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User recommendation aims at recommending users with potential interests in the social network. Previous works havemainly focused on the undirected social networks with symmetric relationship such as friendship, whereas recent advances have been made on the asymmetric relationship such as the following and followed by relationship. Among the few existing direction-aware user recommendation methods, the random walk strategy has been widely adopted to extract the asymmetric proximity between users. However, according to our analysis on real-world directed social networks, we argue that the asymmetric proximity captured by existing random walk based methods are insufficient due to the inbalance in-degree and out-degree of nodes. To tackle this challenge, we propose InfoWalk, a novel informative walk strategy to efficiently capture the asymmetric proximity solely based on random walks. By transferring the direction information into the weights of each step, InfoWalk is able to overcome the limitation of edges while simultaneously maintain both the direction and proximity. Based on the asymmetric proximity captured by InfoWalk, we further propose the qualitative (DNE-L) and quantitative (DNE-T) directed network embedding methods, capable of preserving the two properties in the embedding space. Extensive experiments conducted on six real-world benchmark datasets demonstrate the superiority of the proposed DNE model over several state-of-the-art approaches in various tasks. [ABSTRACT FROM AUTHOR]

Many IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) that are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this article, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relatively computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem, which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes, and the cloud. We conduct extensive evaluations, and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state-of-the-art under dynamic workloads, while incurring acceptable overhead. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of decomposing the end-to-end delay into the per-hop delay for each packet, in multi-hop wireless ad hoc networks. Knowledge on the per-hop per-packet delay can greatly improve the network visibility and facilitate network measurement and management. We propose Domo, a passive, lightweight, and accurate delay tomography approach to decomposing the packet end-to-end delay into each hop. We first formulate the per packet delay tomography problem into a set of optimization problems by carefully considering the constraints among various timing quantities. At the network side, Domo attaches a small overhead to each packet for constructing constraints of the optimization problems. By solving these optimization problems by semi-definite relaxation at the PC side, Domo is able to estimate the per-hop delays with high accuracy as well as give a upper bound and lower bound for each unknown per-hop delay. We implement Domo and evaluate its performance extensively using both trace-driven studies and large-scale simulations. Results show that Domo significantly outperforms two existing methods, nearly tripling the accuracy of the state-of-the-art. [ABSTRACT FROM PUBLISHER]

Inferring fine-grained link metrics by using aggregated path measurements, known as network tomography, is an effective and efficient way to facilitate various network operations, such as network monitoring, load balancing, and failure diagnosis. Given the network topology and a set of interesting links, we study the problem of calculating the link metrics of these links by end-to-end cycle-free path measurements among selected monitors, i.e., preferential link tomography. Since assigning nodes as monitors usually requires non-negligible operational cost, we focus on assigning a minimum number of monitors to identify these interesting links. We propose an optimal monitor assignment (OMA) algorithm for preferential link tomography in communication networks. OMA first partitions the graph representing the network topology into multiple graph components. Then, OMA carefully assigns monitors inside each graph component and at the boundaries of multiple graph components. We theoretically prove the optimality of OMA by proving: 1) the monitors assigned by OMA are able to identify all interesting links and 2) the number of monitors assigned by OMA is minimal. We also implement OMA and evaluate it through extensive simulations based on both real topologies and synthetic topologies. Compared with two baseline approaches, OMA reduces the number of monitors assigned significantly in various network settings. [ABSTRACT FROM PUBLISHER]

Collaborative filtering (CF) is out of question the most widely adopted and successful recommendation approach. A typical CF-based recommender system associates a user with a group of like-minded users based on their individual preferences over all the items, either explicit or implicit, and then recommends to the user some unobserved items enjoyed by the group. However, we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more reasonable to predict preferences through one user's correlated subgroups, but not the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate a new Multiclass Co-Clustering (MCoC) model, which captures relations of user-to-item, user-to-user, and item-to-item simultaneously. Then, we combine traditional CF algorithms with subgroups for improving their top- $N$ recommendation performance. Our approach can be seen as a new extension of traditional clustering CF models. Systematic experiments on several real data sets have demonstrated the effectiveness of our proposed approach. [ABSTRACT FROM PUBLISHER]

Anomaly detection is an essential functionality of Wireless Sensor Networks (WSNs). However, the dynamic nature of WSNs makes the anomaly detection challenging. The environmental dynamics or internal topological dynamics pose difficulties to differentiate abnormal behaviors from normal ones. We observe that indirect correlations among multiple attributes of a sensor node can be utilized to capture and model the dynamic behaviors. Prior studies overlooked indirect correlations while in this study we exploit it for detecting anomaly efficiently and accurately. We propose ICAD, an indirect correlation based anomaly detection approach. By applying the Markov chain, the dynamic behaviors are modeled and subsequently used to detect anomalies. Compared with prior approaches, ICAD can detect different types of anomalies simultaneously and the performance is not sensitive to network dynamics. ICAD is implemented based on TinyOS/TelosB platform. Evaluation results show that ICAD achieves high detection accuracy with low overhead. [ABSTRACT FROM AUTHOR]

Wireless link correlation has shown significant impact on the performance of various sensor network protocols. Many works have been devoted to exploiting link correlation for protocol improvements. However, the effectiveness of these designs heavily relies on the accuracy of link correlation measurement. In this paper, we investigate state-of-the-art link correlation measurement and analyze the limitations of existing works. We then propose a novel lightweight and accurate link correlation estimation (LACE) approach based on the reasoning of link correlation formation. LACE combines both long-term and short-term link behaviors for link correlation estimation. We implement LACE as a stand-alone interface in TinyOS and incorporate it into both routing and flooding protocols. Simulation and testbed results show that LACE: (1) achieves more accurate and lightweight link correlation measurements than the state-of-the-art work; and (2) greatly improves the performance of protocols exploiting link correlation. [ABSTRACT FROM AUTHOR]