scholarly journals Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6384
Author(s):  
Nasser Kimbugwe ◽  
Tingrui Pei ◽  
Moses Ntanda Kyebambe
Keyword(s):  
Quality Of Service ◽  
Deep Learning ◽  
Internet Of Things ◽  
Optimization Techniques ◽  
Security And Privacy ◽  
Potential Candidate ◽  
Critical Systems ◽  
Art Research ◽  
Privacy Breaches

The role of the Internet of Things (IoT) networks and systems in our daily life cannot be underestimated. IoT is among the fastest evolving innovative technologies that are digitizing and interconnecting many domains. Most life-critical and finance-critical systems are now IoT-based. It is, therefore, paramount that the Quality of Service (QoS) of IoTs is guaranteed. Traditionally, IoTs use heuristic, game theory approaches and optimization techniques for QoS guarantee. However, these methods and approaches have challenges whenever the number of users and devices increases or when multicellular situations are considered. Moreover, IoTs receive and generate huge amounts of data that cannot be effectively handled by the traditional methods for QoS assurance, especially in extracting useful features from this data. Deep Learning (DL) approaches have been suggested as a potential candidate in solving and handling the above-mentioned challenges in order to enhance and guarantee QoS in IoT. In this paper, we provide an extensive review of how DL techniques have been applied to enhance QoS in IoT. From the papers reviewed, we note that QoS in IoT-based systems is breached when the security and privacy of the systems are compromised or when the IoT resources are not properly managed. Therefore, this paper aims at finding out how Deep Learning has been applied to enhance QoS in IoT by preventing security and privacy breaches of the IoT-based systems and ensuring the proper and efficient allocation and management of IoT resources. We identify Deep Learning models and technologies described in state-of-the-art research and review papers and identify those that are most used in handling IoT QoS issues. We provide a detailed explanation of QoS in IoT and an overview of commonly used DL-based algorithms in enhancing QoS. Then, we provide a comprehensive discussion of how various DL techniques have been applied for enhancing QoS. We conclude the paper by highlighting the emerging areas of research around Deep Learning and its applicability in IoT QoS enhancement, future trends, and the associated challenges in the application of Deep Learning for QoS in IoT.

A Survey on Internet of Things : Applications and Layered Wise Security Issues

2019 ◽  
pp. 171-180
Author(s):  
Uppuluri Sirisha ◽  
G. Lakshme Eswari
Keyword(s):  
Quality Of Life ◽  
Internet Of Things ◽  
Security And Privacy ◽  
Global Network ◽  
Business Growth ◽  
Paper Briefly ◽  
Smart Meters ◽  
Security Issues ◽  
Data Points

This paper briefly introduces Internet of Things(IOT) as a intellectual connectivity among the physical objects or devices which are gaining massive increase in the fields like efficiency, quality of life and business growth. IOT is a global network which is interconnecting around 46 million smart meters in U.S. alone with 1.1 billion data points per day[1]. The total installation base of IOT connecting devices would increase to 75.44 billion globally by 2025 with a increase in growth in business, productivity, government efficiency, lifestyle, etc., This paper familiarizes the serious concern such as effective security and privacy to ensure exact and accurate confidentiality, integrity, authentication access control among the devices.

scholarly journals A Multi Objective Particle Swarm Optimization for Wireless Sensor Networks and Routing Security

2020 ◽  
Vol 9 (7) ◽  
pp. 1132-1136
Keyword(s):  
Quality Of Service ◽  
Internet Of Things ◽  
Wireless Sensor ◽  
Service Network ◽  
Swarm Optimization ◽  
Routing Security ◽  
Wireless Service ◽  
Node Clustering ◽  
Selection Of

WSN stands for Wireless Sensor Network it is an prefect models of the IoT or Internet of Things that gives checking administrations to catastrophic events, for example, volcanoes ejection and seismic tremor which can influence the life of person. All things considered, the QoS or Quality-of-Service it is a significant problem of the basic application so that it is adequate as well as heartiness is guaranteed. Other than this without a doubt administrations and commitments in checking frameworks, WSN's restricted assets can seriously corrupt the Quality-of-Service in the application of Internet of Things. There will be a decrease in the Quality-of-Service because of the blockage in the wireless service network in the application. For these situtations proficient utilization for the rare assets might be critical for guaranteeing consistent tramission of the information. Decreasing pace in the retransmission of the parcel that occurs due to the blockage diminishes sensor hubs power utilization. PDNC also known as Packet Discarding based Node Clustering that is a specific bundle disposing of technique is presented in this research paper. Every hubs conveyed will be bunched to a few gatherings that focuses on the zone and at once selection of a group head will be done. Parcel disposing of procedure will at that point be conveyed at every hub to diminish the quantity of bundles adding to blockage. Reenactment examination utilizing NS-2 demonstrates that the proposed method can lessen blockage along these lines improve the general execution.

scholarly journals Rancang Bangun Smart Home System Menggunakan NodeMCU Esp8266 Berbasis Komunikasi Telegram Messenger

Techno Com ◽  
2019 ◽  
Vol 18 (4) ◽  
pp. 348-360 ◽  
Author(s):  
Peby Wahyu Purnawan ◽  
Yuni Rosita
Keyword(s):  
Quality Of Service ◽  
User Interface ◽  
Internet Of Things ◽  
Smart Home ◽  
Client Server

Smart Home System bertujuan memaksimalkan pengawasan, pemantauan, keamanan dan sebagainya. sistem ini terintegrasi dari telekomunikasi dan sistem pengendali dari mikrokontroller, sehingga  tercipta Internet Of Things. Pada Penelitian ini dilakukan perancangan sistem Smart Home, dengan sistem client-server berbasis NodeMCU ESP8266 v3 dengan user interface Telegram Messenger yang melakukan komunikasi data melalui wireless. Tahapan perancangan terdiri dari perancangan server, interface, serta sistem kendali Smart Home nya. Hasil akhir pengujian tersebut dapat disimpulkan Aplikasi Telegram Messenger sangat cocok untuk pengontrol dan monitoring Smart Home  jarak jauh, berdasarkan Jarak yang diukur dari 1,7 km sampai 151 km area beda wilayah didapatkan delay rata-rata 20,66 detik, Pada pengujian kinerja Quality of Service dalam sistem komunikasi data ini, berdasarakan standarisasi paramater hasil pengujian bekerja dengan sangat baik. Pada  pengujian nilai RSSI indoor didapat bahwa  kekuatan  komunikasi  wireless  lebih  baik  dibanding outdoor, sehingga RSSI nya lebih kuat. Nilai RSSI  yang tertinggi berada pada -28 dBm dan yang terkecil pada -88 dBm. Berdasarkan pengujian terhadap obstacle, dengan karakteristik redaman yang berbeda - beda dari tiap obstacle nya menghasilkan pengaruh terhadap RSSI dari sinyal wirelessnya.  Obstacle RSSI terkuat dihasilkan oleh pintu kayu dengan nilai -33dbm dBm , serta RSSI terkecil pada obstacle 2 bangunan rumah dengan nilai -78 dBm.  

Security and Privacy Challenges of Deep Learning

2020 ◽  
pp. 42-64
Author(s):  
J. Andrew Onesimu ◽  
Karthikeyan J. ◽  
D. Samuel Joshua Viswas ◽  
Robin D Sebastian
Keyword(s):  
Deep Learning ◽  
Differential Privacy ◽  
Homomorphic Encryption ◽  
Detailed Comparison ◽  
Privacy Preserving ◽  
Research Field ◽  
Security And Privacy ◽  
Security Attacks ◽  
Privacy Breaches ◽  
Comparison Table

Deep learning is the buzz word in recent times in the research field due to its various advantages in the fields of healthcare, medicine, automobiles, etc. A huge amount of data is required for deep learning to achieve better accuracy; thus, it is important to protect the data from security and privacy breaches. In this chapter, a comprehensive survey of security and privacy challenges in deep learning is presented. The security attacks such as poisoning attacks, evasion attacks, and black-box attacks are explored with its prevention and defence techniques. A comparative analysis is done on various techniques to prevent the data from such security attacks. Privacy is another major challenge in deep learning. In this chapter, the authors presented an in-depth survey on various privacy-preserving techniques for deep learning such as differential privacy, homomorphic encryption, secret sharing, and secure multi-party computation. A detailed comparison table to compare the various privacy-preserving techniques and approaches is also presented.

Mathematical Representation of Quality of Service (QoS) Parameters for Internet of Things (IoT)

2020 ◽  
pp. 364-376
Author(s):  
Sandesh Mahamure ◽  
Poonam N. Railkar ◽  
Parikshit N. Mahalle
Keyword(s):  
Mathematical Model ◽  
Quality Of Service ◽  
Internet Of Things ◽  
Gap Analysis ◽  
Literature Survey ◽  
Mathematical Representation ◽  
The World ◽  
Qos Parameters ◽  
The Mathematical Model

Now we are in the era of ubiquitous computing. Internet of things (IoT) is getting matured in various parts of the world. In coming few years' billions and trillions of things will be connected to the internet. To deal with these huge number of devices in a network we need to consider Quality of Service (QoS)parameters so that system operations can be performed in a smoother way. Mathematical modelling of these QoS parameters gives an idea about which factors are needs to consider while designing any IoT-enabled system at the same time it will give the performance analysis of the system before implementation. In this paper comprehensive literature survey is done to discuss various issues related to QoS and gap analysis is also done for IoT Enabled systems. This paper proposes general steps to build a mathematical model for a system. It also proposes the mathematical model for QoS parameters like reliability, communication complexities, latency and aggregation of data for IoT. To support proposed mathematical model proof of concept also given.

Grid Technologies in Epidemiology

2011 ◽  
pp. 426-443
Author(s):  
Ignacio Blanquer ◽  
Vicente Hernandez
Keyword(s):  
Large Scale ◽  
Heterogeneous Data ◽  
Semantic Integration ◽  
Security And Privacy ◽  
Use Case ◽  
Seamless Integration ◽  
Computationally Intensive ◽  
Art Research ◽  
Additional Constraints

Epidemiology constitutes one relevant use case for the adoption of grids for health. It combines challenges that have been traditionally addressed by grid technologies, such as managing large amounts of distributed and heterogeneous data, large scale computing and the need for integration and collaboration tools, but introduces new challenges traditionally addressed from the e-health area. The application of grid technologies to epidemiology has been concentrated in the federation of distributed repositories of data, the evaluation of computationally intensive statistical epidemiological models and the management of authorisation mechanism in virtual organisations. However, epidemiology presents important additional constraints that are not solved and harness the take-off of grid technologies. The most important problems are on the semantic integration of data, the effective management of security and privacy, the lack of exploitation models for the use of infrastructures, the instability of Quality of Service and the seamless integration of the technology on the epidemiology environment. This chapter presents an analysis of how these issues are being considered in state-of-the-art research.

A Trust Based Secure and Privacy Aware Framework for Efficient Taxi and Car Sharing System

2018 ◽  
Vol 2 (1) ◽  
pp. 34-47 ◽  
Author(s):  
Oladayo Olakanmi ◽  
Sekoni Oluwaseun
Keyword(s):  
Quality Of Service ◽  
Service Delivery ◽  
Private Information ◽  
Autonomous Vehicles ◽  
Trust Model ◽  
Security And Privacy ◽  
Car Sharing ◽  
Perfect Model ◽  
Taxi Service

This article describes how taxi service is an essential means of mobility in many cities. Recent findings show that average automobile owners utilize their vehicles for only 5% of its time in a day. Therefore, the advent of autonomous vehicles and car sharing will make it possible for owners to engage their vehicles as taxis when not in use by utilizing its 95% free time for income generation. Sensitive private information is required to be released during a taxi service delivery, which may bring certain security and privacy issues and challenges. This may hinder the prospect of using autonomous vehicles as a form of taxi. As a result of these, the authors propose a secure and privacy-preserving taxi service framework for car sharing, which ensures protection of car owner and passengers personal details, e.g. identity, location, destination, etc. The authors developed a decay-based trust model for a framework in order to monitor and improve the quality of service rendered to passengers by vehicles. The decay-based trust model was simulated on the framework. The simulation of the decay-based trust model shows that it is a perfect model for rewarding vehicles which render good quality of service and blacklisting vehicles with frequent poor service delivery.

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