# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 5 Review on ECS Methods on WSN
### Fec
#### 5.1.1 FEC for Single Hop Asymmetric Structure (SHAS) WSN  
First, we review FECs introduced by research papers for SHAS WSN (refer to Section 3.2 for a brief introduction of an SHAS WSN network). In [72], an asymmetric code concept is introduced for single hop WSN with an SHAS structure where the uplink and downlink communications are treated differently. In downlink, high transmission power is utilized at the base station. No FEC is used; only CRC is used and error correction is performed by ARQ. A highly efficient channel coding with low encoding complexity and high decoding complexity in the uplink is used. The proposed FEC code for the uplink is convolutional code with soft-decision Viterbi decoding. [PERSON] et al. discuss how the SHAS approach can be utilized to reduce power consumption at energy-constrained sensor node and its suitability for centralized MAC design. The paper also compares power consumption (using power consumption model based on [73]) in the uplink between the proposed concept and simplified multi-hop structure without considering several factors such as spatial diversity, data aggregation, etc. The paper claims that the SHAS with convolutional coding in the uplink consumes less power than a multi-hop structure with the same code when distance between nodes is less than 175 m. The paper introduces the use of receiver diversity (by using redundant base station) and the use of multiple transmitting antennae at the sensors to combat slow fading channels. In [74], [PERSON] et al. introduce RA codes in an unbalanced WSN, i.e., WSN with SHAS where the uplink and downlink utilize different error correction techniques. The paper presents BER performance of uncoded, RA and Irregular RA (IRA) (with different code rates) against \(E_{b}/N_{0}\) over an AWGN channel with no interference. The BER results indicate that there is coding gain as expected. This BER result is a standalone simulation outcome over an AWGN channel and does not consider a WSN scenario. Power consumption at sensor nodes versus distance between nodes is presented for coded systems including RA and convolutional code using SHAS and multi hop structure (similar to [72]). The power consumption model is based on coding gain and path loss model. The simulation result confirms that RA code outperforms the convolutional code in the simulation setting. In [75], [PERSON] et al. introduce another way of utilizing properties of SHAS to redistribute energy consumption at WSN with SHAS. In this paper, a complex decoding algorithm (i.e., iterative decoding with a soft decision from a channel using an BCJR algorithm) at base station and rate-1 encoding at an energy-constrained sensor node is considered for an SHAS WSN network. The concept presented in this paper is similar to [72], and the only difference is encoding and decoding techniques used in the simulation setting. Another variation is that the result is presented in terms of coding gain. The assumption of AWGN channel with LoS between nodes is not a realistic situation for practical WSN, and it is only valid for limited applications. In [76], [PERSON] et al. present experimental results (energy consumption and error rates (BER, FER and Message Error Rate (MER)) using MICAz hardware on SHAS WSN using several error correction schemes including repetition code and simplified turbo code. The concept presented in this paper is similar to [72]. The difference is that there are different codes including uncoded, modified turbo codes and repetition code are used in experimental settings. The results are presented in terms of overall energy consumption and energy consumption per successful message for convolutional code,repetition code (rate 1/3 and 1/6) and modified turbo code (1/6). The total measured power consumed by modified turbo code is significantly less than repetition code and uncoded system. There is no clear explanation on experimental settings such as the distance between the nodes.