# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 1 Introduction  
The Internet of Things (IoT) is described as a network of smart objects collecting, processing and sharing data, with sensors and actuators enhancing object functionality. The number of such connected devices, as a result of rapid expansion, is estimated to reach 31 billion by 2020 [1]. For a long time, the IoT infrastructure has been linked to event-based systems. These systems [2] identify events, which cause streaming of structured or scalar data, for example the streaming of the ambient temperature of an object or its energy consumption. On the other hand, camera sensors detect multimedia events and, hence, communicate unstructured data. While event-based systems have hitherto detected mainly structured events, trends in the IoT domain are shifting from structured events to multimedia events. The Internet of Multimedia Things (IoMT) is an aspect of the IoT associated with relaying images, videos, audio, speech, and other multimedia data. Camera sensorscoupled with connected devices, make those devices capable of capturing images and recording videos. Consequently, systems with diverse sensors are able to detect events based on image, video, or audio data. It is estimated [3] that the number of installed cameras will globally exceed 13 billion by 2030. These cameras can exist as either smart phone cameras or security cameras in homes, vehicles, cities, highways, and public places. However, IoMT systems differ from simple event-based systems in terms of: the heterogeneity and complexity of the sensed and communicated data; the high energy consumption of the communication devices and their sensors; their higher bandwidth requirements; the possible use of non-standardized protocols; the possible absence currently of suitable middleware; and the complex processing requirements of the multimedia data analytics at the data sink. Current practices and standards used in the IoT, such as the means of video encoding, the form of the protocol stack, and the cloud services availed of, are not well-suited to the real-time nature of the IoMT. Thus, in an IoMT [4], data is collected, stored, processed and distributed in real-time and, as a result, more resources are required. Another development should be mentioned: There is an obvious increase in mobile multimedia devices and this growth brings with it large quantities of data, which development has been named Multimedia Big Data (MMBD). MMBD in the IoMT comes with challenges of real-time processing in terms of computation, communication and securing or protecting large amounts of data passing through heterogeneous IoT applications, while ensuring Quality-of-Service (QoS) and Quality-of-Experience (QoE) [5,6]. On top of that, real-time streaming and relaying of videos with high bandwidth requirements, video encoding and connectivity also pose potential issues. Cumulatively, the paradigm shift represented by IoMT gives rise to [7] issues of scalability, flexibility, compatibility, security, and mobility. Complexities in the IoMT are a by-product of the massive amount of data and the strain caused by sensing, processing and distributing such large quantities of data. Camera sensors in IoMT tend to transmit videos, a complex type of data requiring storage and subsequent processing. A need then arises for image processing to identify effectively objects within the videos. As the number of camera sensors increases, the amount of video data distributed across multiple applications has escalated. Managing large amounts of video data, because of its complex nature, is a research challenge in general. Videos hold information indicating significant events, as far as applications, such as home security, traffic or military surveillance, are concerned. As a result, not only the protection and privacy of data is important but, for such content-sensitive applications, requires data integrity and data completeness, without loss of any meaningful information. The aim of this study is to reduce the communication load in an IoMT through efficient video compression.