## 1 Introduction  
Fault tolerance of any network is generally defined as the reliability of the availability of the IoT system in working conditions. The more reliable the IoT network, the more tolerable the network, leading to the high acceptance of such a network. The IoT networks relating to mission criticality systems must be tolerable to the extent of 99% [1]. IoT networks use networking topologies (butterfly, Crossbar, Hybrid) in different layers connecting small devices (sensors, actuators, controllers) and big devices such as high-end servers, base stations, gateways, and splitters. Small devices often fail and induce different faults, rendering the entire IoT network less fault tolerant [2]. The devices within an IoT system are heterogenous and are driven through different protocols requiring conversions, speed matching and the use of several sophisticated algorithms for dealing with data transmission for the purpose of performance enhancements [3, 4, 5, 6, 7]. The fault tolerance of an IoT network can be measured in terms of success rate, failure rate, false alarm rate, and power depletion rate [8] and the computation of these metrics. In addition, different computation models are to be used, which include FTA (Fault Tree Analysis) [9] for linear models, probability models [10], hybrid models [11] that combine linear and probability models, empirical model [12], and bipartite flow graph modeling [13]. Small devices tend to fail, inducing different faults, sometimes rendering the entire IoT system non-operational and producing unpredictable behaviour. The faults that are generally induced in the IoT system include Cascading faults [14, 15], Pattern faults [16], and device-to-device communication faults [17]. Various faults occurring within the devices have been reviewed by [PERSON] et al., [18, 19]. Several methods have been implemented to enhance the fault tolerance of an IoT system in the presence of the faults mentioned above. The methods aim to enhance the robustness [14], carry complex event processing [20], minimization of bandwidth [21], alternate Networking butterfly [22], Crossbar[23], and handle demand uncertainty [24]. [PERSON], [PERSON] et al. [25] presented an artificial-intelligence-based method for analyzing the fault tolerance level of the IoT network in the presence of network failures within software-defined IoT networks. The cluster heads transmit the sensed data routed by the devices through base stations to the microcontrollers due to the existence of both devices over long distances. Cellular technologies are used to effect communication between devices. A base station is placed to provide a nearby spectrum for the cluster heads to communicate with the microcontrollers to transmit data and for receiving the actuating commands. The network becomes isolated when the base station fails, and the fault tolerance of such a network is zero. It will be dangerous when the fault tolerance of a mission-critical system becomes zero. [PERSON] et al. [17] presented a D2D-enabled Fault-tolerance in Cellular IoT. They have yet to consider the issue of the remoteness of the second base station and also based their discussion on a single relay system which generally cannot support a huge number of cluster heads. Building an IoT network considering more than two base stations, is very complex. When a base station is placed in a distant location, the latency is bound to increase, affecting the performance of the IoT system. The placement and connectivity of cluster heads and the microcontroller with the base station is a complex issue that must be addressed to minimize the latency. There should be alternate paths of communication to cater for the failure of one of the base stations. It is necessary to build alternative methods of communication between the cluster heads and microcontrollers through a pair of redundant base stations, which are situated between cluster heads and the microcontrollers.