# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 5 Review on ECS Methods on WSN
### Fec
#### 5.1.2 FEC for WSN
However, there is no clarity about types of channels used for the BER simulation result. [PERSON] and [PERSON] illustrate an efficient implementation of quasi cyclic code to save energy consumption that can be used for WSN in [81]. The paper demonstrates the working principle of implementation of quasi cyclic code and claims that the proposed implementation of quasi cyclic code reduces re-transmission and saves energy. However, the energy analysis is limited to an example only, and there is no analytical result. As we discussed in Section 4.2, a model to measure energy efficiency is introduced in [71]. The paper claims that error detection using CRC without ARQ is necessary when the channels between sensor nodes are good (i.e., SNR > 25 dB) and have few hops. However, it is not clear how reliability is maintained without any error correction scheme. The paper claims that re-transmission is necessary when the channel between nodes are bad (i.e., SNR < 20 dB) and have larger number hops. There is no clear understanding and conclusion from the results. For example, what is the use of BCH code in the proposed simulation? [PERSON] presents the performance analysis (energy per bit against distance and overhead versus probability of error) of LT and BCH codes in RF and FSO communication link [82]. The paper presents decoding power consumption versus distance for BCH and Luby Transform (LT) codes under RF channel (considering AWGN and LOS) and FSO channel (considering different Gamma-Gamma distributions for turbulences). However, the paper does not consider power consumption due to encoding and transmission of extra redundant bits. Under those considerations, [PERSON] claims that the performance of LT code is poorer than uncoded systems for the distance between nodes from 0 to 400 m for the RF channel. However, LT code with optimal erasure method is energy efficient for an FSO channel with moderate and strong turbulences. The results presented are standalone results of different codes without considering WSN setting. [PERSON] et al. present simulation results of BER and energy spent per bit versus SNR for BCH and cyclic codes under the Rayleigh fading channel [83] considering IEEE 802.15.4 standard. The BER results for various codes with different rates presented are against SNR instead of \(E_{b}/N_{0}\). Hence, the results do not take the code rate into consideration i.e., do not consider the overhead caused by ECS. Therefore, the discussion of coding gain for several codes are not accurate. In addition, energy spent per bit calculation considers only transmission of total bits, but it does not reflect power consumption of encoding and decoding. Therefore, the results presented are not accurate and do not reflect several aspects. [PERSON] et al. introduce SCCC for a multi-hop WSN scenario [84]. A non-recursive convolutional outer encoder and a recursive convolutional encoder are used in the proposed SCCC. These encoders are separated by a random interleaver. In the proposed error correction method, Viterbi decoding and encoding for inner code is applied at each intermediate node and does not need to do anything for outer code. Turbo decoding is used at the destination node as base station on assuming that base station does not have energy and computational constraints. The additional power consumption (due to encoding and decoding at each intermediate node) by the proposed code and error correction method for multihop WSN [84] is performed using measurement data from [69]. They also calculate transmission and reception energy consumption by coded and uncoded system analytically separately. [PERSON] et al. display total power processing power consumption (numerical) versus the number of hops and claim that there is a linear increase of total processingpower as the number of hops increases. However, there is no clear explanation of how total processing power is evaluated as power consumption due to transmission and reception is in analytical form, and power consumption due to encoding and decoding is in numerical form.