## 1 Introduction
The negotiation-based routing strategy aims to minimize data redundancy by letting both the sender and receiver reach proper negotiation to complete established communication. As a result, mutual understanding happens among sink nodes, and they work together to choose the route for transmission depending on context. In QoS-based routing, the transmission path is planned to use a predetermined and designated route. QoS-based networking aims to help route discovery by allowing protocols to find suitable routes for traffic to flow. This routing's efficiency is evaluated based on the volume of traffic. Most research has described numerous methods and techniques for robust WSNs integrated with IoT networks [11]. However, these studies do not address total centralization, and they create additional issues such as increased overhead in exchanging packets with proper energy efficiency strategies. It is, therefore, necessary to develop a new approach incorporating the combination of both distributive and centralized systems. The network may be partitioned into smaller regions by gathering the network devices in a particular location, known as clustering. Every cluster has one administrative device, known as the cluster head (CH). The CH organizes the monitoring of media access, gathers information, analyzes the data in a certain manner, and interacts with the gateway node of the IoT network or other centralized units. To prevent one device from being overloaded and distribute the energy usage more evenly, the CH responsibility is periodically cycled across units in the cluster. Sensing nodes in WSNs are distributed across vast regions of smart infrastructure to gather the data they are interested in. Because their energy systems are battery-centric and have limited communication range, they need to use a multi-hop routing strategy to transmit the aggregated data to the sink or BS. Thus, clustering is one of the prominent alternate resolutions for this problem. However, it is typically viewed as a single-hop strategy, i.e., each CH sends information directly to its destination. Clustering is driven by the properties of the CH regarding cluster density, the overall lifespan of the network, and its stability. The most critical CH traits are mobility, type, and function. Here, type denotes the general potentialities of the cluster head wherein network administrators can equip such nodes with powerful capabilities and abundant resources that mainly include sufficient energy and decent storage. With mobility, the movement of the CH may create additional challenges for communicating with the cluster members, other CHs, or the BS. Cyber-physical systems based on WSN and IoT usually use CHs to aggregate the sensor data and integration activities or relay information. With increasing urban density, large-scale networks have a scalability issue. Consequently, electing a CH becomes difficult in such situations. In allowing any node to be a CH or normal member node, the decision procedures must abide by many primary concerns. Most research has confirmed that the clustering operation is an Non-deterministic polynomial (NP)-hard optimization issue [11]. Searching across vast spaces of potential outcomes is necessary to find solutions to NP optimization issues. It is therefore essential to know that the fusion of the two techniques, as noted earlier, is also preferred for vast deployment regions. If any CH along the routing path fails, data transfer will be impacted. Hot spots are an issue with this hypothesis. These causes are primarily energy depletion, vital component exhaustion, and other recognized factors. Thus, including Fault-Tolerant (FT) capabilities in network systems is crucial, particularly in smart environments. There are two primary ways of classifying fault tolerance methods in WSNs: they can depend on either the stage when the mechanism activates (during the sensing or routing stage) or the cause of the errors and their propagation (whether they are hardware or software).