scholarly journals Automating Mashup Service Recommendation via Semantic and Structural Features

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wei Xiong ◽  
Zhao Wu ◽  
Bing Li ◽  
Bo Hang
Keyword(s):  
Large Scale ◽  
Structural Features ◽  
Cloud Services ◽  
Semantic Gap ◽  
The Internet ◽  
Semantic Features ◽  
Service Recommendation ◽  
Application Development ◽  
User Expectations ◽  
Smart Things

Increasing physical objects connected to the Internet make it possible for smart things to access all kinds of cloud services. Mashup has been an effective way to the rapid IoT (Internet of Things) application development. It remains a big challenge to bridge the semantic gap between user expectations and application functionality with the development of mashup services. This paper proposes a mashup service recommendation approach via merging semantic features from API descriptions and structural features from the mashup-API network. To validate our approach, large-scale experiments are conducted based on a real-world accessible service repository, ProgrammableWeb. The results show the effectiveness of our proposed approach.

scholarly journals A DHT-Based Discovery Service for the Internet of Things

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Federica Paganelli ◽  
David Parlanti
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Future Internet ◽  
Peer To Peer ◽  
The Internet ◽  
Dangerous Goods ◽  
Discovery Service ◽  
Single Attribute ◽  
Smart Things ◽  
The Internet Of Things

Current trends towards the Future Internet are envisaging the conception of novel services endowed with context-aware and autonomic capabilities to improve end users’ quality of life. The Internet of Things paradigm is expected to contribute towards this ambitious vision by proposing models and mechanisms enabling the creation of networks of “smart things” on a large scale. It is widely recognized that efficient mechanisms for discovering available resources and capabilities are required to realize such vision. The contribution of this work consists in a novel discovery service for the Internet of Things. The proposed solution adopts a peer-to-peer approach for guaranteeing scalability, robustness, and easy maintenance of the overall system. While most existing peer-to-peer discovery services proposed for the IoT support solely exact match queries on a single attribute (i.e., the object identifier), our solution can handle multiattribute and range queries. We defined a layered approach by distinguishing three main aspects: multiattribute indexing, range query support, peer-to-peer routing. We chose to adopt an over-DHT indexing scheme to guarantee ease of design and implementation principles. We report on the implementation of a Proof of Concept in a dangerous goods monitoring scenario, and, finally, we discuss test results for structural properties and query performance evaluation.

scholarly journals Some Aspects of Using the Concept of «the Internet of Things» in Crime Counteraction

2020 ◽  
Vol 14 (3) ◽  
pp. 453-460
Author(s):  
Aleksandr Smuskin
Keyword(s):  
Law Enforcement ◽  
Internet Of Things ◽  
Large Scale ◽  
The Internet ◽  
Practical Implementation ◽  
Smart Environment ◽  
Smart House ◽  
Smart Things ◽  
Smart Cars ◽  
The Internet Of Things

The author states that the era of the Internet of Things has come. It is noted that Russian law publications do not pay sufficient attention to the practical issues of law enforcement that arise from the implementation of the Internet of Things, specifically, criminalistic research and the use of smart things by law enforcement bodies. This study a first attempt at a general criminalistic analysis of implementing the concept of the Internet of Things in Russian research publications. While analyzing the practical implementation of this concept, the author concludes that it is necessary not just to single out a smart house, a smart car or smart things as different categories, but to unite them into a system of smart environment. It is noted that the elements of the public sphere of application for the Internet of Things deserve separate studies, while this article will only focus on everyday application. Modern obstacles to a large-scale implementation of the Internet of Things are identified. The criminalistic research of the Internet of Things and smart environment makes it possible to identify key systems that modern appliances form in this sphere, requirements to them, subsystems of a smart house, functions of smart cars and gadgets. It is stated that the criminalistic research of the subsystems of smart environment is possible with the help of scientific criminalistic findings in the sphere of electronic digital traces and electronic evidence. Key points of finding these traces are identified. The author methodically analyzes the kinds of criminalistically relevant information that could be obtained through the examination of sensors and the memory of smart things, a smart car and a smart house. The author also determines the functions whose analysis is vital for collecting evidentiary and orientation information. It is stated that all information from sensors and information devices is, in the end, accumulated in the management center, as well as in cloud and network services servers that work with the Internet of Things. It is stressed that all interactions with electronic digital traces in the devices that implement the concept of the Internet of Things should happen with the participation of a specialist to avoid a loss of data.

Monitoring and Auditing in the Cloud

2018 ◽  
pp. 856-882
Author(s):  
Indira K. ◽  
Vennila A.
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Cloud Services ◽  
The Internet ◽  
Security Audit ◽  
Security Issues ◽  
Storage Servers ◽  
Monitoring Process ◽  
Cloud Users

The cloud computing is the term which have different services such as storage, servers, and applications which are delivered to an organization's computers and devices through the Internet for both technical and economical reasons. However they are many potential cloud users are reluctant to move to cloud computing on a large scale due to the unaddressed security issues present in cloud computing and so is increased the complexity of the infrastructures behind these services. So in this chapter, the challenges faced on both auditing and monitoring is identified. Accordingly it considers an investigation which uses to produce the major security audit issues present in cloud computing today based on a framework for security subsystems. To overcome the standards of auditing and process of auditing is briefly explained. There are also many platforms that provide cloud services also those domains are listed out with domain based monitoring process.

Monitoring and Auditing in the Cloud

2018 ◽  
pp. 318-350
Author(s):  
Indira K. ◽  
Vennila A
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Cloud Services ◽  
The Internet ◽  
Security Audit ◽  
Security Issues ◽  
Storage Servers ◽  
Monitoring Process ◽  
Cloud Users

The cloud computing is the term which have different services such as storage, servers, and applications which are delivered to an organization's computers and devices through the Internet for both technical and economical reasons. However they are many potential cloud users are reluctant to move to cloud computing on a large scale due to the unaddressed security issues present in cloud computing and so is increased the complexity of the infrastructures behind these services. So in this chapter, the challenges faced on both auditing and monitoring is identified. Accordingly it considers an investigation which uses to produce the major security audit issues present in cloud computing today based on a framework for security subsystems. To overcome the standards of auditing and process of auditing is briefly explained. There are also many platforms that provide cloud services also those domains are listed out with domain based monitoring process.

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

scholarly journals Sensor System: A Survey of Sensor Type, Ad Hoc Network Topology and Energy Harvesting Techniques

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 219
Author(s):  
Phuoc Duc Nguyen ◽  
Lok-won Kim
Keyword(s):  
Energy Harvesting ◽  
Large Scale ◽  
Ad Hoc ◽  
Sensor Nodes ◽  
Sensor System ◽  
Wireless Sensor ◽  
The Internet ◽  
Original Research ◽  
Energy Scavenging ◽  
Term Operation

People nowadays are entering an era of rapid evolution due to the generation of massive amounts of data. Such information is produced with an enormous contribution from the use of billions of sensing devices equipped with in situ signal processing and communication capabilities which form wireless sensor networks (WSNs). As the number of small devices connected to the Internet is higher than 50 billion, the Internet of Things (IoT) devices focus on sensing accuracy, communication efficiency, and low power consumption because IoT device deployment is mainly for correct information acquisition, remote node accessing, and longer-term operation with lower battery changing requirements. Thus, recently, there have been rich activities for original research in these domains. Various sensors used by processing devices can be heterogeneous or homogeneous. Since the devices are primarily expected to operate independently in an autonomous manner, the abilities of connection, communication, and ambient energy scavenging play significant roles, especially in a large-scale deployment. This paper classifies wireless sensor nodes into two major categories based the types of the sensor array (heterogeneous/homogeneous). It also emphasizes on the utilization of ad hoc networking and energy harvesting mechanisms as a fundamental cornerstone to building a self-governing, sustainable, and perpetually-operated sensor system. We review systems representative of each category and depict trends in system development.

scholarly journals Internet of Vehicles: Commencing Intellectual Hoarse Towards Self-Regulating Cars and Vehicular Clouds for Smart Transportation Structure [Vehicular Ad-Hoc Network: A Review and Application in the Internet of Vehicles]

2018 ◽  
Vol 7 (3.12) ◽  
pp. 545
Author(s):  
Risabh Mishra ◽  
M Safa ◽  
Aditya Anand
Keyword(s):  
Internet Of Things ◽  
Wireless Communication ◽  
Automobile Industry ◽  
Information Exchange ◽  
Large Scale ◽  
Intelligent Transportation System ◽  
Information Service ◽  
The Internet ◽  
Internet Of Vehicles ◽  
The Internet Of Things

Recent advances in wireless communication technologies and automobile industry have triggered a significant research interest in the field of Internet of Vehicles over the past few years.The advanced period of the Internet of Things is guiding the development of conventional Vehicular Networks to the Internet of Vehicles.In the days of Internet connectivity there is need to be in safe and problem-free environment.The Internet of Vehicles (IoV) is normally a mixing of three networks: an inter-vehicleNetwork, an intra-vehicle network, and a vehicle to vehicle network.Based on  idea of three networks combining into one, we define  Internet of Vehicles as a large-scale distributed system to wireless communication and information exchange between vehicle2X (X: vehicle, road, human and internet).It is a combined   network for supporting intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control, representation of an application of the Internet of Things (IoT) technology for intelligent transportation system (ITS).  

scholarly journals ALVIC versus the Internet: Redesigning a Networked Virtual Environment Architecture

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Peter Quax ◽  
Jeroen Dierckx ◽  
Bart Cornelissen ◽  
Wim Lamotte
Keyword(s):  
Virtual Environment ◽  
Large Scale ◽  
Virtual Communities ◽  
Real Life ◽  
Point Of View ◽  
The Internet ◽  
Generic Framework ◽  
Short Period ◽  
Networked Virtual Environment ◽  
Near Future

The explosive growth of the number of applications based on networked virtual environment technology, both games and virtual communities, shows that these types of applications have become commonplace in a short period of time. However, from a research point of view, the inherent weaknesses in their architectures are quickly exposed. The Architecture for Large-Scale Virtual Interactive Communities (ALVICs) was originally developed to serve as a generic framework to deploy networked virtual environment applications on the Internet. While it has been shown to effectively scale to the numbers originally put forward, our findings have shown that, on a real-life network, such as the Internet, several drawbacks will not be overcome in the near future. It is, therefore, that we have recently started with the development of ALVIC-NG, which, while incorporating the findings from our previous research, makes several improvements on the original version, making it suitable for deployment on the Internet as it exists today.

scholarly journals Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2473
Author(s):  
Qinglie Yuan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Aidi Hizami Alias ◽  
Shaiful Jahari Hashim
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Spatial Information ◽  
Feature Fusion ◽  
Aerial Images ◽  
Semantic Gap ◽  
Superior Performance ◽  
Lidar Data ◽  
Building Extraction ◽  
Hierarchical Features

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.

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