Towards Trustworthy, Efficient and Scalable Distributed Wireless Systems

Advances in wireless technologies have enabled distributed mobile devices to connect with each other to form distributed wireless systems. Due to the absence of infrastructure, distributed wireless systems require node cooperation in multi-hop routing. However, the openness and decentralized nature of distributed wireless systems where each node labors under a resource constraint introduces three challenges: (1) cooperation incentives that effectively encourage nodes to offer services and thwart the intentions of selfish and malicious nodes, (2) cooperation incentives that are efficient to deploy, use and maintain, and (3) routing to efficiently deliver messages with less overhead and lower delay. While most previous cooperation incentive mechanisms rely on either a reputation system or a price system, neither provides sufficiently effective cooperation incentives nor efficient resource consumption. Also, previous routing algorithms are not sufficiently efficient in terms of routing overhead or delay. In this research, we propose mechanisms to improve the trustworthiness, scalability, and efficiency of the distributed wireless systems. Regarding trustworthiness, we study previous cooperation incentives based on game theory models. We then propose an integrated system that combines a reputation system and a price system to leverage the advantages of both methods to provide trustworthy services. Analytical and simulation results show higher performance for the integrated system compared to the other two systems in terms of the effectiveness of the cooperation incentives and detection of selfish nodes. Regarding scalability in a large-scale system, we propose a hierarchical Account-aided Reputation Management system (ARM) to efficiently and effectively provide cooperation incentives with small overhead. To globally collect all node reputation information to accurately calculate node reputation information and detect abnormal reputation information with low overhead, ARM builds a hierarchical locality-aware Distributed Hash Table (DHT) infrastructure for the efficient and integrated operation of both reputation systems and price systems. Based on the DHT infrastructure, ARM can reduce the reputation management overhead in reputation and price systems. We also design a distributed reputation manager auditing protocol to detect a malicious reputation manager. The experimental results show that ARM can detect the uncooperative nodes that gain fraudulent benefits while still being considered as trustworthy in previous reputation and price systems. Also, it can effectively identify misreported, falsified, and conspiratorial information, providing accurate node reputations that truly reflect node behaviors. Regarding an efficient distributed system, we propose a social network and duration utility-based distributed multi-copy routing protocol for delay tolerant networks based on the ARM system. The routing protocol fully exploits node movement patterns in the social network to increase delivery throughput and decrease delivery delay while generating low overhead. The simulation results show that the proposed routing protocol outperforms the epidemic routing and spray and wait routing in terms of higher message delivery throughput, lower message delivery delay, lower message delivery overhead, and higher packet delivery success rate. The three components proposed in this dissertation research improve the trustworthiness, scalability, and efficiency of distributed wireless systems to meet the requirements of diversified distributed wireless applications

Assessing Influential Users in Live Streaming Social Networks

abstract: Live streaming has risen to significant popularity in the recent past and largely this live streaming is a feature of existing social networks like Facebook, Instagram, and Snapchat. However, there does exist at least one social network entirely devoted to live streaming, and specifically the live streaming of video games, Twitch. This social network is unique for a number of reasons, not least because of its hyper-focus on live content and this uniqueness has challenges for social media researchers. Despite this uniqueness, almost no scientific work has been performed on this public social network. Thus, it is unclear what user interaction features present on other social networks exist on Twitch. Investigating the interactions between users and identifying which, if any, of the common user behaviors on social network exist on Twitch is an important step in understanding how Twitch fits in to the social media ecosystem. For example, there are users that have large followings on Twitch and amass a large number of viewers, but do those users exert influence over the behavior of other user the way that popular users on Twitter do? This task, however, will not be trivial. The same hyper-focus on live content that makes Twitch unique in the social network space invalidates many of the traditional approaches to social network analysis. Thus, new algorithms and techniques must be developed in order to tap this data source. In this thesis, a novel algorithm for finding games whose releases have made a significant impact on the network is described as well as a novel algorithm for detecting and identifying influential players of games. In addition, the Twitch network is described in detail along with the data that was collected in order to power the two previously described algorithms.Dissertation/ThesisDoctoral Dissertation Computer Science 201

A shapley value-based approach to discover influential nodes in social networks

Our study concerns an important current problem, that of diffusion of information in social networks. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing and sales promotions. In this paper, we focus on the target set selection problem, which involves discovering a small subset of influential players in a given social network, to perform a certain task of information diffusion. The target set selection problem manifests in two forms: 1) top-k nodes problem and 2) λ-coverage problem. In the top-k nodes problem, we are required to find a set of k key nodes that would maximize the number of nodes being influenced in the network. The λ-coverage problem is concerned with finding a set of key nodes having minimal size that can influence a given percentage λ of the nodes in the entire network. We propose a new way of solving these problems using the concept of Shapley value which is a well known solution concept in cooperative game theory. Our approach leads to algorithms which we call the ShaPley value-based Influential Nodes (SPINs) algorithms for solving the top-k nodes problem and the λ-coverage problem. We compare the performance of the proposed SPIN algorithms with well known algorithms in the literature. Through extensive experimentation on four synthetically generated random graphs and six-real-world data sets (Celegans, Jazz, NIPS coauthorship data set, Netscience data set, High-Energy Physics data set, and Political Books data set), we show that the proposed SPIN approach is more powerful and computationally efficient