## 1 Introduction  
Recently, many types of network-based systems have become prevalent, such as the Wireless Body Area Network (WBAN) [1], mobile networks [2], spatial-temporal networks [3], and the Internet of Things (IoT) [4]. Since the invention of the IoT by [PERSON], the concept of IoT has become more prosperous and more powerful [5, 6]. IoT has been described as a network in which different objects with exclusive identifiers and the capability to transmit data are interconnected without the need for human interaction over the Internet [7, 8, 9]. The rapid growth of 6G-enabled IoT has recently piqued academia and industry's interest [10, 11, 12]. Further, the issues related to privacy, anomaly, and security are becoming essential as the applications become more prevalent [13, 14, 15, 16]. Safe and robust communication and networks include authentication, data sharing, and analysis [17, 18, 19]. Blockchain is devised to present data availability and tamper resistance in a decentralized environment and an immutable that is tailored to increase IoT data integrity, security, and availability while optimizing IoT applications [20]. It is an encoded, dispersed ledger technology for building tamper-resistant real-time records which is applicable in many fields [21, 22]. Furthermore, this platform provides a reliable environment for IoT devicesby securely interconnecting them and protecting them from the adversarial attacks that plague centralized client/server models [23]. Moreover, the network of IoT is assorted for a particular action, meaning that specific IoT nodes will complete it better than others. The critical problem that has been considered in this paper is determining which nodes are best suited. Obtaining the right nodes at the right time and their selection helps mitigate common IoT-based concerns such as network lifespan, allocation of resources, and trust in the gathered data [24]. The issue of energy and routing could be solved by choosing the node rather than optimizing node numbers and their location [25]. In this case, the nodes can be organized in any order, and only a subsection of nodes is stimulated at any given time for a given mission. By selecting the appropriate subset of nodes based on the task criteria, node selection aids energy efficiency [26]. Furthermore, there is individual-based selection, in which every node is evaluated independently and the best nodes are selected, or group-based selection, in which possible clusters are evaluated as a whole and the finest set is selected. So, evaluations are focused on the task specifications and constraints [27]. The latest selection strategies, in particular, are not well-suited to localization tasks due to several flaws. First, the present selection methods concentrate on comprehensive environmental monitoring, making them less application-oriented [28]. The information gathered is not used in a response system to update the community of nodes, which could be critical for increasing the speed of localization. Most studies on the area of interest coverage in the literature are planned for nodes with sensing ranges. However, this is not true for all sensors, for example, radiation sensors, which do not have a range [29]. These flaws necessitate the use of a dynamic selection outline that acclimates to the localization mission and uses the gathered readings to update the node collection [30]. Fuzzy Logic (FL) is a method that employs approximation logic to manage linguistic and numerical data simultaneously [31; 32]. To achieve an actual output and determine the details of the non-linear mapping, fuzzy logic works on stages of input likelihoods [33]. Furthermore, the models based on Markov Theory and Multi-Criteria Decision-Making (MCDM) have been used to solve engineering problems in science, technology, economics, and other fields in numerous studies [34; 35; 36].