Multimedia Communications in Internet of Things QoT or QoE?

Karaadi, Amulya; Sun, Lingfen; Mkwawa, Is-Haka

doi:10.1109/ithings-greencom-cpscom-smartdata.2017.11

Cited by 17 publications

(8 citation statements)

References 14 publications

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“…BlueVoice has been evaluated for multimedia services in real-time IoT devices in [24]. QoS is analysed in [25]- [29], while QoE is studied in [30]- [35]. The concept of Quality of Information (QoI) and ICN using a distributed algorithm for multimedia data collection is proposed in [36].…”

Section: A Contributions Of This Survey Articlementioning

confidence: 99%

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Multimedia Internet of Things: A Comprehensive Survey

et al. 2020

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The immense increase in multimedia-on-demand traffic that refers to audio, video, and images, has drastically shifted the vision of the Internet of Things (IoT) from scalar to Multimedia Internet of Things (M-IoT). IoT devices are constrained in terms of energy, computing, size, and storage memory. Delaysensitive and bandwidth-hungry multimedia applications over constrained IoT networks require revision of IoT architecture for M-IoT. This paper provides a comprehensive survey of M-IoT with an emphasis on architecture, protocols, and applications. This article starts by providing a horizontal overview of the IoT. Then, we discuss the issues considering the characteristics of multimedia and provide a summary of related M-IoT architectures. Various multimedia applications supported by IoT are surveyed, and numerous use cases related to road traffic management, security, industry, and health are illustrated to show how different M-IoT applications are revolutionizing human life. We explore the importance of Quality-of-Experience (QoE) and Quality-of-Service (QoS) for multimedia transmission over IoT. Moreover, we explore the limitations of IoT for multimedia computing and present the relationship between the M-IoT and emerging technologies including event processing, feature extraction, cloud computing, Fog/Edge computing and Software-Defined-Networks (SDNs). We also present the need for better routing and Physical-Medium Access Control (PHY-MAC) protocols for M-IoT. Finally, we present a detailed discussion on the open research issues and several potential research areas related to emerging multimedia communication in IoT. INDEX TERMS Multimedia Internet of Things (M-IoT), multimedia communication, Internet of Multimedia Things (IoMT), multimedia computing, Quality-of-Experience (QoE), Quality-of-Service (QoS), multimedia routing, medium access control (MAC).

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Section: A Contributions Of This Survey Articlementioning

confidence: 99%

“…The concept of Quality of Information (QoI) and ICN using a distributed algorithm for multimedia data collection is proposed in [36]. Kaaradi et al [30] have presented the concept of Quality of Things (QoT) for M-IoT. However, it lacks QoS objectives.…”

Section: A Contributions Of This Survey Articlementioning

confidence: 99%

Multimedia Internet of Things: A Comprehensive Survey

et al. 2020

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“…Long et al [405] provide an edge computing framework to enable cooperative processing on mobile devices for delaysensitive multimedia IoT tasks. A new architecture based on the concept of Quality of Things (QoT) for multimedia communications in IoTs is proposed in [406], together with its challenges and future research directions. Some of the issues related to video traffic prioritization, virtualization of network elements, mobility management, and security are highlighted in [407].…”

Section: G Multimedia Communications In Internet Of Things (Iots)mentioning

confidence: 99%

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Barakabitze

Barman

Ahmad

et al. 2020

IEEE Commun. Surv. Tutorials

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168

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The highly demanding Over-The-Top (OTT) multimedia applications pose increased challenges to Internet Service Providers (ISPs) for assuring a reasonable Quality of Experience (QoE) to their customers due to lack of flexibility, agility and scalability in traditional networks. The future networks are shifting towards the cloudification of the network resources via Software Defined Networks (SDN) and Network Function Virtualization (NFV). This will equip ISPs with cutting-edge technologies to provide service customization during service delivery and offer QoE which meets customers' needs via intelligent QoE control and management approaches. Towards this end, we provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a highlevel description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoEoriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.

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“…The deployment and application of IoT devices is the cornerstone of the IoT [1,2]. Today, the number of intelligent IoT devices has exceeded 4 billion [3]. These IoT devices are generally embedded in sensors, and scattered throughout the physical world, to be used to sense the external environment and collect data.…”

Section: Introductionmentioning

confidence: 99%

Table Recognition for Sensitive Data Perception in an IoT Vision Environment

et al. 2019

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Internet of Things (IoT) technology allows us to measure, compute, and decide about the physical world around us in a quantitative and intelligent way. It makes all kinds of intelligent IoT devices popular. We are continually perceived and recorded by intelligent IoT devices, especially vision devices such as cameras and mobile phones. However, a series of security issues have arisen in recent years. Sensitive data leakage is the most typical and harmful one. Whether we are just browsing files unintentionally in sight of high-definition (HD) security cameras, or internal ghosts are using mobile phones to photograph secret files, it causes sensitive data to be captured by intelligent IoT vision devices, resulting in irreparable damage. Although the risk of sensitive data diffusion can be reduced by optical character recognition (OCR)-based packet filtering, it is difficult to use it with sensitive data presented in table form. This is because table images captured by the intelligent IoT vision device face issues of perspective transformation, and interferences of circular stamps and irregular handwritten signatures. Therefore, a table-recognition algorithm based on a directional connected chain is proposed in this paper to solve the problem of identifying sensitive table data captured by intelligent IoT vision devices. First, a Directional Connected Chain (DCC) search algorithm is proposed for line detection. Then, valid line mergence and invalid line removal is performed for the searched DCCs to detect the table frame, to filter the irregular interferences. Finally, an inverse perspective transformation algorithm is used to restore the table after perspective transformation. Experiments show that our proposed algorithm can achieve accuracy of at least 92%, and filter stamp interference completely.

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Multimedia Communications in Internet of Things QoT or QoE?

Cited by 17 publications

References 14 publications

Multimedia Internet of Things: A Comprehensive Survey

Multimedia Internet of Things: A Comprehensive Survey

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Table Recognition for Sensitive Data Perception in an IoT Vision Environment

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