A Voronoi- Ant Colony based Routing (VoR-Ant-R) Algorithm for WMSNs

Wireless Multimedia Sensor Networks (WMSNs) have been used in many applications and powerful distributed systems. But the performance of WMSNs is suffering from the occurrence of energy holes. To improve the performance of the network and packet delivery ratio, a Voronoi-Ant colony based Routing (VoR-Ant-R) algorithm is proposed for WMSNs to discover the energy holes and finds the shortest path from the source to destination in the WMSNs even though faces some obstacles. The WMSNs are constructed using the Voronoi structure to bypass energy holes. After bypassing the energy hole in the path; an ACO is introduced to select a neighborhood node for data forwarding. This ACO constructs the shortest optimized path to enhance the performance of the WMSNs. The proposed work is experimentally compared with other algorithms such as IEEABR, EEABR, SC, and BEES. The simulation results show that VoR-Ant-R can increase energy efficiency, success rate, reduces energy consumption, and latency.

Optimization Techniques to Solve Travelling Salesman Problem Using Machine Learning Algorithms

Travelling Salesmen problem is a very popular problem in the world of computer programming. It deals with the optimization of algorithms and an ever changing scenario as it gets more and more complex as the number of variables goes on increasing. The solutions which exist for this problem are optimal for a small and definite number of cases. One cannot take into consideration of the various factors which are included when this specific problem is tried to be solved for the real world where things change continuously. There is a need to adapt to these changes and find optimized solutions as the application goes on. The ability to adapt to any kind of data, whether static or ever-changing, understand and solve it is a quality that is shown by Machine Learning algorithms. As advances in Machine Learning take place, there has been quite a good amount of research for how to solve NP-hard problems using Machine Learning. This reportis a survey to understand what types of machine algorithms can be used to solve with TSP. Different types of approaches like Ant Colony Optimization and Q-learning are explored and compared. Ant Colony Optimization uses the concept of ants following pheromone levels which lets them know where the most amount of food is. This is widely used for TSP problems where the path is with the most pheromone is chosen. Q-Learning is supposed to use the concept of awarding an agent when taking the right action for a state it is in and compounding those specific rewards. This is very much based on the exploiting concept where the agent keeps on learning onits own to maximize its own reward. This can be used for TSP where an agentwill be rewarded for having a short path and will be rewarded more if the path chosen is the shortest. Keywords: LINEAR REGRESSION, LASSO REGRESSION, RIDGE REGRESSION, DECISION TREE REGRESSOR, MACHINE LEARNING, HYPERPARAMETER TUNING, DATA ANALYSIS

Modular Robotic Design and Reconfiguring Path Planning

The modular robot is becoming a prevalent research object in robots because of its unique configuration advantages and performance characteristics. It is possible to form robot configurations with different functions by reconfiguring functional modules. This paper focuses on studying the modular robot’s configuration design and self-reconfiguration process and hopes to realize the industrial application of the modular self-reconfiguration robot to a certain extent. We design robotic configurations with different DOF based on the cellular module of the hexahedron and perform the kinematic analysis of the structure. An innovative design of a modular reconfiguration platform for conformational reorganization is presented, and the collaborative path planning between different modules in the reconfiguration platform is investigated. We propose an optimized ant colony algorithm for reconfiguration path planning and verify the superiority and rationality of this algorithm compared with the traditional ant colony algorithm for platform path planning through simulation experiments.