Karthik Channakeshava

A Study of Information Diffusion over a Realistic Social Network Model

Sociological models of human behavior can explain population-level phenomena within social systems; computer modeling can simulate a wide variety of scenarios and allow one to pose and test hypotheses about the social system. In this paper, we model and examine the spread of information through personal conversations in a simulated socio-technical network that provides a high degree of realism and a great deal of captured detail. To our knowledge thisis the first time information spread via conversation has been modeled against a statistically accurate simulation of peoples daily interactions within a specific urban or rural environment, capturing the points in time and space at which two people could converse, and providing a realistic basis formodeling human behavior during face-to-face interaction.We use a probabilistic model to decide whether two people will converse about a particular topic based on their similarity and familiarity. Similarity is modeled by matching selected demographic characteristics, while familiarity is modeled by the amount of contact required to convey information. We report our findings on the effects of familiarity and similarity on the spread of information over the social network. We resolve the results by age group, daily activities, time, household income, household size and examine the relative effect of these factors.For informal topics where little familiarity is required, shopping and recreational activities predominate; otherwise, home, work, and school predominate. We find that youths play a significant role in spreading information through a community rapidly, mainly through interactions in schools and recreational activities.

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.

EpiNet: a simulation framework to study the spread of malware in wireless networks

We describe a modeling framework to study the spread of malware over realistic wireless networks. We develop (i) methods for generating synthetic, yet realistic wireless networks using activity-based models of urban population mobility, and (ii) an interaction-based simulation framework to study the dynamics of worm propagation over wireless networks. We use the prototype framework to study how Bluetooth worms spread over realistic wireless networks. This required developing an abstract model of the Bluetooth worm and its within-host behavior. As an illustration of the applicability of our framework, and the utility of activity-based models, we compare the dynamics of Bluetooth worm epidemics over realistic wireless networks and networks generated using random waypoint mobility models. We show that realistic wireless networks exhibit very different structural properties. Importantly, these differences have significant qualitative effect on spatial as well as temporal dynamics of worm propagation. Our results also demonstrate the importance of early detection to control the epidemic.

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.

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.

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.

A formal framework for modeling and analysis of system-level dynamic power management

Recent advances in dynamic power management (DPM) techniques have resulted in designs that support a rich set of power management options, both at the hardware and software levels. This has resulted in an explosion of the design space when analyzing the system-level tradeoffs of candidate DPM strategy designs. This paper proposes a design space exploration methodology based on a high-level, multi-layered modeling framework that facilitates rapid estimation of system-wide energy by providing the designer with a global view of the system. The framework is based on the extended finite state machine formalism and abstracts the component power modes, the operating environment and the DPM architecture into interacting, concurrent layers within a single, unified model. The modeling framework is coupled with a symbolic simulation engine to allow for rapid traversal of the large design space. We first illustrate how the proposed model can be constructed by making reasonable assumptions on the system and workload parameters, and then we show how analysis of various candidate strategies can be performed using this model. Our aim is to provide a high-level model that can be used to quickly assess the impact of various power management decisions on the system-wide energy. The framework can also be a formal basis for design of energy efficient power management systems.

Utility accrual channel establishment in multihop networks

We consider real-time CORBA 1.2 (dynamic scheduling) distributable threads operating in multihop networks. When distributable threads are subject to time/utility function-time constraints, and timeliness optimality criteria such as maximizing accrued system-wide utility is desired, utility accrual real-time channels must be established. Such channels transport messages that are generated as distributable threads transcend nodes, in a way that maximizes system-wide, message-level utility. We present 1) a localized utility accrual channel establishment algorithm called localized decision for utility accrual channel establishment (or LocDUCE) and 2) a distributed utility accrual channel establishment algorithm called global decision for utility accrual channel establishment (or GloDUCE). Since the channel establishment problem is NP-complete. LocDUCE and GloDUCE heuristically compute channels, with LocDUCE making decisions based on local information pertaining to the node and GloDUCE making global decisions. We simulate the performance of the algorithms and compare them with the open shortest path first (OSPF) routing algorithm and the optimal algorithm. We also implement these algorithms in a prototype testbed and experimentally compare their performance with OSPF. Our simulation and experimental measurements reveal that GloDUCE and LocDUCE accrue significantly higher utility than OSPF and also perform close to the optimal for some cases. Furthermore, GloDUCE outperforms LocDUCE under high downstream traffic.

From biological and social network metaphors to coupled bio-social wireless networks

Biological and social analogies have been long applied to complex systems. Inspiration has been drawn from biological solutions to solve problems in engineering products and systems, ranging from Velcro to camouflage to robotics to adaptive and learning computing methods. In this paper, we present an overview of recent advances in understanding biological systems as networks and use this understanding to design and analyse wireless communication networks. We expand on two applications, namely cognitive sensing and control and wireless epidemiology. We discuss how our work in these two applications is motivated by biological metaphors. We believe that recent advances in computing and communications coupled with advances in health and social sciences raise the possibility of studying coupled bio-social communication networks. We argue that we can better utilise the advances in our understanding of one class of networks to better our understanding of the other.