## 3 Proposed TIGSO-EDS System Architecture
### Summary and Discussion  
Compared with a traditional network, a wireless sensor network has many constraints. Thus, there is difficulty in applying security methods directly in WSNs. In this work, secured data transmission is ensured by taking into account the trust parameter while choosing the CH. The optimization intends to determine the objective function, whose value may be either the maximum or minimum based on the problem being solved. However, assessment of the convergence rate and computational time is carried out to validate the optimization algorithms.  
Cuckoo search is a general and resilient optimization technique that may be used in a wide variety of optimization problems. Additionally, it possesses desirable characteristics, such as simple implementation, a steady convergence characteristic, and reasonable computing efficiency. This optimization algorithm was inspired and proposed based on the egg laying and hatching life cycle of the cuckoo bird. A few birds such as Guria pigeons and anis (black birds), which belong to the cuckoo species, lay eggs in common nests [35]. However, to improve the probability of hatching their eggs, the process of laying eggs in another bird's nest is termed the parasitic brood method. During the laying period, one egg is laid in the host nest, and the remain ones are in other nests. The egg laid in the host nest will hatch more quickly than the eggs in the other nests. When this happens, the non-hatched eggs are evicted by the foreign cuckoo. This etiquette reduces the hatching probability of legitimate eggs. In addition to this, the chick of the foreign cuckoo tries to get more food by just mimicking the host's chicks. This nature of the cuckoo has been implemented as cluster construction process, as represented in Algorithm 1. Here, nests are considered as clusters, and eggs are considered as IoT devices in the network.  
```
0: Initial population of \(N\) set of nodes.
0:\(N-x\) hosts are identified by an objective function
1: Start
2: Objective function(\(x\))
3: Initiate host nests
4:while ( do\(<MaxGen\))
5: obtains a random cuckoo using levy fights
6: Calculate its quality/fitness \(ff_{i}\)
7: Choose a nest among the n randomly and consider it as \(j\)
8:if\(ff_{i}>ff_{j}\)then
9: Replace \(j\) with a new value
10:endif
11: Fractions of worse nests are unrestricted and so built new one
12: Save the best solution
13:endwhile
```  
**Algorithm 1** Cuckoo Search pseudo-algorithm.  
Various experimental studies concluded that the different insects and animals' flight behaviors exhibit the classic characteristics of Levy flights. The characteristics of Levy flights include a series of straightforward flight paths, which are efficient in examining the large search areas when compared with the traditional random walks due to a faster increase in variances. A Levy flight reduces the iteration of optimization algorithms by four times more than the normal random walk method [36]. The hybridized VIKOR-GSO approach was adopted to choose the optimal CH, and the aggregation of data was carried out on the elected CH. The optimal clusters were searched with the help of a GA. Then, the cumulative data were communicated to BS through the best possible path, selected using the Cuckoo Search algorithm given in Algorithm 1.