# Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN
## 2 Related Work  
WBAN sensor nodes are portable and have the characteristics of small size, light weight, limited energy, inconvenient battery replacement and so on. The dynamic behavior of human limbs maybe due to the communication interruption that is the main cause of the data route reconstruction and retransmission and the reconstruction of network topology. Obviously, energy limitation is an important problem in WBAN, and the research on the lightweight data forwarding mechanism for WBAN will be the basic work to achieve energy-efficient telemedicine services. The data sent by WBAN nodes has a certain continuity and correlation, which represents redundant information. Thus, it is necessary to remove data redundant information to reduce the amount of data sent in the applications, which not only saves a lot of energy, but also reduces network transmission congestion, the transmission delay of data and the conflict when these data are sent, and improves the utilization of wireless channels. Apparently, the energy consumption of WBAN data forwarding mainly depends on the amount of data transmitted by the network and the selection of relay nodes [31]. [PERSON] et al. [16] applied compressed sensing theory to WBAN and proposed a new distributed compressed sensing reconstruction algorithm to effectively reduce node power consumption. [PERSON] et al. [17] applied sparse representation theory and compressed sensing theory to data compression and action number recognition in human activity detection [32; 33; 34; 35], so as to reduce energy consumption in the network as much as possible when a specific action recognition rate is achieved. [PERSON] et al. [21] put forward a method of capturing and detecting EEG based on compressed sensing, which reduces the energy consumption of communication and computing. However, the traditional data compression sensing technology has strict restrictions on the dimension of signal matrices, which is not conducive to dealing with ubiquitous heterogeneous medical signals. [PERSON] et al. [36] proposed an STPCS algorithm to save computing resources, and applied it to the communication of wireless sensor networks. [PERSON] et al. [23] leveraged STPCS to reduce computational and spatial resources when designing a visually secure image encryption scheme. [PERSON] et al. [37] proposed a P tensor product (PTP) based on STPCS. The selection of matrix P is unlimited and can be any kind of matrix. It not only solves the problem of dimension matching in matrix multiplication, but also provides a new method for calculating the angle between different dimensional vectors. Based on PTP, [PERSON] et al. [24] solved dimension matching in the multiplication between large measurements matrices and greatly saved the storage consumption of image encryption. Obviously, the model based on STPCS not only inherits the advantages of compressed sensing, but also overcomes the dimension limitation of matrix multiplication. Applying STPCS to WBAN data forwarding can solve the problems of adaptability, energy, security and so on. In addition, the performance of data forwarding is directly related to the efficiency and energy consumption of the network. In most applications of WBAN, data may be transmitted to target nodes through multi-hop routing. [PERSON] et al. [38] showed that it was feasible to use relay nodes to realize multi-hop data transmission in the human body network, which can greatly improve the robustness of communication between nodes. However, the design of the multi-hop routing mechanism in the human body network has not been explored to a great extent. [PERSON] et al. [39] proposed that the human body relay sensor could cooperate with other nodes of the body to perform the corresponding forwarding operation to achieve smooth data transmission. [PERSON] et al. [40] also showed that the selection of sensor relays would affect the energy consumption and network connection of the human body network [41].