Influence Analysis towards Big Social Data

Large scale social data from online social networks, instant messaging applications, and wearable devices have seen an exponential growth in a number of users and activities recently. The rapid proliferation of social data provides rich information and infinite possibilities for us to understand and analyze the complex inherent mechanism which governs the evolution of the new technology age. Influence, as a natural product of information diffusion (or propagation), which represents the change in an individual’s thoughts, attitudes, and behaviors resulting from interaction with others, is one of the fundamental processes in social worlds. Therefore, influence analysis occupies a very prominent place in social related data analysis, theory, model, and algorithms. In this dissertation, we study the influence analysis under the scenario of big social data. Firstly, we investigate the uncertainty of influence relationship among the social network. A novel sampling scheme is proposed which enables the development of an efficient algorithm to measure uncertainty. Considering the practicality of neighborhood relationship in real social data, a framework is introduced to transform the uncertain networks into deterministic weight networks where the weight on edges can be measured as Jaccard-like index. Secondly, focusing on the dynamic of social data, a practical framework is proposed by only probing partial communities to explore the real changes of a social network data. Our probing framework minimizes the possible difference between the observed topology and the actual network through several representative communities. We also propose an algorithm that takes full advantage of our divide-and-conquer strategy which reduces the computational overhead. Thirdly, if let the number of users who are influenced be the depth of propagation and the area covered by influenced users be the breadth, most of the research results are only focused on the influence depth instead of the influence breadth. Timeliness, acceptance ratio, and breadth are three important factors that significantly affect the result of influence maximization in reality, but they are neglected by researchers in most of time. To fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio, and broad diffusion for influence breadth has been investigated. In our model, the breadth of influence is measured by the number of covered communities, and the tradeoff between depth and breadth of influence could be balanced by a specific parameter. Furthermore, the problem of privacy preserved influence maximization in both physical location network and online social network was addressed. We merge both the sensed location information collected from cyber-physical world and relationship information gathered from online social network into a unified framework with a comprehensive model. Then we propose the resolution for influence maximization problem with an efficient algorithm. At the same time, a privacy-preserving mechanism are proposed to protect the cyber physical location and link information from the application aspect. Last but not least, to address the challenge of large-scale data, we take the lead in designing an efficient influence maximization framework based on two new models which incorporate the dynamism of networks with consideration of time constraint during the influence spreading process in practice. All proposed problems and models of influence analysis have been empirically studied and verified by different, large-scale, real-world social data in this dissertation

Incentive-compatible route coordination of crowdsourced resources

Technical ReportWith the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen-ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1-approximation algorithm to solve the 2 problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments

What’s in it for me? Incentive-compatible route coordination of crowdsourced resources

With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresence-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1/2-approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Opportunistic Networks: Present Scenario- A Mirror Review

Opportunistic Network is form of Delay Tolerant Network (DTN) and regarded as extension to Mobile Ad Hoc Network. OPPNETS are designed to operate especially in those environments which are surrounded by various issues like- High Error Rate, Intermittent Connectivity, High Delay and no defined route between source to destination node. OPPNETS works on the principle of “Store-and-Forward” mechanism as intermediate nodes perform the task of routing from node to node. The intermediate nodes store the messages in their memory until the suitable node is not located in communication range to transfer the message to the destination. OPPNETs suffer from various issues like High Delay, Energy Efficiency of Nodes, Security, High Error Rate and High Latency. The aim of this research paper is to overview various routing protocols available till date for OPPNETs and classify the protocols in terms of their performance. The paper also gives quick review of various Mobility Models and Simulation tools available for OPPNETs simulation

Incentive compatible route coordination of crowdsourced resources and its application to GeoPresence-as-a-Service

With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen- ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent's exibility is exploited to maximize the coverage of a mo- bility field, with an objective to maximize the revenue collected from sat- isfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1 2 -approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent's truthfulness about its exibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798

A Novel Theoretical Probabilistic Model for Opportunistic Routing with Applications in Energy Consumption for WSNs

This paper proposes a new theoretical stochastic model based on an abstraction of the opportunistic model for opportunistic networks. The model is capable of systematically computing the network parameters, such as the number of possible routes, the probability of successful transmission, the expected number of broadcast transmissions, and the expected number of receptions. The usual theoretical stochastic model explored in the methodologies available in the literature is based on Markov chains, and the main novelty of this paper is the employment of a percolation stochastic model, whose main benefit is to obtain the network parameters directly. Additionally, the proposed approach is capable to deal with values of probability specified by bounded intervals or by a density function. The model is validated via Monte Carlo simulations, and a computational toolbox (R-packet) is provided to make the reproduction of the results presented in the paper easier. The technique is illustrated through a numerical example where the proposed model is applied to compute the energy consumption when transmitting a packet via an opportunistic network

Message forwarding techniques in Bluetooth enabled opportunistic communication environment

These days, most of the mobile phones are smart enough with computer like intelligence and equipped with multiple communication technologies such as Bluetooth, wireless LAN, GPRS and GSM. Different communication medium on single device have unlocked the new horizon of communication means. Modern mobile phones are not only capable of using traditional way of communication via GSM or GPRS; but, also use wireless LANs using access points where available. Among these communication means, Bluetooth technology is very intriguing and unique in nature. Any two devices equipped with Bluetooth technology can communicate directly due to their unique IDs in the world. This is opposite to GSM or Wireless LAN technology; where devices are dependent on infrastructure of service providers and have to pay for their services. Due to continual advancement in the field of mobile technology, mobile ad-hoc network seems to be more realised than ever using Bluetooth. In traditional mobile ad-hoc networks (MANETs), before information sharing, devices have partial or full knowledge of routes to the destinations using ad-hoc routing protocols. This kind of communication can only be realised if nodes follow the certain pattern. However, in reality mobile ad-hoc networks are highly unpredictable, any node can join or leave network at any time, thus making them risky for effective communication. This issue is addressed by introducing new breed of ad-hoc networking, known as opportunistic networks. Opportunistic networking is a concept that is evolved from mobile ad-hoc networking. In opportunistic networks nodes have no prior knowledge of routes to intended destinations. Any node in the network can be used as potential forwarder with the exception of taking information one step closer to intended destination. The forwarding decision is based on the information gathered from the source node or encountering node. The opportunistic forwarding can only be achieved if message forwarding is carried out in store and forward fashion. Although, opportunistic networks are more flexible than traditional MANETs, however, due to little insight of network, it poses distinct challenges such as intermittent connectivity, variable delays, short connection duration and dynamic topology. Addressing these challenges in opportunistic network is the basis for developing new and efficient protocols for information sharing. The aim of this research is to design different routing/forwarding techniques for opportunistic networks to improve the overall message delivery at destinations while keeping the communication cost very low. Some assumptions are considered to improved directivity of message flow towards intended destinations. These assumptions exploit human social relationships analogies, approximate awareness of the location of nodes in the network and use of hybrid communication by combining several routing concept to gain maximum message directivity. Enhancement in message forwarding in opportunistic networks can be achieved by targeting key nodes that show high degree of influence, popularity or knowledge inside the network. Based on this observation, this thesis presents an improved version of Lobby Influence (LI) algorithm called as Enhanced Lobby Influence (ELI). In LI, the forwarding decision is based on two important factors, popularity of node and popularity of node’s neighbour. The forwarding decision of Enhanced Lobby Influence not only depends on the intermediate node selection criteria as defined in Lobby Influence but also based on the knowledge of previously direct message delivery of intended destination. An improvement can be observed if nodes are aware of approximate position of intended destinations by some communication means such as GPS, GSM or WLAN access points. With the knowledge of nodes position in the network, high message directivity can be achieved by using simple concepts of direction vectors. Based on this observation, this research presents another new algorithm named as Location-aware opportunistic content forwarding (LOC). Last but not least, this research presents an orthodox yet unexplored approach for efficient message forwarding in Bluetooth communication environment, named as Hybrid Content Forwarding (HCF). The new approach combines the characteristics of social centrality based forwarding techniques used in opportunistic networks with traditional MANETs protocols used in Bluetooth scatternets. Simulation results show that a significant increase in delivery radio and cost reduction during content forwarding is observed by deploying these proposed algorithms. Also, comparison with existing technique shows the efficiency of using the new schemes