Computing最新文献

Abstract

Deep learning has shown superior performance in various applications. The emergence of graph convolution neural networks (GCNs) enables deep learning to learn the latent representation from graph-structured data with rich attributes. To be specific, the message passing mechanism of GCNs can aggregate and update messages through the topological relationship between nodes in a graph. The graph attention network (GAT) introduces the attention mechanism into GCNs when aggregating messages and achieves significant performance on the node classification task. However, focusing on each node in the neighborhood, GAT becomes extremely complex. In addition, although stacking network layers could obtain a wider receptive field, it also brings high time cost and leads to the difficulty of training. To handle this problem, this paper only divides the messages into two types, i.e. self message and neighborhood message. The neighborhood message comes from the neighborhood with(out) self-loop while the self message comes from the node itself. Then, we design a light attention mechanism that only focuses on two weights, one for the self message, and the other for the neighborhood message, to adaptively reveal the different contributions of messages from a node as well as its neighborhood. In addition, we also adopt linear propagation, a shallow and efficient method, to aggregate messages from distant neighbors and thus obtain a wider neighborhood receiving field. To verify the effectiveness of our proposed approach, extensive experiments have been conducted on the semi-supervised node classification task. Results show that our proposed approach achieves comparable or even better performance than the baseline methods with complicated GCN structures on the benchmark datasets. Specifically, the proposed light attention mechanism focusing on message passing exhibits a great efficiency improvement with the training time cost less than half of GAT.

Many modern day cloud hosted applications such as virtual reality, real time games require low latency data access and computation to improve response time. So it is essential to bring the computation and data storage edge servers closer to the user’s geographical location to improve response times and save bandwidth. In particulars, in online gaming and on demand video services, the required application data present at cloud servers need to be placed on the edge servers to provide low latency app-functionalities. The transfer of huge amount of data from cloud server to edge server incurs high cost and time penalties. Thus, we need an efficient way to solve edge data distribution (EDD) problem which distribute the application data to the edge servers that minimizes transfer cost. In this work, we provide a refined formulation of an optimal approach to solve the EDD problem using integer linear programming (ILP) technique. Due to the time complexity limitation of the ILP approach, we propose an O(k) approximation algorithm based on network Steiner tree estimation (EDD-NSTE) for estimating solutions to dense large-scale EDD problem. The proposed approach is analyzed to be 11/6 approximation which is better than the state-of-the-art 2 approximation EDD-A approach. The experimental evaluation through simulation using real world EUA data set demonstrate that the EDD-NSTE outperform state-of-the-art approach and other representative approaches.

Abstract

In this paper, we develop computing machinery within the framework of the String Multiset Rewriting calculus (SMSR), as defined by Barbuti et al. [4], to solve the SAT problem in linear time regarding the number of variables of a given conjunctive normal form. This shows that SMSR can be considered a computational model capable of significantly reducing the time requirement of classical decision problems.

Abstract

Internet of Vehicles (IoV) aka V2X is a growing area of research that aims at information exchange between vehicles and all other related objects to develop intelligent transportation systems. IoVs are characterized by high mobility, high-speed internet, varying node density, and dynamic topology and aim to minimize and communicate situations like traffic congestion, accidents, etc. Discovering a routing path in a highly unstable network environment to ensure the successful delivery of packets with minimal overheads. Finding reliable routing links as against shortest path routing is a necessity in IoV networks. In this paper, a routing protocol coined as the dynamic opportunistic routing protocol for IoV (DORP-IoV) is presented. DORP-IoV is an on-demand position-based protocol that seeks the advantage of wireless broadcast advantage to select a hop close to the virtual line of sight between source and destination for communicating the information to the destination. Vehicle movement direction and vehicle density around the ideal hop location are also considered while selecting the next hop for information forwarding. Communicating the information through an optimal number of intermediate nodes is the novelty of the work which ensures better packet delivery with minimized delay and routing overheads. The performance of DORP-IoV is evaluated and results are compared with the performance of Weighted- Greedy Perimeter Coordinator Routing (W-GPCR), Greedy Perimeter Coordinator Routing, and Greedy Perimeter Stateless Routing for varying node density and network connections for various metrics. DORP-IoV shows an improved performance in the range of 8–12% for packet delivery with similar performance for average end-to-end delay compared to W-GPCR. The optimal hop selection mechanism in DORP-IoV reduces the number of hops by 10–30% compared to W-GPCR.

Smart objects in the Internet of Things (IoT) communicate with one another, gather information, and respond to users requests. In these systems, wireless sensors are used to collect data and monitor the environment at the lowest level. In IoT applications, wireless sensor networks play a pivotal role. Since IoT devices often use batteries, efficiency is important to them such that IoT-related standards and research efforts focus more on energy saving or conservation. In this paper, we have used two meta-heuristics algorithm for clustering and routing in IoT. We cluster the network using a clustering method called WOA-clustering based on the meta-heuristic Whale Optimization Algorithm (WOA) and select the optimal cluster heads. We then use a routing method called HHO-Routing based on the Harris Hawks Optimization (HHO) algorithm, a novel meta-heuristic algorithm, to route the cluster heads to BS. The use of the above methods results in reduced power consumption for reaching the base station (BS). Also, to prove the optimal performance of the proposed methods, these methods were simulated and compared with five different methods in a similar context. It was observed that the consumed energy, the number of survival cycles for the death of the first node, and the data transmission rate were improved. The proposed method is simulated in cooja simulator, and for a more accurate evaluation, we compare it with UCCGRA, PSO-SD, PUDCRP, EECRA, EEMRP algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan.

In Flying Ad Hoc Networks (FANETs), the critical resource resides in an Unmanned Aerial Vehicle (UAV) and the user nodes within the UAV’s neighborhood defined by its transmission range can request for it. In Local Mutual Exclusion (LME), two nodes in the same neighborhood cannot execute the Critical Section (CS) simultaneously, but two non-neighboring nodes can be in the CS at the same time. This is a variation of traditional Mutual Exclusion (ME). The proposed Priority Aware - Request Collector Local Mutual Exclusion (PA-RCLME) algorithm ensures prioritized LME in such FANET structures. The proposed PA-RCLME algorithm is token-based and takes into account the priority of CS requests. It leverages a slow ageing technique to prevent starvation, to avoid a profusion of priority inversions, and to ensure the bounded waiting property of mutual exclusion algorithms. This algorithm introduces a neighborhood search technique that makes the token holder a secondary request collector, thereby reducing average request latency and increasing efficiency. The rapid movement of UAVs and other user nodes makes FANET topology highly dynamic and fault-prone. PA-RCLME algorithm handles it gracefully. Opportunistic Node Simulator (ONE) is used to simulate the algorithm and appropriate performance metrics have been recorded. A comparative analysis with the existing algorithm in the literature is also presented, and the proposed algorithm performs better.

The K-means algorithm has been successfully applied to many complex network analysis problems. However, this method is sensitive to how the first cluster centers are chosen. It is possible to minimize superfluous runs by choosing the first cluster center in advance because each run produces a unique set of results. To overcome this issue, an algorithm for Community Detection based on Transitive Closure (CoDTC) has been introduced. In this algorithm, the initial cluster center is provided by degree centrality and T-transitive closure. The algorithm initializes with the calculation of the similarity relation matrix. Then, to avoid the limitation of sparse problems in complex network analysis, we offer the idea of transitive closure on weighted networks to solve the sparsity issue. This notion is based on imposing a t-norm inequality on the connection weights and providing a method to compute them. Finally, based on T-transitive closure, new cluster centers are calculated iteratively to avoid random selection of cluster centers. In this paper, we demonstrate the efficacy of the CoDTC approach on a diverse range of real and artificial networks, encompassing both big and small communities.

Abstract

With the popularity of the Internet of Things (IoT), massive amounts of data are generated every second. By analyzing this data, attackers can launch kinds of attacks for their own profits, such as data tampering, malicious data injection, identity deception etc. To solve these problems, in this paper, we propose a Trusted Execution Environment-and-Blockchain-based data protection architecture (TEDA). In TEDA, edge devices in different IoTs maintain a consortium blockchain to achieve the secure read/write operations and verification of data together with cloud. Besides, to secure the local data processing in edge devices and manage internal light-weight devices, an Intel SGX-based module is designed. Furthermore, a new transaction structure is introduced to protect user’s access pattern. The experimental results show that the space occupancy rates of write and read of TEDA with SGX are 0.84 (times ) and 1.07 (times ) than that of TEDA without SGX, and the time occupancy rates of write and read of TEDA with SGX are 0.94 (times ) and 0.90 (times ) than that of TEDA without SGX, which indicate TEDA has a good performance.

The incompleteness of the knowledge graph limits its applications to various downstream tasks. To this end, numerous influential knowledge graph embedding models have been presented and have made great achievements in the domain of knowledge graph completion. However, most of these models only pay attention to the extraction of latent knowledge or translational features, and cannot comprehensively capture the surface semantics, latent interactions, and translational characteristics of triples. In this paper, a novel multi-channel convolutional model, MConvKGC, is presented for knowledge graph completion, which has three feature extraction channels and employs them to simultaneously extract shallow semantics, latent interactions, and translational characteristics, respectively. In addition, MConvKGC adopts an asymmetric convolutional block to comprehensively extract the latent interactions from triples, and process the generated feature maps with various attention mechanisms to further learn local dependencies between entities and relations. The results of the conducted link prediction experiments on FB15k-237, WN18RR, and UMLS indicate that our proposed MConvKGC shows excellent performance and outperforms previous state-of-the-art KGE models in the majority of cases.

Smartphones have become highly personalized interfaces between people and the technology ecosystem around them. In this sense, they play a key role for a technology shift from the current Internet of Things to a future human-centric paradigm of an Internet of People, automatically adapting smart things and services to the preferences and context of their users. In this paper, we propose the use of Complex Event Processing (CEP) engines deployed in the users’ smartphones granting them context-awareness capabilities in order to react to external stimulus, and enabling them to interact both with smart things and services in the surroundings of the users. With that purpose, we have designed a communication architecture that interconnects CEP engines running on smartphones, providing a framework for building applications for Mobile-based Collaborative Social Computing (MCSC). For that, we make use of previous works of the authors with Digital Avatars, a framework which promotes the use of smartphones for inferring and sharing a unique digital avatar or virtual profile of each user. The resulting framework, which we have called Collaborative CEP, allows to implement complex interactions among users, and between them and the IoT, a common need in Collaborative Social Computing applications. We provide a proof of concept based on the implementation of a Cops and Robbers game to test the expressiveness and correct functioning of the framework, and we evaluate its performance and efficiency.