What are the emerging trends and applications of data compression and transmission in WSNs?

2 Distributed vs Centralized Compression

Data compression can also be performed at different levels of the WSN architecture: distributed or centralized. Distributed compression means that each sensor node compresses its own data before sending it to the next node or the sink. Centralized compression means that the sensor nodes send their raw data to a central node or the sink, where the compression is done. Distributed compression can save energy and bandwidth, but it may introduce errors or dependencies among the nodes. Centralized compression can improve the quality and consistency of the data, but it may increase the latency and congestion.

3 Adaptive Compression

Adaptive compression is a technique that adjusts the compression parameters according to the characteristics of the data or the network conditions. For example, adaptive compression can change the compression ratio, the compression algorithm, or the compression mode (lossless or lossy) based on the data type, the data distribution, the data correlation, the data redundancy, the data importance, the network topology, the network traffic, or the network quality. Adaptive compression can enhance the flexibility and robustness of the WSNs, but it may also increase the complexity and overhead.

4 Compressive Sensing

Compressive sensing is a technique that exploits the sparsity or structure of the data to reduce the amount of data that needs to be collected and transmitted. Compressive sensing allows the sensor nodes to sample the data at a rate lower than the Nyquist rate, which is the minimum rate required to reconstruct the data without aliasing. Compressive sensing can also recover the data from incomplete or noisy measurements using optimization algorithms. Compressive sensing can improve the energy efficiency and bandwidth utilization of the WSNs, but it may also require high computational power and sophisticated algorithms.

5 Data Aggregation

Data aggregation is a technique that combines the data from multiple sensor nodes to reduce the number of transmissions and eliminate redundant or irrelevant data. Data aggregation can be performed at different levels of the WSN hierarchy: local, cluster, or global. Data aggregation can also use different methods such as averaging, filtering, fusion, or extraction. Data aggregation can reduce the energy consumption and network congestion of the WSNs, but it may also affect the accuracy and timeliness of the data.

6 Data Coding

Data coding is a technique that encodes the data using mathematical or logical operations to improve the reliability and security of the data transmission. Data coding can use different schemes such as error correction codes, network coding, or encryption codes. Error correction codes can detect and correct errors caused by noise or interference in the communication channel. Network coding can combine and transform the data packets from different sources to increase the network throughput and resilience. Encryption codes can protect the data from unauthorized access or modification. Data coding can enhance the quality and safety of the data transmission, but it may also increase the data size and processing time.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?