## 2 Related Work  
Clustering and fault tolerance are the focus of various researchers working on wireless sensor networks [3; 18]. Clustering is generally studied as either centralised or distributed [18]. Centralised clustering involves clustering that is dictated by the base station and the decisions are communicated in the field to the elected CHs; it is generally used in location-aware sensor networks. On the other hand, distributed clustering can be utilised in the case of location-unaware sensor networks, where the sensors are not aware of their relative positioning in the network. Various hierarchical routing protocols for WSNs have also been presented in the past [15; 19; 20; 21; 22]. [PERSON] et al. [15] presented a two-phase (setup phase and steady  
Figure 3: Scenario where most of the area is unmonitored in a live network. phase) cluster-based protocol for WSNs, known as Low Energy Adaptive Clustering Hierarchy (LEACH). Though LEACH was one of the earliest clustering protocols and formed the basis for various forthcoming protocols, it has its own share of shortcomings, which have been addressed by various researchers [23; 24; 25; 26; 27; 28; 29]. [PERSON] et al. [30] have proposed a protocol named LEACH-CKM that makes use of k-means classification for grouping the nodes. The proposed protocol is based on Centralised LEACH, where CHs are appointed centrally with the help of the CH's and sensing node's local information. LEACH-CKM addresses the node isolation problem where a remote node is not able to communicate with the base station. It utilises a Minimum Transmission Energy routing algorithm for routing the information from remote nodes. An energy-efficient, fault-tolerant, distributed clustering and routing scheme for wireless sensor networks has been proposed by [PERSON] et al. [31]. The proposed scheme is a set of two algorithms jointly named Distributed Fault-tolerant Clustering and Routing (DFCR). It makes use of a distributed run time recovery strategy for the sensor nodes in case of unexpected failure of the cluster heads. The scheme is shown to be capable of handling the sensor nodes that do not have any cluster head to be associated with. In another work, LEACH's threshold formula has been modified and presented by [PERSON] et al. [20], where the residual energy is taken into account while selecting a cluster head, as the traditional LEACH does not pay attention to the residual energy of the nodes while choosing the cluster heads, which might lead to a low energy sensor node being elected as the cluster head, which will exhaust early, leading to wastage of resources utilised for cluster head selection. The protocol uses direct BS and CH communication. [PERSON] et al. [32] have proposed a clustering technique using fuzzy c-means. The paper presents a cluster-based cooperative spectrum sensing scheme to reduce energy consumption and proposes an algorithm that creates clusters and elects the cluster heads in such a way that focuses on both the issues of energy consumption and efficiency in a positive manner. The proposed scheme ensures maximum probability of detection under an imperfect channel, utilising minimal consumption of energy when compared with the conventional clustering approaches. Further, the role of energy balancing in extending the network lifespan has been explored by [PERSON] et al. [33]. The authors propose a Particle Swarm Optimization (PSO)-based routing and clustering algorithm for wireless sensor networks, where the routing algorithm creates a balance between energy efficiency and energy balancing. The clustering algorithm is responsible for the CH and normal sensing node's energy consumption. The proposed algorithms are also sophisticated enough to handle the failure of cluster heads. A comparison between Particle Swarm Optimisation and Genetic Algorithm when applied to wireless sensor networks for network optimisation has been presented by [PERSON] et al. [34]. It shows how both the algorithms can optimise cluster formation.