# A Bee Colony-Based Optimized Searching Mechanism in the Internet of Things
## 3 Proposed Solution
### Optimized Replication Position  
The E-ABC is applied to assess replicas and place them through nodes in a cloud environment considering a shorter path. The E-ABC offers cost-effective optimal solutions and attains burden sharing or load balancing via data centers (DCs). Accessing and placing the DCs at the correct positions is crucial. The suggested solution demonstrates replication access and suitable deployment in the cloud through nodes. We obtain the quickest route between the DCs at the lowest cost and utilize a heterogeneous method to locate replicas at the finest position, in an optimized way, using statistical distribution. The costs and quantity of available data replications differ amongst the DCs. In the given scheme, employee bees are capable of acquiring and depositing data correctly at the DCs at the lowest possible cost. All the DCs are linked hierarchically and circularly across all levels. Users are at the system's exterior level, and they may use the DCs to transmit jobs to replicas in order to obtain the best results at the lowest cost, duration and distance. Placing data replications at locations closer to users, at an acceptable cost, via the DCs is a difficult task; thus, AI-based techniques are used to achieve the objective of optimized data replication and placement in the DCs. The E-ABC employs an improved variant of the ABC, which decreases the number of comparisons and achieves high data accessibility while reducing delays. Next, we will discuss the optimal replica position. The users reside at the system's outermost position and have the ability to transmit tasks to replication in order to attain the best possible place in terms of time, distance and cost. Accessing and placing the DCs at the appropriate position is achieved through nodes in cloud computing. Thus, to achieve the excellent capability of selecting nodes with cost-effectiveness and a shorter path among data centers, and to accomplish optimum data replication, a heterogeneous approach is utilized. Moreover, at the same time, all the DCs are connected at all levels, both hierarchically and circularly. Considering these points, it is concluded that accessing the DCs and placing them at appropriate positions is very important concerning honeybees, since these bees are liable to explore the lowest-cost path efficiently. Data replicas reside at the DCs in order to fulfill the user's tasks. Geometric distribution and Zipf are used with the purpose of allocating replicas and placing files between data centers that are nearer to users. Zipf is calculated as \(p(f_{i})=\frac{1}{\pi}\).It is used for the placement of replicated files among the different DCs that are very close to the users. Here, \(\alpha\) represents the factor data replication distribution and \(\alpha\) ranges between \(0\leq\alpha<1\), while \(i=1,\ldots,n\). The geometric distribution is another important factor that is used to randomly allocate and place replication files ideally together with various parameters. The value for the GD can be obtained using \(p(i)=(1-p)^{i-1}.p\). Here, \(p\) represents access to a replica file. The DC provides better efficiency with respect to speed, data accessibility and reliability and has a higher fault tolerance rate, which results in a higher cost. Considering the cloud environment, the replication cost is a crucial element. Thus, the total cost of the system should be managed in a better way. Every DC has a cost to replicate data, which is strongly associated with each data center. The cost among various data centers is calculated on the basis of the replication number across each data center. In cloud computing, it is very important to access replicas with the minimum cost and place them closer to the user. The replica cost for data file \(f_{k}\) can be computed using Equation (3), where \(x\) represents the total number of DCs, \(cost\) (\(dc_{i}\)) represents the cost for file replication at data center \(dc_{i}\) and \(br_{k}(dc_{i})\) represents the number of replicas of \(f_{k}\) at \(dc_{i}\).