| 1. |
- Niu, Yuechao, et al.
(författare)
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Design of a Digital Baseband Processor for UWB Transceiver on RFID Tag
- 2007
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Ingår i: 21st International Conference on Advanced Networking and Applications Workshops/Symposia, Vol 2, Proceedings. - : IEEE Computer Society. - 9780769528472 ; , s. 358-361
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Konferensbidrag (refereegranskat)abstract
- In this paper we present a novel digital baseband processor designed for UWB transceiver on RFID tag. It is a low power and low voltage (1.8V) full digital ASIC which is implemented in 0.18 mu m CMOS technology. The processor receives serial signals (consist of data and commands) from the RF Receiver, and based on received command carries out various functions such as receive data and write to the memory, compare data, send data, set/reset tag, kill tag and etc. The processor mainly consists of eight sub modules: Receive Buffer, Transmit Buffer, Random Number Generator (RNG), Slot Counter, Memory Controller, Reset Counter, Comparator, Controller.
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| 2. |
- Wang, Qinghua, et al.
(författare)
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Detecting anomaly node behavior in wireless sensor networks
- 2007
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Ingår i: Proceedings - 21st International Conference on Advanced Information Networking and Applications Workshops/Symposia, AINAW'07. - USA : IEEE conference proceedings. - 9780769528472 ; , s. 451-456
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Konferensbidrag (refereegranskat)abstract
- Wireless sensor networks are usually deployed in a way "once deployed, never changed". The actions of sensor nodes are either pre-scheduled inside chips or triggered to respond outside events in the predefined way. This relatively predictable working flow make it easy to build accurate node profiles and detect any violation of normal profiles. In this paper, traffic patterns observed are used to model node behavior in wireless sensor networks. Firstly, selected traffic related features are used to translate observed packets into different events. Following this, unique patterns based on the arriving order of different packet events are extracted to form the normal profile for each sensor node during the profile learning stage. Finally, real time anomaly detection can be achieved based on the profile matching.
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