The key internal node attack known as the selfish node is the one which intercepts the communication and results in an increased communication loss. In this paper, a trustworthy multipath routing algorithm for a wireless network has been constructed, and the paper has also discovered the selfish node. The author characterised their work as an enhancement to the AOMDV protocol (Wang, Y. et al. 2008). The author (Wang, Y. and Singhal, M. 2007) has also utilised a new game theory technique to reduce communication costs over the balancing network information set and has identified a self-centered node in wireless networks. The author determined the most cost-effective route by looking at the overall cost of packet transmission and selecting the one with the lowest total cost. The highlighted technique recognised both the path that had been destroyed and the next available ranking path to guarantee that data would be delivered successfully. A fresh approach to game theory was utilised in the research conducted by Das, D. et al. (2015), which observed the self-centered node in wireless networks. The author determined the most cost-effective route by looking at the overall cost of packet transmission and selecting the one with the lowest total cost. The highlighted technique recognised both the path that had been destroyed and the next available ranking path to guarantee that data would be delivered successfully. The author was able to identify the most power-efficient method, which minimises (Anuradha, D. and Srivatsa, S.K. 2019) the amount of power consumed for retransmission when a node fails and the maximum amount of time that propagation takes. This paper used the neighbour node analysis to determine the cooperative behaviour of mobile nodes. A fuzzy driven trustworthiness framework for selfish node detection was supplied by the author (Ullah,Z et al. 2016). In order to classify the node and determine its level of vulnerability, trust value-based fuzzy functions have been devised. There are seven distinct trust classes that can be determined on the basis of the number of dropped and created packets. During the process of carrying out the exchange of information, the nodes that have a greater trust reputation are chosen. A two-hops reputation analysis scheme has been defined in the publication (Chakrabarti.C et al. 2015) that was written for Delay Tolerant Networks. This scheme is used to rate the node and find the selfish node dynamically. For the purpose of selecting the forwarder, a joint transmission analysis of patterns at the node level has been defined. Following this step, a reliable authority is outlined in order to construct the reputation structure for efficient route formation.
An approach to observing neighbouring nodes that takes into account the amount of memory, bandwidth, and battery power that is available has been outlined by the author (Das,S.K. et al., 2014). In order to determine which node in the sensor network was responsible for the attack, a time-scheduled investigation of the communication flow was designed. The author used a variety of restrictions in order to identify both the fully complete and the partially completed selfish behavioural node contained within this system. A new observer-based distributed strategy for finding the selfish behaviour node in a Delay Tolerant Network has been defined in the paper written by Chakrabarti, C., et al. (2014). The dynamic reputation scheme, which was applied to nodes and was based on individual, group, and periodic statistical findings, was employed by the observer node. The author was kind enough to supply the performance evaluation that was needed for creating a safe communication path. The author kindly supplied the analysis of the encrypted token along with the reputation evaluation. An approach for the discovery of selfish nodes that is based on non-cooperative action analysis has been devised by the author (Muthumalathi, N. and Raseen, M.M. 2013). In order to cut down on the amount spent on communication, a secure hill cypher technique was implemented at the network nodes. The author additionally increased the connection performance and made use of the memory resources available. The article titled "Random Cluster Deployment of Sensors in Mobile Underwater Acoustic Wireless Sensor Networks" (Priyadarshini, R.R., and Sivakumar, N. 2019) discusses random cluster deployment of sensors in MUAWSN. The scientists created an energy prediction algorithm by employing the Markov chains Monte Carlo approach. This technique improves the greatest degree of coverage in data transmission by making use of the sample's value of a known variable in the ocean's surface.Ant Colony Optimization (ACO) used for solving the problems in a probabilistic method where to discover an optimum path. It depends on an iterative algorithm in which we consider a number of ants. An ant, whereas moving from its settle to the food, lays pheromone on the ground. This makes a way which is followed by the other ants. The overall behaviour of the ants is involved in the form of positive inputs. So, a greater number of ants take after a specific path that path becomes more attractive and, in this way, it'll be the way taken by neighbouring ants in the colony (Dorigo, M. et al. 2006). In reality, ants look for food, they begin moving in an irregular way. In searching the food, they return back to the colony in conjunction with laying down pheromone trails (Socha, K.2004). This approach has been demonstrated to be an ideal way to check ways which is able offer help coordinate other ants conjointly make ideal ways from the generally conduct of the colony ([Zengin, A. and Tuncel, S. 2010).
At first, we constantly release ants from different nodes. This not only helps make a difference in creating partial solutions to the issue but also navigate through all the diverse stages of the issue. The agents take after a Greedy Local Decision approach whereas navigating through the diverse nodes. This Approach depends on two fundamental parameters, engaging quality and data we get from the path (Jiang,N. et al. 2009). The ways that are looked by long-standing time ants are coordinated the path value which is upgraded by the ants and has prior investigated intensive to the same path (Zungeru, A.M et al. 2012). Asynchronous agents or ants are continually discharged from diverse nodes to deliver partial solution to the problem whereas navigating through distinctive stages of the problems. Whereas navigating, those agents take a greedy local choice approach which depend over two parameters, specifically, engaging quality and path data (Saleem, M et al. 2011). Each ant whereas navigating through distinctive stages of issue produces a partial solution to the problems in incremental manner. Finding the future ants were coordinated by the path value which can be upgraded by the ants in prior navigated by the same path (Nasir, H.J.A et al. 2019).