# Green IoT: A Review and Future Research Directions
## 4 Energy-Efficient M2M Communications
### Offloading Computation/Task Offloading  
Offloading computation refers to the process of transferring computation-intensive tasks from M2M devices to external resources in order to reduce energy consumption. This can be achieved through the use of edge, fog, or cloud computing, which allows M2M devices to access the resources of a remote server or a nearby edge device to perform complex tasks. There are several benefits to offloading computation for energy-efficient data transmission in M2M communications. One significant advantage is that lessening the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & **Description** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Disadvantages**} \\ \hline \multirow{8}{*}{Huffman coding} & Lossless data compression & \(\bigcirc\) & Simple to implement. & & Not suitable for highly \\  & algorithm that encodes data & \(\bigcirc\) & An adaptive algorithm & \(\bigcirc\) & Not suitable for streaming data. \\  & using a variable-length & \(\bigcirc\) & Good compression ratios. & \(\bigcirc\) & Requires additional space to store \\  & prefix code based on & \(\bigcirc\) & Lossless compression. & & the prefix code. \\  & character frequencies. & & & \(\bigcirc\) & Slow (not suitable for real-time \\  & & & & applications). \\ \hline \multirow{8}{*}{Lempel-Ziv-Welch (LZW) algorithm} & Encodes data by identifying & \(\bigcirc\) & High compression ratio. & \(\bigcirc\) & Limited to text and other \\  & and replacing repeated & \(\bigcirc\) & Suitable for both lossless and & \(\bigcirc\) & non-random data. \\  & patterns with references. & & lossy compression. & \(\bigcirc\) & High computational power for \\  & & & lossy compression. & & some implementations. \\  & & & & & Not suitable for streaming data. \\ \hline \multirow{8}{*}{Run-length encoding (RLE)} & A technique that reduces & \(\bigcirc\) & Efficient for repetitive data. & & \\  & data size by replacing & \(\bigcirc\) & Low computational complexity. & \(\bigcirc\) & Not efficient for \\  & repeated characters with a & \(\bigcirc\) & Low memory requirements. & & non-repetitive data. \\  & single character and a count & \(\bigcirc\) & Suitable for both lossless and & \(\bigcirc\) & Limited in compression ratio \\  & of their occurrences. & & lossy compression. & & \\ \hline \multirow{8}{*}{Delta encoding} & Encodes data by & \(\bigcirc\) & High compression ratio for time & & \\  & representing the difference & & series data and numerical data. & \(\bigcirc\) & May not retain all original \\  & between successive values & \(\bigcirc\) & Low computational complexity. & & information. \\  & rather than the values & \(\bigcirc\) & Suitable for both lossless and & \(\bigcirc\) & May not be suitable for \\  & themselves. & & lossy compression. & & sensitive data. \\ \hline \multirow{8}{*}{Predictive coding} & A technique that predicts & \(\bigcirc\) & High compression ratio for time & & \\  & values and encodes the & & series and other correlated data. & \(\bigcirc\) & May not retain all original \\  & difference between the & \(\bigcirc\) & Low computational complexity. & & information. \\  & prediction and actual value. & \(\bigcirc\) & Suitable for both lossless and & \(\bigcirc\) & May not be suitable for \\  & & lossy compression. & & sensitive data. \\ \hline \multirow{8}{*}{Transform coding} & Uses a mathematical & \(\bigcirc\) & High compression ratio for certain & \(\bigcirc\) & May not be suitable for \\  & transformation to convert & & types of data (e.g., images, audio). & & streaming data. \\ \cline{1-1}  & the data into a different & \(\bigcirc\) & Can be used for feature extraction. & \(\bigcirc\) & High computational complexity. \\ \cline{1-1}  & domain, where it can be & \(\bigcirc\) & Suitable for both lossless and & \(\bigcirc\) & May not retain all original \\ \cline{1-1}  & more efficiently encoded. & & lossy compression. & & information. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data compression techniques.