# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 5 Discussion  
For WSNs with limited energy, the energy management of nodes is still one of the most important factors restricting their large-scale and long-term deployment and application.  
(1) Energy harvesting technology  
It is an effective power supply technology for nodes to harvest the energy produced by the surrounding environment. In the energy storage module, we use capacitors to store energy, which can avoid the problems of node failure caused by frequent charging and discharge of batteries, but the capacitors have the shortcomings of less storage energy and large leakage current. In addition, the energy collection, storage, and management circuit will also consume electricity so that the utilization rate of energy is not high, and only when the solar panel voltage is higher than the capacitor voltage is the solar energy used by the system.  
(2) Neighbor discovery mechanism  
Low-duty cycle can reduce the idle listening time and save a lot of energy. However, the current mechanism of neighbor beacon discovery based on low-duty cycle is too idealistic in analyzing the discovery probability and beacon conflict between nodes, which is quite different from the actual working situation between nodes. In addition, the factors such as the size of time slot and the number of beacons in the active time slot will have an important impact on the performance of the neighbor discovery. In this paper, our time slot model is closer to the actual working process of sensor nodes, and the sending and receiving separation beacon mechanism based on reply to ACK can effectively reduce the beacon conflict. In addition, the low-duty cycle mechanism can result in longer detection delays. In general, longer detection delays can miss some communication opportunities, which is not acceptable. However, shorter detection delays can consume more energy; thus,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **File Size** & **Compression** & **Uncompressing** & **Compression** \\  & **(Byte)** & **Time (ms)** & **Time (ms)** & **Ratio** \\ \hline Integral Wavelet & 151,982 & 32 & 15 & 5.42 \\ Huffman & 151,982 & 44 & 18 & 2.05 \\ LZSS & 151,982 & 62 & 37 & 2.04 \\ LZW & 151,982 & 49 & 25 & 2.36 \\ WinRAR & 151,982 & 31 & 16 & 2.52 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Compression ratio of different lossless compression algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{3}{c}{**Number of Data Stored**} \\ \cline{2-4}  & **Bottom Node** & **Convergence Node** & **Sink Node** \\ \hline Traditional Model & 96 & 96 & 96 \\ Integral Wavelet & 52 & 30 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the data traffic.  
a balance is required between the detection delay and energy expenditure in the application of WSNs.  
(3) Data fusion mechanism based on integer wavelet transform  
Data fusion can greatly reduce the amount of data in the network, which is conducive to reducing the energy expenditure of nodes by data transmission. Lossless data compression based on the integer wavelet transform can reach a compression ratio of 4-19 times, which can better remove the spatial and temporal correlation of the data. In addition, the distributed preservation of multiresolution data can solve the limitation of the storage space of nodes and improve the disaster recovery capacity of the network. However, the integer wavelet transform has some requirements on the computational power of the nodes.