Epidemic spreading is one of the most popular bio-inspired principles, which has made its way into computer networking. This principle naturally applies to Opportunistic or Delay Tolerant Networks (DTNs), where nodes probabilistically meet their neighbors thanks to mobility. Epidemic-based algorithms are often the only choice for DTN problems such as broadcast and unicast routing, distributed estimation etc. Existing analyses of epidemic spreading in various contexts only consider specific graph geometries (complete, random, regular etc) and/or homogeneous exponential node meeting rates. In addition, in wired networks, synchronous communication is usually assumed.In this paper, we relax these assumptions and provide a detailed analysis of epidemic spreading in DTNs with heterogeneous node meeting rates. We observe the special properties of a Markov model, describing the epidemic process and use them to derive bounds for the delay (expectation and distribution). We apply our analysis to epidemic-based DTN algorithms for routing and distributed estimation and validate the bounds against simulation results, using various real and synthetic mobility scenarios. Finally, we empirically show that the delay distribution is relatively concentrated, and that, depending on graph properties (communities, scale-freeness), the delay scales very well with network size.
By leveraging device-to-device communication, opportunistic networks promise to complement infrastructure-based networks, by enabling communication in remote areas or during disaster and emergency situations, as well as by giving rise to novel applications, such as location-based sharing. Yet, to become feasible in practice and accepted by users, it is crucial that opportunistic communication be energy-efficient. Through extensive and detailed measurements and analysis, we show in this paper, that all of today's device-to-device communication technologies suffer from two grave energy consumption problems: very expensive neighbor discovery and unfair connection maintenance. We consider the two most well-known technologies -- Wi-Fi Direct and Bluetooth, and a third solution based on the WLAN access point mode -- WLAN-Opp. We carefully design a measurement setup which allows us to isolate the energy consumption of individual operations (e.g. CPU sleeping/waking up, scanning/listening for neighbors etc) for thesetechnologies and compare the technologies based on these measurements. Our analysis reveals that neighbor discovery can quickly drain a device's battery, depending on the required scanning frequency. In addition, once a connection is established, the "host" of the connection consumes two to five times the energy needed by a "client". To solve this unfairness problem, we propose a strategy that periodically alternates the hosting role among the peers. Further, we minimize the cost of the role switching operation by using the distribution of the residual connection time of two peers to calculate an adaptive switching period. We examine the trade-off between fairness and switching cost on real-world connection traces and show that our scheme largely outperforms static role switching. Finally, we demonstrate that our fair role switching scheme is also effective when run on real devices.
Opportunistic or Delay Tolerant Networks (DTNs) may be used to enable communication in case of failure or lack of infrastructure (disaster, censorship, remote areas) and to complement existing wireless technologies (cellular, WiFi). Wireless peers communicate when in contact, forming an impromptu network, whose connectivity graph is highly dynamic and only partly connected. In this harsh environment, communication algorithms are mostly greedy, choosing the best solution among the locally available ones. Furthermore, they are routinely evaluated through simulations only, as they are hard to model analytically. Even when more insight is sought from models, they usually assume homogeneous node meeting rates, thereby ignoring the attested heterogeneity and non-trivial structure of (human) mobility.We propose DTN-Meteo: a new unified analytical model that maps an important class of DTN optimization problems and the respective (greedy) algorithms into a Markov chain traversal over the relevant solution space. Fully heterogeneous node contact patterns and a range of algorithmic actions jointly (but separably) define transition probabilities. Thus, we provide closed-form solutions for crucial performance metrics under generic settings. While DTN-Meteo has wider applicability, in this paper, we focus on algorithms with explicitly controlled replication. We apply our model to two problems: routing and content placement. We predict the performance of state of the art algorithms (SimBet, BubbleRap) in various real and synthetic mobility scenarios and show that surprising precision can be achieved against simulations, despite the complexity of the problems and diversity of settings. To our best knowledge, this is the first analytical work that can accurately predict performance for utility-based algorithms and heterogeneous node contact rates.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.