QoS Strategies for Wireless Multimedia Sensor Networks in the Context of IoT at the MAC Layer, Application Layer, and Cross-Layer Algorithms

Bernard, Muwonge Ssajjabbi; Pei, Tingrui; Kimbugwe, Nasser

doi:10.1155/2019/9651915

Cited by 13 publications

(8 citation statements)

References 52 publications

(112 reference statements)

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“…The work in Reference 29 proposed a smart‐contract based blockchain architecture for effective and secure data communication. Better QoS strategies mentioned in Reference 30 can be utilized to enhance the IoT network's performance further. It is observed that the AR‐MAC [17:27] protocol outperforms other protocols in terms of QoS performance and thus must be used for the deployment of efficient IoT networks.…”

Section: Literature Surveymentioning

confidence: 99%

KMFA2 based QoS improvement for multi‐channel IoT networks

Shankhpal

Hanumantha

2022

Concurrency and Computation

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Internet of Things (IoT) networks are designed to obtain the best possible end-user experience. This experience is measured in terms of security and speed of operation. In order to achieve high security, encryption, and authentication algorithms are proposed by researchers. The speed of operation is generally measured using the network's response time, and it can be improved using light-weighted algorithms for data processing and actuation. Most of these techniques are applied at the application level, making the network ineffective in its originally deployed form. Moreover, due to the addition of security and data processing layers, the overall IoT network's QoS reduces, reducing the end user's experience. In order to remove these issues, this work proposes a novel kernel-level multi-channel fuzzy-authentication and aggregation framework (KMFA2). The KMFA2 framework can solve authentication issues by using a kernel-level lightweight and secure authentication protocol for all IoT devices. KMFA2 also enables the IoT devices to possess multi-channel capabilities, with inbuilt aggregation capabilities. The overall implementation is carried out on the NS2 tool.The proposed KMFA2 protocol is tested on different IoT networks, and it is observed that the protocol improves the QoS by almost 10% and simplifies security using the authentication framework. The comparisons were performed with state-of-the-art IoT deployments.

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Section: Literature Surveymentioning

confidence: 99%

KMFA2 based QoS improvement for multi‐channel IoT networks

Shankhpal

Hanumantha

2022

Concurrency and Computation

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“…QoS is the measurement of the general performance of any service, mainly the performance seen by the users of the service [36][37][38]. Owing to the widespread usage and application of IoT services in our daily life situations, the cost of IoT devices should be low without compromising the level of QoS.…”

Section: Quality Of Service In Internet Of Thingsmentioning

confidence: 99%

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

2021

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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.

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“…Tere are several uses for evolving wireless sensor networks (WSNs) in business, education, commuting, infrastructure, and protection [1]. Massive, widely dispersed sensing gadgets having data management capabilities, sensors, actuators, energy harvester devices, various forms of memory, radio signal-based transmitters, and receivers are all included in WSNs [2][3][4]. Such networked devices are placed across a sizable operational region to acquire data by detecting or monitoring the testbed.…”

Section: Introductionmentioning

confidence: 99%

Requirements, Deployments, and Challenges of LoRa Technology: A Survey

Kamal

Alam

Sajak

et al. 2023

Computational Intelligence and Neuroscience

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LoRa is an ISM-band based LPWAN communication protocol. Despite their wide network penetration of approximately 20 kilometers or higher using lower than 14 decibels transmitting power, it has been extensively documented and used in academia and industry. Although LoRa connectivity defines a public platform and enables users to create independent low-power wireless connections while relying on external architecture, it has gained considerable interest from scholars and the market. The two fundamental components of this platform are LoRaWAN and LoRa PHY. The consumer LoRaWAN component of the technology describes the network model, connectivity procedures, ability to operate the frequency range, and the types of interlinked gadgets. In contrast, the LoRa PHY component is patentable and provides information on the modulation strategy which is being utilized and its attributes. There are now several LoRa platforms available. To create usable LoRa systems, there are presently several technical difficulties to be overcome, such as connection management, allocation of resources, consistent communications, and security. This study presents a thorough overview of LoRa networking, covering the technological difficulties in setting up LoRa infrastructures and current solutions. Several outstanding challenges of LoRa communication are presented depending on our thorough research of the available solutions. The research report aims to stimulate additional research toward enhancing the LoRa Network capacity and allowing more realistic installations.

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QoS Strategies for Wireless Multimedia Sensor Networks in the Context of IoT at the MAC Layer, Application Layer, and Cross-Layer Algorithms

Cited by 13 publications

References 52 publications

KMFA2 based QoS improvement for multi‐channel IoT networks

KMFA2 based QoS improvement for multi‐channel IoT networks

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

Requirements, Deployments, and Challenges of LoRa Technology: A Survey

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