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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Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physicallayer channel models, however, do not explicitly consider qualityof-service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing works, that use the EC model in various wireless networks. We summarize the work related to EC for different networks such as cognitive radio networks (CRNs), cellular networks, relay networks, adhoc networks, and mesh networks. We explore five case studies encompassing EC operation with different design and architectural requirements. We survey various delay-sensitive applications such as voice and video with their EC analysis under certain delay constraints. We finally present the future research directions with open issues covering EC maximization.

There has been a rapid increase in dietary ailments during the last few decades, caused by unhealthy food routine. Mobile-based dietary assessment systems that can record real-time images of the meal and analyze it for nutritional content can be very handy and improve the dietary habits and, therefore, result in a healthy life. This paper proposes a novel system to automatically estimate food attributes such as ingredients and nutritional value by classifying the input image of food. Our method employs different deep learning models for accurate food identification. In addition to image analysis, attributes and ingredients are estimated by extracting semantically related words from a huge corpus of text, collected over the Internet. We performed experiments with a dataset comprising 100 classes, averaging 1000 images for each class to acquire top 1 classification rate of up to 85%. An extension of a benchmark dataset Food-101 is also created to include sub-continental foods. Results show that our proposed system is equally efficient on the basic Food-101 dataset and its extension for sub-continental foods. The proposed system is implemented as a mobile app that has its application in the healthcare sector. INDEX TERMS Food recognition, convolutional neural networks, vector embeddings, attribute estimation. I. INTRODUCTION