Nowadays, we can get the wireless devices easily, such as cell phone, PDA, etc. It can make the life convenient. The P2P network which has been constructed by the wireless devices does not need the central server. They can communicate by themselves. Not only have protected in the privacy, but also add the convenience. The reason is that the devices are portable, we can get the newest information. Some P2P software focuses on the sharing. They can share files with other peer. The file can separate into several modes. The software will share these nodes. Every peer shares his own node, and it will speed up the rate of sharing. There are some selfish peers in this environment, and they will not want to share their own node. Moreover, some of them share the incorrect file. The software will solve this kind of problem by some mechanism. And it set some incentive mechanism to make the sharing to go on. Because the wireless devices are portable, we can use these devices for exchanging immediate information. Sharing the files is similar to the exchanging. Both of the users trust each other. They can exchange automatically. So, our research has designed a reputation based mechanism for exchanging. The users can evaluate each other to exchanging the information automatically. By this mechanism, the user in our system will exchange continuously. We can reach our purpose which makes the user in our environment can exchange automatically.

The multifaceted utilization of mobile computing devices, including smart phones, PDAs, tablet computers with increasing functionalities and the advances in wireless technologies, has fueled the utilization of collaborative computing (peer-to-peer) technique in mobile environment. Mobile collaborative computing, known as mobile peer-to-peer (MP2P), can provide an economic way of data access among users of diversified applications in our daily life (exchanging traffic condition in a busy high way, sharing price-sensitive financial information, getting the most-recent news), in national security (exchanging information and collaborating to uproot a terror network, communicating in a hostile battle field) and in natural catastrophe (seamless rescue operation in a collapsed and disaster torn area). Nonetheless, data/content dissemination among the mobile devices is the fundamental building block for all the applications in this paradigm. The objective of this research is to propose a data dissemination scheme for mobile distributed systems using an MP2P technique, which maximizes the number of required objects distributed among users and minimizes to object acquisition time. In specific, we introduce a new paradigm of information dissemination in MP2P networks. To accommodate mobility and bandwidth constraints, objects are segmented into smaller pieces for efficient information exchange. Since it is difficult for a node to know the content of every other node in the network, we propose a novel Spatial-Popularity based Information Diffusion (SPID) scheme that determines urgency of contents based on the spatial demand of mobile users and disseminates content accordingly. The segmentation policy and the dissemination scheme can reduce content acquisition time for each node. Further, to facilitate efficient scheduling of information transmission from every node in the wireless mobile networks, we modify and apply the distributed maximal independent set (MIS) algorithm. We also consider neighbor overlap for closely located mobile stations to reduce duplicate transmission to common neighbors. Different parameters in the system such as node density, scheduling among neighboring nodes, mobility pattern, and node speed have a tremendous impact on data diffusion in an MP2P environment. We have developed analytical models for our proposed scheme for object diffusion time/delay in a wireless mobile network to apprehend the interrelationship among these different parameters. In specific, we present the analytical model of object propagation in mobile networks as a function of node densities, radio range, and node speed. In the analysis, we calculate the probabilities of transmitting a single object from one node to multiple nodes using the epidemic model of spread of disease. We also incorporate the impact of node mobility, radio range, and node density in the networks into the analysis. Utilizing these transition probabilities, we construct an analytical model based on the Markov process to estimate the expected delay for diffusing an object to the entire network both for single object and multiple object scenarios. We then calculate the transmission probabilities of multiple objects among the nodes in wireless mobile networks considering network dynamics. Through extensive simulations, we demonstrate that the proposed scheme is efficient for data diffusion in mobile networks.

Despite recommender systems being useful, for some applications it is hard to accumulate all the required information needed for the recommendation. In today‟s ubiquitous environment, mobile devices with different characteristics are widely available. Our work focuses on the recommendation service built on mobile environment to support tourists‟ traveling need. When tourists visit a new attraction, their recommender systems can exchange data with the attraction system to help obtain rating information of people with similar tastes. Such asynchronous rating exchange mechanisms allow a tourist to receive ratings from other people even though they may not collocate at the same time. We proposed four data exchange methods between a user and an attraction system. Our recommendation mechanism incorporates other users‟ opinions to provide recommendations once the user has collected enough ratings. Every method is compared under four conditions which attraction systems carry different amount of existing data. Then we compare these methods under different amount of existing rating data and shed the light on their advantages and disadvantages. Finally, we compare our proposed asynchronous methods with other synchronous data exchange methods proposed previously.

As the global wireless network becoming mature, the growing number of mobile devices, and the personal need of keeping in touch with others daily, mobile communication is becoming a necessity of life. Using mobile devices, such as cell phones or PDAs, everyone could communicate with each other independently; and this communication environment is similar with P2P network. Utilizing the connection network of cell phones is becoming a brand new business channel. Since group-buying is part of our daily life, people live in the same area could buy products or services together based on mutual needs, and this could enhance the bargaining power of customers and lower the purchase price. This research proposes a group-buying system architecture under mobile environment, and discusses the problems that customers might encounter in every stage of the buying process. Mobile device users under the environment could exchange information with each other and this could help customers search group-buying information efficiently. Sensor Network combines with MP2P Network accelerates the spread of group-buying information in a marketplace, and helps the originator and other buyers to negotiate with the supplier. Based on the observation of the variation of the group-buying information distribution in the experiment, we summarize under what situation a better information distribution would take place in a specific marketplace.

The goodness of group buying is consumers can buy product with lower price, and seller can reduce the bargain cost by collecting orders. But it is hard to be realized in the mall. A recent survey shows most everyone has his own mobile device. In this research, we organize a mobile P2P network by exchanging between two mobile devices. We propose a system for consumers and sellers to exchange group buying information. In Mobile P2P Network environment, group buying initiator is hard to decide the best timing to end the group buying. Because buyers can easily participate or leave the group buying group, and initiator may not know the exactly how many buyers participate the group buying. So we simulate a virtual mall with the realistic data and try to find the suitable group buying model in this environment. Then we examine if participation externality effect, price drop effect and ending effect will appear in this model. Finally, we observe the trend of the number of buyers in group buying to suggest the group buying ending time. The research result indicates that participation externality effect and price drop effect still appear in the Mobile P2P Network environment. But ending effect is not obvious. Because the group buying information is distributed by participating buyers, the distribution will affect the trend of participation and participation externality effect. With suggestion of the system, initiator will not waste much time to wait few participations, and participators can reduce the time cost.

¡@¡@Recommendation techniques are developed to uncover users¡¥ real needs among large volume of information. Recommender systems help us filter information and present those similar to our tastes. As wireless technology develops and mobile devices become more and more powerful, new recommender systems appear to adapt to new implementation environment. We focus on travel recommender systems implemented in a mobile P2P environment using collaborative filtering recommendation algorithms which intend to provide real-time suggestions to travelers when they are on the move. Using the concept of incorporating other travelers¡¥ suggestions to the next attraction, we let users exchange their ratings toward visited attractions and use these ratings as a basis of recommendation. ¡@¡@We proposed six data exchange algorithms for travelers to exchange their ratings. The proposed methods were experimented in the homogeneous and heterogeneous environment. The experimental results show that the proposed data exchange methods have better recommendation hit ratio than content-based recommendation methods and better performance compared with other methods only using ratings of users when they meet face-to-face. Finally, all methods are compared and evaluated. An optimal method should be able to strike a balance between algorithm performance and the amount of data communication.

The multifaceted utilization of mobile computing devices, including smart phones, PDAs, tablet computers with increasing functionalities and the advances in wireless technologies, has fueled the utilization of collaborative computing (peer-to-peer) technique in mobile environment. Mobile collaborative computing, known as mobile peer-to-peer (MP2P), can provide an economic way of data access among users of diversified applications in our daily life (exchanging traffic condition in a busy high way, sharing price-sensitive financial information, getting the most-recent news), in national security (exchanging information and collaborating to uproot a terror network, communicating in a hostile battle field) and in natural catastrophe (seamless rescue operation in a collapsed and disaster torn area). Nonetheless, data/content dissemination among the mobile devices is the fundamental building block for all the applications in this paradigm. The objective of this research is to propose a data dissemination scheme for mobile distributed systems using an MP2P technique, which maximizes the number of required objects distributed among users and minimizes to object acquisition time. In specific, we introduce a new paradigm of information dissemination in MP2P networks. To accommodate mobility and bandwidth constraints, objects are segmented into smaller pieces for efficient information exchange. Since it is difficult for a node to know the content of every other node in the network, we propose a novel Spatial-Popularity based Information Diffusion (SPID) scheme that determines urgency of contents based on the spatial demand of mobile users and disseminates content accordingly. The segmentation policy and the dissemination scheme can reduce content acquisition time for each node. Further, to facilitate efficient scheduling of information transmission from every node in the wireless mobile networks, we modify and apply the distributed maximal independent set (MIS) algorithm. We also consider neighbor overlap for closely located mobile stations to reduce duplicate transmission to common neighbors. Different parameters in the system such as node density, scheduling among neighboring nodes, mobility pattern, and node speed have a tremendous impact on data diffusion in an MP2P environment. We have developed analytical models for our proposed scheme for object diffusion time/delay in a wireless mobile network to apprehend the interrelationship among these different parameters. In specific, we present the analytical model of object propagation in mobile networks as a function of node densities, radio range, and node speed. In the analysis, we calculate the probabilities of transmitting a single object from one node to multiple nodes using the epidemic model of spread of disease. We also incorporate the impact of node mobility, radio range, and node density in the networks into the analysis. Utilizing these transition probabilities, we construct an analytical model based on the Markov process to estimate the expected delay for diffusing an object to the entire network both for single object and multiple object scenarios. We then calculate the transmission probabilities of multiple objects among the nodes in wireless mobile networks considering network dynamics. Through extensive simulations, we demonstrate that the proposed scheme is efficient for data diffusion in mobile networks.

Wireless mobile internet market is still an unprecedented, uncaptured territory for cellular service providers. The shortage and high cost of downlink data bandwidth in a cellular network has remained a huge factor for the slow growth of data services in mobile devices. Although there has been a significant evolution in telephony infrastructures in form of 3G and 4G systems, the potential of high speed ad hoc network for sharing cellular spectrum have not been realized to its full potential. Like (e.g. Verizon) users can share voice minutes with friends, there is a potential for sharing the unutilized cellular bandwidth among friends to increase net data speed. In a scenario like a football stadium where people visit in groups, although a lone phone cannot stream a high quality replay video, unused cellular bandwidth of proximate friend’s devices can automatically be used in real time to view the replays. An available secondary ad hoc network such as Wi-Fi or Bluetooth in phone can be used for sharing this cellular bandwidth. Thus, we propose BuddyShare, a novel social-based automatic bandwidth sharing overlay platform on short range ad hoc devices to increase net data speed. The motivation stems from the fact that the location of mobile users tends to be clustered to form “people hotspots” such as conferences, stadiums, stations, buses and trains. For example, in a scenario like a football stadium where people visit in groups, although a lone phone cannot stream a high quality replay video, unused cellular bandwidth of proximate friends’ devices can automatically be used in real time to view the replays. Our work creates an overlay on horizontal ad hoc network to enable users to form a group among socially trusted members who can collaboratively share their data connections. Social trust is automatically derived from social relationships obtained by mining mobile-phone behavior pattern. This work aims to improve the overall utilization of the data connection, and increase the data rate of individual users without compromising their privacy and unauthenticated usage. The user privacy is preserved by using the bandwidth resources of only socially trusted member of the user, which also guarantees against unauthenticated exploitation of expensive bandwidth. Our proposed work promises to deliver win-win situation to users, content providers and service providers. The advantages of users are: 1) Increased data rate for the same cost.2) Secure and trusted overlay based communication for sharing resources. The advantages for the service providers are manifold: 1) Customer increase: More customers will avail the data plan due to social influence. 2) Customer retention: [18] Customers part of the social-cum-adhoc network are least likely to leave the network.3) Group subscription: Service provider can get bulk subscriptions as collaborative groups increase data rate. In this work, we address some key technical issues of developing a socially aware overlay collaborating medium. Some of the addressed functionalities associated with the overlay formations are group discovery, creation, management and actual data distribution. This proposal also accounts the computation of social trustworthiness by using standard social networking analytics. We also account the several key technical challenges associated with management of overlay on mobile nodes and trust computation using abstract social network. In order to verify the usefulness of BuddyShare, we collected realistic datasets from various sources (questionnaires, mobile device logs, social networking portal) and conducted analyses and simulations on it. The analyses concluded that sample users from the dataset shared sufficient social trustworthiness. The real events from the datasets were captured in the simulations. These simulations showed that, by using Bluetooth as a horizontal ad hoc medium, an user can scale his data speed three times on average for sufficient duration per day. This thesis achieves the following objectives: 1) It presents a comprehensive design for an overlaid social based internet sharing platform called BuddyShare. 2) It presents a social analysis to validate the concept of social trust among users. 3) It delivers a flexible simulation platform to realistically simulate mobile phones with dual interfaces. 4) It presents the results of simulations of real events captured from the device logs of sample users. These results conclude the usefulness of BuddyShare work.

With online social networks such as Facebook and Twitter becoming globally popular, there is renewed interest in understanding the structural and dynamical properties of social networks. In this thesis we study several stochastic models arising in the context of the spread of influence or information in social networks. Our objective is to provide compact and accurate quantitative descriptions of the spread processes, to understand the effects of various system parameters, and to design policies for the control of such diffusions. One of the well established models for influence spread in social networks is the threshold model. An individual’s threshold indicates the minimum level of “influence” that must be exerted, by other members of the population engaged in some activity, before the individual will join the activity. We begin with the well-known Linear Threshold (LT) model introduced by Kempe et al. [1]. We analytically characterize the expected influence for a given initial set under the LT model, and provide an equivalent interpretation in terms of acyclic path probabilities in a Markov chain. We derive explicit optimal initial sets for some simple networks and also study the effectiveness of the Pagerank [2] algorithm for the problem of influence maximization. Using insights from our analytical characterization, we then propose a computationally efficient G1-sieving algorithm for influence maximization and show that it performs on par with the greedy algorithm, through experiments on a coauthorship dataset. The Markov chain characterisation gives only limited insights into the dynamics of influence spread and the effects of the various parameters. We next provide such insights in a restricted setting, namely that of a homogeneous version of the LT model but with a general threshold distribution, by taking the fluid limit of a probabilistically scaled version of the spread Markov process. We observe that the threshold distribution features in the fluid limit via its hazard function. We study the effect of various threshold distributions and show that the influence evolution can exhibit qualitatively different behaviors, depending on the threshold distribution, even in a homogeneous setting. We show that under the exponential threshold distribution, the LT model becomes equivalent to the SIR (Susceptible-Infected-Recovered) epidemic model [3]. We also show how our approach is easily amenable to networks with heterogeneous community structures. Hundreds of millions of people today interact with social networks via their mobile devices. If the peer-to-peer radios on such devices are used, then influence spread and information spread can take place opportunistically when pairs of such devices come in proximity. In this context, we develop a framework for content delivery in mobile opportunistic networks with joint evolution of content popularity and availability. We model the evolution of influence and content spread using a multi-layer controlled epidemic model, and, using the monotonicity properties of the o.d.e.s, prove that a time-threshold policy for copying to relay nodes is delay-cost optimal. Information spread occurs seldom in isolation on online social networks. Several contents might spread simultaneously, competing for the common resource of user attention. Hence, we turn our attention to the study of competition between content creators for a common population, across multiple social networks, as a non-cooperative game. We characterize the best response function, and observe that it has a threshold structure. We obtain the Nash equilibria and study the effect of cost parameters on the equilibrium budget allocation by the content creators. Another key aspect to capturing competition between contents, is to understand how a single end-user receives and processes content. Most social networks’ interface involves a timeline, a reverse chronological list of contents displayed to the user, similar to an email inbox. We study competition between content creators for visibility on a social network user’s timeline. We study a non-cooperative game among content creators over timelines of fixed size, show that the equilibrium rate of operation under a symmetric setting, exhibits a non-monotonic behavior with increasing number of players. We then consider timelines of infinite size, along with a behavioral model for user’s scanning behavior, while also accounting for variability in quality (influence weight) among content creators. We obtain integral equations, that capture the evolution of average influence of competing contents on a social network user’s timeline, and study various content competition formulations involving quality and quantity.

This thesis focuses on two important problems related to DDDAS: interest management (data distribution) and prediction models. In order to reduce communication overhead, we propose a new interest management mechanism for mobile peer-to-peer systems. This approach involves dividing the entire space into cells and using an efficient sorting algorithm to sort the regions in each cell. A mobile landmarking scheme is introduced to implement this sort-based scheme in mobile peer-to-peer systems. The design does not require a centralized server, but rather, every peer can become a mobile landmark node to take a server-like role to sort and match the regions. Experimental results show that the scheme has better computational efficiency for both static and dynamic matching. In order to improve communication efficiency, we present a travel time prediction model based on boosting, an important machine learning technique, and combine boosting and neural network models to increase prediction accuracy. We also explore the relationship between the accuracy of travel time prediction and the frequency of traffic data collection with the long term goal of minimizing bandwidth consumption. Several different sets of experiments are used to evaluate the effectiveness of this model. The results show that the boosting neural network model outperforms other predictors.