# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 5 Review on ECS Methods on WSN
### Adaptive Technique
#### 5.2.1 Single-Hop Network
The paper outlines a bit error rate vector to exchange bit errors status and a version used between sender and receiver. The adaptive algorithm is implemented in real WSN with sender and receiver 65 m apart and LOS exists between nodes. The paper outlines the experimental setup and the result in terms of version changes and bit error rates during the experiment. The results are compared with adaptive BCH and different versions of BCH codes. In [90], [PERSON] et al. present a framework for selection of ECC in WSN. However, the framework lacks clarity and followed by standalone BER performance of uncoded, convolutional code, Hamming code, Goley code and Reed-Solomon code against \(E_{b}/N_{0}\). Additionally, the paper presents power consumption by several RS codes, but there is no clear connection between the energy models introduced earlier in the paper. Therefore, there is no clarity on the outcome of the paper. In [91], [PERSON] et al. propose BCH based adaptive error correction code where the code rate of BCH code varies according to distance between nodes and ACK. The energy efficiency against communication distance for BCH code and the proposed adaptive code results are compared. The results are based on Mica 2 series WSN platform and considering a Rayleigh fading channel. However, the simulation setting is not clearly explained. The paper claims that the proposed adaptive code is more energy efficient than non-adaptive code. In [92], [PERSON] et al. introduce adaptive LDPC code with SPA decoding for WSN. The code rate of LDPC is adapted according to required BER or QoS, received SNR and data rate demand. The LDPC code rate varies among 1/3, 1/2 and 3/4. The LDPC encoding complexity is discussed in detail, but it lacks discussion of decoding complexity which is very crucial for complex LDPC decoding as discussed in Section 2.2.3. Additionally, the energy consumption at the transmitter and the receiver for bit transmission is presented using \(\mu\)AMPS-1 Note. However, how the decoding energy consumption LDPC decoding with SPA is measured is not clear. [PERSON] et al. present a comparison of BER results of LDPC, proposed code and uncoded system without specifying channel SNR range, which is a very critical parameter for wireless channels. Another key issue in the paper is the use of LDPC code with SPA as decoding algorithm. As we discussed in Section 2.2.3, SPA is one of the complex decoding algorithm in the industry. However, how the use of this complex algorithm with energy and computationally constrained node of WSN is possible is not justified. Additionally, the results are also not compared with other codes. There are two major findings from the review. First, there is a limited research work on the effect of using extra works (channel estimation, reverse link for channel estimation update at the transmitter from receiver) associated with an adaptive technique while implementing in a practical setting. Therefore, researchers should incorporate this extra power consumption during energy analysis while implementing adaptive techniques. Another key issue is the practicality of re-configurable transmitters and receivers at the energy and computationally constrained WSN node. There is no research work reported on this issue.