Efficient Route Optimization Research Articles

Efficient Route Optimization for Ice Distribution: Enhanced VRPTW with Customer Retention Strategies

Efficient Route Optimization for Ice Distribution: Enhanced VRPTW with Customer Retention Strategies

Secure Patient Data Monitoring and Efficient Routing Optimization using a Hyperelliptic Curve Cryptography with Fuzzy-based Priority in WBSN

Aims and Background: Wireless Body Sensor Network (WBSN) technology is one of the major research areas in the medical and entertainment industries. A wireless sensor network (WSN) is a dense sensor network that senses environmental conditions, processes, and outgoing data at the sink node. A WBSN develops patient monitoring systems that provide the flexibility and mobility needed to monitor patient health. In data communications, it is difficult to find flexible optical routing paths, switching capabilities, and packet processing in the composition of optical networks. Information-centric networks (ICNs) are a new network model and are different from information- centric models. The priority of the information-centric model is the communication network. Objectives: In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. Methodology: A fuzzy–based Priority Aware Data Sharing (FPADS) method is introduced to schedule the priority data and monitor the transmission length. The child node adjusts the transmission speed of the cluster head with the help of a fuzzy logic controller (FLC). Results: The proposed model estimated the traffic load of the child node and the priority of the different amounts of data to be transmitted. The principle of scheduling data packets to be developed is based on the precedence of the data with the lowest transmit length in the network. Conclusion: The proposed FPADS performance increases in terms of scheduling time utilisation, traffic distribution, and mean delay. Simulations have been done using NS2, and the outcomes have shown that the proposed methodology is efficient and improves the overall QoS of the system.

Vehicle Routing Optimization Algorithms for Pharmaceutical Supply Chain: A Systematic Comparison

Abstract Nowadays, supply chain and road networks are full of complexity. Companies can differentiate themselves by offering superior customer service. Therefore, pharmaceutical distributors need to optimize route distribution for efficient and effective delivery to pharmacies, enhancing their competitiveness. In the field of operations research (OR), efficient route optimization is a significant function of the Vehicle Routing Problem (VRP) where the goal is to find the optimal distance travelled. The VRP can be solved using a range of algorithms, comprising exact methods, heuristics and meta-heuristics. This paper gives the basic concepts of some algorithms and these algorithms are compared based on the results. This study also extends its focus to the variation known as the Vehicle Routing Problem with Time Windows (VRPTW), that closely mirrors real-world scenarios, allowing for a more practical assessment of pharmaceutical distribution challenges and optimization solutions.

Multiobjective-energy centric honey badger optimization based routing for wireless body area network

Wireless Body Area Network (WBAN) is an interconnection of tiny biosensors that are organized in/on several parts of the body. The developed WBAN is used to sense and transmit health-related data over the wireless medium. Energy efficiency is the primary challenges for increasing the life expectancy of the network. To address the issue of energy efficiency, one of the essential approaches i.e., the selection of an appropriate relay node is modelled as an optimization problem. In this paper, energy efficient routing optimization using Multiobjective-Energy Centric Honey Badger Optimization (M-ECHBA) is proposed to improve life expectancy. The proposed M-ECHBA is optimized by using the energy, distance, delay and node degree. Moreover, the Time Division Multiple Access (TDMA) is used to perform the node scheduling at transmission. Therefore, the M-ECHBA method is used to discover the optimal routing path for enhancing energy efficiency while minimizing the transmission delay of WBAN. The performances of the M-ECHBA are analyzed using life expectancy, dead nodes, residual energy, delay, packets received by the Base Station (BS), Packet Loss Ratio (PLR) and routing overhead. The M-ECHBA is evaluated with some classical approaches namely SIMPLE, ATTEMPT and RE-ATTEMPT. Further, this M-ECHBA is compared with the existing routing approach Novel Energy Efficient hybrid Meta-heuristic Approach (NEEMA), hybrid Particle Swarm Optimization-Simulated Annealing (hPSO-SA), Energy Balanced Routing (EBR), Threshold-based Energy-Efficient Routing Protocol for physiological Critical Data Transmission (T-EERPDCT), Clustering and Cooperative Routing Protocol (CCRP), Intelligent-Routing Algorithm for WBANs namely I-RAW, distributed energy-efficient two-hop-based clustering and routing namely DECR and Modified Power Line System (M-POLC). The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR.

Logistics Web Application for the Tracking of Parcels

Firms can save operating expenses and improve customer satisfaction by managing their logistics well. Delivering goods and services to customers with the highest standards while reducing operating costs is the aim of the logistics management philosophy. As a result, logistics management is a crucial component of the supply chain process, which also includes other tasks including organizing, directing, planning, storing, communicating, and providing support. Web applications tracking allow easy access to goods and services over the internet. It allows for easy detection of the state, location of goods and services at any given instance. This web application gives the users easy accessisibility to the platform. The logistics web application for the tracking of parcels was developed using Angular Js, Node and Express Js, and MongoDB. Hosted on Heroku. The aim of the project which is to meet the demands of the users while offering real-time visibility, efficient route optimization, as well as the overall streaming of the supply chain process was achieved. With this application, users can finally be able to know the current and real time location of their packages so long as they have access to the internet.

Voyage optimization using dynamic programming with initial quadtree based route

Abstract This research proposes an integrated voyage optimization algorithm that combines quadtree graph generation, visibility graph simplification, Dijkstra’s algorithm, and a 3D dynamic programming (3DDP) method. This approach enables the determination of a minimum distance initial reference route and the creation of a 2D navigational graph for efficient route optimization. We effectively store and process complex terrain information by transforming the GEBCO uniform grid into a quadtree structure. By utilizing a nearest neighbour search algorithm, edges are connected between adjacent ocean nodes, facilitating the generation of a quadtree graph. Applying Dijkstra’s algorithm to the quadtree graph, we derive the shortest initial route and construct a visibility graph based on the waypoints. This results in a simplified reference route with reduced search distance, allowing for more efficient navigation. For each waypoint along the reference route, a boundary is defined angled at 90 degrees to the left and right, based on the waypoint’s reference bearing. A line segment formed by the waypoint and both boundaries is defined as a navigational stage. A navigational graph is defined by connecting adjacent stages. Employing a 3DDP method on the navigational graph, and incorporating weather forecasting data, including wind, wave, and currents, we search for a route that minimizes fuel oil consumption with estimated time of arrival restrictions. Our approach is tested on several shipping routes, demonstrating a fuel consumption reduction compared to other voyage optimization routes. This integrated algorithm offers a potential solution for tackling complex voyage optimization problems in marine environments while considering various weather factors.

Efficient routing optimization with discrete penguins search algorithm for MTSP

The Travelling Salesman Problem (TSP) is a well-known combinatorial optimization problem that belongs to a class of problems known as NP-hard, which is an exceptional case of travelling salesman problem (TSP), which determines a set of routes enabling multiple salesmen to start at and return to home cities (depots). The penguins search optimization algorithm (PeSOA) is a new metaheuristic optimization algorithm. In this paper, we present a discrete penguins search optimization algorithm (PeSOA) for solving the multiple travelling salesman problem (MTSP). The PeSOA evaluated by a set of benchmarks of TSP instance from TSPLIB library. The experimental results show that PeSOA is very efficient in finding the right solutions in a reasonable time

RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimization in SDN

Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE=15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.

Routing optimization for cloud services in SDN-based Internet of Things with TCAM capacity constraint

Distributed in-network cloud architecture is a promising solution to efficiently host next generation internet-of-things (IoT) services. With the rapid increase of IoT devices and applications, the backhaul or backbone networks, which transmit IoT traffic to various in-network clouds, will experience a predicted explosion in the volume of carried traffic. To guarantee the QoS of IoT cloud services and improve the network performance, it is crucial for network operator to implement efficient routing optimization strategies for IoT traffic. As a promising networking paradigm, software-defined networking (SDN) has flexible and programmable control capability for fine-grained flows. The emergence of SDN paves a way for implementing high-performance routing optimization in networks. In SDN networks, the routing strategies are realized through flow rules, which are usually stored in TCAM with very limited capacity. However, the number of IoT flows are enormous. Thus, in this paper, we address the routing optimization problem in SDN-based IoT with TCAM capacity constraint. We first formulate the problem as a mixed integer linear programming problem and prove the problem is NP-hard. Then to solve the problem efficiently, we propose several approximate algorithms, which solve the problem in two stages. In the first stage, the algorithms calculate the routing strategies for flows without considering the TCAM capacity constraint. To meet the TCAM capacity constraint, the algorithms using different strategies to adjust the paths of some flows in the second stage. Extensive simulations are conducted on both real ISP and synthetic topologies to evaluate the performance of the algorithms. The simulation results verify that the algorithms can achieve promising load balancing performance in SDN-based IoT, where the capacity of TCAM in SDN switches is very limited.

Token system‐based efficient route optimization in mobile ad hoc network for vehicular ad hoc network in smart city

AbstractMobile ad hoc network (MANET) is a well‐known wireless hoc network, which several nodes combined together to form a network. These nodes do not have any structure that forms network by self‐configuring. In the global area, MANET is one of the popular technologies. At present, the most important real‐time challenge in MANET is efficient routing. This is because of dynamic topology and resource constraints. Several existing researcher's workfare were done in an achieving efficient routing mechanism. In our work, we propose a token agent‐based route planning protocol for achieving reliable routing. Vehicular ad hoc network (VANET) is advanced concept of the MANET. VANET can be performed in various smart cities, and it travels each city from source to destination. This token system consists of a token agent that monitors and operates the whole network. The tokens are responsible for collecting nodes activity details such as mobile node energy, node location, and speed. Later, its current and previous information is up‐to‐date and inmate to the token agent for further process in the network. Our proposed routing protocol is well applicable for typical application and a multimedia application. In order to transfer typical packets, our proposed scheme finds the shortest path based on the collect the information.

Mobility Support for Fog Computing: An SDN Approach

The emerging real-time and computation-intensive services driven by the Internet of Things, augmented reality, automatic driving, and so on, have tight quality of service and quality of experience requirements, which can hardly be supported by conventional cloud computing. Fog computing, which migrates the features of cloud computing to the network edge, guarantees low latency for location-aware services. However, due to the locality feature of fog computing, maintaining service continuity when mobile users travel across different access networks has become a challenging issue. In this article, we propose a novel software-defined-networking-based fog computing architecture by decoupling mobility control and data forwarding. Under the proposed architecture, we design efficient signaling operations to provide seamless and transparent mobility support to mobile users, and present an efficient route optimization algorithm by considering the performance gain in data communications and system overhead in mobile fog computing. Numerical results from extensive simulations have demonstrated that the proposed scheme can not only guarantee service continuity, but also greatly improve handover performance and achieve high data communication efficiency in mobile fog computing.

Efficient Route Optimization using Distance Matrices for Generation of Latin Square

Efficient Route Optimization using Distance Matrices for Generation of Latin Square

Efficient route optimization scheme for nested-NEMO

Network mobility (NEMO) basic support protocol maintains the connectivity when mobile router (MR) of a mobile network changes its point of attachment to the Internet by establishing a bi-directional tunnel between the MR and the home agent (HA). A packet from a correspondent node (CN) traverses through the tunnel to reach the mobile network. Nesting occurs in NEMO when a MR's new attachment point is in another mobile network that has also moved away from its home link. The level of tunneling increases as the level of nesting increases. Multiple levels of tunneling in nested NEMO adds multiple legs to a non-optimized routing path that the IP packets have to traverse in order to reach the final destination. As per our study, an efficient route optimization technique in NEMO, particularly in nested NEMO, is still a research challenge. In this paper, we propose an efficient route optimization scheme for nested NEMO. We use two care-of Addresses for each MR, as well as two types of entries, such as fixed and visiting, in the routing table in each MR. Our route optimization scheme removes the tunnels completely from the nested NEMO in a single step using only one binding update message irrespective of the number of levels in the nest. Our route optimization scheme also works for non-nested NEMO.

Optimum routing of a sailing wind farm

To acquire the wind energy of the oceans, a sailing wind farm has been proposed. The wind farm considered is composed of a semisubmersible floating structure, 11 windmills, four sails, six thrusters, and storage for hydrogen. To maximize the acquired energy, an effective algorithm to search for optimum routes was developed. The algorithm is based on the fact that beam winds yield a maximum of acquired energy. This feature reduces the computation time, and, consequently, efficient route optimization becomes possible in a reasonable time. After setting an operational area for the wind farm, navigation simulations for a 1-year period were carried out. A numerical weather forecast was used as well as the responses of the floating structure, such as the speed of the structure, the output power of the windmills, and the time of course changes. In the simulation, the wind farm evades rough seas to avoid structural damage, and an optimum route is searched for. The capacity factor of the system was used to evaluate the efficiency of the optimized routes. From the simulations, the maximum capacity factor achieved was 42.6%. The dependency of the capacity factor on the initial position of the wind farm was also examined. It was shown that offshore from Sanriku in northeastern Japan is an area suited to the operation of the wind farm. The effect of the initial position on consecutive periods of operation is discussed.