Read e-book online Ad-hoc, Mobile, and Wireless Networks: 14th International PDF

By Symeon Papavassiliou, Stefan Ruehrup

ISBN-10: 3319196618

ISBN-13: 9783319196619

ISBN-10: 3319196626

ISBN-13: 9783319196626

This e-book constitutes the lawsuits of the 14th overseas convention on advert Hoc Networks and instant, ADHOC-NOW 2015, held in Athens, Greece in June/July 2015. The 25 complete papers offered during this quantity have been conscientiously reviewed and chosen from fifty two submissions. The booklet additionally comprises three full-paper invited talks. The contributions are prepared in topical sections named: routing, connectivity, and source allocation; localization, sensor deployment, and mobility administration; dispensed computing with cellular brokers; effective, trustworthy, and safe clever power networks; and rising communications, networking and computing applied sciences for VANETs 2.0.

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Additional resources for Ad-hoc, Mobile, and Wireless Networks: 14th International Conference, ADHOC-NOW 2015, Athens, Greece, June 29 -- July 1, 2015, Proceedings

Example text

Optimal transmission ranges for randomly distributed packet radio terminals. IEEE Trans. Com. 32, 246–257 (1984) 5. : Transmission range control in multihop packet radio networks. IEEE Trans. Com. 34, 38–44 (1986) 6. : Compass routing on geometric networks. In: Canadian Conference on Computational Geometry (1999) 7. : Routing and addressing problems in large metropolitan-scale internetworks. ISI Research Report (1987) 8. : An information model for geographic greedy forwarding in wireless ad-hoc sensor networks.

Let G, w be an instance of MinAvgMult-EMC and let G be a random orientation of G in which each edge receives each of the two possible directions with probability 12 . The expected fraction of locally optimal nodes in G is at least 21w . Theorem 4. There exists a Mult-EMC. 2− 1 2w -approximation algorithm for MinAvg 44 E. Bampas et al. Proof. By Lemma 4, if we assign random directions to the edges of G, we get at least 21w · n locally optimal nodes in expectation. This algorithm can be derandomized by a straightforward application of the method of conditional expectations.

We take advantage of this property and first show that MinAvgMult-EMC can be solved exactly if the number of available colors equals 2. Next, we further fine-tune the algorithm, by adding a random orientation step together with a derandomization process, in order to come up with an algorithm having an approximation ratio of 2 − 21w , where w is the number of available colors. Since the ratio of 2 of the algorithm in [21] is tight, our results represent the first, to the best of our knowledge, algorithm with an approximation ratio strictly better than 2, for both MinAvgMult-EMC in general graphs and MinAvgMult-PMC in stars.

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Ad-hoc, Mobile, and Wireless Networks: 14th International Conference, ADHOC-NOW 2015, Athens, Greece, June 29 -- July 1, 2015, Proceedings by Symeon Papavassiliou, Stefan Ruehrup


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