In the field of mobile robot navigation, challenges such as nonlinear conditions, uncertainties, and the advancement of methods have made accurate position estimation essential. This study evaluates the effectiveness of a fuzzy-based adaptive unscented Kalman filter (FAUKF) for improving the state estimation accuracy for mobile robot localization. The proposed approach leverages the FAUKF algorithm to address noise uncertainty effectively by adaptively adjusting the covariance of the measurement noise based on a defined adaptation law. The Mamdani Fuzzy Inference System (FIS) serves as an observer, enhancing the matching law and improving overall system performance. The findings of this study demonstrate that the FAUKF algorithm provides superior position estimation accuracy compared to conventional Unscented Kalman Filter (UKF) methods. Furthermore, the research introduces an innovative navigation framework for mobile robots by integrating the Random tree routing algorithm with Rapidly exploring Random tree Star (RRT*) for optimal path planning in indoor environments. The RRT* integration aims to generate efficient and optimal paths while addressing safety considerations and environmental constraints. By combining the prediction and update phases of the Kalman filter, the proposed methodology effectively minimizes the propagation of uncertainty during the localization process, thereby enabling precise localization and robust path planning for designated targets. The simulation results confirmed the effectiveness of this method in maintaining constant uncertainty levels in localization over time. The proposed adaptive method enables efficient navigation in complex environments. Path planning is a critical element in robotics applications, and the RRT*-based approach presented herein offers a comprehensive solution for generating optimal and efficient paths. By providing an up-to-date perspective, this research contributes to the evolving landscape of mobile robot localization methods. The proposed method highlights the importance of utilizing adaptive algorithms and advanced path-planning techniques to enhance navigation capabilities in indoor environments.

The Internet of Things (IOT) has advanced in parallel with the wireless sensor network (WSN) and the WSN is an IOT empowerment. The IOT, through the internet provides the connection between the defined objects in apprehending and supervising the environment. In some applications, the IOT is converted into WSN with the same descriptions and limitations. Working with WSN is limited to energy, memory and computational ability of the sensor nodes. This makes the energy consumption to be wise if protection of network reliability is sought. The newly developed and effective hierarchical and clustering techniques are to overcome these limitations. The method proposed in this article, regarding energy consumption reduction is tree-based hierarchical technique, used clustering based on dynamic structure. In this method, the location-based and time-based properties of the sensor nodes are applied leading to provision of a greedy method as to form the subtree leaves. The rest of the tree structure up to the root, would be formed by applying the centrality concept in the network theory by the base station. The simulation reveals that the scalability and fairness parameter in energy consumption compare to the similar method has improved, thus, prolonged network lifetime and reliability.

Today, one of the new technologies in the modern era is Vehicular Ad-hoc Network which has taken enormous attention in the recent years. Because of rapid topology changing and frequent disconnection makes it difficult to design an efficient routing protocol for routing data between vehicles, called V2V or vehicle to vehicle communication and vehicle to roadside infrastructure, called V2I. Designing an efficient routing protocol has taken significant attention because existing routing protocols for VANET are not efficient to meet every traffic scenario. For this reason it is very necessary to identify the pros and cons of routing protocols as well as simulation of protocols is essential to understand existing routing protocols behavior. In this research paper, we focus on VANET topology based routing protocols and also measure the performance of two on-demand routing protocols AODV & DSR in the Random waypoint scenario.

In this paper, a bi-level decision making model is proposed for a vehicle routing problem with multiple decision-makers (VRPMD) in a fuzzy Random environment. In our model, the objective of the leader is to minimize total costs by deciding the customer sets, while the follower is trying to minimize routing costs by choosing routes for each vehicle. Demand for each item has considerable uncertainty, so customer demand is considered a fuzzy Random factor in this paper. After setting up the bilevel programming model for VRPMD, a bi-level global-local-neighbor particle swarm optimization with fuzzy Random simulation (bglnPSO-frs) is developed to solve the bi-level fuzzy Random model. Finally, the proposed model and method are applied to construction material transportation in the Yalong River Hydropower Base in China to illustrate its effectiveness.

Nowadays, wireless sensor network has been of interest to investigators and the greatest challenge in this part is the limited energy of sensors. Sensors usually are in the harsh environments and transit in these environments is hard and impossible and moreover the nodes use non- replaceable batteries. Because of this, saving energy is very important. In this paper we tried to decrease hard and complex computing with using soft computing such as fuzzy logic and we used it in cluster head selection part. Hard and complex computing can waist energy, because of this, we used some techniques to solve this problem. Minimum cost tree (MCT) helps to find minimum path, so we used this technique for intra cluster routing and again more over we used distributed source coding (DSC) technique for aggregating data. Finally all of methods could reduce energy consumption and increase network lifetime. Proposed algorithm is called FCMCT and simulation and results show improvement.

A well-planned humanitarian logistics involving rescuing people and providing on-time lifesaving facilities to disaster-affected areas can significantly mitigate the aftermath of disasters. However, damaged bridges and blocked roads can hinder last-mile deliveries in disaster-affected areas by ground vehicles only. So, in this paper, we propose a ground vehicle (GV) and unmanned air vehicle (UAV) collaborative delivery system in such areas. Here, a fleet of homogenous ground vehicles each equipped with a certain number of UAVs is deployed for last-mile deliveries. UAVs make the flight from GVs, deliver to end locations and return to the GV for battery replacement and/or start another flight. The objective of the model is to minimize the total delivery time within UAV flight endurance and payload constraints. Firstly K-means clustering algorithm has been used to cluster the disaster-affected region into different sectors. Then GV_Touring and UAV_routing have been scheduled using nearest neighbor heuristic to serve ground approachable locations and UAV served locations respectively. Finally, the Random walk based ant colony optimization-based (ACS_RW) has been developed to further optimize the overall travel time. Experimentation results show the potential benefits of the proposed algorithm over other available truck-drone collaborative transportation models.