Multimedia Internet of Things: A Comprehensive Survey <s> V. M-IoT COMPUTING PARADIGM <s> With the proliferation of social media, which is largely fostered by the boom of the Internet and mobile ecosystems, a huge amount of multimedia data has been generated, forming the multimedia big data. However, because multimedia data is unstructured and multimodal in nature, with real-time and Quality of Experience requirements, multimedia big data computing has not only created unprecedented opportunities but also imposed fundamental challenges in storage, processing, and analysis. Here, the authors introduce the principal concepts for multimedia big data computing, discuss what the scientific problems and fundamental challenges are, and present methodologies and approaches from the perspectives of the multimedia life cycle and multimedia big data computing life cycle. They also speculate on the research opportunities and directions for multimedia big data computing. <s> BIB001 </s> Multimedia Internet of Things: A Comprehensive Survey <s> V. M-IoT COMPUTING PARADIGM <s> Crowdsourced multimedia data poses several challenges when it is collected, stored, indexed, retrieved, and visualized. Examples of crowd source multimedia data are social sensors, vehicle sensors, physical sensors, human sensors, etc. Analyzing such multimodal and diversified crowdsourced data provides very rich understanding about the need of individuals within a crowd. Such understanding makes it possible to tailor services to individuals' needs, also called context-aware services. In this paper, we propose a spatial multimedia big data framework that can collect multimedia data from 1) a very large crowd equipped with multi-sensory smartphones, 2) vehicles, and 3) social networks. A set of multimedia services are offered to users to support their spatio-temporal activities. These include but not limited to 1) simple user interfaces to utilize multimedia services for instant guidance, 2) navigation to points of interests (POI), and 3) efficient and cost effective intra-city rides to users. The big data framework is designed to handle a very large number of multimedia spatio-temporal queries in real-time. The system is a pilot project and will be deployed during the event of Hajj 2015 when over three million pilgrims from all over the world will visit Makkah, Saudi Arabia to perform their Hajj rituals. <s> BIB002 </s> Multimedia Internet of Things: A Comprehensive Survey <s> V. M-IoT COMPUTING PARADIGM <s> Voluminous amounts of data have been produced, since the past decade as the miniaturization of Internet of things (IoT) devices increases. However, such data are not useful without analytic power. Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices. However, these solutions are still in their infancy, and the domain lacks a comprehensive survey. This paper investigates the state-of-the-art research efforts directed toward big IoT data analytics. The relationship between big data analytics and IoT is explained. Moreover, this paper adds value by proposing a new architecture for big IoT data analytics. Furthermore, big IoT data analytic types, methods, and technologies for big data mining are discussed. Numerous notable use cases are also presented. Several opportunities brought by data analytics in IoT paradigm are then discussed. Finally, open research challenges, such as privacy, big data mining, visualization, and integration, are presented as future research directions. <s> BIB003 </s> Multimedia Internet of Things: A Comprehensive Survey <s> V. M-IoT COMPUTING PARADIGM <s> Abstract With an exponential increase in the provisioning of multimedia devices over the Internet of Things (IoT), a significant amount of multimedia data (also referred to as multimedia big data – MMBD) is being generated. Current research and development activities focus on scalar sensor data based IoT or general MMBD and overlook the complexity of facilitating MMBD over IoT. This paper examines the unique nature and complexity of MMBD computing for IoT applications and develops a comprehensive taxonomy for MMBD abstracted into a novel process model reflecting MMBD over IoT. This process model addresses a number of research challenges associated with MMBD, such as scalability, accessibility, reliability, heterogeneity, and Quality of Service (QoS) requirements. A case study is presented to demonstrate the process model. <s> BIB004
A significant increase in multimedia big data generated from IoT devices, this volume increases with diminishing size and mobile nature of IoT devices. The amount of data generated is expected to be 600 Zettabytes (ZB) annually by 2020 BIB003 . One of the particular multimedia IoT data generated online is audio, video, images, and graphics BIB001 . The characteristics of multimedia data are specified as huge volume, structured and unstructured data, velocity, unpredictability (frequently changing), and accuracy BIB002 . The current IoT frameworks to analyze and process scalar data are unsuitable for multimedia data in IoT. Multimedia data in IoT poses challenges including storage and sharing, real-time computation, processing and provision, energy optimization, resource allocation, feature extraction from unstructured multimedia data, addressing and routing, QoS and QoE preservation, delay sensitivity, data reduction, compression and encryption, security and privacy BIB001 . Previously we have discussed M-IoT architectures, use cases, QoS, and QoE optimization techniques. In this section, various computation, processing, compression techniques have been discussed. Fig. 10 shows the classification of multimedia computing in IoT. Routing and resource allocation MAC approaches are present in the later sections. Four main phases of multimedia data computation are shown in Fig. 11 . A detailed survey on multimedia big data computing in IoT is presented by Kumari et al. BIB004 . The authors have discussed in detail the difference between big data and big multimedia data. This section has been specifically subdivided into multimedia data compression and event processing, fog/edge frameworks for M-IoT computation, cloud computation for M-IoT and SDNs for M-IoT computing. Table 7 classifies and summarizes the existing work related to multimedia data computing in IoT.