Implementation of Ant Colony Routing in Internet of Things
Rewrite similar literature review about Ant Colony Routing Implementation in Internet of Things
Implementation of Ant Colony Routing in Internet of Things
In recent years, many scholars have put forward their own solutions for IoT routing. They have solved energy, reliability and other issues by using various methods.
Reference [7] using fog computing. The paper proposed an adaptive edge calculation solution based on Regression Admission Control (REAC) and Fuzzy Weighted Queueing (FWQ).
In the algorithm, adaptive edge routers use the REAC method to monitor link performance to handle congestion and maintain the routing quality of priority users. Using FWQ in heterogeneous networks based on primary services.
Through the decision-making process, the system adjusts the flow of information or assigns bandwidth weights to react to network changes, thereby bringing REAC and FWQ awareness to network routing.
Reference [8~9] originate from the fault-tolerant routing algorithm based on Learning Automata (LA) proposed by Misra for Mobile Ad Hoc Networks. In order to improve network reliability IoT, the authors are further proposed Mixed Cross- Layered and Learning Automata-based(MCLA) fault-tolerant routing protocol.
In the case of a failure between the source node and the destination node, the success of the packet can also be guaranteed. It dynamically adapts to changing environments and chooses the best route. Nodes placed in an unused path will sleep.
The optimization of multiple metrics in the IoT is a NP-Complete problem. Many scholars try to use heuristic algorithms to solve this problem and get good results [10~12].
A heuristic algorithm is an empirically constructed algorithm that searches for the best solution within the acceptable computational cost, but does not necessarily guarantee that the resulting feasible solution is the optimal solution.
The literature [10] proposed a Particle Multi-Swarm Optimization routing algorithm that can build, recover, and select multiple disjoint routes that tolerate failures while satisfying the quality of service.
The algorithm is mainly to use multi-group strategy to determine the best direction when selecting multi-path routing at the same time. Speed updates are introduced in the form of individual best locations and global locations to enhance the performance of the routing algorithm.
The literature [11] selected a Fuzzy C-means algorithm to complete the selection of cluster heads and proposed a new method combining Optimal Secured Energy Aware Protocol (OSEAP) and Improved Bacterial Foraging Optimization (IBFO) algorithms.
This method can improve network security and save energy. In OSEAP, energy is saved by closing part of the sending nodes, and the receiving end node selects whether to accept and forward the route request message to its neighbor nodes according to the battery level.
In [12], IoT routing is optimized by a k-means clustering algorithm and a Genetic Algorith. Sensor nodes are clustered based on energy, and an optimal cluster head is selected by using a k-means clustering algorithm. Then determine the energy cost of the cluster head and the level of the sensor node.
The optimal path is selected by genetic algorithm. The genetic algorithm is based on the energy cost of the cluster head, the level of the sensor node and the path length.
REFERENCES
[7] M. Jutila, “An Adaptive Edge Router Enabling Internet of Things,” in IEEE Internet of Things Journal, vol. 3, no. 6, pp. 1061-1069, Dec. 2016.
[8] S. Misra, A. Gupta, P. V. Krishna, H. Agarwal and M. S. Obaidat, “An adaptive learning approach for fault-tolerant routing in Internet of Things,” 2012 IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, 2012, pp. 815-819.
[9] S. Misra, P. V. Krishna, A. Bhiwal, A. S. Chawla, B. E. Wolfinger, C. Lee, “A learning automata-based fault-tolerant routing algorithm for mobile ad hoc networks,” The Journal of Supercomputing, vol. 62, no. 1, pp. 4-23, 2012.
[10] M. Z. Hasan and F. Al-Turjman, “Optimizing Multipath Routing With Guaranteed Fault Tolerance in Internet of Things,” in IEEE Sensors Journal, vol. 17, no. 19, pp. 6463-6473, Oct.1, 1 2017.
[11] P. K. Reddy, R. Babu, “An Evolutionary Secure Energy Efficient Routing Protocol in Internet of Things,” International Journal of Intelligent Engineering and Systems, vol. 10, no. 3, pp. 337-346, 2017.
[12] A. V. Dhumane, R. S. Prasad, “Prasad J R. An Optimal Routing Algorithm for Internet of Things Enabling Technologies,” International Journal of Rough Sets & Data Analysis, vol. 4,no. 3, pp. 1-16, 2017.
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