# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 2 Related Research and Background
### Challenges in IoT
#### 2.1.2 Management of Limited Resources  
Resource management is one of the challenges faced by the IoT. Some applications require the processing of large amounts of data using existing technologies and limited resources while being in a heterogeneous environment and retaining QoS targets. Data in massive amounts flow through IoT devices. Once that data is collected from sensors or other devices, it goes through multiple stages. After acquisition of the data, it is stored in memory for further processing. Then it is filtered using multiple techniques. The filtered data is then checked for inaccuracies or errors. If needed, data are then compressed and, additionally in some cases, it is changed into some other format or type for data homogeneity across applications or for security purposes. The resulting data is subsequently sent as an image, video, email, or whatever. All this data is processed either in the device memory or through some cloud-processing platforms. Data must not only be scalable but meaningful too. While using data in the IoT [13], it should be made useful by filtering it before carrying out any other operation on it and, therefore, it must be safe to use for applications. As there are multiple stages involved, managing data and resources remains a challenge.  
To manage resources, IoT devices and applications must be sufficiently adaptable to their dynamically changing environment. However, frameworks designed for IoT resource management, such as optimization theory, result in additional energy consumption when communicating to a wireless network Base Station (BS). A learning framework in Reference [14] helps adapt to diverse environments by adjusting parameters of performance, processing and learning from the data, while keeping within the resource constraints. In addition, by means of learning algorithms, data analytics can be made easier in respect to large amounts of data, resulting in lower power consumption and, thus, increasing lifetime of IoT devices. Again tackling energy conservation, a concept of Green IoT was presented in Reference [15]. In that study, the authors described how conservation of energy is possible using renewable energy resources. In addition, data size reduction and efficient routing protocols were suggested as two ways to save energy.  
Managing huge amounts of data arising from camera sensors has nowadays become a concern. Cloud computing has paved the way for massive storage but there is a downside to cloud computing: Applications suffer from latencies in video processing and, thus, real-time performance is compromised. To maintain QoS, the spatial resolution of the videos is commonly decreased, which, however, may affect applications requiring high resolutions to function in an optimal manner. Therefore, bandwidth and storage constraints both need to be properly accommodated. Resource management is able to make sure that despite resource constraints and a system's capabilities, QoS is not compromised. A methodology for stabilizing the amount of video data produced by camera sensors works by determining the resolution of a camera sensor and analyzing the chores of each IoT layer. This methodology [16] has been presented for content-sensitive applications such as face recognition. Although, cloud-based IoT applications, have been addressed in the literature, for small-scale applications and small devices such as mobile phones, a need remains to take measures to properly utilize data generated in bulk [17].