KEYWORDS: Sensors, Surveillance, Surveillance systems, Data storage, Control systems, Video surveillance, Computer security, Video, Intelligence systems, Data processing
Surveillance systems became powerful. Objects can be identified and intelligent surveillance services can generate
events when a specific situation occurs. Such surveillance services can be organized in a Service Oriented
Architecture (SOA) to fulfill surveillance tasks for specific purposes. Therefore the services process information
on a high level, e.g., just the position of an object. Video data is still required to visualize a situation to an
operator and is required as evidence in court. Processing of personal related and sensitive information threatens
privacy. To protect the user and to be compliant with legal requirements it must be ensured that sensitive
information can only be processed for a defined propose by specific users or services. This work proposes an
architecture for Access Control that enforces the separation of data between different surveillance tasks. Access
controls are enforced at different levels: for the users starting the tasks, for the services within the tasks processing
data stored in central store or calculated by other services and for sensor related services that extract information
out of the raw data and provide them.
KEYWORDS: Sensors, Smart sensors, Sensor networks, Data storage, Data modeling, Systems modeling, Data processing, Cameras, Surveillance systems, Astatine
Smart sensors can gather all kind of information and process it. Cameras are still dominating and smart cameras can offer
services for face recognition or person tracking. Operators are building collaborations to cover a larger area, to save costs
and to add more and different sensors. Cryptographic methods may achieve integrity and confidentiality between
operators, but not trust. Even if a partner or one of his sensors is authenticated, no statements can be made about the
quality of the sensor data. Hence, trust must be established between the partners and their sensors. Trust can be built
based on past experience. A reputation system collects opinions of operators about the behavior of sensors and calculates
trust based on these opinions. Many reputation systems have been proposed, e.g., for authentication of files in peer-topeer
networks. This work presents a new reputation system, which is designed to calculate the trustworthiness of smart
sensors and smart sensor systems. A new trust model, including functions to calculate and update trust on past
experiences, is proposed. When fusing information of multiple sensors, it cannot always be reconstructed, which
information led to a bad result. Hence, an approach for fair rating is shown. The proposed system has been realized in a
Service-Oriented Architecture for easy integration in existing smart sensor systems, e.g., smart surveillance systems. The
model itself can be used in every decentralized heterogeneous smart sensor network.
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