Viral marketing is becoming important due to the popularity of online social networks (OSNs). Companies may provide incentives (e. g., via free samples of a product) to a small group of users in an OSN, and these user...
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Viral marketing is becoming important due to the popularity of online social networks (OSNs). Companies may provide incentives (e. g., via free samples of a product) to a small group of users in an OSN, and these users provide recommendations to their friends, which eventually increases the overall sales of a given product. Nevertheless, this also opens a door for malicious behaviors: dishonest users may intentionally give misleading recommendations to their friends so as to distort the normal sales distribution. In this paper, we propose a detection framework to identify dishonest users in the OSNs. In particular, we present a set of fully distributed and randomized algorithms, and also quantify the performance of the algorithms by deriving probability of false positive, probability of false negative, and the distribution of number of detection rounds. Extensive simulations are also carried out to illustrate the impact of misleading recommendations and the effectiveness of our detection algorithms. The methodology we present here will enhance the security level of viral marketing in the OSNs.
distributed algorithms executed by a network of nodes with limited computational resources have many practical applications, not only in computer science, but also in other areas of engineering, and in physics, biolog...
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ISBN:
(纸本)9781665414906
distributed algorithms executed by a network of nodes with limited computational resources have many practical applications, not only in computer science, but also in other areas of engineering, and in physics, biology, and the social sciences. This paper studies one such algorithm in which the nodes update their states by iteratively voting for one of a finite number of candidates. Our numerical simulations show that convergence for this algorithm depends on a parameter m that determines the mean of the normal distribution that generates the weights in the network. When m is above an upper threshold, the algorithm always converges to a consensus candidate. When m is below a lower threshold, the algorithm does not converge and there is no consensus. When m is between these two thresholds, then the algorithm converges, but not necessarily to a consensus candidate. The values of the thresholds depend on the size of the network.
A general open problem in networking is: what are the fundamental limits to the performance that is achievable with some given amount of resources? More specifically, if each node in the network has information about ...
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ISBN:
(纸本)9781450389334
A general open problem in networking is: what are the fundamental limits to the performance that is achievable with some given amount of resources? More specifically, if each node in the network has information about only its 1-hop neighborhood, then what are the limits to performance? This problem is considered for wireless networks where each communication link has a minimum bandwidth quality-of-service (QoS) requirement. Links in the same vicinity contend for the shared wireless medium. The conflict graph captures which pairs of links interfere with each other and depends on the MAC protocol. In IEEE 802.11 MAC protocol-based networks, when communication between nodes i and j takes place, the neighbors of both i and j remain silent. This model of interference is called the 2-hop interference model because the distance in the network graph between any two links that can be simultaneously active is at least 2. In the admission control problem studied in the present paper, the objective is to determine, using only localized information, whether a given set of flow rates is feasible. In the present work, a distributed algorithm is proposed for this problem, where each node has information only about its 1-hop neighborhood. The worst-case performance of the distributed algorithm, i.e. the largest factor by which the performance of this distributed algorithm is away from that of an optimal, centralized algorithm, is analyzed. Lower and upper bounds on the suboptimality of the distributed algorithm are obtained, and both bounds are shown to be tight. The exact worst-case performance is obtained for some ring topologies. While distance-d distributed algorithms have been analyzed for the 1-hop interference model, an open problem in the literature is to extend these results to the K-hop interference model, and the present work initiates the generalization to the K-hop interference model.
The emerging power grid is characterized by a high penetration of distributed energy resources (DERs), particularly in the distribution grid. There is potential to leverage these DERs to provide grid-level support or ...
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ISBN:
(纸本)9781728181929
The emerging power grid is characterized by a high penetration of distributed energy resources (DERs), particularly in the distribution grid. There is potential to leverage these DERs to provide grid-level support or reduce operating costs. However, economic dispatch of these DERs in a centralized fashion is computationally intractable. Rather, the use of distributed algorithms which leverage grid edge intelligence and peer-to-peer communication is preferred. To this end, we propose a distributed algorithm for the recursive economic dispatch of DERs for radial distribution grids, based on the backward/forward sweep method, using the LinDistFlow equations. We aggregate costs through recursive bid functions using local information, which are then cleared using local computational resources. We motivate our design of bid functions using the KKT optimality conditions, and provide numerical results for the IEEE-13 bus network.
LP relaxations of Maximum Weighted Independent Set (MWIS) problems have been widely studied. A key motivation for this prior work comes from the central role that MWIS plays in designing throughput-optimal algorithms ...
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ISBN:
(纸本)9781479933617
LP relaxations of Maximum Weighted Independent Set (MWIS) problems have been widely studied. A key motivation for this prior work comes from the central role that MWIS plays in designing throughput-optimal algorithms for wireless networks. However, to the best of our knowledge, the actual packet delay performance of these algorithms has not been studied in the context of wireless networks. In this paper, we first present an algorithm for solving the LP relaxation of MWIS which exhibits faster convergence to an optimal solution. Further, we show that one does not have to wait for infinite time for convergence to occur, but a simple rounding technique can be used to identify the ON/OFF states of the wireless links in finite time. As in prior work, such an approach only identifies the optimal MWIS states of some of the links in the network. Therefore, we present a scheme to combine this solution with Q-CSMA. Simulations indicate that the proposed scheme significantly improves the performance of Q-CSMA. Further, the proposed algorithm is shown to perform much better than previously suggested LP relaxation schemes due to its superior convergence properties.
Doubly-stochastic matrices are usually required by consensus-based distributed algorithms. We propose a simple and efficient protocol and present some guidelines for implementing doubly-stochastic combination matrices...
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Doubly-stochastic matrices are usually required by consensus-based distributed algorithms. We propose a simple and efficient protocol and present some guidelines for implementing doubly-stochastic combination matrices even in noisy, asynchronous and changing topology scenarios. The proposed ideas are validated with the deployment of a wireless sensor network, in which nodes run a distributed algorithm for robust estimation in the presence of nodes with faulty sensors.
We study the gathering problem to make multiple agents initially scattered in arbitrary networks gather at a single node. There exist k agents with unique identifiers (IDs) in the network, and.. of them are weakly Byz...
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ISBN:
(纸本)9781450392624
We study the gathering problem to make multiple agents initially scattered in arbitrary networks gather at a single node. There exist k agents with unique identifiers (IDs) in the network, and.. of them are weakly Byzantine agents, which behave arbitrarily except for falsifying their IDs. The agents behave in synchronous rounds, and each node does not have any memory like a whiteboard. In the literature, there exists a gathering algorithm that tolerates any number of Byzantine agents, while the fastest gathering algorithm requires Omega(f(2)) non-Byzantine agents. This paper proposes an algorithm that solves the gathering problem efficiently with Omega(f(2)) non-Byzantine agents since there is a large gap between the number of non-Byzantine agents in the previous works. The proposed algorithm achieves the gathering in O(f center dot |Lambda(good) | center dot X (Nu)) rounds in case of 9f + 8 <= k and simultaneous startup if.. is given to agents, where ||Lambda(good) | | is the length of the largest ID among nonByzantine agents, and |Lambda(good) | is the number of rounds required to explore any network composed of n nodes. This algorithm is faster than the most fault-tolerant existing algorithm and requires fewer non-Byzantine agents than the fastest algorithm if.. is given to agents, although the guarantees on simultaneous termination and startup delay are not the same. To achieve this property, we propose a new technique to simulate a Byzantine consensus algorithm for synchronous message-passing systems on agent systems.
We consider the generalized Nash equilibrium (GNE) problem via distributed computation. Specifically, we consider a partial-decision information setting where each agent has no direct access to the decisions of all ot...
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ISBN:
(纸本)9781665436595
We consider the generalized Nash equilibrium (GNE) problem via distributed computation. Specifically, we consider a partial-decision information setting where each agent has no direct access to the decisions of all others while its cost function depends on them. Instead, each agent is assumed to exchange information with its neighbors via a communication network. To enhance communication efficiency, we propose a distributed event-triggered scheme such that each agent can independently determine when to transmit information to its neighbors. Thus, a fully-distributed, event-triggered GNE seeking algorithm is designed by combining event-triggered scheme, consensus, and projected-pseudo-gradient dynamics. Through primal-dual analysis, we prove convergence to a variational GNE by recasting the overall scheme as an inexact forward-backward iteration.
This paper studies distributed resource allocation problems of multi-agent systems, where the decisions of agents are subject to inequality network resource constraints and local inequality constraints. Compared with ...
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This paper studies distributed resource allocation problems of multi-agent systems, where the decisions of agents are subject to inequality network resource constraints and local inequality constraints. Compared with well-known resource allocation problems, our problem considers the high-order dynamics of agents. Without involving the control of high-order dynamics, existing distributed resource allocation algorithms cannot deal with our problem. Meanwhile, the high-order dynamics together with the inequality constraints makes it difficult to design and analyze distributed resource allocation algorithms, because the outputs of agents cannot be decided by their inputs directly and the optimal decisions of agents must satisfy the inequality constraints. In order to control the high -order agents to accomplish the resource allocation tasks autonomously, a distributed algorithm is designed by state feedback, gradient descent and primal-dual methods. Moreover, the convergence of the algorithm is analyzed by convex analysis and Lyapunov stability theory. With the algorithm, the high-order agents converge to the optimal allocation. Finally, numerical simulations verify the effectiveness of the algorithm. (C) 2022 Published by Elsevier Ltd.
distributed state estimation (DSE) is considered as a more robust and reliable alternative for centralized state estimation (CSE) in power system. Especially, taking into account the future power grid, so called smart...
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ISBN:
(纸本)9781728176604
distributed state estimation (DSE) is considered as a more robust and reliable alternative for centralized state estimation (CSE) in power system. Especially, taking into account the future power grid, so called smart grid in which bi-directional transfer of energy and information happens, and renewable energy sources with huge indeterminacy are applied more than before. Combining the mentioned features and complexity of the power network, there is a high probability that CSE face problems such as communication bottleneck or security/reliability issues. So, DSE has the potential to be considered as a solution to solve the mentioned issues. In this paper, first, a modified convergence criterion is proposed and has been tested for different approaches of DSE problem, considering the most important factors such as iteration number, convergence rate, and data needed to be transferred to/from each area. Then, an optimal partitioning technique has been implemented for clustering the system into different areas. Besides the detailed analysis and comparison of recent DSE methods, the proposed partitioning method's effectiveness and scalability has been shown in this paper.
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