Real-time streaming challenges in Internet of Video Things (IoVT)

Sammoud, Ahmed; Kumar, Ashok; Bayoumi, Magdy; Elarabi, Tarek

doi:10.1109/iscas.2017.8050815

Cited by 22 publications

(15 citation statements)

References 11 publications

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“…A compressed sensing technique is proposed for M-IoT in [40]. Various video encoding schemes and multimedia sensing are outlined in [41], [42]. Performance comparison of Advance Video Codings (AVC) H. 265 and H.264 is presented in [43].…”

Section: A Contributions Of This Survey Articlementioning

confidence: 99%

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%

Multimedia Internet of Things: A Comprehensive Survey

et al. 2020

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“…The authors of Reference [19] also suggested the use of compression standards such as H.264/Advanced Video Coding (AVC), which is suitable for video streaming with low bitrates. The remaining video streaming challenges faced by IoT are highlighted in Reference [20], where possible solutions to those problems were presented.…”

Section: Video Streaming In the Iotmentioning

confidence: 99%

“…The fast coding scheme proposed in Reference [35] serves that purpose, reducing processing time by 12.35% as a result of decreased complexity, while, compared to more complex codecs only results in a bit-rate increment of 0.81% for the reduced computing time. For a reliable video streaming in real-time, there is IoVT platform in Reference [20]. This platform combines efficient video encoding and streaming with low power consumption.…”

Section: Video Compressionmentioning

confidence: 99%

Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things

et al. 2020

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There is an increasing number of devices available for the Internet of Multimedia Things (IoMT). The demands these ever-more complex devices make are also increasing in terms of energy efficiency, reliability, quality-of-service guarantees, higher data transfer rates, and general security. The IoMT itself faces challenges when processing and storing massive amounts of data, transmitting it over low bandwidths, bringing constrained resources to bear and keeping power consumption under check. This paper’s research focuses on an efficient video compression technique to reduce that communication load, potentially generated by diverse camera sensors, and also improve bit-rates, while ensuring accuracy of representation and completeness of video data. The proposed method applies a video content-based solution, which, depending on the motion present between consecutive frames, decides on whether to send only motion information or no frame information at all. The method is efficient in terms of limiting the data transmitted, potentially conserving device energy, and reducing latencies by means of negotiable processing overheads. Data are also encrypted in the interests of confidentiality. Video quality measurements, along with a good number of Quality-of-Service measurements demonstrated the value of the load reduction, as is also apparent from a comparison with other related methods.

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“…The Raspberry Pi 3 3 model B (RPi) is one of the most common devices for emulating an IoT edge node [44].…”

Section: A Device and Hardware Configurationmentioning

confidence: 99%

IoT-Guard: Event-Driven Fog-Based Video Surveillance System for Real-Time Security Management

Sultana

Wahid

2019

IEEE Access

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In this paper, we design and implement a distributed Internet of Things (IoT) framework called IoT-guard, for an intelligent, resource-efficient, and real-time security management system. The system, consisting of edge-fog computational layers, will aid in crime prevention and predict crime events in a smart home environment (SHE). The IoT-guard will detect and confirm crime events in real-time, using Artificial Intelligence (AI) and an event-driven approach to send crime data to protective services and police units enabling immediate action while conserving resources, such as energy, bandwidth (BW), and memory and Central Processing Unit (CPU) usage. In this study, we implement an IoT-guard laboratory testbed prototype and perform evaluations on its efficiency for real-time security application. The outcomes show better performance by the proposed system in terms of resource efficiency, agility, and scalability over the traditional IoT surveillance systems and state-of-the-art (SoA) approaches. INDEX TERMS IoT, edge, fog, video surveillance, convolutional neural network, motion detection, gun-knife detection, real-time security, message queuing telemetry transport (MQTT).

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Real-time streaming challenges in Internet of Video Things (IoVT)

Cited by 22 publications

References 11 publications

Multimedia Internet of Things: A Comprehensive Survey

Multimedia Internet of Things: A Comprehensive Survey

Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things

IoT-Guard: Event-Driven Fog-Based Video Surveillance System for Real-Time Security Management

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