# Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN
## 3 Two-Tier Collaboration Based High-Reliable and Lightweight Forwarding (TTCF) Mechanism for WBAN  
In the field of information technology, high availability refers to a system or component whose running time can meet the expected time [56]. The less time it takes to efficiently and accurately transmit data to the target node within the expected time, the higher the reliability of the data forwarding operation. The efficiency of the data forwarding operation is directly related to the size of the forwarding data itself and the performance of the forwarding algorithm. The smaller the amount of data transmitted, the less energy is consumed to transmit the same data. The faster the forwarding algorithm runs and the higher the accuracy of the results, the higher the efficiency of the data transmission and the more reliable the data forwarding is. Therefore, this paper proposes the TTCF model, which intends to improve the problems of low reliability and high energy consumption in the existing research by reducing the amount of data transmitted and improving the efficiency and accuracy of the forwarding. The design and implementation of TTCF is shown in Figure 1.  
In Figure 1, the bottom part represents various types of sensing devices, which are mainly responsible for identifying various physical objects and collecting WBAN context and indicator data, such as ECG, EEG, EMG, medical imaging and other heterogeneous data. The top layer is a variety of smart devices that act as data sink nodes, such as mobile phones, computers, vehicles, etc., which are mainly responsible for calculating, storing, analyzing and providing feedback on the collected data, or transmitting it to more powerful network devices for further processing. The middle part is the TTCF strategy proposed in this paper. Data is measured from the individual and its surrounding environment through small sensors, embedded systems, RFID tags and readers, small and medium-sized to large diagnostic and healthcare equipment, medical and clinical imaging equipment and any sensing equipment that supports data acquisition and transmission, and is then transmitted to the corresponding sink node through the TTCF strategy running in a heterogeneous WBAN environment. The TTCF strategy includes two parts: the STPCSE model and the forwarding model based on multi attributes and improved Dijkstra algorithm. The former mainly leverages the STPCS model to develop a new and more powerful data compression model which can use the same measurement matrix to measure signals of various lengths, and can compress various data by reducing the number of rows and columns of the measurement matrix, thus effectively reducing the amount of data transmission, improving the forwarding efficiency and saving the energy of the WBAN. The latter focuses on the improvement of the performance and reliability of the forwarding model. It firstly leverages multiple attributes closely related to nodes and the energy consumption between two nodes to measure the forwarding ability of the encounter nodes, and then uses the improved Dijkstra algorithm to compute the shortest path between the source-goal nodes based on the above forwarding ability measurement values. TTCF makes every effort to improve the correctness and efficiency of each step of forwarding to ensure the reliability of data transmission. Moreover, because each node in the actual WBAN application needs to be powered by batteries, and most of the nodes are relatively static, the proposed TTCF algorithm follows the following preconditions:  
* Compress data on nodes adaptively before transmission based on the correlation between them. * Each node is relatively static, and the sink node receives data information transmitted by other nodes. * In WBAN, the energy of the sink node is infinite, and the initial energy, data rate and sampling frequency of the other nodes are assigned according to the type of sensor. * The node status is classified according to the importance of its collected data.