Browsing by Subject "sensor networks"

Now showing 1 - 3 of 3
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    Cluster Base Network: A Neighborhood Watch Approach
    (Office of the Vice Chancellor for Research, 2013-04-05) ZareAfifi, Saharnaz; King, Brian
    Sensors can significantly impact one’s life, they can be used to measure various phenomena such as CO2, temperature, chemicals, water quality, etc. They can also be used in surveillance situations. The data collected by the sensors is processed into information from which decisions are made. Faulty information could cause severe problems. For example, a CO2 sensor with a low battery charge may trigger a false CO2 alarm, causing emergency personnel to respond, thus causing a temporary shortage of personnel able to respond to real emergencies. This situation is exacerbated in a sensor network where data collected is highly sensitive and decisions are important. Furthermore, sensor networks are ad-hoc in nature, with no central authority to analyze network behavior. A goal of our research is to construct energy efficient mechanisms that increase the integrity of the data collected within the sensor network in the presence of potential malicious behavior, sensors with weakened battery power, and/or faulty sensors. A mechanism that we have used in our research is a cluster-based approach. Here the ad-hoc network is partitioned into small clusters. Data collection, communications and processing can be observed by cluster members. The cluster members can police each other, assessing the trustworthiness of each member and collectively signing this assessment. Thus sensors can act as neighborhood watch in a large city, in the sense neighbors watch each other's house to protect each other and enhance the security of the neighborhood. In this research, we developed an energy efficient network protocol that constructs clusters without the use of a central authority. We have also constructed an energy efficient protocol for a cluster to assess members’ trustworthiness and mechanisms that allow the cluster to sign this assessment.
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    Ontological Problem-Solving Framework for Assigning Sensor Systems and Algorithms to High-Level Missions
    (MDPI, 2011-08-29) Qualls, Joseph; Russomanno, David J.; Electrical and Computer Engineering, School of Engineering and Technology
    The lack of knowledge models to represent sensor systems, algorithms, and missions makes opportunistically discovering a synthesis of systems and algorithms that can satisfy high-level mission specifications impractical. A novel ontological problem-solving framework has been designed that leverages knowledge models describing sensors, algorithms, and high-level missions to facilitate automated inference of assigning systems to subtasks that may satisfy a given mission specification. To demonstrate the efficacy of the ontological problem-solving architecture, a family of persistence surveillance sensor systems and algorithms has been instantiated in a prototype environment to demonstrate the assignment of systems to subtasks of high-level missions.
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    Ontological Problem-Solving Framework for Dynamically Configuring Sensor Systems and Algorithms
    (MDPI, 2011-03-15) Qualls, Joseph; Russomanno, David J.; Electrical and Computer Engineering, School of Engineering and Technology
    The deployment of ubiquitous sensor systems and algorithms has led to many challenges, such as matching sensor systems to compatible algorithms which are capable of satisfying a task. Compounding the challenges is the lack of the requisite knowledge models needed to discover sensors and algorithms and to subsequently integrate their capabilities to satisfy a specific task. A novel ontological problem-solving framework has been designed to match sensors to compatible algorithms to form synthesized systems, which are capable of satisfying a task and then assigning the synthesized systems to high-level missions. The approach designed for the ontological problem-solving framework has been instantiated in the context of a persistence surveillance prototype environment, which includes profiling sensor systems and algorithms to demonstrate proof-of-concept principles. Even though the problem-solving approach was instantiated with profiling sensor systems and algorithms, the ontological framework may be useful with other heterogeneous sensing-system environments.