Research Projects
-
Information Gathering Markets For Opportunistic Networks
Collaborators: Ranjan Pal (University of Southern California) and Dr. Pan Hui (Detusche Telekom Research Laboratories, Berlin)
Summary: The power of portable computing devices such as smartphones are increasing at a rapid rate in terms of data storage, computing, and communication capabilities. Opportunistic ad-hoc networking is a technology that enables these devices to effectively exchange information in infrastructure-less communication environments by exploiting human mobility and opportunistic contacts. In this work, we investigate simple markets for information gathering in opportunistic networks (ONs). The term `information gathering’ refers to the process of certain network users requesting information on a topic followed by responders (also network users) responding with information. By the term `market’, we imply a platform via which the process of information gathering can be commercialized using payments. QoS-driven information gathering will be an important application in ONs in future given the increasing power of portable computing devices to support a variety of applications. Researchers have already proposed the idea of mobile crowd computing that is based on smartphones participating in large scale distributed application tasks in an ON and providing information/solutions to queries made by certain users in the ON. We propose two types of markets: (1) a distributed market where information requesters gather information from information providers, without any central control and (2) a centralized market where information requesters gather information from information providers, with a profit-maximizing central controller mediating on the information gathering process. We study the equilibrium number of market users in both the market types and investigate whether the market equilibria equals the socially optimal number of market users. We mathematically show that the social optimal solution is not equal to the market equilibrium in both the market types. To alleviate the latter issue, we design and analyze mechanisms to make the market equilibrium equal to the socially optimal number of market users.
-
GlobalSense: Modeling Using Planet-scale Vehicular Imagery Data
Collaborators: Dr. Pan Hui & Dr. Hamed Ketabdar (Detusche Telekom Research Laboratories, Berlin) and Ahmed Helmy (CISE, University of Florida)
Summary: A new generation of “behavior-aware” delay tolerant networks is emerging in what may define future mobile social networks. With the introduction of novel behavior-aware protocols, services and architectures, there is a pressing need to understand and realistically model mobile users behavioral characteristics, their similarity and clustering. Such models are essential for the analysis, performance evaluation, and simulation of future DTNs. This paper addresses issues related to mobile user similarity, its definition, analysis and modeling. To define similarity, we adopt a behavioral-profile based on users location preferences using their on-line association matrix and its SVD, then calculate the behavioral distance to capture user similarity. This measures the difference of the major spatio-temporal behavioral trends and can be used to cluster users into similarity groups or communities. We then analyze and contrast similarity distributions of mobile user populations in two settings: (i) based on real measurements from four major campuses with over ten thousand users for a month, and (ii) based on existing mobility models, including random direction and time-varying community models. Our results show a rich set of similar communities in real mobile societies with distinct behavioral clusters of users. This is true for all the traces studied, with the trend being consistent over time. Surprisingly, however, we find that the existing mobility models do not explicitly capture similarity and result in homogeneous users that are all similar to each other. Thus the richness and diversity of user behavioral patterns is not captured to any degree in the existing models. These findings strongly suggest that similarity should be explicitly captured in future mobility models, which motivates the need to re-visit mobility modeling to incorporate accurate behavioral models in the future.
-
Mobility Modeling in Mobile Societies
Collaborators: Udayan Kumar, Ahmed Helmy (CISE, University of Florida) and Wei-Jen Hsu (Cisco Systems)
Summary: A new generation of “behavior-aware” delay tolerant networks is emerging in what may define future mobile social networks. With the introduction of novel behavior-aware protocols, services and architectures, there is a pressing need to understand and realistically model mobile users behavioral characteristics, their similarity and clustering. Such models are essential for the analysis, performance evaluation, and simulation of future DTNs. This paper addresses issues related to mobile user similarity, its definition, analysis and modeling. To define similarity, we adopt a behavioral-profile based on users location preferences using their on-line association matrix and its SVD, then calculate the behavioral distance to capture user similarity. This measures the difference of the major spatio-temporal behavioral trends and can be used to cluster users into similarity groups or communities. We then analyze and contrast similarity distributions of mobile user populations in two settings: (i) based on real measurements from four major campuses with over ten thousand users for a month, and (ii) based on existing mobility models, including random direction and time-varying community models. Our results show a rich set of similar communities in real mobile societies with distinct behavioral clusters of users. This is true for all the traces studied, with the trend being consistent over time. Surprisingly, however, we find that the existing mobility models do not explicitly capture similarity and result in homogeneous users that are all similar to each other. Thus the richness and diversity of user behavioral patterns is not captured to any degree in the existing models. These findings strongly suggest that similarity should be explicitly captured in future mobility models, which motivates the need to re-visit mobility modeling to incorporate accurate behavioral models in the future.
-
Detection of Local Community Structures in Complex Dynamic Networks
Collaborators: Ravi Tiwari and Prof. My Tra Thai, Prof. S.-S. Chen (CISE, University of Florida), A.W.M. Dress (Chinese Academy of Sciences)
Summary: Identification of interaction patterns in complex networks via community structures has gathered a lot of attention in recent research studies. Local community structures provide a better measure to understand and visualize the nature of interaction when the global knowledge of networks is unknown. Recent research on local community structures, however, lacks the feature to adjust itself in the dynamic networks and heavily depends on the source vertex position. This work propose a novel approach to identify local communities based on iterative agglomeration and local optimization. The proposed solution has two significant improvements: (i) In each iteration, agglomeration strengthens the local community measure by selecting the best possible set of vertices (ii) The proposed vertex and community rank criterion are suitable for the dynamic networks where the interactions among vertices may change over time. In order to evaluate the proposed algorithm, extensive experiments and benchmarking on computer generated networks as well as real-world social and biological networks have been conducted. The experiment results reflect that the proposed algorithm can identify local communities, irrespective of the source vertex position, with more than 92% accuracy in the synthetic as well as in the real world networks.
-
SHIELD: Social sensing and Help In Emergency using mobiLe Devices
Collaborators: Mukul Sharma and Ahmed Helmy (CISE, University of Florida)
Summary: School and College campuses face a perceived threat of violent crimes and require a realistic plan against unpredictable emergencies and disasters. Existing emergency systems (e.g., 911, campus-wide alerts) are quite useful, but provide delayed response (often tens of minutes) and do not utilize proximity or locality. There is a need to exploit proximity- based help for immediate response and to deter any crime. In this project, authors propose SHIELD, an on-campus emergency rescue and alert management service. It is a fully distributed infrastructure- less platform based on proximity-enabled trust and cooperation. It relies on nearby localized responses sent using Bluetooth and/or WiFi to achieve minimal response time and maximal availability thereby augmenting the traditional notion of centralized emergency services. Analysis of campus crime statistics and WLAN traces surprisingly show a strong positive correlation (over 55%) between on-campus crime statistics and spatio- temporal density distribution of on-campus mobile users. This result is promising to develop a platform based on mutual trust and cooperation. A prototype application used in such scenarios is also implemented. -
CINORA: Cell Based Identification of NOde Replication Attack in Wireless Sensor Networks
Collaborators: -
Summary: Now a days, thousands of low-cost Wireless Sensor Network (WSN) devices are often deployed in remote and hostile locations for monitoring and information processing purposes. However, it also makes them susceptible to external attacks and physical tempering. An attacker can easily capture and replicate them to deploy surreptitiously at strategic location for its own benefit. These replicated nodes might disrupt the communication channel, flood false information or even jam and control other near-by sensor nodes. To handle this problem we propose CINORA protocol suite that employees distributed cell based replicated node detection and revocation schemes to secure WSN. In CINORA-Inset, we form non-null intersecting subset of cells to perform distributed authentication. While CINORAHybrid uses a two-phase restricted cell distributed authentication scheme to detect and revoke node replication attacks. Theoretical and experimental analysis show that the proposed approaches are very efficient on memory and communication overheads. Finally, the percentage detection accuracy of the node replication attack is closest to certainty as the number of nodes and cells in the sensor networks increases.