By Song Guo, Jaime Lloret Mauri, Pietro Manzoni, Stefan Ruehrup
This booklet constitutes the refereed court cases of the thirteenth foreign convention on Ad-hoc, cellular and instant Networks, ADHOC-NOW 2014, held in Benidorm, Spain, in June 2014. The 33 revised complete papers offered have been rigorously reviewed and chosen from seventy eight submissions. The papers tackle such assorted subject matters as routing, mobile networks, MAC and actual layer, cellular advert hoc, sensor and robotic networks, localization and defense, vehicular ad-hoc networks.
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Extra info for Ad-hoc, Mobile, and Wireless Networks: 13th International Conference, ADHOC-NOW 2014, Benidorm, Spain, June 22-27, 2014 Proceedings
S follows a log-normal distribution with zero mean and 38 S. Naimi et al. Table 2. 25 [sec] 2 [sec] diﬀerent standard deviations σ (σ 2 ranges from 0 to 25). The received signal strength is then P L(d) + S (expressed in dBm). 3 Simulation Results In all curves, each point is the average of 20 simulations and is shown with a conﬁdence interval of 95%. It may appear that the conﬁdence interval lengths are smaller than the symbols used in the plots, making them barely visible. Chain Scenario The ﬁrst topology considered is a chain of 11 nodes which are separated from a distance of 100 meters A further mobile node moves at constant speed along the chain of ﬁxed nodes.
This bias is two-fold: a time shift of the measured quality exists due to frequency of the probes used to manage the link, and a measurement error is caused by the lack of information from lower layers. Metrics obtained from link states information suﬀer from these two problems. Moreover, metrics dissemination by the routing protocol can cause an appreciable delay between the time at which the link states are estimated and their inclusion in the route calculations. All these uncertainties lead to high packet loss rate in presence of mobility.
IMPORTANT: a framework to systematically analyze the Impact of Mobility on Performance of Routing Protocols for Adhoc Networks. In: Twenty-Second Annual Joint Conference of the IEEE Computer and Communications (INFOCOM), pp. 825–835. IEEE Societies (2003) 21. org/ 22. : Long Range Prediction of Fading Signals: Enabling Adaptive Transmission for Mobile Radio Channels. IEEE Signal Processing Magazine 17, 62–75 (2000) 23. : A Real-Time Algorithm for Long Range Signal Strength Prediction in Wireless Networks.