WikiServe: Using Wikipedia to Match IoT based Services for Situation Response

2021 ◽  
Author(s):  
Sazid Zaman Khan ◽  
Alan Colman ◽  
Iqbal H. Sarker
Keyword(s):  
Internet Of Things ◽  
Natural Disaster ◽  
Smart Devices ◽  
Baseline Method ◽  
Service Matching ◽  
Ontological Model ◽  
Ranked List ◽  
F Measure ◽  
The Web ◽  
Better Than

A large number of smart devices (things) are being deployed with the swift development of Internet of Things (IOT). These devices, owned by different organizations, have a wide variety of services to offer over the web. During a natural disaster or emergency (i.e., a situation), for example, relevant IOT services can be found and put to use. However, appropriate service matching methods are required to find the relevant services. Organizations that manage situation responses and organizations that provide IOT services are likely to be independent of each other, and therefore it is difficult for them to adopt a common ontological model to facilitate the service matching. Moreover, there exists a large conceptual gap between the domain of discourse for situations and the domain of discourse for services, which cannot be adequately bridged by existing techniques. In this paper, we address these issues and propose a new method, WikiServe, to identify IOT services that are functionally relevant to a given situation. Using concepts (terms) from situation and service descriptions, WikiServe employs Wikipedia as a knowledge source to bridge the conceptual gap between situation and service descriptions and match functionally relevant IOT services for a situation. It uses situation terms to retrieve situation related articles from Wikipedia. Then it creates a ranked list of services for the situation using the weighted occurrences of service terms in weighted situation articles. WikiServe performs better than a commonly used baseline method in terms of Precision, Recall and F measure for service matching.

scholarly journals WikiServe: Using Wikipedia to Match IoT based Services for Situation Response

2021 ◽  
Author(s):  
Sazid Zaman Khan ◽  
Alan Colman ◽  
Iqbal H. Sarker
Keyword(s):  
Natural Disaster ◽  
New Method ◽  
Smart Devices ◽  
Baseline Method ◽  
Service Matching ◽  
Ontological Model ◽  
Ranked List ◽  
F Measure ◽  
The Web ◽  
Better Than

A large number of smart devices (things) are being deployed with the swift development of Inter- net of Things (IOT). These devices, owned by different organizations, have a wide variety of services to offer over the web. During a natural disaster or emergency (i.e., a situation), for example, relevant IOT services can be found and put to use. However, appropriate service matching methods are required to find the relevant services. Organizations that manage situation responses and organizations that provide IOT services are likely to be independent of each other, and therefore it is difficult for them to adopt a common ontological model to facilitate the service matching. Moreover, there exists a large conceptual gap between the domain of discourse for situations and the domain of discourse for services, which cannot be adequately bridged by existing techniques. In this paper, we address these issues and propose a new method, WikiServe, to identify IOT services that are functionally relevant to a given situation. Using concepts (terms) from situation and service descriptions, WikiServe employs Wikipedia as a knowledge source to bridge the conceptual gap between situation and service descriptions and match functionally relevant IOT services for a situation. It uses situation terms to retrieve situation related articles from Wikipedia. Then it creates a ranked list of services for the situation using the weighted occurrences of service terms in weighted situation articles. WikiServe performs better than a commonly used baseline method in terms of Precision, Recall and F measure for service matching.

Web-Based IoT Application Development

2019 ◽  
pp. 62-86
Author(s):  
S. Gopikrishnan ◽  
P. Priakanth
Keyword(s):  
Internet Of Things ◽  
Economic Value ◽  
Wireless Sensor ◽  
Smart Devices ◽  
The Internet ◽  
Application Development ◽  
Web Based ◽  
Physical Changes ◽  
The Internet Of Things ◽  
The Web

Wireless sensor network (WSN) is an outdated technology that is used to monitor the physical changes in environment and take necessary actions. The advancement in WSN leads to automation in physical environment by uploading the sensed data to internet or cloud. The internet of things concept deals with the issues of making things connected to the internet as well as in a network of smart devices. IoT application development presents an enormous opportunity to reshape entire industries. According to McKinsey & Co, the merging of the physical and digital worlds via IoT could generate up to $11.1 trillion a year in economic value by 2025. Hence, the development of the web-based IoT applications will take automation research to the next level. Many authors have proposed many solutions to make internet of things possible in day-to-day life. This chapter gives an introduction about the web-based application development based on internet of things. The major objective of this chapter is to discuss and resolve the challenges in IoT to automate the real-time problems.

scholarly journals Multi-Layer Blockchain-Based Security Architecture for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 772 ◽  
Author(s):  
Houshyar Honar Pajooh ◽  
Mohammad Rashid ◽  
Fakhrul Alam ◽  
Serge Demidenko
Keyword(s):  
Internet Of Things ◽  
Clustering Algorithm ◽  
Model Development ◽  
Base Stations ◽  
Smart Devices ◽  
Security Model ◽  
Cluster Heads ◽  
Authentication Mechanism ◽  
Proposed Model ◽  
Authentication And Authorization

The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain.

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 Recent Patient Health Monitoring Platforms Incorporating Internet of Things-Enabled Smart Devices

2018 ◽  
Vol 22 (Suppl 2) ◽  
pp. S76-82 ◽  
Author(s):  
Minhee Kang ◽  
Eunkyoung Park ◽  
Baek Hwan Cho ◽  
Kyu-Sung Lee
Keyword(s):  
Internet Of Things ◽  
Health Monitoring ◽  
Smart Devices ◽  
Patient Health ◽  
Recent Patient

From Users’ Intentions to IF-THEN Rules in the Internet of Things

2021 ◽  
Vol 39 (4) ◽  
pp. 1-33
Author(s):  
Fulvio Corno ◽  
Luigi De Russis ◽  
Alberto Monge Roffarello
Keyword(s):  
Internet Of Things ◽  
Smart Devices ◽  
The Internet ◽  
Short Term ◽  
Real World Data ◽  
Original Intention ◽  
Recommendation Accuracy ◽  
High Level ◽  
The Internet Of Things

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as “IF the entrance Nest security camera detects a movement, THEN blink the Philips Hue lamp in the kitchen.” Unfortunately, the spread of new supported technologies makes the number of possible combinations between triggers and actions continuously growing, thus motivating the need of assisting users in discovering new rules and functionality, e.g., through recommendation techniques. To this end, we present , a semantic Conversational Search and Recommendation (CSR) system able to suggest pertinent IF-THEN rules that can be easily deployed in different contexts starting from an abstract user’s need. By exploiting a conversational agent, the user can communicate her current personalization intention by specifying a set of functionality at a high level, e.g., to decrease the temperature of a room when she left it. Stemming from this input, implements a semantic recommendation process that takes into account ( a ) the current user’s intention , ( b ) the connected entities owned by the user, and ( c ) the user’s long-term preferences revealed by her profile. If not satisfied with the suggestions, then the user can converse with the system to provide further feedback, i.e., a short-term preference , thus allowing to provide refined recommendations that better align with the original intention. We evaluate by running different offline experiments with simulated users and real-world data. First, we test the recommendation process in different configurations, and we show that recommendation accuracy and similarity with target items increase as the interaction between the algorithm and the user proceeds. Then, we compare with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of in recommending IF-THEN rules that satisfy the current personalization intention of the user.

scholarly journals Proposal of a Big data System for an Intelligent Management of Water Resources

2021 ◽  
Vol 314 ◽  
pp. 02002
Author(s):  
Sara Bouziane ◽  
Badraddine Aghoutane ◽  
Aniss Moumen ◽  
Ali Sahlaoui ◽  
Anas EL Ouali
Keyword(s):  
Big Data ◽  
Water Resources ◽  
Internet Of Things ◽  
Water Resources Management ◽  
Smart Devices ◽  
Water Losses ◽  
Resources Management ◽  
Water Leak ◽  
Intelligent Management ◽  
Agriculture Water

Today, advanced technologies like Big Data, IoT, and Cloud Computing can provide new opportunities and applications in all sectors. In the water sector, water scarcity has become a common concern of different institutions and actors worldwide. In this context, several approaches and systems have been proposed and developed, using these technologies, allowing intelligent water resources management. Internet of Things can be used for assisting the Water Industry to collect data, manage and monitor the water infrastructures using smart devices. Big Data is a strategic technology for analyzing and interpreting collected data into valuable and helpful information for better decision making. This paper presents Big Data and Internet of Things technologies. It addresses theirs uses in some use cases such as municipal water losses, water pollution in agriculture, water Leak detection, etc., to provide new systems and innovative solutions for intelligent water resources management. Based on this study, we propose a Big Data and IoT architecture for intelligent water resources management.

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