Guanhong Pei

Cellular traffic offloading through opportunistic communications: a case study

Due to the increasing popularity of various applications for smartphones, 3G networks are currently overloaded by mobile data traffic. Offloading cellular traffic through opportunistic communications is a promising solution to partially solve this problem, because there is no monetary cost for it. As a case study, we investigate the target-set selection problem for information delivery in the emerging Mobile Social Networks (MoSoNets). We propose to exploit opportunistic communications to facilitate the information dissemination and thus reduce the amount of cellular traffic. In particular, we study how to select the target set with only k users, such that we can minimize the cellular data traffic. In this scenario, initially the content service providers deliver information over cellular networks to only users in the target set. Then through opportunistic communications, target-users will further propagate the information among all the subscribed users. Finally, service providers will send the information to users who fail to receive it before the delivery deadline (i.e., delay-tolerance threshold). We propose three algorithms, called Greedy, Heuristic, and Random, for this problem and evaluate their performance through an extensive trace-driven simulation study. The simulation results verify the efficiency of these algorithms for both synthetic and real-world mobility traces. For example, the Heuristic algorithm can offload cellular traffic by up to 73.66% for a real-world mobility trace.

Cascading failures in multiple infrastructures: From transportation to communication network

This research conducts a systematic study of human-initiated cascading failures in critical inter-dependent societal infrastructures. The focus is on three closely coupled systems: (i) cellular and mesh networks, (ii) transportation networks and (iii) social phone call networks. We analyze cascades that occur in inter-dependent infrastructures due to behavioral adaptations in response to a crisis. During crises, changes in individual behavioral lead to altered calling patterns and activities, which influence the urban transport network. This, in turn, affects the loads on wireless networks. The interaction between these systems and their co-evolution poses significant technical challenges for representing and reasoning about these systems. We develop interaction-based models in which individuals and infrastructure elements are placed in a common geographic coordinate system. The goal is to study the impact of a chemical plume in a densely populated urban region. Authorities order evacuation of the affected area which leads to change in peoples activity patterns as they are forced to drive home or to evacuation shelters. They also use the wireless networks for coordination among family members and information sharing. These two behavioral adaptations, cause flash-congestion in the urban transport network and the wireless network. We analyze how extended periods of unanticipated road congestion can result in failure of infrastructures, starting with the servicing base stations in the congested area. Finally, we study the criticality and robustness of the various base stations and measure how congestion in the transportation network impacts communication infrastructure.

Implications of Dynamic Spectrum Access on the Efficiency of Primary Wireless Market

In this paper, we develop a microscopic, agent-based simulation tool, called SIGMA-SPECTRUM to study the dynamics of the primary wireless spectrum market. A detailed, synthetic demand model, is used to produce disaggregated spectrum demand profiles that vary spatially and temporally for each individual in the population. We implement a truthful and efficient auction mechanism, proposed by Ausubel, that results in more efficient allocations than the current auction mechanisms used by the FCC. This research analyzes the effect of recent advances in cognitive radio technology, the DSA (Dynamic Spectrum Access) and the possibility of active trading in the secondary market, on the allocation outcomes in the primary market. Provision of active trading in the secondary market invites speculators and sometimes encourages collusive behavior among bidders, which can significantly alter the primary market outcomes in terms of winners and their allocations.

Low-complexity scheduling for wireless networks

Designing efficient scheduling and power control algorithms for distributed wireless communication has been a challenging issue, especially in the physical interference model based on SINR constraints. In this paper, we discuss the first local distributed scheduling and power control algorithm in the SINR model that achieves an O(g(L)) approximation factor of the rate region, where O(g(L)) denotes the link diversity. As an intermediate step, we develop a scheduling algorithm in a k-hop interference model, which is used in the analysis of the more general model. Our algorithms are based on random-access and use local queue size information. Exchanging queue size information is challenging, and the complexity of this step is often ignored in prior local distributed algorithms. A novel aspect of our paper is the use of stale and infrequently updated queue size info, which significantly improves the complexity of our algorithms.

Integrated Multi-Network Modeling Environment for Spectrum Management

We describe a first principles based integrated modeling environment to study urban socio-communication networks which represent not just the physical cellular communication network, but also urban populations carrying digital devices interacting with the cellular network. The modeling environment is designed specifically to understand spectrum demand and dynamic cellular network traffic. One of its key features is its ability to support individual-based models at highly resolved spatial and temporal scales. We have instantiated the modeling environment by developing detailed models of population mobility, device ownership, calling patterns and call network. By composing these models using an appropriate in-built workflow, we obtain an integrated model that represents a dynamic socio-communication network for an entire urban region. In contrast with earlier papers that typically use proprietary data, these models use open source and commercial data sets. The dynamic model represents for a normative day, every individual in an entire region, with detailed demographics, a minute-by-minute schedule of each persons activities, the locations where these activities take place, and calling behavior of every individual. As an illustration of the applicability of the modeling environment, we have developed such a dynamic model for Portland, Oregon comprising of approximately 1.6 million individuals. We highlight the unique features of the models and the modeling environment by describing three realistic case studies.

Approximation algorithms for throughput maximization in wireless networks with delay constraints

We study the problem of throughput maximization in multihop wireless networks with end-to-end delay constraints for each session. This problem has received much attention starting with the work of Grossglauser and Tse (2002), and it has been shown that there is a significant tradeoff between the end-to-end delays and the total achievable rate. We develop algorithms to compute such tradeoffs with provable performance guarantees for arbitrary instances, with general interference models. Given a target delay-bound Δ(c) for each session c, our algorithm gives a stable flow vector with a total throughput within a factor of O(log Δ_m/loglog Δ_m) of the maximum, so that the per-session (end-to-end) delay is O(((log Δ_m/loglog Δ_m)Δ(c))²), where Δ_m=max_c{Δ(c)}; note that these bounds depend only on the delays, and not on the network size, and this is the first such result, to our knowledge.

Human Initiated Cascading Failures in Societal Infrastructures

In this paper, we conduct a systematic study of human-initiated cascading failures in three critical inter-dependent societal infrastructures due to behavioral adaptations in response to a crisis. We focus on three closely coupled socio-technical networks here: (i) cellular and mesh networks, (ii) transportation networks and (iii) mobile call networks. In crises, changes in individual behaviors lead to altered travel, activity and calling patterns, which influence the transport network and the loads on wireless networks. The interaction between these systems and their co-evolution poses significant technical challenges for representing and reasoning about these systems. In contrast to system dynamics models for studying these interacting infrastructures, we develop interaction-based models in which individuals and infrastructure elements are represented in detail and are placed in a common geographic coordinate system. Using the detailed representation, we study the impact of a chemical plume that has been released in a densely populated urban region. Authorities order evacuation of the affected area, and this leads to individual behavioral adaptation wherein individuals drop their scheduled activities and drive to home or pre-specified evacuation shelters as appropriate. They also revise their calling behavior to communicate and coordinate among family members. These two behavioral adaptations cause flash-congestion in the urban transport network and the wireless network. The problem is exacerbated with a few, already occurring, road closures. We analyze how extended periods of unanticipated road congestion can result in failure of infrastructures, starting with the servicing base stations in the congested area. A sensitivity analysis on the compliance rate of evacuees shows non-intuitive effect on the spatial distribution of people and on the loading of the base stations. For example, an evacuation compliance rate of 70% results in higher number of overloaded base stations than the evacuation compliance rate of 90%.

Synthesis and Analysis of Spatio-Temporal Spectrum Demand Patterns: A First Principles Approach

Modeling and analysis of Primary User (PU) spectrum requirement is key to effective Dynamic Spectrum Access (DSA). Allocation of long term licenses, as well as opportunistic spectrum usage by secondary users (SU) cannot be done without accurate modeling of PU behavior. This is especially important in the case of cellular network traffic, which exhibits a significant spatio-temporal variation. Recently, there has been a lot of interest in modeling PU behavior in cellular networks by means of detailed analysis of proprietary data from wireless providers (e.g., Willcomm et al., IEEE DySpan 2008). While such analysis gives useful insights, major shortcomings of such an approach include (i) unavailability of data for open scientific study, and (ii) hard to predict future trends, and changes resulting from behavioral modifications. In this paper, we develop a methodology to generate synthetic network traffic data to model primary usage, by combining a number of different data sets for mobility, device ownership and call generation in a large synthetic urban population. Unlike simple random graph techniques, these methods use real world data sources and combine them with behavioral and social theories to synthesize spatial and dynamic relational networks. We use our tool to model the network traffic in the region of Portland, Oregon, calibrated by using published aggregate measurements of Wilcomm et al. As an illustration of our approach, we study the variation in demand as a result of changes in calling patterns based on user activities, and the impact of increased user demand on hotspots and their cascades within the region.

Distributed approximation algorithms for maximum link scheduling and local broadcasting in the physical interference model

In this paper, we develop the first rigorous distributed algorithm for link scheduling in the SINR model under any length-monotone sub-linear power assignments. Our algorithms give constant factor approximation guarantees, matching the bounds of the sequential algorithms for these problems, with provable bounds on the running time in terms of the graph topology. We also study a related and fundamental problem of local broadcasting for uniform power levels, and obtain similar bounds. These problems are much more challenging in the SINR model than in the more standard graph based interference models, because of the non-locality of the SINR model. Our algorithms are randomized and crucially rely on physical carrier sensing for the distributed communication steps. We find that the specific wireless device capability of duplex/halfduplex communication significantly impacts the performance. Our main technique involves the distributed computation of affectance and a construct called a ruling, which are likely to be useful in other scheduling problems in the SINR model. We also study the empirical performance of our algorithms, and find that the performance depends on the topology, and the approximation ratio is very close to the best sequential algorithm.

Brief announcement: distributed algorithms for maximum link scheduling in the physical interference model

We develop distributed algorithms for the maximum independent link set problem in wireless networks in a distributed computing model based on the physical interference model with SINR constraints -- this is more realistic and more challenging than the traditional graph-based models. Our results give the first distributed algorithm for this problem with polylogarithmic running time with a constant factor approximation guarantee, matching the sequential bound.