Now showing 1 - 10 of 13
  • Publication
    PI : perceiver and interpreter of smart home datasets
    Pervasive healthcare systems facilitate various aspects of research including sensor technology, software technology, artificial intelligence and human-computer interaction. Researchers can often benefit from access to real-world data sets against which to evaluate new approaches and algorithms. Whilst more than a dozen data sets are currently publicly available, their use of heterogeneous mark-up impedes easy and widespread use. We describe PI – the Perceiver and semantic Interpreter – which offers a workbench API for the querying, re-structuring and re-purposing of a range of diverse data formats currently in use. The use of a single API reduces cognitive overload, improves access, and supports integration of generic and domain-specific information within a common framework.
      748
  • Publication
    Packet-Level Attestation (PLA):A framework for in-network sensor-data reliability
    (Association for Computing Machinery (ACM), 2013-03) ; ;
    Wireless sensor networks (WSN) show enormous potential for collection and analysis of physical data in real-time. Many papers have proposed methods for improving the network reliability of WSNs. However, real WSN deployments show that sensor data-faults are very common. Several server-side data reliability techniques have been proposed to detect these faults and impute missing or erroneous data. Typically, these techniques reduce the lifetime of the network due to redundant data transmission, increase latency, and are computation and storage intensive. Herein, we propose Packet-Level Attestation (PLA), a novel framework for sensor data reliability assessment. It exploits the spatial correlation of data sensed at nearby sensors. The method does not incur additional transmission of control message between source and sink; instead, a verifier node sends a validation certificate as part of the regular data packet. PLA was implemented in TinyOS on TelosB motes and its performances was assessed. Simulations were performed to determine its scalability. It incurs only an overhead of 1.45% in terms of packets transmitted. Fault detection precision of the framework varied from 100% to 99.48%. Comparisons with existing methods for data reliability analysis showed a significant reduction in data transmission, prolonging the network lifetime.
    Scopus© Citations 23  446
  • Publication
    From physical models to well-founded control
    Mobile sensors are an attractive proposition for environmental sensing, but pose significant engineering problems. Not least amongst these is the need to match the behaviour of the sensor platform to the physical environment in which it operates. We present initial work on using models of physical processes to generate models for autonomic control, and speculate that these can be used to improve the confidence we can place in sensed data.
    Scopus© Citations 2  980
  • Publication
    Failure Detection in Wireless Sensor Networks: A Sequence-based Dynamic Approach
    (Association for Computing Machinery, 2014-01-02) ; ;
    Wireless Sensor Network (WSN) technology has recently moved out of controlled laboratory settings to real-world deployments. Many of these deployments experience high rates of failure. Common types of failure include node failure, link failure, and node reboot. Due to the resource constraints of sensor nodes, existing techniques for fault detection in enterprise networks are not applicable. Previously proposed WSN fault detection algorithms either rely on periodic transmission of node status data or inferring node status based on passive information collection. The former approach significantly reduces network lifetime, while the latter achieves poor accuracy in dynamic or large networks. Herein, we propose Sequence-Based Fault Detection (SBFD), a novel framework for network fault detection in WSNs. The framework exploits in-network packet tagging using the Fletcher checksum and server-side network path analysis to efficiently deduce the path of all packets sent to the sink. The sink monitors the extracted packet paths to detect persistent path changes which are indicative of network failures. When a failure is suspected, the sink uses control messages to check the status of the affected nodes. SBFD was implemented in TinyOS on TelosB motes and its performance was assessed in a testbed network and in TOSSIM simulation. The method was found to achieve a fault detection accuracy of 90.7% to 95.0% for networks of 25 to 400 nodes at the cost of 0.164% to 0.239% additional control packets and a 0.5% reduction in node lifetime due to in-network packet tagging. Finally, a comparative study was conducted with existing solutions.
    Scopus© Citations 40  460
  • Publication
    Using situation lattices in sensor analysis
    Highly sensorised systems present two parallel challenges: how to design a sensor suite that can efficiently and cost-effectively support the needs of given services; and to extract the semantically relevant interpretations, or “situations”, from the flood of context data collected by the sensors. We describe mathematical structures called situation lattices that can be used to address these two problems simultaneously, allowing designers to both design and refine situation identification whilst offering insights into the design of sensor suites. We validate the accuracy and efficiency of our technique against a third-party data set and demonstrate how it can be used to evaluate sensor suite designs.
    Scopus© Citations 16  708
  • Publication
    Activity recognition using temporal evidence theory
    The ability to identify the behavior of people in a home is at the core of Smart Home functionality. Such environments are equipped with sensors that unobtrusively capture information about the occupants. Reasoning mechanisms transform the technical, frequently noisy data of sensors into meaningful interpretations of occupant activities. Time is a natural human way to reason about activities. People's activities in the home often have an identifiable routine; activities place at distinct times throughout the day and last for predicable lengths of time. However, the inclusion of temporal information is still limited in the domain of activity recognition. Evidence theory is gaining increasing interest in the field of activity recognition, and is suited to the incorporation of time related domain knowledge into the reasoning process. In this paper, an evidential reasoning framework that incorporates temporal knowledge is presented. We evaluate the effectiveness of the framework using a third party published smart home dataset. An improvement in activity recognition of 70% is achieved when time patterns and activity durations are included in activity recognition. We also compare our approach with Naïve Bayes classifier and J48 Decision Tree, with temporal evidence theory achieving higher accuracies than both classifiers.
    Scopus© Citations 76  1236
  • Publication
    Ontology-based query recommendation as a support to image retrieval
    Stock photo libraries are the most common means for publishers and advertisers to find images for their media. Searching for the perfect photo can be a time-consuming and frustrating task. This is because searching is often dependent on the descriptors or tags given to each photo by the editors and contributors to the library. The tagging process is subjective, further complicating the search process. We describe an algorithm that uses domain ontologies to improve the interactions with these libraries. Ontologies are used to expand query terms based on users' initial search queries.We present results that demonstrate that the use of ontologies greatly improves users ability to retrieve photos when undertaking a number of search tasks.
      478
  • Publication
    Robust High Accuracy Ultrasonic Range Measurement System
    This paper presents a novel method for ultrasonic range estimation. The method uses a wideband frequency-hop spread spectrum ultrasonic signal to increase robustness to noise and reverberation. The method applies cross-correlation with earliest peak search and a novel minimum variance search technique to correct the error in the cross-correlation time-of-flight estimate to within one wavelength of the carrier before applying a phase-shift technique for subwavelength range refinement. The method can be implemented digitally in software and only requires low-cost hardware for signal transmission and acquisition. Experimental results show an accuracy of better than 0.5 mm in a typical office environment.
    Scopus© Citations 75  2093
  • Publication
    Compression in wireless sensor networks: A survey and comparative evaluation
    (Association for Computing Machinery, 2013-11-01) ; ;
    Wireless sensor networks (WSNs) are highly resource constrained in terms of power supply, memory capacity, communication bandwidth, and processor performance. Compression of sampling, sensor data, and communications can significantly improve the efficiency of utilization of three of these resources, namely, power supply, memory and bandwidth. Recently, there have been a large number of proposals describing compression algorithms for WSNs. These proposals are diverse and involve different compression approaches. It is high time that these individual efforts are put into perspective and a more holistic view taken. In this article, we take a step in that direction by presenting a survey of the literature in the area of compression and compression frameworks in WSNs. A comparative study of the various approaches is also provided. In addition, open research issues, challenges and future research directions are highlighted.
    Scopus© Citations 151  949
  • Publication
    Adaptive management of shared resource pools with decentralized optimization and epidemics
    Shared resource pools are facilities featuring a certain amount of resources which can be used by different applications. For managing resources in such pools, the demand of each application can be used. Such a demand, however, is driven by the workload, which varies over time. For that reason, adaptive approaches have been proposed for the management of shared resources pools. Whereas a number of solutions exist in this context, they are either not truly decentralized or do not apply to the problem we are dealing with. In this paper, we then present Darma, an approach for managing shared resource pools in a truly decentralized, adaptive, and optimal way.
      360Scopus© Citations 5