scholarly journals Pushing the Scalability of RDF Engines on IoT Edge Devices

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2788
Author(s):  
Anh Le-Tuan ◽  
Conor Hayes ◽  
Manfred Hauswirth ◽  
Danh Le-Phuoc
Keyword(s):  
Buffer Management ◽  
Semantic Integration ◽  
Management Technique ◽  
Computer Design ◽  
Join Algorithm ◽  
Memory Footprint ◽  
Description Framework ◽  
Resource Description ◽  
The Internet Of Things ◽  
Better Than

Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. In this paper, we have first carried out an empirical study of the scalability and behaviour of solutions for RDF data management on standard computing hardware that have been ported to run on lightweight devices at the network edge. The findings of our study shows that these RDF store solutions have several shortcomings on commodity ARM (Advanced RISC Machine) boards that are representative of IoT edge node hardware. Consequently, this has inspired us to introduce a lightweight RDF engine, which comprises an RDF storage and a SPARQL processor for lightweight edge devices, called RDF4Led. RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. The design constitutes a flash-aware storage structure, an indexing scheme, an alternative buffer management technique and a low-memory-footprint join algorithm that demonstrates improved scalability and robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than popular RDF engines such as Jena TDB (Tuple Database) and RDF4J, while consuming the same amount of memory. In particular, RDF4Led requires 10%–30% memory of its competitors to operate on datasets of up to 50 million triples. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J.

Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

2021 ◽  
Vol 21 (3) ◽  
pp. 1-22
Author(s):  
Celestine Iwendi ◽  
Saif Ur Rehman ◽  
Abdul Rehman Javed ◽  
Suleman Khan ◽  
Gautam Srivastava
Keyword(s):  
Internet Of Things ◽  
Detection System ◽  
Experimental Testing ◽  
Cyber Attacks ◽  
Internet Technology ◽  
The Internet ◽  
Business Operations ◽  
Private Data ◽  
The Internet Of Things ◽  
Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

scholarly journals IoT Resource Allocation and Optimization Based on Heuristic Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 539 ◽  
Author(s):  
Arun Kumar Sangaiah ◽  
Ali Asghar Rahmani Hosseinabadi ◽  
Morteza Babazadeh Shareh ◽  
Seyed Yaser Bozorgi Rad ◽  
Atekeh Zolfagharian ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Distributed System ◽  
Communication Cost ◽  
The Internet ◽  
Multiple Resources ◽  
Whale Optimization ◽  
Total Communication ◽  
The Internet Of Things ◽  
Proper Performance ◽  
Better Than

The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of “total communication cost”, is better than other ones.

Pidgin Metadata Framework as a Mediator for Metadata Interoperability

Libri ◽  
2021 ◽  
Vol 71 (4) ◽  
pp. 375-387
Author(s):  
Seungmin Lee
Keyword(s):  
Structural Characteristics ◽  
Semantic Association ◽  
Semantic Relationships ◽  
Semantic Categories ◽  
External Function ◽  
Layer 2 ◽  
Description Framework ◽  
Internal Function ◽  
Resource Description ◽  
Metadata Interoperability

Abstract A pidgin metadata framework based on the concept of pidgin metadata is proposed to complement the limitations of existing approaches to metadata interoperability and to achieve more reliable metadata interoperability. The framework consists of three layers, with a hierarchical structure, and reflects the semantic and structural characteristics of various metadata. Layer 1 performs both an external function, serving as an anchor for semantic association between metadata elements, and an internal function, providing semantic categories that can encompass detailed elements. Layer 2 is an arbitrary layer composed of substantial elements from existing metadata and performs a function in which different metadata elements describing the same or similar aspects of information resources are associated with the semantic categories of Layer 1. Layer 3 implements the semantic relationships between Layer 1 and Layer 2 through the Resource Description Framework syntax. With this structure, the pidgin metadata framework can establish the criteria for semantic connection between different elements and fully reflect the complexity and heterogeneity among various metadata. Additionally, it is expected to provide a bibliographic environment that can achieve more reliable metadata interoperability than existing approaches by securing the communication between metadata.

scholarly journals COMBINE Archive Specification Version 1

2015 ◽  
Vol 12 (2) ◽  
pp. 104-118 ◽  
Author(s):  
Frank T. Bergmann ◽  
Nicolas Rodriguez ◽  
Nicolas Le Novère
Keyword(s):  
Modeling And Simulation ◽  
Relevant Information ◽  
Essential Information ◽  
Media Type ◽  
Additional Information ◽  
Internet Media ◽  
Metadata File ◽  
Description Framework ◽  
Audit Trails ◽  
Resource Description

Summary Several standard formats have been proposed that can be used to describe models, simulations, data or other essential information in a consistent fashion. These constitute various separate components required to reproduce a given published scientific result.The Open Modeling EXchange format (OMEX) supports the exchange of all the information necessary for a modeling and simulation experiment in biology. An OMEX file is a ZIP container that includes a manifest file, an optional metadata file, and the files describing the model. The manifest is an XML file listing all files included in the archive and their type. The metadata file provides additional information about the archive and its content. Although any format can be used, we recommend an XML serialization of the Resource Description Framework.Together with the other standard formats from the Computational Modeling in Biology Network (COMBINE), OMEX is the basis of the COMBINE Archive. The content of a COMBINE Archive consists of files encoded in COMBINE standards whenever possible, but may include additional files defined by an Internet Media Type. The COMBINE Archive facilitates the reproduction of modeling and simulation experiments in biology by embedding all the relevant information in one file. Having all the information stored and exchanged at once also helps in building activity logs and audit trails.

Resource Description Framework

2017 ◽  
pp. 1-4
Author(s):  
Christian Bizer ◽  
Maria-Esther Vidal ◽  
Michael Weiss
Keyword(s):  
Resource Description Framework ◽  
Description Framework ◽  
Resource Description

scholarly journals Fine-Grained Pests Recognition Based on Truncated Probability Fusion Network via Internet of Things in Forestry and Agricultural Scenes

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 290
Author(s):  
Kai Ma ◽  
Ming-Jun Nie ◽  
Sen Lin ◽  
Jianlei Kong ◽  
Cheng-Cai Yang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Insect Pests ◽  
Accurate Identification ◽  
Technical Application ◽  
Network Mining ◽  
Fine Grained ◽  
Feature Expression ◽  
The Internet Of Things ◽  
Different Levels ◽  
Better Than

Accurate identification of insect pests is the key to improve crop yield and ensure quality and safety. However, under the influence of environmental conditions, the same kind of pests show obvious differences in intraclass representation, while the different kinds of pests show slight similarities. The traditional methods have been difficult to deal with fine-grained identification of pests, and their practical deployment is low. In order to solve this problem, this paper uses a variety of equipment terminals in the agricultural Internet of Things to obtain a large number of pest images and proposes a fine-grained identification model of pests based on probability fusion network FPNT. This model designs a fine-grained feature extractor based on an optimized CSPNet backbone network, mining different levels of local feature expression that can distinguish subtle differences. After the integration of the NetVLAD aggregation layer, the gated probability fusion layer gives full play to the advantages of information complementarity and confidence coupling of multi-model fusion. The comparison test shows that the PFNT model has an average recognition accuracy of 93.18% for all kinds of pests, and its performance is better than other deep-learning methods, with the average processing time drop to 61 ms, which can meet the needs of fine-grained image recognition of pests in the Internet of Things in agricultural and forestry practice, and provide technical application reference for intelligent early warning and prevention of pests.

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