Social networks allow rapid spread of ideas and innovations while negative information can also propagate widely. When a user receives two opposing opinions, they tend to believe the one arrives first. Therefore, once misinformation or rumor is detected, one containment method is to introduce a positive cascade competing against the rumor. Given a budget $k$, the rumor blocking problem asks for $k$ seed users to trigger the spread of a positive cascade such that the number of the users who are not influenced by rumor can be maximized. The prior works have shown that the rumor blocking problem can be approximated within a factor of $(1 - 1/e)$ by a classic greedy algorithm combined with Monte Carlo simulation. Unfortunately, the Monte Carlo simulation based methods are time consuming and the existing algorithms either trade performance guarantees for practical efficiency or vice versa. In this paper, we present a randomized approximation algorithm which is provably superior to the state-of-the art methods with respect to running time. The superiority of the proposed algorithm is demonstrated by experiments done on both the real-world and synthetic social networks.

Online social networks such as Facebook, Twitter and Wechat have become major social tools. The users can not only keep in touch with family and friends, but also send and share the instant information. However, in some practical scenarios, we need to take effective measures to control the negative information spreading, e.g, rumors spread over the networks. In this paper, we first propose the Minimizing Influence of Rumors (MIR) problem, i.e., selecting a blocker set $\mathcal{B}$ with $k$ nodes such that the users' total activation probability by rumor source set $s$ is minimized. Then we employ the classical Independent Cascade (IC) model as information diffusion model. Based on the IC model, we prove the objective function is monotone decreasing and non-submodular. To address the MIR problem effectively, we propose a two-stages method enerating Candidate Set $\&$ Selecting Blockers (GCSSB) for the general networks. Furthermore, we also study the MIR problem on the tree network and propose a dynamic programming guaranteeing the optimal solution. Finally, we evaluate proposed algorithms by simulations on synthetic and real-life social networks, respectively. Experimental results show our algorithms are superior to the comparative heuristic approaches such as ut-Degree (OD), Betweenness Centrality (BC) and PageRank (PR).

The world-changing blockchain technique provides a novel method to establish a secure, trusted, and decentralized system for solving the security and personal privacy problems in Internet of Things (IoT) applications. As the mining process in blockchain requires high computational power, the lightweight IoT devices need to purchase computational resources from edge servers and thus can offload their computational tasks. In this paper, we investigate the incentive mechanism for the blockchain platform to attract IoT devices to purchase more computational power from edge servers to participate in the mining process, thereby building a secure blockchain network while guaranteeing the profits of the blockchain platform. We model the interaction between the blockchain platform and IoT devices as a two-stage Stackelberg game, where the blockchain platform act as the leader and IoT devices act as followers. We analyze the existence and uniqueness of the Stackelberg equilibrium, and propose an efficient algorithm to compute the Stackelberg equilibrium point. Furthermore, we evaluate the performance of our algorithm through extensive simulations, and analyze the strategies of the blockchain platform and IoT devices under different situations.

For a network of robots working in a specific environment, relative localization among robots is the basis for accomplishing various upper-level tasks. To avoid the latency and fragility of long-range or multi-hop communication, distributed relative localization algorithms, in which robots take local measurements and calculate localizations and poses relative to their neighbors distributively, are highly desired. Distributed relative localization has the advantages of a low communication burden and better system robustness but encounters challenges in the distributed algorithm design, communication protocol design, local network organization, etc. This paper presents a detailed survey of the key methodologies designed for distributed relative localization for robot networks. We classify the distributed localization algorithms regarding to the types of measurements, i.e., distance-based, bearing-based, and multiple-measurement-fusion-based. The detailed design methodologies, advantages, drawbacks, and application scenarios of different distributed localization algorithms are introduced and summarized. Then, the research works that support distributed localization, including local network organization, communication efficiency, and the robustness of distributed localization algorithms, are surveyed. Finally, popular simulation platforms are summarized and compared in order to facilitate future research and experiments on distributed relative localization algorithms.

A swarm of unmanned air vehicles (UAVs) may form a dynamic 3-D network whose topology changes frequently. Tracking the geometric formation of the network is a critical problem. Recent advantage of wireless ranging technologies (e.g., ultrawideband) enables inter-UAV distance measurement up to hundreds meters with errors in centimeter level. This makes it possible to track the network topology by the partially measured distance matrix among the UAVs, which is known as the formation tracking problem. But the measured distances are generally sparse and noisy, and the topology of UAV network is changing continuously. These cause the formation tracking highly challenging. Existing methods are generally fragile to the measurement noises and network sparsity. This paper exploits a fact that well-connected subcomponents, whose local structures can be calculated reliably, exist widely because the unevenness of node distribution in sparse networks. Therefore, a weighted component stitching (WCS) method to find the reliable components and stitch their local structures with weights is proposed for calculating the formation of the network accurately. In particular, we propose efficient two-center four-vertex-connected star-graph (2-4-star) detection and merging algorithms to extract the reliable global rigid components. A WCS algorithm and a weighted component-based Kalman filter algorithm with complexity both O(n ³ ) are proposed for robust formation tracking in n vertex UAV networks. Extensive experiments were conducted, showing that the proposed methods can improve the formation tracking accuracy 21%-48% over existing state-of-the-art methods, especially in sparse, noisy UAV networks under different parameter settings.