Jianhui Huang

Opportunistic Routing in Intermittently Connected Mobile P2P Networks

Mobile P2P networking is an enabling technology for mobile devices to self-organize in an unstructured style and communicate in a peer-to-peer fashion. Due to user mobility and/or the unrestricted switching on/off of the mobile devices, links are intermittently connected and end-to-end paths may not exist, causing routing a very challenging problem. Moreover, the limited wireless spectrum and device resources together with the rapidly growing number of portable devices and amount of transmitted data make routing even harder. To tackle these challenges, the routing algorithms must be scalable, distributed, and light-weighted. Nevertheless, existing approaches usually cannot simultaneously satisfy all these three requirements. In this paper, we propose two opportunistic routing algorithms for intermittently connected mobile P2P networks, which exploit the spatial locality, spatial regularity, and activity heterogeneity of human mobility to select relays. The first algorithm employs a depth-search approach to diffuse the data towards the destination. The second one adopts a depth-width-search approach in a sense that it diffuses the data not only towards the destination but also to other directions determined by the actively moving nodes (activists) to find better relays. We perform both theoretical analysis as well as a comparison based simulation study. Our results obtained from both the synthetic data and the real world traces reveal that the proposed algorithms outperform the state-of-the-art in terms of delivery latency and delivery ratio.

The Tempo-Spatial Information Dissemination Properties of Mobile Opportunistic Networks with Levy Mobility

Mobile opportunistic networks make use of a new networking paradigm that takes advantage of node mobility to distribute information. Studying their inherent properties of information dissemination can provide a straightforward explanation on the potentials of mobile opportunistic networks to support emerging applications such as mobile commerce, emergency services, and so on. In this paper, we investigate the inherent properties of information dissemination using the Lévy mobility model to characterize the movement pattern of the nodes. Because Lévy mobility can closely mimic human walk, the analysis model we adopt is practical. Our analyses are taken from the perspectives of small- and large-scales. From the perspective of small-scale, the distribution of the minimum time needed by the information to spread to a given region is investigated, from the perspective of large-scale, the bounds of the probability of the earliest time at which the information arrives in a region that is sufficiently farther away are obtained. We also provide the rate that such probability approaches zero as the distance to the region increases to infinity. Finally, our main results are validated by the numerical simulations.

Mobility-Assisted Routing in Intermittently Connected Mobile Cognitive Radio Networks

In mobile ad-hoc cognitive radio networks (CRNs), end-to-end paths with available spectrum bands for secondary users may exist temporarily, or may never exist, due to the dynamism of the primary user activities. Traditional CRN routing algorithms, which typically ignore the intermittent connectivity of network topology, and traditional mobility-assisted routing algorithms, which generally overlook the spectrum availability, are obviously unsuitable. To tackle this challenge, we propose a Mobility-Assisted Routing algorithm with Spectrum Awareness (MARSA) to select relays based on not only the probability that a node meets the destination but also the chance at which there exists at least one available channel when they meet. To the best of our knowledge, this paper is the first to bring the idea of mobility-assisted routing to deal with the intermittently connected attribute of mobile ad-hoc CRNs, and the first to enhance the mobility-assisted routing by considering the temporal, spatial, and spectrum domains at the same time. Our simulation results demonstrate the superiority of MARSA over traditional algorithms in intermittently connected mobile CRNs.

Analyzing the potential of mobile opportunistic networks for big data applications

The advantages brought by opportunistic data delivery make mobile opportunistic networking a promising technology for big data computing. Thus, it is important to study the potential of mobile opportunistic networks in supporting big data applications by analyzing their fundamental data dissemination properties. However, such an analysis is nontrivial as a large dimension of elements may affect the data spreading behavior in mobile opportunistic networks. In this article, we first outline the challenges of analyzing the potential of mobile opportunistic networks for big data applications. Then we survey the state-of-the-art analytical results on data dissemination properties in mobile opportunistic networks. Finally, we summarize our analytical results and present a few open research issues that may foster future research endeavors on mobile opportunistic networks to support big data applications.

Coverage adjustment for load balancing with an AP service availability guarantee in WLANs

In wireless local area networks, adjusting the coverage of access points (APs) may force the clients near the coverage boundaries of congested APs to associate with lightly-loaded ones, thus realizing load-balancing. Such an approach has the advantage of requiring no modification on the client software/hardware compared to other load-balancing techniques. However, its applicability is undermined by the problems of AP service cheating and AP service loophole resulted from coverage adjustment, which significantly affect the AP service availability. Nevertheless, these two problems are largely ignored by the existent research. To tackle this challenge, a variable polyhedron genetic algorithm (GA) is proposed, which not only provides an AP service availability guarantee but also yields a near-optimal beacon range for each AP when the number of evolutions is large enough. Simulation study indicates that our algorithm is superior over the default 802.11 AP association model in terms of load-balancing and network throughput enhancement. In addition, the variable polyhedron GA outperforms the traditional GA in terms of fitness value and convergence speed.

HERO – A Home Based Routing in Pocket Switched Networks

Pocket switched networks (PSNs) take advantage of human mobility to distribute data. Investigations on real-world trace data indicate that human mobility follows a simple reproducible pattern: a human being usually visits a few places at high frequencies. These most frequently visited places form the home of a node, which is exploited in this paper to design two HomE based ROuting (HERO) algorithms. In the basic HERO, the first encountered relay whose home contains the place where the destination resides is selected to deliver the data. The enhanced HERO, on the other hand, continuously selects a better relay that visits the destination place at a higher frequency. In both algorithms, each node only needs to maintain and exchange its relatively stable home information and/or the corresponding visiting frequencies; therefore no global networking information and no frequent information update are needed, resulting in a low burden on the network due to its low communication and storage overheads. Moreover, HERO involves only simple arithmetic operations, thus causing little computation overhead at the mobile nodes. The simulation results indicate that both HERO algorithms outperform the state-of-the art.

The Potential of Mobile Opportunistic Networks for Data Disseminations

Mobile opportunistic networks make use of node mobility to provide occasional contact opportunities for mobile devices to deliver data. In this paper, we investigate the inherent properties of data disseminations in mobile opportunistic networks by answering the question regarding how far and how fast a packet can be disseminated in mobile opportunistic networks. This problem has been overlooked by recent research, but it is very important because it reveals the potential for mobile opportunistic networks to support emerging applications such as mobile commerce and emergency services that may involve time and location sensitive information dissemination. Our investigations are taken from the perspectives of small and large scales. From the perspective of small scale, the probability distributions of the minimum time needed by the data to spread to a given region, namely, T, are deduced for both the one-copy case and the multiple-copy case. From the perspective of large scale, by using the large deviation theory, the probability distributions of T are deduced for both the one-copy case and the multiple-copy case when the destination region is far enough from the data origin.

Fundamental Analysis on Data Dissemination in Mobile Opportunistic Networks With Lévy Mobility

Mobile opportunistic networks take advantage of contact opportunities due to the mobility of individual nodes for distributing data. Studying the inherent traits of data dissemination in mobile opportunistic networks can reveal their potential to support emerging applications, such as emergency services and mobile commerce. In this paper, we employ the Lévy mobility model to characterize the movement pattern of nodes. Because the Lévy mobility closely mimics human walking patterns, the analysis model that we adopt is realistic. Our analyses consider small- and large-scale perspectives. In the small-scale case, we investigate the distribution of the minimum time needed by the data to spread to a given region. In the large-scale case, we examine the probability bounds of the earliest time at which the data arrives in a region that is sufficiently far away. We also derive the rate at which such a probability tends to zero as the distance to the region increases to infinity. The results of extensive numerical simulations validate our analysis.

Stackelberg Game Based Incentive Mechanism for Data Transmission in Mobile Opportunistic Networks

Mobile opportunistic networks (MONs) can utilize the random contacts among nodes to deliver data, which is a crucial complement of the traditional infrastructure-based communications. However, due to the limited resource and the issues of security and privacy, the selfishness of nodes proves to bring severe negative impact on the data transmission for MONs. Therefore, how to motivate nodes to contribute their transmission capacity turns to be an important topic. Because of the time-varying trait of MONs, it is challenging to incent selfish nodes to help others, because it is hard to control the data forwarding process and the corresponding cost. In this paper, we propose a Stackelberg game based incentive mechanism for data dissemination in MONs, which realizes the optimal resource allocation, including the task assignment and pricing of data forwarding for the relays. In addition, we analyze the existence of the equilibrium state, which theoretically presents some requirements of parameter setting for real deployment of our mechanism. In the end, we demonstrate the effectiveness of our proposed mechanism.

Wireless Relay Selection in Pocket Switched Networks Based on Spatial Regularity of Human Mobility

Pocket switched networks (PSNs) take advantage of human mobility to deliver data. Investigations on real-world trace data indicate that human mobility shows an obvious spatial regularity: a human being usually visits a few places at high frequencies. These most frequently visited places form the home of a node, which is exploited in this paper to design two HomE based Relay selectiOn (HERO) algorithms. Both algorithms input single data copy into the network at any time. In the basic HERO, only the first node encountered by the source and whose home overlaps a destination’s home is selected as a relay while the enhanced HERO keeps finding more optimal relay that visits the destination’s home with higher probability. The two proposed algorithms only require the relays to exchange the information of their home and/or the visiting frequencies to their home when two nodes meet. As a result, the information update is reduced and there is no global status information that needs to be maintained. This causes light loads on relays because of the low communication cost and storage requirements. Additionally, only simple operations are needed in the two proposed algorithms, resulting in little computation overhead at relays. At last, a theoretical analysis is performed on some key metrics and then the real-world based simulations indicate that the two HERO algorithms are efficient and effective through employing only one or a few relays.