By A. A. Bertossi, S. Olariu, M. C. Pinotti (auth.), Sotiris E. Nikoletseas, José D. P. Rolim (eds.)
This booklet constitutes the reviewed court cases of the second one overseas Workshop on Algorithmic features of instant Sensor Networks, ALGOSENSORS 2006, held in Venice, Italy in July 2006, in organization with ICALP 2006.
The 15 revised complete papers and 5 revised brief papers offered have been rigorously reviewed and chosen from sixty eight submissions; they're absolutely revised to include reviewers' reviews and discussions on the workshop.
Topics addressed are foundational and algorithmic features of the instant sensor networks examine. particularly, ALGOSENSORS makes a speciality of summary types, complexity-theoretic effects and lower-bounds, in addition to the layout and research of algorithms for instant sensor networks.
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Extra resources for Algorithmic Aspects of Wireless Sensor Networks: Second International Workshop, ALGOSENSORS 2006, Venice, Italy, July 15, 2006, Revised Selected Papers
Pseudo-code of our algorithm executed by a single station, parameter version indicates the ﬁrst or the second version of choosing backoﬀ values (y) By B(x, y) we denote the Euler beta function deﬁned by B(x, y) = ΓΓ(x)Γ (x+y) . We will use the fact that the function B(n + 1, z) is analytic everywhere except for z = 0, −1, −2, . , its residue at z = −k equals Resz=−k B(n + 1, z) = nk (−1)k . By Hn = nk=1 k1 we denote the n-th harmonic number. 5772 is the Euler-Mascheroni constant. 1 39 The Number of Final Winners First we calculate the probability distribution of the number of winners after k tosses of a biased coin.
Zollinger, A robust interference model for wireless ad-hoc networks, 5th International Workshop on Algorithms for Wireless, Mobile, Ad-hoc and Sensor Networks (WMAN’05), Denver, Colorado, USA, April 2005. A Context Interpretation Based Wireless Sensor Network for the Emergency Preparedness Class of Applications* Azzedine Boukerche1, Regina B. S. br Abstract. Emergency Preparedness is one of the most appealing classes of applications for context-aware wireless sensor networks (WSN). In such environments, contexts can be captured and interpreted in the WSN application layer to help preventing, fighting, rescuing and checking against fire, explosions, leaking of toxic gases etc.
Lemma 2. Let Y (n) be a random variable denoting the number of ﬁnal winners, when we start with n stations. Let Yn (z) = Ez Y (n) . Then E[Y (1)] = 1, 2 E[Y (2)] = 1+p and if n > 2, then E[Y (n)] = (1 − p) n(1 − p) +2 p log(1/p) p log(1/p) ∞ B n, 1 + k=1 2kπi log(p) . (1) n t Proof. We split the function Yn (z) = t=1 Pr[Y (n) = t] · z into a sum of k a sequence of functions Yn (z), namely we put ∞ Ynk (z) , Yn (z) = (2) k=1 where Ynk (z) = n Pr[X 1 (n) > 0, . . , X k−2 (n) > 0, X k−1 (n) = t, X k (n) = 0] · z t .
Algorithmic Aspects of Wireless Sensor Networks: Second International Workshop, ALGOSENSORS 2006, Venice, Italy, July 15, 2006, Revised Selected Papers by A. A. Bertossi, S. Olariu, M. C. Pinotti (auth.), Sotiris E. Nikoletseas, José D. P. Rolim (eds.)