The massive amount of data generated by the Internet-of-Things (IoT) devices places enormous pressure on sensory data query processing. Due to the limitations of computation and data transmission capabilities in traditional wireless sensor networks (WSNs), the current query processing methods are no longer effective. Furthermore, processing vast amount of sensory data also overloads the cloud. To address these problems, we investigate query processing in an edge-assisted IoT data monitoring system (EDMS). Multiaccess edge computing (MEC) is an emerging topic in IoTs. Unlike WSNs, the edge servers in an EDMS can deploy the computation and storage resources to nearby IoT devices and offer data processing services. Therefore, queries toward massive sensory data can be processed in an EDMS in a distributed manner and the edge servers can handle the sensory data in a distributed manner, reducing the workload of the cloud. In this article, we define a query processing problem in an EDMS, which aims to derive a distributed query plan with the minimum query response latency. We prove that this problem is NP-Hard and propose a corresponding approximation algorithm. The performance of the proposed algorithm is bounded. Furthermore, we evaluate the performance of the proposed algorithm through extensive simulations.

Distributed Query Processing in the Edge-Assisted IoT Data Monitoring System

Battery-Free Wireless Sensor Networks (BF-WSNs) have been attracting increasing interests in the recent years. To reduce the latency in BF-WSNs, the Minimum Latency Aggregation Scheduling (MLAS) problem with coverage requirement $q$ is proposed recently, which tries to choose $q$ percent of nodes for communication and aggregation. In the existing method, the authors try to select nodes adaptively according to their energy status and schedule these nodes to achieve the minimum latency. Unfortunately, it cannot guarantee the distribution of the aggregated nodes and may result in these nodes being squeezed in a small area and a poor aggregation quality. Thus, we re-investigate the $q$ -coverage MLAS problem in this article, which can guarantee that the aggregated nodes are distributed evenly. Firstly, the 1-coverage MLAS problem, in which each node can be covered by at least one aggregated node, is studied. To reduce the latency, we intertwine the selection of aggregated nodes and the computation of a collision-free communication schedule simultaneously. Two algorithms are proposed by scheduling the communication tasks in the bottom-up and top-down manner respectively. Secondly, to satisfy the arbitrary coverage requirement $q$ , three algorithms are proposed to guarantee the aggregated nodes are evenly distributed in the network with a low latency. Additionally, the method to extend the proposed algorithms for the BF-WSNs with multiple channels is also studied. The theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

Latency-and-Coverage Aware Data Aggregation Scheduling for Multihop Battery-Free Wireless Networks

This article studies the batch processing of truss maintenance in large graphs. Trussness is a widely used index in graph analytics for cohesive subgraph mining. It is defined on edges to reflect the closeness of vertices connected by the edges. The trussness maintenance problem, i.e., updating trussness after edge insertions/deletions and avoiding recomputation, was proposed by Cohen (2008) with the assumption that real graphs are continuously evolving in nature and their dynamicity usually only affects few edges. Different from the existing work that mainly focuses on simple single edge/vertex insertion/deletion, we propose batch processing algorithms for truss maintenance with multiedge insertions/deletions. By presenting an edge structure called triangle disjoint set, whose insertion/deletion can make each edge change its trussness by at most 1, we tackle the difficulty in quantifying the trussness changes of the edges in batch processing. More specifically, we propose two indices, namely sustain support and pivotal support, to help measure whether the edges have the potential to change their trussness, such that the search range of the potential edges is greatly reduced. The extensive experiments on real-world graphs illustrate that compared with the single-edge processing approach, our batch processing algorithms can significantly improve the processing efficiency, and the improvement becomes more obvious when more edges are inserted/deleted.

Batch Processing for Truss Maintenance in Large Dynamic Graphs

Community search has been extensively studied in large networks, such as Protein-Protein Interaction (PPI) networks, citation graphs, and collaboration networks. However, in terms of widely existing multi-valued networks, where each node has d (d ≥ 1) numerical attributes, almost all existing algorithms either completely ignore the attributes of node at all or only consider one attribute. To solve this problem, the concept of skyline community was presented, based on the concepts of k-core and skyline recently. The skyline community is defined as a maximal k-core that satisfies some influence constraints, which is very useful in depicting the communities that are not dominated by other communities in multi-valued networks. However, the algorithms proposed on skyline community search can only work in the special case that the nodes have different values on each attribute, and the computation complexity degrades exponentially as the number of attributes increases. In this work, we turn our attention to the general scenario where multiple nodes may have the same attribute value. Specifically, we first present an algorithm, called MICS, which can find all skyline communities in a multi-valued network. To improve computation efficiency, we then propose a dimension reduction based algorithm, called P-MICS, using the maximum entropy method. Our algorithm can significantly reduce the skyline community searching time, while is still able to find almost all cohesive skyline communities. Extensive experiments on real-world datasets demonstrate the efficiency and effectiveness of our algorithms.

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Fast Skyline Community Search in Multi-Valued Networks

In this paper, we study exact hypercore maintenance in large-scale dynamic hypergraphs. A hypergraph, whose hyperedges may contain a set of vertices rather than two vertices in pairwise graphs, can represent complex interactions in more sophisticated applications. However, the exponential number of hyperedges incurs unaffordable costs to recompute the hypercore number of vertices and hyperedges when updating a hypergraph. This motivates us to propose an efficient approach for exact hypercore maintenance with the intention of significantly reducing the hypercore updating time comparing with recomputation approaches. The proposed algorithms can pinpoint the vertices and hyperedges whose hypercore numbers have to be updated by only traversing a small sub-hypergraph. Extensive experiments on real-world and temporal hypergraphs demonstrate the superiority of our algorithms in terms of efficiency.

Hypercore Maintenance in Dynamic Hypergraphs

This paper initializes distributed algorithm studies for core decomposition in probabilistic graphs. Core decomposition has been proven to be a useful primitive for a wide range of graph analyses, but it has been rarely studied in probabilistic graphs, especially in a distributed environment. In this work, under a distributed model underlying Pregel and live distributed systems, we present the first known distributed solutions for core decomposition in probabilistic graphs, where there is an existence probability for each edge. In the scenario that the existence probability of edges are known to nodes, the proposed algorithm can get the exact coreness of nodes with a high probability guarantee. In the harsher case that the existence probability is unknown, we present a novel method to estimate the existence probability of edges, based on which the coreness of nodes with small approximation ratio guarantee can be computed. Extensive experiments are conducted on different types of real-world graphs and synthetic graphs. The results illustrate that the proposed algorithms exhibit good efficiency, stability and scalability.

Qi Luo

Papers

Batch Processing for Truss Maintenance in Large Dynamic Graphs

Fast Skyline Community Search in Multi-Valued Networks

Hypercore Maintenance in Dynamic Hypergraphs

Distributed Core Decomposition in Probabilistic Graphs

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