Zhenning Kong

On the latency for information dissemination in mobile wireless networks

In wireless networks, node mobility may be exploited to assist in information dissemination over time. We analyze the latency for information dissemination in large-scale mobile wireless networks. To study this problem, we map a network of mobile nodes to a network of stationary nodes with dynamic links. We then use results from percolation theory to show that under a constrained i.i.d. mobility model, the scaling behavior of the latency falls into two regimes. When the network is not percolated (subcritical), the latency scales linearly with the initial Euclidean distance between the sender and the receiver; when the network is percolated (supercritical), the latency scales sub-linearly with the distance.

Decentralized Coding Algorithms for Distributed Storage in Wireless Sensor Networks

We consider large-scale wireless sensor networks with n nodes, out of which k are in possession, (e.g., have sensed or collected in some other way) k information packets. In the scenarios in which network nodes are vulnerable because of, for example, limited energy or a hostile environment, it is desirable to disseminate the acquired information throughout the network so that each of the n nodes stores one (possibly coded) packet so that the original k source packets can be recovered, locally and in a computationally simple way from any k(1 + ¿) nodes for some small ¿ > 0. We develop decentralized Fountain codes based algorithms to solve this problem. Unlike all previously developed schemes, our algorithms are truly distributed, that is, nodes do not know n, k or connectivity in the network, except in their own neighborhoods, and they do not maintain any routing tables.

Fountain Codes Based Distributed Storage Algorithms for Large-Scale Wireless Sensor Networks

We consider large-scale networks with n nodes, out of which k are in possession, (e.g., have sensed or collected in some other way) k information packets. In the scenarios in which network nodes are vulnerable because of, for example, limited energy or a hostile environment, it is desirable to disseminate the acquired information throughout the network so that each of the n nodes stores one (possibly coded) packet and the original k source packets can be recovered later in a computationally simple way from any (1 + isin)k nodes for some small isin > 0. We developed two distributed algorithms for solving this problem based on simple random walks and Fountain codes. Unlike all previously developed schemes, our solution is truly distributed, that is, nodes do not know n, k or connectivity in the network, except in their own neighborhoods, and they do not maintain any routing tables. In the first algorithm, all the sensors have the knowledge of n and k. In the second algorithm, each sensor estimates these parameters through the random walk dissemination. We present analysis of the communication/transmission and encoding/decoding complexity of these two algorithms, and provide extensive simulation results as well.

Connectivity and Latency in Large-Scale Wireless Networks with Unreliable Links

We study connectivity and transmission latency in wireless networks with unreliable links from a percolation-based perspective. We first examine static models, where each link of the network is functional (active) with some probability, independently of all other links, where the probability may depend on the distance between the two nodes. We obtain analytical upper and lower bounds on the critical density for phase transition in this model. We then examine dynamic models, where each link is active or inactive according to a Markov on- off process. We show that a phase transition also exists in such dynamic networks, and the critical density for this model is the same as the one for static networks under some mild conditions. Furthermore, due to the dynamic behavior of links, a delay is incurred for any transmission even when propagation delay is ignored. We study the behavior of this transmission delay and show that the delay scales linearly with the Euclidean distance between the sender and the receiver when the network is in the subcritical phase, and the delay scales sub-linearly with the distance if the network is in the supercritical phase.

Distributed energy management algorithm for large-scale wireless sensor networks

In battery-constrained wireless sensor networks, it is important to employ effective energy management while maintaining some level of network connectivity. Viewing this problem from a percolation-based connectivity perspective, we propose a fully distributed energy management algorithm for large-scale wireless sensor networks. This algorithm allows each sensor to probabilistically schedule its own activity based on its node degree. This mechanism is modelled by a degree-dependent dynamic site percolation process on random geometric graphs. We specify the conditions under which the resulting network is guaranteed to be percolated at all the time. We further study the delay performance of the proposed energy management algorithm by modelling the problem as a degree-dependent first passage percolation process on random geometric graphs.

Raptor codes based distributed storage algorithms for wireless sensor networks

We consider a distributed storage problem in a large-scale wireless sensor network with n nodes among which k acquire (sense) independent data. The goal is to disseminate the acquired information throughout the network so that each of the n sensors stores one possibly coded packet and the original k data packets can be recovered later in a computationally simple way from any (1 + isin)k of nodes for some small isin Gt 0. We propose two Raptor codes based distributed storage algorithms for solving this problem. In the first algorithm, all the sensors have the knowledge of n and k. In the second one, we assume that no sensor has such global information.

Resilience to Degree-Dependent and Cascading Node Failures in Random Geometric Networks

We study the problem of resilience to node failures in large-scale networks modelled by random geometric graphs. Adopting a percolation-based viewpoint, we investigates the ability of the network to maintain global communication in the presence of dependent node failures. Degree-dependent site percolation processes on random geometric graphs are examined, and the first known analytical conditions are obtained for the existence and non-existence, respectively, of a large connected component of operational network nodes after degree-dependent node failures. In electrical power networks or wireless communication and computing networks, cascading failure from power blackouts or virus epidemics may result from a small number of initial node failures triggering global failure events affecting the whole network. With the use of a simple but descriptive model, it is shown that the cascading failure problem is equivalent to a degree-dependent percolation process. The first analytical conditions are obtained for the occurrence and non-occurrence of cascading failures, respectively, in large-scale networks with geometric constraints.

Analytical Lower Bounds on the Critical Density in Continuum Percolation

Percolation theory has become a useful tool for the analysis of large-scale wireless networks. We investigate the fundamental problem of characterizing the critical density lambda_c ^(d) for d-dimensional Poisson random geometric graphs in continuum percolation theory. By using a probabilistic analysis which incorporates the clustering effect in random geometric graphs, we develop a new class of analytical lower bounds for the critical density lambda_c ^(d) in d-dimensional Poisson random geometric graphs. The lower bounds are the tightest known to date. In particular, for the two-dimensional case, the analytical lower bound is improved to lambda_c ⁽²⁾ ges 0.7698. For the three-dimensional case, we obtain lambda_c ⁽³⁾ ges 0.4494.

Characterization of the Critical Density for Percolation in Random Geometric Graphs

Percolation theory has become a useful tool for the analysis of large-scale wireless networks. We investigate the fundamental problem of characterizing the critical density lambdac(d) for d-dimensional Poisson random geometric graphs in continuum percolation theory. By using a probabilistic analysis which incorporates the clustering effect in random geometric graphs, we develop a new class of analytical lower bounds for the critical density lambdac(d). These analytical lower bounds are the tightest known to date, and reveal a deep underlying relationship between clustering effects and percolation phenomena.

Coding Improves the Throughput-Delay Tradeoff in Mobile Wireless Networks

This paper studies the throughput-delay performance tradeoff in large-scale wireless ad hoc networks. It has been shown that the per source-destination pair throughput can be improved from Θ(1/√{nlogn}) to Θ(1) if nodes are allowed to move and a two-hop relay scheme is employed. The price paid for such a throughput improvement is large delay. Indeed, the delay scaling of the two-hop relay scheme is Θ(nlogn) under the random walk mobility model. In this paper, coding techniques are used to improve the throughput-delay tradeoff for mobile wireless networks. For the random walk mobility model, the delay is reduced from Θ(nlogn) to Θ(n) by employing a maximum distance separable Reed-Solomon coding scheme. This coding approach maintains the diversity gained by mobility while decreasing the delay.