scholarly journals Network-State-Aware Quality of Service Provisioning for the Internet of Things

2016 ◽  
Vol 7 (6) ◽  
Author(s):  
Shafique Ahmad ◽  
Jun Zhang
Keyword(s):  
Quality Of Service ◽  
Internet Of Things ◽  
The Internet ◽  
Service Provisioning ◽  
Network State ◽  
The Internet Of Things ◽  
Quality Of Service Provisioning

scholarly journals PERBANDINGAN QUALITY OF SERVICE PROTOKOL KOMUNIKASI DATA PADA SISTEM DETEKSI ASAP ROKOK BERBASIS INTERNET OF THINGS

2020 ◽  
Vol 16 (1) ◽  
pp. 19-24
Author(s):  
Pether V B Romony ◽  
Lanny Sitanayah ◽  
Junaidy B Sanger
Keyword(s):  
Quality Of Service ◽  
Internet Of Things ◽  
Transmission Control Protocol ◽  
The Internet ◽  
Data Loss ◽  
Transmission Control ◽  
User Datagram Protocol ◽  
Control Protocol ◽  
The Internet Of Things

Asap rokok adalah salah satu asap beracun yang berbahaya bagi kesehatan manusia dari sisi biologis maupun sisi kimiawi. Pada penelitian ini, penulis mengimplementasikansebuah sistem deteksi asap rokok berbasis The Internet of Things menggunakan sensor MQ135, Arduino board dan NodeMCU. Kemudian, penulis melakukan perbandingan Quality of Service dari dua protokol komunikasi data, yaitu Transmission Control Protocol dan User Datagram Protocol pada sistem tersebut. Parameter Quality of Service yang dibandingkan saat proses pengiriman data adalah delay dan data loss. Untuk setiap protokol, simulasi dilakukan selama 1 jam dengan pengiriman data setiap 5 detik, 10 detik, sampai 1 menit. Hasil yang diperoleh adalah data loss dengan Transmission Control Protocol lebih rendah dari pada data loss dengan User Datagram Protocol, sedangkan delay dengan User Datagram Protocol lebih rendah dari pada delay dengan Transmission Control Protocol.

Algorithms Optimization for Intelligent IoV Applications

2021 ◽  
pp. 1-25
Author(s):  
Elmustafa Sayed Ali Ahmed ◽  
Zahraa Tagelsir Mohammed ◽  
Mona Bakri Hassan ◽  
Rashid A. Saeed
Keyword(s):  
Quality Of Service ◽  
Internet Of Things ◽  
Network Topology ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Topology Changes ◽  
The Internet Of Things

Internet of vehicles (IoV) has recently become an emerging promising field of research due to the increasing number of vehicles each day. It is a part of the internet of things (IoT) which deals with vehicle communications. As vehicular nodes are considered always in motion, they cause frequent changes in the network topology. These changes cause issues in IoV such as scalability, dynamic topology changes, and shortest path for routing. In this chapter, the authors will discuss different optimization algorithms (i.e., clustering algorithms, ant colony optimization, best interface selection [BIS] algorithm, mobility adaptive density connected clustering algorithm, meta-heuristics algorithms, and quality of service [QoS]-based optimization). These algorithms provide an important intelligent role to optimize the operation of IoV networks and promise to develop new intelligent IoV applications.

scholarly journals Ontological base models machine-to-machine M2M applied to the internet of things IOT

2021 ◽  
Vol 10 (12) ◽  
pp. 148-161
Author(s):  
Mauricio Orlando Bermúdez Amaya ◽  
Octavio José Salcedo Parra ◽  
Juan Pablo Rodríguez Miranda
Keyword(s):  
Quality Of Service ◽  
Internet Of Things ◽  
Present Article ◽  
The Internet ◽  
Conceptual Scheme ◽  
Machine To Machine ◽  
Intelligent Devices ◽  
The Internet Of Things

Machine-to-Machine M2M   technology   being a specific discourse universe of the Internet of Things IoT for the connectivity of intelligent devices, the support of said environment requires a basic conceptual scheme; for which the present article, proposes an evaluation about the different ontological models that consider the M2M and the IoT in simultaneous, recognizing the syntactic and semantic capacity of the interoperability of such devices, from the study of the basic schemes in mention, and identifying its most outstanding properties according to the Quality of Service QoS metric, obtaining the oneM2M ontology as the most appropriate.

A Review of Quality of Service in Fog Computing for the Internet of Things

2020 ◽  
Vol 3 (1) ◽  
pp. 22-40 ◽  
Author(s):  
William Tichaona Vambe ◽  
Chii Chang ◽  
Khulumani Sibanda
Keyword(s):  
Quality Of Service ◽  
Internet Of Things ◽  
Fog Computing ◽  
The Internet ◽  
Open Problems ◽  
Primary Focus ◽  
Time Critical ◽  
Work Done ◽  
The Internet Of Things

With the advent of the paradigm of the Internet of Things, many computing elements need many modifications to promote Quality of Service (QoS). Quality of Service is a pillar that promotes real-time reaction to time-critical tasks. Any impediments to QoS should be resolved and handled. In 2012, fog computing was implemented to enhance QoS in current systems in a bid to tackle QoS problems encountered by using cloud computing alone. Currently, the primary focus in fog computing is now on enhancing QoS. The primary goal of this study is, therefore, to critically review and evaluate the literature on the work done to improve elements of QoS in fog computing. This study begins by examining the roots of history, characteristics, and advantages of fog computing. Secondly, it discusses the important elements of QoS parameters. Finally, open problems that still affect fog computing are identified and discussed in order to achieve enhanced QoS.

scholarly journals User preference–based QoS-aware service function placement in IoT-Edge cloud

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110199
Author(s):  
Briytone Mutichiro ◽  
Younghan Kim
Keyword(s):  
Quality Of Service ◽  
Internet Of Things ◽  
Response Time ◽  
User Preference ◽  
Mixed Integer ◽  
The Internet ◽  
Service Function ◽  
Cost Constraints ◽  
The Internet Of Things

In the Internet of Things-Edge cloud, service provision presents a challenge to operators to satisfy user service-level agreements while meeting service-specific quality-of-service requirements. This is because of inherent limitations in the Internet of Things-Edge in terms of resource infrastructure as well as the complexity of user requirements in terms of resource management in a heterogeneous environment like edge. An efficient solution to this problem is service orchestration and placement of service functions to meet user-specific requirements. This work aims to satisfy user quality of service through optimizing the user response time and cost by factoring in the workload variation on the edge infrastructure. We formulate the service function placement at the edge problem. We employ user service request patterns in terms of user preference and service selection probability to model service placement. Our framework proposal relies on mixed-integer linear programming and heuristic solutions. The main objective is to realize a reduced user response time at minimal overall cost while satisfying the user service requirements. For this, several parameters, and factors such as capacity, latency, workload, and cost constraints, are considered. The proposed solutions are evaluated based on different metrics and the obtained results show the gap between the heuristic user preference placement algorithm and the optimal solution to be minimal.

scholarly journals A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 880
Author(s):  
Imran ◽  
Zeba Ghaffar ◽  
Abdullah Alshahrani ◽  
Muhammad Fayaz ◽  
Ahmed Mohammed Alghamdi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
Internet Of Things ◽  
Software Defined Networking ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Resources Management ◽  
Learning Techniques ◽  
The Internet Of Things

In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.

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