Embedded sensor network

The Sensor network is responsible for all the collected data that are fed in to the system. Presence at home, activity, sleep, and other activities should be extracted by using the data collected from this first layer. The implementation of this layer was based on Crossbow wireless sensors. These are small programmable wireless-communication motes that can be connected to a variety of sensors. The Crossbow motes are equipped with TinyOS, an open-source operating system designed for wireless embedded sensor networks. 

Keywords Mobile ad-hoc networks Distributed sensor networks Secure routing Vulnerability Embedded systems Low-power design... 

The transition from desktop computing to embedded systems is associated with price, power and timing constrains. A special class embedded systems, termed distributed sensor networks (DSN), are characterized by extra requirements small size and sufficient battery lifetime. Distributed sensor networks can be alternatively labeled mobile ad-hoc networks (MANET). While the term DSN is associated with data acquisition applications, MANET emphasizes mobility and the lack of infrastructure. Distributed sensor networks can be scalable to thousands of nodes that cooperatively perform complex tasks. The interaction between the nodes is based on wireless communication [Kah 00, War 01, Hil 02], Wireless sensor networks (WSN) is yet another synonym. 

Intrusion detection systems (IDS) are used as a computer network security tool and permit to alert an administrator in case of attack. The main goal of IDS is to detect and recognize network attacks in real time. Nowadays there exist different approaches for intrusion detection. It is signature analysis, rule-based method, embedded sensors, neural networks, artificial immune systems [1]—[6] and so on. The most of these IDS can detect the known attacks and have poor ability to detect new attacks. 

Goldfine NJ. Embedded EC MWM sensors network 2012. Available http // jenteksensors.com/embeddednetworks.php. 

Erickson V, Kamthe A.U., and Cerpa A.E. 2009. Measuring foot pronation using RFID sensor networks. Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems. Berkeley, California, ACM 325-326. 

Kansal A, Hsu J, Zahedi S, Srivastava MB. Power management in energy harvesting sensor networks. ACM transactions on embedded computing systems 2006. 

Buettner, M., Yee, G.V., Anderson, E., Han, R. X-MAC a Short Preamble MAC Protocol for Duty-Cycled Wireless Sensor Networks. In SenSys Proc. of the ACM Int. Conference on Embedded Networked Sensor Systems (2006)... 

Merrill, D., Kalanithi, J., Maes, P. Siftables towards sensor network user interfaces. In Proceedings of the 1st international Conference on Tangible and Embedded interaction. TEI 07, pp. 75-78. ACM Press, New York (2007)... 

Jian-She, J., Jing, J., Yong-Hui, W., Ke, Z., Jia-Jun, H. (2008). Developmentofremote-eontroUed home automation system with wireless sensor network. In Proceedings of the Fifth TREE International Symposium on Embedded Computing, (pp 169-173). IEEE Press. 

Andre L. C. Araujo was bom in Fortaleza, Brazil, in 1973. He is currently an Associate Professor with the Department of Telecommunications Engineering, Federal Institute for Education, Science, and Technology of Ceara (IFCE), Fortaleza, Brazil. He works toward the Ph.D. degree in Robust Control Theory with the Federal University of Ceara (UFC), Fortaleza, Brazil. His research interests include telemetry, wireless sensor networks, embedded systems, and data communications. 

At UCLA, Deborah Estrin is heading up the Centre for Embedded Networked Sensors or CENs, a 40 million, 10-year NSF-backed centre... 

This is apparent in the case of industrial systems [6]0, which are making increasing use of the capabilities offered by open networks for remotely accessing sensors and actuators for their operation and maintenance. Control and supervision systems are typically embedded in other critical infrastructures electric power, potable water, oil and gas, railways, etc. The potential advantages from the economic and functional viewpoints are unquestionable less often studied are the risks that these connections might... 

After introduction a prototype of intelligent multi-sensor system for driver status monitoring— DeCaDrive is presented in Section 2. The system expansion with embedded impedance spectroscopy sensor, its analog front-end and sensor data preprocessing are addressed in Section 3. Multi-sensor feature computation and data fusion as well as neural network based pattern classification are discussed in Section 4. The extended system is validated and evaluated by presenting the experimental results in Section 5. Finally, with future perspectives the current work is concluded in Section 6. 

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