Research on rechargeable agricultural wireless sensor network based on ZigBee immune routing repair algorithm

WSN (wireless sensor network) plays a very important role in the agricultural environment monitoring. Although solar energy and other power supply methods are used to solve the node energy problem, the monitoring equipment works outdoors for a long time, which is easily affected by the environment. The supply is unstable to cause abnormalities in some nodes. So this study proposes a ZIRRA algorithm (ZigBee immune routing repair algorithm) for the rechargeable agricultural WSN. It simulates the working mechanism of the immune system and designs modules such as identification, processing, cloning and storage, which can provide a better repair strategy for abnormal nodes. Then it compares the quality of the backup nodes and replaces the backup nodes with poor quality, so that the optimal paths are maintained between source nodes and middle relay nodes, which increases the optimization ability of the algorithm. The experimental results show that the ZIRRA algorithm shows significant advantages in routing node repair mechanism. Compared with the LFRA, AR-TORA and ICCO algorithms, the average routing energy consumption of the ZIRRA algorithm reduced 35.33%, 58.37% and 45.15% , the data transmission delay reduced by 23.72%, 36.74% and 16.28%, and the average node survival time extended 25.08%, 33.55% and 13.88%. In addition, the maximum communication time and network throughput of the ZIRRA algorithm increased 44.49% and 13.03% at the scale of 1000 to 2000 nodes. These quantitative results show that the ZIRRA algorithm can improve the energy efficiency, transmission reliability and stability. The ZIRRA algorithm draws on the working principle of the immune system and repairs abnormal nodes through identification, processing, cloning and storage modules. Unlike the traditional node repair algorithms, the ZIRRA algorithm has higher efficiency and accuracy in identifying and processing abnormal nodes through the improved clone tracking algorithm. It uses an improved clone tracking algorithm in the learning module, improves the cloning and mutation mechanisms, and generates the optimal antibodies for repairing abnormal nodes. It also integrates an adaptive energy management strategy to cope with fluctuations in energy levels by prioritizing the transmission of critical data and reducing the frequency of non-essential communications, which improves the network stability and data transmission reliability.