# An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks
## 2 Related Work  
In this section, we will summarize some of the key research papers that have motivated us to develop our mechanism. A wide variety of works have addressed various aspects in this domain and we have obtained critical information from each of these works.  
[PERSON] and [PERSON] explored fault tolerance, fault recovery, fault prevention, and fault detection techniques in WSNs in [31]. In [32], [PERSON] and [PERSON] proposed a fault-tolerant technique for the detection of faults at the sensor node level where, depending on the group detection method, each node can be monitored by several other nodes. The fault detection is performed using a detection tree in which a hierarchy is defined for the identification of failed nodes. The proposed formula was analyzed according to a probabilistic approach. [PERSON] et al. introduced a fault tolerance model in [33] to improve the WSN lifetime by reducing the energy consumption over the network nodes; to do this, a pollination algorithm for was used. MATLAB 2018a has been utilized as a simulation environment for the anticipated model. In [34], in order to achieve a balance between the two metrics of energy consumption and packet loss, [PERSON] et al. proposed a data dissemination protocol based on probabilistic network coding. This was carried out by using a subset of the network nodes in a directed acyclic graph.  
There are also several fault-tolerant routing protocols proposed in the existing literature. A selected few are summarized here. [PERSON] et al. developed swarm genetic optimization (FTGSO) for fault-tolerant routing path identification in [35]. In this work, the selection of the Cluster Head (CH) was carried out using several metrics such as residual energy, coverage, communication cost, and proximity. The proposed model was compared with other optimization algorithms and the evaluation results verified the superior performance of the proposed scheme. In [36], [PERSON] et al. proposed a method that aimed to detect network holes. In this method, the network is geographically _cellulated_, and a cell agent is selected for each cell. Then, the coverage of nodes within each cell is calculated. After that, it is possible to detect holes in the network cells. Finally, the holes are recovered using mobile nodes using the grasshopper optimization algorithm. In [37], [PERSON] and [PERSON] introduced a method to maintain coverage in WSNs. The proposed method is composed of two phases: the first phase aims to detecting holes through a simple distributed method by which each node explores whether there are holes around it or not. In the second phase of the proposed method, the holes are covered using mobile nodes.  
A regression-based fault detection and tolerance strategy for WSNs was introduced in [38] by [PERSON] et al. The proposed strategy diagnoses the different types of faulty nodes with the help of the data sensed by the fault-free neighbors. In [39], [PERSON] et al. proposed a fault detection and recovery technique (FDRT) for a cluster-based network. Essentially, the principle is to choose a Cluster Head (CH) and to select a Backup Cluster Head (BCH) using the fuzzy logic technique. In the case of failure of the CH, the BCH will continue the task of the failed CH. In [40], [PERSON] et al. provided a potential game and the cut vertex detection mechanism, which are utilized to establish a fault-tolerant topology which consists of a set of single hop clusters. The main objective was to reduce the energy consumption of the network. Therefore, an algorithm based on a double price function was designed.