Secure Integration of Wireless Sensor Networks into Applications

Christophe Trefois
2008
Student project

Wireless sensors are small devices that are able to gather, process and deliver information from a physical environment to an external system. By doing so, they open new applications in different domains, such as healthcare, traffc control, defense and agriculture. The integration of Wireless Sensor Networks (WSN) with Business Applications (BA) raises technical and security related challenges. Existing approaches target technical issues such as interoperability between WSN and BAs or heterogeneity of acquired sensor data. In this work, we start by performing an analysis of the risks that such an integration of WSNs with BAs may present using the NIST SP 800-30 recommendations. We then introduce and analyze an effcient security scheme that does not use complex operations and guarantees end-to-end confidentiality of sensor data. Finally, we provide an in silico proof-of-concept and validate it using a real WSN co-developed with Cisco Systems France.

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Christophe Trefois
2008
Student project

Abstract

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https://infoscience.epfl.ch/record/161912?ln=en

About this result

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Mechanical intelligence for learning embodied sensor-object relationships

Ahalya Prabhakar

Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsupervised learning problem. Current machine learning methods process new sensory data using prior knowledge defined by either domain knowledge or datasets. When datasets are not available, data acquisition is needed, though automating exploration in support of learning is still an unsolved problem. Here we develop a method that enables agents to efficiently collect data for learning a predictive sensor model-without requiring domain knowledge, human input, or previously existing data-using ergodicity to specify the data acquisition process. This approach is based entirely on data-driven sensor characteristics rather than predefined knowledge of the sensor model and its physical characteristics. We learn higher quality models with lower energy expenditure during exploration for data acquisition compared to competing approaches, including both random sampling and information maximization. In addition to applications in autonomy, our approach provides a potential model of how animals use their motor control to develop high quality models of their sensors (sight, sound, touch) before having knowledge of their sensor capabilities or their surrounding environment. Information-based search strategies are relevant for the learning of interacting agents dynamics and usually need predefined data. The authors propose a method to collect data for learning a predictive sensor model, without requiring domain knowledge, human input, or previously existing data.

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