Optimal Routing Strategy Research Articles

Levy flight based momentum search and Circle Rock Hyrax swarm optimization algorithms for energy effective clustering and optimal routing in wireless body area networks

In recent years, the connectivity of a new dimension has been brought to Wireless Body Area Networks (WBANs) through the concept of the Wearable Internet of Things (WIoT). WIoT offers valuable functionalities like data collection, analysis, and transmission for real-time health monitoring services. However, WBANs are characterized by a large number of battery-powered sensor nodes, making them resource-constrained. While keeping standard WSN abilities, WBAN's significant obstacles are data communication, social mobility, high interference in dense deployment, latency, and energy consumption of sensor nodes. As a result, the implementation and design of an application require dependable data transfer and an energy-efficient architecture. It proved critical to have as little delay as possible while delivering correct data. To overcome these issues, this study developed a novel energy-efficient clustered and optimized routing scheme in WBAN. Due to its effective scalability, this work employs an Improved Self-Executing Active Cross-Propagated (ISAC) clustering approach to form clusters in Wireless Body Area Networks (WBANs). Subsequently, the selection of the best Cluster Heads (CHs) is facilitated by the Levy Flight-based Momentum Search Algorithm (LFMSA). The Circle Rock Hyrax Swarm Optimization (CRH) algorithm is then utilized for energy-efficient routing. In determining the CH in each cluster, consideration is given to both residual energy and connectivity. The proposed framework minimizes the number of transmissions and overall transmission distance, optimizing inter and intra-cluster transmission distances. Additionally, the framework ensures energy-efficient packet delivery from CHs to the sink node. Performance comparisons, conducted using various statistical measures and simulated on the NS-2 platform, demonstrate that the proposed framework outperforms existing routing protocols and clustering methodologies. In comparison to previous methodologies, the proposed framework consistently achieves superior results.

Energy Efficient strategy for Wireless Sensor Networks lifetime endurance using Genetic Algorithm

This study propose an optimized routing scheme for multiple hops Wireless Sensor Networks to enhance the lifetime of the network. Since the wireless sensor networks work as application-centric having the capabilities of data gathering and transmission to the base station. Therefore, a significant amount of sensor networks` energy spends during such operation of data collection and transmission if the route is not optimized. So, a meta-heuristic genetic algorithm plays a vital role in finding the optimized route. We will modify the genetic algorithm for better optimization results so that the best route can be selected among all possible routes and will save the wireless sensor networks` energy. All information is routed towards the sink via intermediate neighbor nodes, which only relay the data without performing any computation or operation. The genetic algorithm determines the route information to all nodes and sinks. Then, the data takes the most optimal route for data transmission. Thus, this study proved a considerably saved extra waste of energy.

Genetic Algorithm based Secure Hybrid Routing Technique for IoT Framework

Internet is gaining new heights in today’s technological environment. Internet of things (IoT) is a new class of Internet-basedheterogeneous networked application systems that uses various types of sensors/detectors and devices for the exchange and collection of data. Due to their applications in situations such as building home automation, emergencies, crisis management, energy management, and healthcare, path optimization and message security becomes of topmost importance in IoT. An optimized routing scheme using intelligent mathematical techniques including Genetic Algorithms (GA) and Analytical Hierarchy Process (AHP), is proposed here and an optimized route can be encrypted using cryptographic techniques. From the simulation results, it has been found that the overall efficiency of the IoT system can be greatly improved with the proposed model. A comparison is also provided in the discussion section which demonstrates that hybrid algorithms developed for IoT systems perform much better than traditional routing algorithms.

ER-OCN: Toward efficient network routing in ocean city based on deep reinforcement learning

Smart ocean cities are crucial to future city development, and offshore networks are an essential part of smart ocean cities. Nowadays, communication in offshore networks is becoming increasingly important. However, the offshore networks are highly heterogeneous, with low reliability, narrow bandwidth, and dramatic dynamics. Offshore networks are more challenging to manage than traditional terrestrial networks, and the standard quality of service routing faces significant challenges. Currently, artificial intelligence provides a way to meet the requirements. This paper proposes ER-OCN (Efficient Routing in Ocean City Networks), a two-stage routing optimization scheme driven by artificial intelligence to cope with the complex offshore networks environment. The first stage will calculate the flow paths based on Lagrangian relaxation. Besides, the ocean environment is under dramatic dynamics, leading to unpredictable network topology or link bandwidth changes. Thus, the second stage deployed a deep reinforcement learning algorithm to monitor the environment in real-time and update the routing strategy when needed. We conducted experiments based on actual network topology and traffic data sets of offshore platforms’ network systems. The results prove that our scheme improves communication cost performance by 36.61% compared to traditional methods.

Electric Vehicle Routing Problem with States of Charging Stations

This paper proposes an electric vehicle routing problem, considers the states of charging stations and suggests solution strategies. The charging of electric vehicles is a main issue in the field of electric vehicle routing. There are many studies that find the locations of charging stations, recharging functions for the batteries of vehicles, and so on. However, the state of charging stations significantly affects the routes of electric vehicles, which is not much explored. The states may include open or closed charging stations, occupied or empty charging slots, and so on. This paper investigates how the states of charging stations are estimated and how routing strategies are determined. We formulate a mixed integer programming model and suggest how to solve the problem with an exact method. Numerical examples provide the optimal routing strategies of electric vehicles for the changing environments regarding the states of charging stations.

Multi-Agent Deep Reinforcement Learning-Based Fine-Grained Traffic Scheduling in Data Center Networks

In data center networks, when facing challenges such as traffic volatility, low resource utilization, and the difficulty of a single traffic scheduling strategy to meet demands, it is necessary to introduce intelligent traffic scheduling mechanisms to improve network resource utilization, optimize network performance, and adapt to the traffic scheduling requirements in a dynamic environment. This paper proposes a fine-grained traffic scheduling scheme based on multi-agent deep reinforcement learning (MAFS). This approach utilizes In-Band Network Telemetry to collect real-time network states on the programmable data plane, establishes the mapping relationship between real-time network state information and the forwarding efficiency on the control plane, and designs a multi-agent deep reinforcement learning algorithm to calculate the optimal routing strategy under the current network state. The experimental results demonstrate that compared to other traffic scheduling methods, MAFS can effectively enhance network throughput. It achieves a 1.2× better average throughput and achieves a 1.4–1.7× lower packet loss rate.

A Genetic Algorithm Based Hybrid Routing Technique for IOT Systems

Internet is gaining new heights in the today’s technological environment. Internet of things (IoT) is a new class of Internet based heterogeneous networked application systems which uses various types of sensor/detectors and devices for the exchange and collection of data. Devices need to be in the radio range and must remain continuously connected to the internet. IoT can sense and control the objects remotely across existing networked architecture and builds chances for direct combination among the physical world and computer-based systems. It is believed that, in future many of the household application will be based on IoT. Due to their applications in situations such as building home automation, emergencies, crisis management, energy management and healthcare, message security becomes of top importance in IoT. An optimized routing scheme using the intelligent mathematical techniques, which includes Genetic Algorithms(GA) and Analytical Hierarchy Process(AHP), is proposed here and an optimized route can be encrypted using cryptanalytic techniques. Simulation results of GA and AHP are also presented here for the proposed network. It has been found that overall efficiency of the IoT system can be greatly improved with the proposed model. A comparison is also provided in discussion section which demonstrates that hybrid algorithms developed for IoT systems performs much better than traditional routing algorithms. Keywords: IoT; AHP;GA; Cryptanalytic Techniques; WSN

A new quantum key distribution resource allocation and routing optimization scheme

Quantum key distribution (QKD) is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols. However, due to the stringent requirements of the quantum key generation environment, the generated quantum keys are considered valuable, and the slow key generation rate conflicts with the high-speed data transmission in traditional optical networks. In this paper, for the QKD network with a trusted relay, which is mainly based on point-to-point quantum keys and has complex changes in network resources, we aim to allocate resources reasonably for data packet distribution. Firstly, we formulate a linear programming constraint model for the key resource allocation (KRA) problem based on the time-slot scheduling. Secondly, we propose a new scheduling scheme based on the graded key security requirements (GKSR) and a new micro-log key storage algorithm for effective storage and management of key resources. Finally, we propose a key resource consumption (KRC) routing optimization algorithm to properly allocate time slots, routes, and key resources. Simulation results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate, among others.

An Optimized Routing Scheme in SDN-Based IoT Using the Lion Optimization Algorithm

The Internet of Things (IoT) speaks to the current and future state of the Internet. Numerous things (objects) related to the Internet generate a large amount of information that requires a ton of labor and prepare tasks to move to valuable data. In addition, for the association and control of this enormous information, the original ideas in the project and the executives of the IoT system need to expedite and improve its presentation. A software-defined networking (SDN) system is another worldview that has recently emerged to cover all the complexities in the traditional system architecture by summarizing all the controls and management functions from the basic devices (things in IoT). The open-flow protocol is used to make messages between switches and controllers. It is a correspondence conference, through which the controller integrates the path of the network pocket with switches. Therefore, data packets should be directed to the optimum path to improve system operation. The best or optimal path for packet routing in the SDN data plane is found using the Lion Optimization Algorithm (LOA), which is proposed in this study. Response times and packet loss rates can both be decreased by choosing the best route between the hosts. The suggested LOA outperforms current optimal path detection methods in terms of performance, delay, packet delivery rate, performance, and pocket loss rate, according to simulation findings.

Wireless sensor network routing optimization based on improved ant colony algorithm in the Internet of Things

The Internet of Things (IoT) connects devices, enabling real-time data acquisition, automation, and collaboration. Wireless sensor networks are one of the important components of the Internet of Things, consisting of many wireless sensor nodes distributed in space. These nodes can perceive environmental information and transmit it to other nodes through wireless communication. In wireless sensor network routing optimization methods, improved ant colony algorithm can be used to find the optimal routing scheme. Ant colony algorithm simulates the behavior of ants in the process of searching for food, and optimizes factors such as transfer probability and pheromone concentration to enable ants to find the shortest path. In wireless sensor networks, node positions can be used as reference nodes and anchor nodes, combined with the objective function of wireless sensor network routing optimization, and improved ant colony algorithm can be used to solve the optimal path, thus obtaining the optimal wireless sensor network routing optimization scheme. Through experimental results, it can be found that the proposed method performs well in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. These optimization results have positive implications for the sustainable development and practical application of the Internet of Things, which can improve the development of the digital economy and enhance the construction of smart cities.

Novel Cross Optimization based Trusted and Opportunistic Routing Scheme in Wireless Sensor Network

Opportunistic routing in wireless networks under mobile computing domain is one of the point of attraction for the research. Consistency along with routing security are the challenges faced by many old schemes which results performance to be compromised. However the existing techniques developed for effective routing are cost effective without the concern for the exact positioning of nodes in the network but while designing the scheme, security still requires attention. A novel cross optimization based trusted and opportunistic routing scheme for WSN is proposed in this paper which is referred as Unified Trusted and Optimized Routing (UTOR) scheme. The prime intention of UTOR is to realize optimized data movement along with routing and data security. UTOR functions in two stages, the first stage recognizes and unites the unified nodes depending on defined liberal constant(LC) which has definite trust, link stability, and quality components. Definite trust as well as link stability can be measured directly but time to live and the associated delay are considered for measuring the quality. Whereas in second stage optimized nodes selection takes place with the help of proposed UTOR over defined proclivity function(PF) based on definite_trust(DT), link_stability (LS), quality_of_node(QN) and distance. Proposed UTOR’s performance is evaluated based on performance measures for Ad-Hoc sensor network of varying range of dynamic nodes in the presence of black_hole and DoS attacks. UTOR exposes relatively superior throughput and detection rate at the same time showcases minimal distance and delay, which are comparatively better than competing schemes. Significantly higher throughput and detection_rate as 44.1 and 55.7 respectively alongside low distance and delay as 168.2 and 13 are shown by UTOR which are comparatively better performance parameters than competing schemes. UTOR’s effective use in WSN may be under real-time scenarios such as environmental monitoring, smart farming, automation industries etc.

Time-Sensitive Networking-Driven Deterministic Low-Latency Communication for Real-Time Telemedicine and e-Health Services

With the convergence of telemedicine and electronic health (e-health) services, Consumer Internet of Things (CIoT) enabled smart healthcare has attracted much attention from researchers. Telemedicine and e-health care can help to eliminate geographical barriers and provide high-quality medicine services. Whereas, deterministic low-latency communication is a major problem in smart healthcare system with commercial network, since the latency can affect the process of diagnosis and treatment while producing inaccuracy. To address this issue, Time Sensitive Networking (TSN) is introduced in this article for bounded lowlatency network services. First, we present a Digital Twin based TSN (DTTSN) framework where digital modeling and intelligent simulation are performed under a virtual environment. Second, a digital twin scheduling model is created to predict the forwarding delay. In order to enhance model adaption in different scenarios, cycle generative adversarial network (CycleGAN) is adopted to train the scheduling model. Finally, the optimal routing strategy deduced in digital twin network is fed back to physical network for automated configuration. Simulation results verify that our digital twin network can promote visibility of operations and predictions of physical network scheduling and routing, while optimizing delay and balancing link load.

Low-Delay Routing Scheme for UAV Communications in Smart Cities

UAV, particularly UAV self-assembly network or flying ad hoc network, is one of the most promising wireless network technologies in smart cities and the future 6G era. To satisfy the low delay demands of communication routing in FANETs, we offer an optimized routing scheme based on OLSR routing to overcome additional routing overhead caused by link interruptions due to node movement. With the use of the NS3 network simulation platform, the performance metrics of OLSRs improved and original algorithms are evaluated. Simulations demonstrate that this improved algorithm is significantly more usable and scalable with respect to a number of important metrics, including routing overhead, average delay, and packet delivery rate.

Accessible taxi routing strategy based on travel behavior of people with disabilities incorporating vehicle routing problem and Gaussian mixture model

The government of Seoul has implemented accessible taxis, following the Convention on the Rights of Persons with Disabilities established by the United Nations in 2006. Accessible taxis are an emerging transportation mode that improves transportation accessibility for people with disabilities (PWD). The main idea of this study is to classify PWD into short-distance and long-distance groups and operate corresponding short-distance and long-distance taxis to improve the operational efficiency and user convenience of accessible taxi services. This study aims to find an optimal routing strategy for accessible taxis considering the travel behavior of PWD. Specifically, the travel distance distribution of PWD was divided into multiple distributions, i.e., short-distance and long-distance distributions, using Gaussian Mixture Model. Then, a vehicle routing problem with time windows was developed to find the optimal routing strategy for accessible taxis. The objective function was set to minimize users’ total wait time, and the decision variables included the number of user classifications, the number of accessible taxis, and the route for each vehicle. The results of the proposed strategy showed about an 8.0% reduction in total wait time and a 1.3% reduction in the total operating distance of accessible taxis compared to the current strategy. Specifically, the wait time of the short-distance user group with a distance of less than 4.3 km was reduced by about 26%. An economic benefit analysis was also performed to identify the applicability of the proposed strategy, and the results showed that the proposed model was economically beneficial.

A Graph reinforcement learning based SDN routing path selection for optimizing long-term revenue

Software-Defined Network (SDN) paradigm decouples control plane from data plane and provides a logically-centralized control to whole underlying network, which enables the controller to centrally configure the network, and has demonstrated great potential in fully utilizing network capacity. However, the traditional SDN does not consider the accumulative long-term revenue in routing configuration for network service provider (SP). For example, it is prone to choosing the path passing through important nodes, which may increase the possibility of future congestion, and hamper the SP’s long-term revenue. Deep reinforcement learning (DRL) has brought about the transformative advances in decision-making when facing uncertainty. As a well-known type of DRL, Deep Q-network (DQN) can explore optimal routing schemes from high dimensional network state. However, most of existing DRL based methods fail to provide satisfactory configuration when confronted with a topology unseen during training. Meanwhile, many existing network optimization schemes ignore the impact of nodes and global network attributes on the routing configuration. Consequently, they may perform poorly in real scenarios. In this paper, we propose a graph reinforcement learning (GRL) based SDN routing selection scheme, GN-DQN for optimizing SP’s accumulative long-term revenue. First, besides the link embeddings, GN-DQN explicitly learns the representations of the routers and globe network topology with full graph neural network block. Then, through utilizing the formed network representation as the state, DQN is used to learn to sequentially choose the optimal routing path from the candidate path set to maximize the accumulative long-term revenue. Thorough experiments on multiple real network topologies show that our proposed GN-DQN outperforms other GRL based and traditional routing selection schemes, and can generalize over arbitrary topologies. Moreover, GN-DQN also demonstrates great robustness to the link failure scenario.

A stochastic formulation of the two-echelon vehicle routing and loading bay reservation problem

This paper studies a goods delivery problem for multiple courier companies in central city areas integrated with a loading bay scheduling problem. The goods delivery problem is formulated as a stochastic two-echelon vehicle routing problem, while the loading bays are managed through a central reservation system, which assigns loading bays to courier vehicles taking into account their delivery demands. A stochastic programming model with chance constraints is formulated to find the optimal vehicle routing and loading bay reservation strategy, and a hybrid genetic algorithm is developed to solve the model. Numerical results suggest that the reservation system can help coordinate deliveries from different courier companies and substantially improve the overall delivery efficiency and loading space utilisation.

A comprehensive cost performance analysis for a QoS-based scheme in network mobility (NEMO)

By shifting the portability task away from a mobile network node and onto a mobile router, the NEMO BS protocol has been given the green light to run by the Internet Engineering Task Force (IETF) working group. It is not effective to anticipate the mobility of each node in a train, bus, or ship individually. Hence, it would be reasonable to hire a Mobile Router (MR) that collectively handles the mobility of the entire mobile network. The NEMO BS protocol encourages efficient mobility for groups. Devices on a mobile network do not recognize the mobility of their network. Uninterrupted Internet connectivity is still given to mobile network nodes (i.e. the devices) despite the fact that the network's connection point is shifted on the Internet. The NBS solution has severe performance limitations (e.g. triangular routing and signalling cost). To address the aforementioned issues, the Diff-FH NEMO pattern has formerly been proposed. This article built a methodology to evaluate signalling costs for major Diff-FH NEMO entities. For verification, the effectiveness of the proposed scheme Diff-FH NEMO is measured against that of the industry-standard NEMO BS protocol and the MIPv6-based Route Optimization (MIRON) scheme. Many important indicators, such as the length of time a user spends in a subnet and the total number of hops, are used to compare the signalling cost to (DiffServ Mobile Router (DMRs), Correspondent Nodes (CNs), Local Fixed Nodes (LFNs), and mobile nodes). The analytical findings indicate that the suggested approach gained considerable act enhancement by shrinking the total signalling cost when the network size was enlarged.

Dynamic Vehicle Routing Problem with Fuzzy Customer Response

This paper proposes a dynamic vehicle routing problem (DVRP) model with fuzzy customer responses and suggests optimal routing strategies. Most DVRP studies have focused on how to create a new route upon the occurrence of dynamic situations such as unexpected demands. However, the customer responses have received little attention. When a pop-up demand is added to one of the planned routes, the service for some optimally planned demands may be delayed. Customers may file complaints or cancel their orders as a result of the delays. As a result, the customer response has a significant impact on current profits as well as future demands. In this research, we consider the customer response in DVRP and address it with a fuzzy number. Changing distances or defining time windows can resolve the problem of customer response. The customer responses are represented by a fuzzy rule. The new routing strategy provides the viability to reduce customer complaints and avoid losing potential customers.

Fuzzy Logic Intelligent Space Routing Convolutional Decision and Optimization Scheme

Fuzzy Logic Intelligent Space Routing Convolutional Decision and Optimization Scheme

Vehicle Dispatch and Route Optimization Algorithm for Demand-Responsive Transit

Giving priority to the development of public transit is an important way to achieve efficient, convenient, safe, comfortable, economic, reliable, green and low-carbon sustainable development. In view of the highly dispersed and regular passenger flow, demand responsive transit is an important complementary means for traditional public transport to improve passenger satisfaction. However, high operating costs and low load factor will have a bad impact on the operation of public transport and reduce passenger satisfaction. In this work, firstly, by analyzing the demand frequency of historical travel stations, the stations with high demand are extracted by time periods as high probability travel points; On this basis, a dynamic vehicle dispatching optimization model is established, and the static vehicle dispatching is carried out with the goal of minimizing the running mileage of the bus system; Finally, based on the initial static route and the later real-time travel demand, the accurate dynamic planning algorithm is used to optimize the dynamic route with the goal of minimizing the change of the system mileage, so as to achieve timely response to the demand. The results show that the two-phase scheduling optimization model based on the station extraction strategy can provide a reasonable real-time vehicle scheduling and route optimization scheme, improve the utilization rate of vehicles and the passenger load factor, and provide a theoretical basis and application guidance for actual vehicle scheduling.