Routing Scheme Research Articles

Retraction Note: Optimizing IoT-enabled WSN routing strategies using whale optimization-driven multi-criterion correlation approach employs the reinforcement learning agent

Retraction Note: Optimizing IoT-enabled WSN routing strategies using whale optimization-driven multi-criterion correlation approach employs the reinforcement learning agent

Node Localization Based on Unsupervised Approach and Secure Routing for Healthcare Management in Delay‐Tolerant Network

ABSTRACTThe ability of the current global healthcare system to continue providing high‐quality medical care has been questioned. By using delay‐tolerant networks (DTNs) in healthcare management, people that spend a lot of time treating illnesses are exposed to more people while also putting their lives in danger. Unsupervised approach‐based node localization and secure routing in DTNs are developed in the proposed method. The proposed adaptive density‐based localization algorithm is constructed in two modules. The entire network is clustered depending on density utilizing density‐based spatial clustering of applications with noise (DBSCAN). An RSA‐based location estimation algorithm is employed to identify the unlocalized node within each cluster. After finding the location of each node, routing mechanism is carried out. An adaptive spray and wait routing strategy is utilized in the proposed method. When a node only has one copy of a message, the spray and wait, routing mechanism moves the relay node while it waits for the destination to be countered. Then modified DNA encryption algorithm is applied for secure data transmission. It is used for encrypting the information before storing it in the network. The trusted center will generate the private key needed for encrypting the data based on the sensitivity level of the information. This proposed algorithm is tested with several metrics which attain better performance, like 94% localization accuracy, 6% localization error, average residual energy of 93%, 5.7 Mbps throughput, 18% encryption time, and 14% decryption time. Thus, the techniques used in the proposed approach are the best choice for node localization, energy efficiency, and secure data transmission in DTN.

Randomly Twisted Hypercubes: Between Structure and Randomness

ABSTRACTTwisted hypercubes are generalizations of the Boolean hypercube, obtained by iteratively connecting two instances of a graph by a uniformly random perfect matching. Dudek et al. showed that when the two instances are independent, these graphs have optimal diameter. We study twisted hypercubes in the setting where the instances can have general dependence, and also in the particular case where they are identical. We show that the resultant graph shares properties with random regular graphs, including small diameter, large vertex expansion, a semicircle law for its eigenvalues and no non‐trivial automorphisms. However, in contrast to random regular graphs, twisted hypercubes allow for short routing schemes.

Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion

The growth of urbanization, population, and economic activity has led to a substantial increase in freight transportation demand, exceeding the capacity of existing infrastructure and creating new challenges across various regions. This has resulted in significant traffic congestion, increased travel times, and higher operational costs for commercial vehicle fleets. Leveraging topological data, such as road networks and traffic patterns, can enable more efficient routing strategies to navigate around congested areas. This study presents a comprehensive approach to truck rerouting strategy by integrating spatial analysis, truck characteristics, traffic conditions, road geometry, and cost–benefit analysis to select alternative routes suitable for commercial vehicle fleets. Incorporating real-time traffic information and predictive analytics, commercial vehicle operators can optimize their routes, reduce fuel consumption, and improve overall delivery efficiency. Three case studies were presented to demonstrate the proposed diversion decision framework. Two scenarios were designed for each case study: a base scenario with no diversion and an optimized scenario with a diversion strategy. The travel times, fuel consumption, and economic impacts between the two scenarios were compared and quantified as a total annual saving of USD 52 million. This approach goes beyond selecting alternative routes and provides decision makers with measurable benefits that justify diversion strategies.

An Energy-efficient Routing Protocol Based on Elephant Herding Optimization in MANET

Background: A mobile ad hoc network (MANET) is a collection of self-organizing mobile nodes creating an ad hoc network without fixed infrastructure. Routing is a major issue in mobile networks that may reduce network performance due to frequent network topology changes. Routing protocols are so important in dynamic multi-hop networks that many studies have focused on the routing problem in MANETs. Methods: In this research work, a proposed ad hoc on-demand distance vector (AODV) with an elephant herding optimization (EHO) called AODVEHO is used as an optimal path selection method that creates a set of best paths between destination and source. Results: To validate the performance of proposed AODVEHO, AntHocNet, AODV, and designation sequence distance vector (DSDV). Various performance measures are considered to validate our proposed research work, such as packet delivery ratio (PDR), end-to-end delay (E2ED), and average energy consumption (AEC). Conclusion: Experimental results are indicated that the AODVEHO achieved a higher routing overhead (73 %), PDR (89.87 %), low E2ED rate (95.22 %), AEC (75.60 %), and dead nodes (75%) when compared to other routing schemes.

Spatiotemporal Precise Routing Strategy for Multi-UAV-Based Power Line Inspection With Integrated Satellite-Terrestrial Network

Spatiotemporal Precise Routing Strategy for Multi-UAV-Based Power Line Inspection With Integrated Satellite-Terrestrial Network

OSC-GOR: Optimal Security-Aware Cluster-Based Hybrid Geographical and Opportunistic Routing for Vehicular ad hoc Networks

Vehicular ad hoc networks (VANETs) are crucial for infrastructure-less vehicular communication. The dynamic and complex nature of VANETs necessitates efficient routing strategies. This paper introduces an optimal security-aware cluster-based hybrid geographical and opportunistic routing (OSC-GOR) for VANETs. By utilizing the improved bald eagle optimization (IBEO) algorithm, we achieve optimal clustering that enhances energy efficiency. Cluster heads (CHs) are elected based on trust, calculated from metrics like energy consumption and signal strength. A Poplar optimization algorithm (POA) further refines these metrics. Subsequently, an optimal min–max recurrent neural network (OMM-RNN) determines the next forwarder nodes, facilitating data transfer in VANETs. Implemented using the NS-2 tool and SUMO traffic generator, OSC-GOR showcases optimized clustering and superior network efficiency. Comparative simulations reveal OSC-GOR’s advantages in energy use, latency, delivery ratio, goodput, and routing overhead, outperforming existing methods. Notably, OSC-GOR effectively counters VANET attacks such as GPS spoofing and Sybil threats.

Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

ABSTRACTIn the context of Mobile Ad Hoc Networks (MANETs), the dynamic and decentralized topology poses significant challenges like unreliable connectivity, limited bandwidth, node mobility, and vulnerability to security threats from malicious nodes. Ensuring secure and energy‐efficient data transmission in such environments is crucial for mission‐critical applications. This research addresses these pressing challenges by introducing a robust routing protocol capable of detecting and mitigating malicious nodes, thereby enhancing MANET's Quality of Service (QoS). The proposed approach, the Dendritic Cell with Adaptive Trust Q‐learning Protocol (dDC‐ATQP), integrates several innovative techniques to tackle these issues. Firstly, the trust evaluation mechanism assesses the behavior of nodes to identify potential malicious actors, mitigating the effect of malicious nodes on system execution. Secondly, the adaptive routing strategy optimizes data transmission paths based on real‐time network conditions, reducing latency and packet loss. To evaluate the effectiveness of this approach, extensive simulations are conducted using a range of performance metrics. The results demonstrate significant improvements over existing methods, including a throughput increase of (79.2% in 50 s), lower end‐to‐end‐delay (0.075 s for 20 nodes), energy consumption of (38.55/J), higher packet delivery ratio (98% for 20 nodes), reduced packet loss ratio (5% for 100 nodes), enhanced security (80% for 70 nodes).

DAR-DRL: A dynamic adaptive routing method based on deep reinforcement learning

Mobile-centric wireless networks offer users a diverse range of services and experiences. However, existing intelligent routing methods often struggle to make suitable routing decisions during dynamic network changes, significantly limiting transmission performance. This paper proposes a dynamic adaptive routing method based on Deep Reinforcement Learning (DAR-DRL) to effectively address these challenges. First, to accurately model network state information in complex and dynamically changing routing tasks, we introduce a link-aware graph learning model (LA-GNN) that efficiently senses network information of varying structures through a hierarchical aggregated message-passing neural network. Second, to ensure routing reliability in dynamic environments, we design a hop-by-hop routing strategy featuring a large acceptance domain and a reliability guarantee reward function. This mechanism adaptively avoids routing holes and loops across various network scenarios while enhancing the robustness of routing under dynamic conditions. Experimental results demonstrate that the proposed DAR-DRL method achieves the network routing task with shorter end-to-end delays, lower packet loss rates, and higher throughput compared to existing mainstream methods across common dynamic network scenarios, including cases with dynamic traffic variations, random link failures (small topology changes), and significant topology alterations.

Elderly care and health monitoring using smart healthcare technology: An improved routing scheme for wireless body area networks

AbstractHypertensive patients need regular checkups and constant monitoring for taking time critical decisions by the medical experts. Unfortunately, it is hard to maintain uninterrupted patient health surveillance due to limited medical staff resulting in an increasing mortality rate annually. Thanks to recent developments in wireless sensor networking, we can monitor constantly and efficiently diverse parameters of a network. Similarly, Wireless Body Area Networks (WBANs) have become a well‐known sub‐branch of Wireless Sensor Networks. Such sensor networks can be leveraged for patient health monitoring, minimising the medical staff workload. Wireless Body Area Networks require tiny sensor nodes with limited battery power. Therefore, it is always desirable to design effective routing schemes that can enhance network lifetime, and reduce packet drop ratio. In this paper, we re‐simulate and explain in detail the results of a selected published journal article for WBANs and provide some modifications to improve the network's overall performance. Based on these amendments, the modified protocol successfully extends the operational time of the network than the original. Our performance evaluation parameters are dead nodes, throughput, residual energy, and path loss versus the number of rounds. These analyses support effective solutions that improve network performance and data delivery ratio.

IFKC and Optimized DeTrac Deep Convolutional Neural Network Based Location Prediction in the Mobile IOT

Wireless Sensor Network (WSN) is a noteworthy technological advancement in the Internet of Things (IoT). Due to its severe resource constraints, this network necessitates the development of energy-efficient routing strategies. In the context of the Internet of Things, a cluster-based routing problems frequently struggle with unequal power usage. Therefore, this paper proposes an Improved Fractional Rough Fuzzy K-means Clustering and Optimized DeTraC Deep Convolutional Neural Network based Location Prediction in Mobile Internet of Things (IFKC-ODTN-LP-MIoT) system to address unequal power consumption in cluster-based IoT routing. By proposing Improved Fractional Rough Fuzzy K-means (IF-RFKM) for cluster head selection and an optimized DeTraC Deep Convolutional Neural Network (DT-DCNN) the system aims to enhance energy efficiency for location prediction. Then Adolescent Identity Search Algorithm (AISA) optimizes DT-DCNN parameters for improved accuracy. The experimental results demonstrate significant improvements in Computational efficiency, network lifetime and delay compared to the existing approaches.

Towards an energy-aware two-way trust routing scheme in fog computing environments

Towards an energy-aware two-way trust routing scheme in fog computing environments

Active Disturbance Rejection Flight Control and Simulation of Unmanned Quad Tilt Rotor eVTOL Based on Adaptive Neural Network

The unmanned quad tilt-rotor eVTOL (QTRV) is a variable-configuration aircraft that combines the features of vertical takeoff and landing (VTOL), hovering, and high-speed cruising, making its control system design particularly challenging. The flight dynamics of the QTRV differ significantly between the VTOL and cruise modes, and are further influenced by rotor tilt and external wind disturbances. Developing a unified, highly coupled nonlinear full-flight dynamics model facilitates flight control system design and simulation verification. To ensure stable tilt of the QTRV, a tilt corridor was established, along with the design of its tilt route and manipulation strategy. An adaptive neural network active disturbance rejection controller (ANN-ADRC) is proposed to ensure stable flight across all modes, reducing the control parameters and simplifying tuning while effectively estimating and compensating for unknown disturbances in real time. A hardware-in-the-loop (HIL) simulation system was designed for full-mode flight control simulation, and the results demonstrated the effectiveness of the proposed control method.

Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks

The advancement of communication technologies has facilitated the deployment of numerous sensors, terminal human–machine interfaces, and smart devices in various complex environments for data collection and analysis, providing automated and intelligent services. The increasing urgency of monitoring demands in complex environments necessitates low-cost and efficient network deployment solutions to support various monitoring tasks. Distributed networks offer high stability, reliability, and economic feasibility. Among various Low-Power Wide-Area Network (LPWAN) technologies, Long Range (LoRa) has emerged as the preferred choice due to its openness and flexibility. However, traditional LoRa networks face challenges such as limited coverage range and poor scalability, emphasizing the need for research into distributed routing strategies tailored for LoRa networks. This paper proposes the Optimizing Link-State Routing Based on Load Balancing (LB-OLSR) protocol as an ideal approach for constructing LoRa distributed multi-hop networks. The protocol considers the selection of Multipoint Relay (MPR) nodes to reduce unnecessary network overhead. In addition, route planning integrates factors such as business communication latency, link reliability, node occupancy rate, and node load rate to construct an optimization model and optimize the route establishment decision criteria through a load-balancing approach. The simulation results demonstrate that the improved routing protocol exhibits superior performance in node load balancing, average node load duration, and average business latency.

Enhancing interconnection network topology for chiplet-based systems: An automated design framework

Chiplet-based systems integrate discrete chips on an interposer and use the interconnection network to enable communication between different components. The topology of the interconnection network poses a significant challenge to overall performance, as it can greatly affect both latency and throughput. However, the design of the interconnection network topology is not currently automated. They rely heavily on expert knowledge and fail to deliver optimal performance. To this end, we propose an automated design framework for chiplet interconnection network topology, called CINT-AD. To implement CINT-AD, we first investigate topology-related properties from the perspective of design constraints and structural symmetry. Then, using these properties, we develop an automated framework to generate the topology for interposer interconnections between different chiplets. A deadlock-free routing scheme is proposed for the topologies generated by CINT-AD to fully utilize the resources of the interconnection network. Experimental results show that CINT-AD achieves lower average latency and higher throughput compared to existing state-of-the-art topologies. Furthermore, power and area analysis show that the overhead of CINT-AD is negligible.

An optimal scheduling method for heavy haul trains with virtual coupling technology

ABSTRACT Heavy haul transportation enjoys the benefits of cost-efficiency by using long trains, but suffers from high cost in combination process and low efficiency in bottleneck areas. Virtual coupling technology (VCT) is helpful in resolving the dilemma. In this paper, we propose an optimal method for scheduling the route, timetable, and combination scheme of heavy haul trains with VCT in an integrated way. Specifically, six groups of constraints are first established, in which the sequence and train length problems incorporated by virtual coupling are properly handled. Then, a combined objective function comprised makespan and total flowtime indicators is proposed to optimize the throughput and turnover rate and further speeds up the optimization convergence. Finally, the optimization problem equipped with the proposed constraints and the combined objective function is transformed into an integer linear programming problem and solved by GUROBI. Simulation results demonstrate the effectiveness and advantages of the proposed method in leveraging VCT, and the benefits of virtual coupling over traditional physical coupling for heavy haul transportation.

Time Evolution of Orbital Angular Momentum Modes for Deep-Routing Multiplexing Channels

Optical orbital angular momentum (OAM) mode multiplexing has emerged as a promising technique for boosting communication capacity. However, most existing studies have concentrated on channel (de)multiplexing, overlooking the critical aspect of channel routing. This challenge involves the reallocation of multiplexed OAM modes across both spatial and temporal domains—a vital step for developing versatile communication networks. To address this gap, we introduce a novel approach based on the time evolution of OAM modes, utilizing the orthogonal conversion and diffractive modulation capabilities of unitary transformations. This approach facilitates high-dimensional orthogonal transformations of OAM mode vectors, altering both the propagation direction and the spatial location. Using Fresnel diffraction matrices as unitary operators, it manipulates the spatial locations of light beams during transmission, breaking the propagation invariance and enabling temporal evolution. As a demonstration, we have experimentally implemented the deep routing of four OAM modes within two distinct time sequences. Achieving an average diffraction efficiency above 78.31%, we have successfully deep-routed 4.69 Tbit·s−1 quadrature phase-shift keying (QPSK) signals carried by four multiplexed OAM channels, with a bit error rate below 10–6. These results underscore the efficacy of our routing strategy and its promising prospects for practical applications.

Discussion on the Automatic Design Method of Well Trajectory for Cluster Well Platform

Abstract Shale oil and gas and other unconventional oil and gas resources adopt the development mode of cluster well group to achieve economic benefit development. Cluster well groups have the characteristics of small spacing between wellheads and limited space, and generally adjust trajectory parameters by designing individual artificial Wells one by one to meet the needs of geological target hitting and anti-collision and circumnavigation. Trajectory design affects the whole body, and the change of trajectory of one well causes all Wells on the same platform to readjust design parameters, which seriously affects the design efficiency. Therefore, on the basis of summarizing the experience of manual trajectory design, combining classical path planning algorithm and global planning algorithm, an automatic trajectory design model is established. A* path optimization algorithm and ant colony algorithm are used to construct the routing strategy method and rule set, and adapt the efficient manual guidance strategy for different risk classifications to achieve efficient automatic routing. A cluster well trajectory automatic design and routing module has been developed. Automatic trajectory design tested 15 platforms, with an average time of 60s. The efficiency of trajectory design was greatly improved, and it has important guiding significance for the development of automated drilling technology.

Achieving Reliability of Privacy-preserving Phantom Routing Protocols in Multi-hop Wireless Sensor Networks

Due to the open nature of wireless channels and sensor node resource constraints, it is challenging to secure the communication in wireless sensor networks (WSNs) while simultaneously protecting the privacy of node location data. Therefore, a significant amount of research focusing on source location privacy (SLP) protocols has been conducted. The amount of research on SLP reliability, meanwhile, is insignificant. This study explores the operational features of various privacy-preserving phantom routing protocols and simulates WSNs with varied network configurations to investigate how different routing strategies affect SLP reliability. Safety period and capture ratio metrics are used to compute SLP reliability. Simulation results show that integration of phantom routing with fake packet distribution mechanisms adversely impacts SLP reliability. SLP reliability decreases also as the number of fake packet sources increases. Research proves that a protocol with many fake packet sources achieves SLP reliability for a mission duration of 940 rounds, while a protocol with no fake packet sources achieves SLP reliability for 1658 rounds.

A Link’s Fineness Based on Spatial Relation for Multipoint Relays Selection Algorithm in Mobile Ad Hoc Networks Algorithm

The propagation of wireless mobile equipment has given rise to mobile ad hoc networks (MANET), which allow the establishment of networks deprived of infrastructure support. The improvements in these technologies have also resulted in the variety of link quality, scalability, stability and reachability limits. Principally, due to their incapability to guarantee node reliability, MANETs are weak in highly dynamic environment. Since high-speed nodes, low density, and rapid changes in the topology are directly damage networks performances and lifetime. In response, routing is an inspiring issue in these networks. This paper proposed a new routing scheme in MANETS. This technique is integrated in the optimized link state routing (OLSR). In the projected scheme, the author defines a link fineness based on spatial relation between mobile nodes, which exploits different parameters, speed, direction and acceleration, including the sent and received of hello packets. Correspondingly, the proposed selected multipoint relays (MPRs) based the projected methods is included in the algorithm. The scheme elected more stable MPRs with high link’s quality, more neighborhood degree and higher willingness as MPR that can increase the reachability and stability of the network even in an extremely dynamic situation. This version of OLSR is verified by the network simulator (NS3) under the Radom Waypoint mobility model. Results revealed an important improvement for SLF_OLSR. In addition, this scheme can be integrated into other protocols in MANETs.