Download Distributed Computing in Sensor Systems: 4th IEEE by Joon Ahn, Bhaskar Krishnamachari (auth.), Sotiris E. PDF

By Joon Ahn, Bhaskar Krishnamachari (auth.), Sotiris E. Nikoletseas, Bogdan S. Chlebus, David B. Johnson, Bhaskar Krishnamachari (eds.)

The booklet constitutes the refereed court cases of the 4th foreign convention on allotted Computing in Sensor structures, DCOSS 2008, hung on Santorini Island, Greece, in June 2008.

The 29 revised complete papers and 12 revised brief papers awarded have been rigorously reviewed and chosen from 116 submissions. The papers suggest a large number of novel algorithmic layout and research ideas, systematic techniques and alertness improvement methodologies for dispensed sensor networking. The papers hide points together with power administration, conversation, assurance and monitoring, time synchronization and scheduling, key institution and authentication, compression, medium entry keep watch over, code replace, and mobility.

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Extra info for Distributed Computing in Sensor Systems: 4th IEEE International Conference, DCOSS 2008 Santorini Island, Greece, June 11-14, 2008 Proceedings

Example text

Xn can be split into two groups, say X = (X1 , . . , Xk ) and X = (Xk+1 , . . , Xn ), so that X and X are independent, then κn (X1 , . . , Xn ) = 0. Property 4: If the Xi are different notations for the same random variable X, then κn (X, . . , X) = cn (X), the n-th factorial coefficient of the cumulant generating function of X. Stochastic Counting in Sensor Networks, or: Noise Is Good 5 37 Sensing Area Intersections and Cumulants Let us return to the original problem of estimating the number of targets detected in the sensing domains of a collection of sensors.

To appreciate the basic ideas behind our counting algorithm, we need only consider a system of two sensors. For this case, if A1 and A2 denote the two sensing areas, then the sensor-field partition of interest consists of the disjoint subsets A1 \ A2 , A2 \ A1 , and A1 A2 , which we denote by A1\2 , A2\1 , A12 . , N1\2 , N12 , . . and to the counts C1\2 , C12 , . .. To verify the fact that the cumulant for the two sensor counts C1 and C2 is simply their covariance, write κ2 (C1 , C2 ) = κ2 (C1\2 + C12 , C2 ) = κ2 (C12 , C2 ) = κ2 (C12 , C2\1 + C12 ) = κ2 (C12 , C12 ) = N12 c2 (Bernoulli(p)) = p(1 − p)N12 where Properties 2 and 3 have been applied in the first two lines and Property 4 in the last line.

Although we did see error in the tens of microseconds, the maximum did not exceed 50μs, and the average was below 10μs. This is significant, because it demonstrates that microsecond accuracy synchronization can be achieved between mote and PC networks, enabling the development of HSN applications that require precision time synchronization. In addition, because this technique uses UART communication, it can easily be adapted for synchronization with UART-supported peripheral sensing devices. 8 Conclusion Time synchronization is an important and necessary component in most wireless sensor network applications.

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