By Sotiris Nikoletseas, José D.P. Rolim
Wireless advert hoc sensor networks has lately develop into a truly energetic study topic. attaining effective, fault-tolerant realizations of very huge, hugely dynamic, complicated, unconventional networks is a true problem for summary modelling, algorithmic layout and research, yet an outstanding foundational and theoretical heritage appears to be like missing. This e-book offers fine quality contributions through prime specialists around the globe at the key algorithmic and complexity-theoretic elements of instant sensor networks. The meant viewers contains researchers and graduate scholars engaged on sensor networks, and the wider parts of instant networking and dispensed computing, in addition to practitioners within the suitable software parts. The booklet may also function a textual content for complex classes and seminars.
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Extra info for Theoretical Aspects of Distributed Computing in Sensor Networks
J. Heo, D. Henriksson, X. Liu, and T. Abdelzaher. Integrating adaptive components: An emerging challenge in performance-adaptive systems and a server farm case-study. In Real-Time Systems Symposium, Tuscon, AZ, December 2007. 15. -H. Huang, S. Amjad, and S. Mishra. Cenwits: a sensor-based loosely coupled search and rescue system using witnesses. In Proceedings of SenSys, pages 180–191, San Diego, CA, 2005. 1 Composition and Scaling Challenges in Sensor Networks 27 16. Z. Huang, W. Du, and B. Chen.
In this chapter we focus on distance-based power assignments because of their simplicity and locality, which is a striking conceptual advantage in distributed wireless systems. An oblivious (or distance-based) power assignment p is given by pi = φ(d(u i , vi )) with a function φ : [1, Δ] → (0, ∞). For uniqueness we assume that φ is always scaled such that φ(1) = 1. Examples are the uniform φ(d(u i , vi )) = 1 or the linear φ(d(u i , vi )) = d(u i , vi )α power assignment. Recently, the square root assignment φ(d(u i , vi )) = d(u i , vi )α/2 has attracted some 2 Scheduling and Power Assignments in the Physical Model 35 interest [5, 9] as it yields better approximation ratios for request scheduling than the uniform and the linear power assignment.
It can be seen that the estimates obtained from perturbed data are reasonably accurate. Several research questions remain. For example, what is a good upper bound on the reconstruction error of the data aggregation result as a function of the noise statistics introduced to perturb the individual inputs? What are noise generation techniques that minimize the former error (to achieve accurate aggregation results) while maximizing the noise (for privacy)? How to ensure that data of individual data streams cannot be inferred from the perturbed signal?