With the enhancement of data collection capabilities, massive streaming data have been accumulated in numerous application scenarios. Specifically, the issue of classifying data streams based on mobile sensors can be formalized as a multi-task multi-view learning problem with a specific task comprising multiple views with shared features collected from multiple sensors. Existing incremental learning methods are often single-task single-view, which cannot learn shared representations between relevant tasks and views. An adaptive multi-task multi-view incremental learning framework for data stream classification called MTMVIS is proposed to address the above challenges, utilizing the idea of multi-task multi-view learning. Specifically, the attention mechanism is first used to align different sensor data of different views. In addition, MTMVIS uses adaptive Fisher regularization from the perspective of multi-task multi-view learning to overcome catastrophic forgetting in incremental learning. Results reveal that the proposed framework outperforms state-of-the-art methods based on the experiments on two different datasets with other baselines.

Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners; for example, the Proof-of-Work (PoW) and Practical Byzantine Fault Tolerant (PBFT) schemes, which have a high consumption of computing/communication resources and usually require reliable communications with bounded delay. However, these protocols may be unsuitable for Internet of Things (IoT) networks because the IoT devices are usually lightweight, battery-operated, and deployed in an unreliable wireless environment. Therefore, this paper studies an efficient consensus protocol for blockchain in IoT networks via reinforcement learning. Specifically, the consensus protocol in this work is designed on the basis of the Proof-of-Communication (PoC) scheme directly in a single-hop wireless network with unreliable communications. A distributed MultiAgent Reinforcement Learning (MARL) algorithm is proposed to improve the efficiency and fairness of consensus for miners in the blockchain system. In this algorithm, each agent uses a matrix to depict the efficiency and fairness of the recent consensus and tunes its actions and rewards carefully in an actor-critic framework to seek effective performance. Empirical results from the simulation show that the fairness of consensus in the proposed algorithm is guaranteed, and the efficiency nearly reaches a centralized optimal solution.

Large-scale graphs usually exhibit global sparsity with local cohesiveness, and mining the representative cohesive subgraphs is a fundamental problem in graph analysis. The $k$-truss is one of the most commonly studied cohesive subgraphs, in which each edge is formed in at least $k\mathrm{-}\mathrm{2}$ triangles. A critical issue in mining a $k$-truss lies in the computation of the trussness of each edge, which is the maximum value of $k$ that an edge can be in a $k$-truss. Existing works mostly focus on truss computation in static graphs by sequential models. However, the graphs are constantly changing dynamically in the real world. We study distributed truss computation in dynamic graphs in this paper. In particular, we compute the trussness of edges based on the local nature of the $k$-truss in a synchronized node-centric distributed model. Iteratively decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition algorithm. Moreover, the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the computation. Extensive experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

The prevalence of graph data has brought a lot of attention to cohesive and dense subgraph mining. In contrast with the large number of indexes proposed to help mine dense subgraphs in general graphs, only very few indexes are proposed for the same in bipartite graphs. In this work, we present the index called $\alpha \mathsf{}\mathrm{(}\beta \mathrm{)}$-core number on vertices, which reflects the maximal cohesive and dense subgraph a vertex can be in, to help enumerate the $\mathrm{(}\alpha \mathrm{,}\beta \mathrm{)}$-cores, a commonly used dense structure in bipartite graphs. To address the problem of extremely high time and space cost for enumerating the $\mathrm{(}\alpha \mathrm{,}\beta \mathrm{)}$-cores, we first present a linear time and space algorithm for computing the $\alpha \mathsf{}\mathrm{(}\beta \mathrm{)}$-core numbers of vertices. We further propose core maintenance algorithms, to update the core numbers of vertices when a graph changes by avoiding recalculations. Experimental results on different real-world and synthetic datasets demonstrate the effectiveness and efficiency of our algorithms.

Scalability has long been a major challenge of cryptocurrency systems, which is mainly caused by the delay in reaching consensus when processing transactions on-chain. As an effective mitigation approach, the payment channel networks (PCNs) enable private channels among blockchain nodes to process transactions off-chain, relieving long-time waiting for the online transaction confirmation. The state-of-the-art studies of PCN focus on improving the efficiency and availability via optimizing routing, scheduling, and initial deposits, as well as preventing the system from security and privacy attacks. However, the behavioral decision dynamics of blockchain nodes under potential malicious attacks is largely neglected. To fill this gap, we employ the game theory to study the characteristics of channel interactions from both the micro and macro perspectives under the situation of channel depletion attacks. Our study is progressive, as we conduct the game-theoretic analysis of node behavioral characteristics from individuals to the whole population of PCN. Our analysis is complementary, since we utilize not only the classic game theory with the complete rationality assumption, but also the evolutionary game theory considering the limited rationality of players to portray the evolution of PCN. The results of numerous simulation experiments verify the effectiveness of our analysis.

In recent years, due to the wide implementation of mobile agents, the Internet-of-Things (IoT) networks have been applied in several real-life scenarios, servicing applications in the areas of public safety, proximity-based services, and fog computing. Meanwhile, when more complex tasks are processed in IoT networks, demands on identity authentication, certifiable traceability, and privacy protection for services in IoT networks increase. Building a blockchain system in IoT networks can greatly satisfy such demands. However, the blockchain building in IoT brings about new challenges compared with that in the traditional full-blown Internet with reliable transmissions, especially in terms of achieving consensus on each block in complex wireless environments, which directly motivates our work. In this study, we fully considered the challenges of achieving a consensus in a blockchain system in IoT networks, including the negative impacts caused by contention and interference in wireless channel, and the lack of reliable transmissions and prior network organizations. By proposing a distributed consensus algorithm for blockchains on multi-hop IoT networks, we showed that it is possible to directly reach a consensus for blockchains in IoT networks, without relying on any additional network layers or protocols to provide reliable and ordered communications. In our theoretical analysis, we showed that our consensus algorithm is asymptotically optimal on time complexity and is energy saving. The extensive simulation results also validate our conclusions in the theoretical analysis.

A public-private-graph (pp-graph) is developed to model social networks with hidden relationships, and it consists of one public graph in which edges are visible to all users, and multiple private graphs in which edges are only visible to its endpoint users. In contrast with conventional graphs where the edges can be visible to all users, it lacks accurate indexes to evaluate the importance of a vertex in a pp-graph. In this paper, we first propose a novel concept, public-private-core (pp-core) number based on the k-core number, which integrally considers both the public graph and private graphs of vertices, to measure how critical a user is. We then give an efficient algorithm for the pp-core number computation, which takes only linear time and space. Considering that the graphs can be always evolving over time, we also present effective algorithms for pp-core maintenance after the graph changes, avoiding redundant re-computation of pp-core number. Extension experiments conducted on real-world social networks show that our algorithms achieve good efficiency and stability. Compared to recalculating the pp-core numbers of all vertices, our maintenance algorithms can reduce the computation time by about 6–8 orders of magnitude.

In the past decades, with the widespread implementation of wireless networks, such as the Internet of Things, an enormous demand for designing relative algorithms for various realistic scenarios has arisen. However, with the widening of scales and deepening of network layers, it has become increasingly challenging to design such algorithms when the issues of message dissemination at high levels and the contention management at the physical layer are considered. Accordingly, the abstract medium access control (absMAC) layer, which was proposed in 2009, is designed to solve this problem. Specifically, the absMAC layer consists of two basic operations for network agents: the acknowledgement operation to broadcast messages to all neighbors and the progress operation to receive messages from neighbors. The absMAC layer divides the wireless algorithm design into two independent and manageable components, i.e., to implement the absMAC layer over a physical network and to solve higher-level problems based on the acknowledgement and progress operations provided by the absMAC layer, which makes the algorithm design easier and simpler. In this study, we consider the implementation of the absMAC layer under jamming. An efficient algorithm is proposed to implement the absMAC layer, attached with rigorous theoretical analyses and extensive simulation results. Based on the implemented absMAC layer, many high-level algorithms in non-jamming cases can be executed in a jamming network.

In the era of big data, sensor networks have been pervasively deployed, producing a large amount of data for various applications. However, because sensor networks are usually placed in hostile environments, managing the huge volume of data is a very challenging issue. In this study, we mainly focus on the data storage reliability problem in heterogeneous wireless sensor networks where robust storage nodes are deployed in sensor networks and data redundancy is utilized through coding techniques. To minimize data delivery and data storage costs, we design an algorithm to jointly optimize data routing and storage node deployment. The problem can be formulated as a binary nonlinear combinatorial optimization problem, and due to its NP-hardness, designing approximation algorithms is highly nontrivial. By leveraging the Markov approximation framework, we elaborately design an efficient algorithm driven by a continuous-time Markov chain to schedule the deployment of the storage node and corresponding routing strategy. We also perform extensive simulations to verify the efficacy of our algorithm.