In this era of digitization, we are moving more and more towards the usage of smart devices. These smart devices includes Smart watches, Smart Phones, Tablet PCs and many other smart hand held devices. These devices comes along with many powerful sensors which are used to collect real time data from the surroundings. These real time data can be later used for various purposes like data analytics, controlling of devices and etc. Fundamentally IoT Architecture consist of 3 layers:
- Layer-1: This layer deals with sensors, which are used to collect real-time data.
- Layer-2: This is the transmission layer with or without filtering out the useless data.
- Layer-3: It is the Application layer.
In Compression Sensing Based IoT Applications there are 3 layers, but there is a slight difference in the layer 2:-
- Layer-1: This layer collects real-time data just like the one I have mentioned above. This is also called as CS-Based Acquisition Layer.
- Layer-2: This layer is called as the CS-Based Reconstruction layer. This layer plays a crucial role in this entire system. Here the data is filtered out and only the salient features are transmitted to the Cloud. This in turn significantly reduces the network bandwidth utility.
- Layer-3: It is the Application layer. Just like the previous layer.
Importance of Layer 2 in Compression Sensing Based IoT Application: Usually while collecting real time data from smart devices or from sophisticated IoT devices, the information contains redundant data, which is actually of no use. And if these redundant data is transmitted across the internet then there is unnecessary utilization of the network bandwidth. So to avoid these, help of edge computing is taken. Here at first the collected data is transmitted to a local processing unit that has got ample amount of space and computing capacity to perform various pre-processing techniques to extract various info and features. Only the important information and features that are required is transmitted to the Cloud which in turn significantly reduces the network bandwidth.
Advantages of Using Compression Sensing Based IoT Applications:
- Energy efficient.
- Only significant data is transmitted to the Cloud.
- In the Application layer, Data Analytics algorithms can be implemented on these selectively transmitted data as per the requirements.
- Cost-effective: Compression sensing technology is cost-effective as it reduces the amount of data transmitted over the network, which lowers the cost of data storage and transmission.
- Improved Security: Compression sensing technology can enhance the security of IoT applications. Since only relevant data is transmitted, the risk of a data breach or cyber attack is reduced.
- Faster Data Processing: By reducing the amount of data transmitted, compression sensing technology can speed up the processing of data. This allows for quicker analysis and decision making, which is critical in time-sensitive IoT applications.
- Improved Battery Life: Compression sensing technology can extend the battery life of IoT devices as it reduces the amount of energy required for data transmission. This is especially important for IoT applications in remote areas or where frequent battery replacement is not feasible.
- Better Network Bandwidth Utilization: By transmitting only relevant data, compression sensing technology can optimize network bandwidth utilization. This is beneficial in areas where network connectivity is limited or unreliable.
- Real-Time Monitoring: Compression sensing technology enables real-time monitoring of IoT applications. Since only relevant data is transmitted, it can be processed and analyzed in real-time, providing instant feedback and alerts when necessary.
- Scalability: Compression sensing technology is highly scalable, making it suitable for IoT applications of all sizes. As the amount of data generated by IoT devices continues to increase, compression sensing technology can help manage this growth by reducing the amount of data transmitted.
Disadvantages of Using Compression Sensing Based IoT Applications:
- Reduced Accuracy: One of the main disadvantages of compression sensing technology is that it can reduce the accuracy of the data being transmitted. Since only relevant data is transmitted, some important details may be lost, leading to inaccurate analysis and decision making.
- Limited Applications: Compression sensing technology may not be suitable for all types of IoT applications. Applications that require high levels of accuracy, such as medical devices or precision equipment, may not benefit from compression sensing technology.
- Increased Complexity: Implementing compression sensing technology in IoT applications can increase the complexity of the system. This can result in higher development and maintenance costs, as well as increased risk of system failure.
- Dependence on Data Analytics: Since compression sensing technology relies on data analytics to process and analyze the selectively transmitted data, the effectiveness of the system is highly dependent on the quality of the algorithms used. Poorly designed algorithms can lead to inaccurate analysis and decision making.
- Compatibility Issues: Compression sensing technology may not be compatible with all types of IoT devices and systems. Integration with existing systems can be challenging, especially in cases where different devices use different compression algorithms.
- Increased Latency: In some cases, compression sensing technology can increase latency in IoT applications. This can be especially problematic in time-sensitive applications such as industrial automation or real-time monitoring of critical infrastructure.
- Risk of Data Loss: Compression sensing technology relies on transmitting only relevant data, which means that some data may be lost in the compression process. This can result in incomplete data sets, leading to inaccurate analysis and decision making.