on the ESB platform. They are suitable for several applications scenarios and can easily be adapted even to other wireless sensor platforms

Open Infrared : enhancing environmental monitoring through accessible remote sensing, in Indonesia and beyond

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013.Cataloged from PDF version of thesis.Includes bibliographical references.As the human landscape changes ever more rapidly, environmental change accelerates. Much environmental information is publicly available as infrared satellite data. However, for the general user, this information is difficult to obtain, and even more difficult to interpret. With this in mind, my team and I launched OpenIR (Open Infrared), an ICT (Information Communication Technology) that provides geo-located IR (infrared) satellite data as ondemand map layers, automates environmental feature classification, experiments with flood risk mapping, and interfaces IR data with crowd- and citizen-maps. OpenIR's initial use case is emergency management and environmental monitoring in the economically developing and ecologically vulnerable archipelago of Indonesia, where we conduced initial usability tests in January 2013.by Arlene Ducao.S.M

Federal Register

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