Multimedia Internet of Things: A Comprehensive Survey <s> B. M-IoT ARCHITECTURES <s> In this paper, we propose an agent-based architecture for remote collaboration support systems that enables the exchange of synchronous and asynchronous multimedia streams at remote sites using an Internet of Multimedia Things (IoMT) approach. First, we design and implement Internet of Things (IoT) applications that contain simple sensors and actuators. These applications are modularized into agent-based subsystems that can be incorporated into an IoT application with agent operations. Our aim is to develop remote collaboration support applications composed of video, data, and document channels. Because the applications will exchange enormous multimedia streams between remote sites, we propose a novel IoMT system architecture composed of several channel types that consist of various resource and network components. Users can dynamically incorporate these channels into applications to update the IoMT system’s effects. Finally, we demonstrate and discuss the experimental results of our application to validate its ability to rapidly supply multimedia resources via multi-agent collaboration. Adding to its novelty, the developed system is in practical use for collaboration between France and Japan. <s> BIB001 </s> Multimedia Internet of Things: A Comprehensive Survey <s> B. M-IoT ARCHITECTURES <s> Multimedia traffic has drastically grown in the last few years. In addition, some of the last paradigms proposed, like the Internet of Things (IoT), adds new types of traffic and applications. Software-defined networks (SDNs) improve the capability of network management. Combined with SDN, artificial intelligence (AI) can provide solutions to network problems based on classification and estimation techniques. In this paper, we propose an artificial intelligence system for detecting and correcting errors in multimedia transmission in a surveillance IoT environment connected through a SDN. The architecture, algorithm, and messages of the SDN are detailed. The AI system design is described, and the test-bed and the data set are explained. The AI module consists of two different parts. The first one is a classifying part, which detects the type of traffic that is sent through the network. The second part is an estimator that informs the SDN controller on which kind of action should be executed to guarantee the quality of service and quality of experience. Results show that with the actions performed by the network, like jitter can be reduced up to 70% of average and losses can be reduced from 9.07% to nearly 1.16%. Moreover, the presented AI module is able to detect critical traffic with 77% accuracy. <s> BIB002 </s> Multimedia Internet of Things: A Comprehensive Survey <s> B. M-IoT ARCHITECTURES <s> We propose a fog-cloud hybrid architecture that can support a massive ad hoc crowd composed of a massive social network and distributed IoT nodes around a smart city environment. The fog computing framework is introduced to support energy efficiency by incorporating IoT nodes that act as an interface to the ad hoc crowd, is aware of user contexts within its vicinity, can do real-time processing of user requests, supports a constrained amount of data offloading, and pass the geo-tagged multimedia data available from the subset of the ad hoc crowd to the big data repository in the cloud. The framework leverages a sustainable crowdsourcing incentive model for both the ad hoc crowd and the IoT infrastructure provider. The proposed sustainable framework can potentially support context-aware smart city services such as finding a lost person within the crowd, showing green and health risk prone zones, semantic and location-aware notifications of events of interest, semantic IoT-based routing, and dealing with emergency situations. We present a communication architecture between mobile users and fog nodes, and between fog nodes and the cloud, a sustainability and energy efficiency model, and massive geo-tagged, multimedia big data architecture. <s> BIB003 </s> Multimedia Internet of Things: A Comprehensive Survey <s> B. M-IoT ARCHITECTURES <s> The escalating growth of multimedia content in Internet of Things (IoT) applications leads to a huge volume of unstructured data being generated. Unstructured Big data has no particular format or structure and can be in any form such as text, audio, images, and video. Furthermore, current IoT systems cannot successfully realize the notion of having ubiquitous connectivity of everything if they are not capable to include ‘multimedia things’. In this paper, we address two issues by proposing a new architecture for the Multimedia Internet of Things (MIoT) with Big multimodal computation layer. We first introduce MIoT as a novel paradigm in which smart heterogeneous multimedia things can interact and cooperate with one another and with other things connected to the Internet to facilitate multimedia-based services and applications that are globally available to the users. The MIoT architecture consists of six layers. The computation layer is specially designed for Big multimodal analytics. This layer has four important functional units: Data Centralized Unit, Multimodal Data Aggregation Unit, Multimodal Data Divide & Conquer Computation Unit, and Fusion & Decision Making Unit. A novel and highly scalable technique called the Divide & Conquer Principal Component Analysis (DC-PCA) for feature extraction in the divide and conquer mechanism is proposed to be used together with the Divide & Conquer Linear Discriminant Analysis (DC-LDA) for multimodal Big data analytics. Experiments are conducted to confirm the good performance of these techniques in the functional units of the Divide & Conquer computational mechanisms. The final section of the paper gives application on a camera sensing IoT platform and real-world data analytics on multicore architecture implementations. <s> BIB004
The characteristics of IoT make it possible to support multimedia applications. However, it poses enormous challenges as multimedia applications are bandwidth-hungry and delaysensitive. Radio and computational resources are preciously scarce for IoT devices. The rapid surge of multimedia data in IoT leads to vast amorphous data. Researchers have proposed different M-IoT architectures to efficiently analyze, process, and utilize the resources with more reliability. Novel M-IoT architectures are presented in this section. Fig. 4 and Table 4 summarizes existing M-IoT architectures.  , agent-based M-IoT architecture BIB001 , AI-based SDNs for multimedia traffic management architecture BIB002 , Fog-Cloud hybrid M-IoT architecture BIB003 , and Big data layered M-IoT architecture BIB004 . 