Reliable Routing Research Articles

Energy aware and secure routing for hierarchical cluster through trust evaluation

With the rapid growth of technology advancement in wireless communication have resulted in interconnection of large number of devices covering wide range of application services. Wireless sensor network (WSN) have become popular and provide various benefits, including low-power smart computation, compact size, self organizing and ability to route via routing protocols. WSN covers a wide range of application services, including home automation, smart cities, fire detection, unmanned aerial vehicle (UAV), and object tacking. Sensor nodes are tiny, battery operated, and suffer from fundamental restrictions such as available bandwidth, memory space, power restrictions, and limited resources. Due to their communication characteristics and working environment, WSN faces energy optimization and secure data communication challenges to increase network lifetime and defend against malicious nodes. To overcome energy scarcity and consumption issues, clustering methods have proven smart and effective approaches to reduce energy consumption and extend network life. Low energy adaptive clustering hierarchy (LEACH), well-known clustering method, enhances energy efficiency by selecting cluster head (CH) on a rotation basis by employing random integer technique. To extend network stability period, extensions of LEACH was proposed. LEACH and its extensions mainly focus on optimal selection of CH and fail to concentrate on secure communication. Integration of blockchain and real-time message content validation (RMVC) in LEACH was modelled to detect malicious node through secure hash algorithm. However, existing scheme had complex cryptographic functions and higher computation overhead. In this paper, we present energy aware and secure routing (EASR) for hierarchical cluster based WSN. Existing LEACH is modified and an improved threshold value is adapted to select secure optimal CH and trust based routing model which combines direct and indirect trust opinion values to resist routing attacks for establishing trustworthy and reliable routes towards base station.

Longevity-Oriented and Reliable Forwarding Percolation Routing in Underwater Acoustic Sensor Networks

Longevity-Oriented and Reliable Forwarding Percolation Routing in Underwater Acoustic Sensor Networks

Strategies to improve the isochrone algorithm for ship voyage optimisation

ABSTRACT An optimisation algorithm is an essential part of voyage planning systems to achieve autonomous and intelligent operations. Various algorithms have been proposed for voyage planning to minimise fuel consumption and increase punctuality. Among them, the isochrone method has been recognised for its robustness and efficiency in voyage optimizations. This paper improves it to overcome its incompetence in multi-objective optimisation and reliable route convergence towards the destination. Five improved methods are proposed and compared, to investigate the most effective enhancing strategy to achieve robustness and practicality in real-time application. By changing search space after the middle stage of the voyage, and formulating an augmented cost function to refine search criteria according to different optimisation objectives, one improved isochrone method (named Isochrone-A*) shows more competitive capability, with potential in real-time implementations. The effectiveness and efficiency of these five improved strategies are compared using four ocean-crossing voyages collected by a chemical tanker.

An optimized crypto‐based routing protocol for secure routing in wireless sensor networks

SummaryIn Wireless Sensor Networks (WSN), energy‐efficient, reliable routing is the core objective for the data transmission process. Anomaly nodes in the communication environment can affect reliable routing and network efficiency. Therefore, the present research created a novel Arithmetic Optimization‐based Rumor Routing Protocol with SKINNY Crypto mechanism (AORRP‐SCrypt) for secure Routing in WSN. Primarily, the required nodes are deployed in the related WSN environment. The arithmetic function traced the nodes' higher energy consumption and malicious action to avoid security vulnerabilities during data transmission. The sensed data packets are transmitted based on the Rumor routing function of the proposed system. Furthermore, to avoid data access by unauthorized users, it is encrypted using the SKINNY Crypto mechanism. Finally, the network efficiency and security performance are validated in the NS3 platform. The model gained 98.1% network throughput, 95.6% PDR, a lower energy consumption of between 0.001 and 0.01 J, and a 99% confidentiality rate. The developed system provided increased network security and efficiency.

Efficient energy consumption and fault tolerant method for clustering and reliable routing in wireless sensor network

Efficient energy consumption and fault tolerant method for clustering and reliable routing in wireless sensor network

Reliable paths prediction with intelligent data plane monitoring enabled reinforcement learning in SD-IoT

Legacy routing with conventional distributed networking in the Internet-of-Things (IoT) networks needs to work on efficiently handling service requests, leading to compromised Quality of Service (QoS) for user demands. In this research, we introduce Reliable routing based on Reinforcement learning in SD-IoT Networks (RRSN), an intelligent routing technique leveraging Software-Defined Networking (SDN) in IoT networks. RRSN utilizes a hybrid mechanism that combines Machine Learning (ML) and Reinforcement Learning (RL) within SDN’s centralized control to optimize QoS-aware reliable routing. In addition, network telemetry is utilized for intelligent network monitoring in the data plane to get the underlying network’s status efficiently. The proposed approach consists of three main modules: a Support Vector Regression (SVR) machine learning algorithm to predict link reliability, a traffic classifier for QoS-based traffic classification, and a reliability-aware reinforcement learning algorithm to compute optimal routing paths for IoT sensors. SVR algorithm has a high prediction accuracy, is lightweight, less susceptible to noisy data, and its computational complexity is independent of the dimensions of the input space. RRSN leverages intelligent network monitoring with RL, global view, and centralized SDN control to compute and implement optimal routes in data plane switches efficiently. We conduct extensive Proof-Of-Concept (POC) experimental evaluations, comparing RRSN with state-of-the-art models, including POC experiments in real Internet topologies. The results demonstrate that RRSN outperforms existing schemes in network performance metrics such as throughput, jitter, packet loss ratio, and delay, showcasing its effectiveness in improved QoS for IoT networks.

IDR: Intent‐driven routing strategy for reliable and cost‐effective multicasting in airborne tactical networks

AbstractTraditional multicast routing strategies can hardly meet the demanding requirements of multicasting mission assignment messages in airborne tactical networks. This letter proposes an intent‐driven routing strategy, termed as IDR, to realize reliable and efficient multicast. IDR exploits bit index explicit replication technique to encode not only the destination information but also the retransmission information into packet headers. If part of the multicast members failed to receive the packet, IDR can perceive the error and retransmit the packet to the precise set of nodes. Compared to the state‐of‐the‐art reliable multicast routing methods, IDR can enhance multicast retransmission success rate by about 10% or more and reduce retransmission overhead by around 50%.

Ethosomes: Novel Vesicular Carriers for Effective Transdermal Delivery of Natural Therapeutics

Abstract: The topical delivery, being the most reliable route for drug administration, offers multiple advantages. The conventional topical dosage forms deliver a relatively higher amount of drug to achieve therapeutic action triggering hypersensitivity reactions accompanied by greasiness and staining issues. The advent of transdermal nanocarriers has waived off some of these limitations and assisted in achieving enhanced therapeutic efficacy with sustained release and minimal/no instances of systemic toxicity. The ethosome, one of the novel lipid carrier systems, has eased the administration of many hydrophilic and lipophilic drugs through the stratum corneum. It is a non-invasive drug carrier consisting of 45% ethanol, phospholipids, and non-ionic surfactants. The discovery of ethosomal technology has proved to be fruitful in delivering drugs with a wide range of polarity and other physicochemical parameters across skin. In this review, the ethosomal system has been explored for the delivery of complex phytoconstituents across the skin along with the key building material of ethosomes, associated mechanism of drug delivery, recent developments in ethosomes as a drug carrier, reported ethosomal formulations encapsulating various plant metabolites, conducted clinical trials and available ethosomal formulations for the delivery of phytocomponents across stratum corneum.

Recourse strategy for the routing problem of mobile parcel lockers with time windows under uncertain demands

It is expected that mobile parcel lockers (MPLs) will provide more responsive and flexible service to the increasing variation of demand than fixed parcel lockers. However, the service provided by MPLs following planned routes can be degraded and even fail due to the demand uncertainty, such as demand volume and pickup time. In this study, we propose an optimization approach integrated with the recourse strategies to overcome this barrier. Three types of uncertain demands and two types of service failures are addressed in this study, with consideration of uncertain demands and time window constraint. To optimally determine the a priori routes for a set of MPLs, we propose a tailored tabu search algorithm by taking into account the recourse cost. A series of numerical experiments were conducted to examine the effectiveness of the proposed approach in enhancing route reliability with a comparison to ordinary express vehicles. The results reveal that the service reliability can be significantly improved with only a marginal increase in the operational cost by the proposed approach.

Designing a Fuzzy Mathematical Model for a Two-Echelon Allocation-Routing Problem by Applying Route Conditions: A New Interactive Fuzzy Approach

In vehicle routing problems (VRP), the optimal allocation of transportation by considering factors such as route hardness, driver experience and vehicle worn-out has a significant effect on costs reduction and approaching real-world conditions. In this paper, a novel fuzzy mixed integer non-linear mathematical model to address the two-echelon allocation-routing problem under uncertainty is proposed by applying route and fleet conditions. The cost of allocating drivers to diverse vehicles is computed at the first echelon of the problem, considering factors such as vehicle type, vehicle wear-out, and driver experience. Additionally, different routes are defused with varying levels of hardness. The goal of the second echelon of the model is to improve reliability by defining the reliability of routes within each section. To solve the model, the Torabi and Hessini (TH), the Selimi and Ozkarahan (SO) methods, and a newly proposed approach (PIA) were utilized to transform the multi-objective model into a single-objective one. Numerical tests and performance indicators were used to validate the effectiveness of both the multi-objective mathematical model and the proposed solution method. The validation computation results indicate that the proposed solution approach outperforms both the TH and SO approaches.

Assessment of stress levels based on biosignal during the simulator-based maritime navigation training and its impact on sailing route reliability

Maritime training can improve safety by equipping seafarers with the knowledge and skills to manage risk. However, designing a quality training program can be challenging and stress can negatively impact performance and safety. To address this, the present study aims to investigate the relationship between stress and training outcomes, with the goal of developing more effective stress-based training systems. Two stressful scenarios were designed with varying safety factors involved during navigation tasks. The study examines the impact of stress levels on training outcomes and performance based on safety factors and the correlation between self-assessed stress levels and objective stress levels obtained from biosignal data. The study was conducted in a simulated bridge environment in Tromsø, Norway, and analyzed using statistical tests and machine learning models. The findings of this study indicate that training scenarios can be classified by stress levels, which were found to be associated with reduced visibility, equipment failures, and severe weather conditions. Additionally, the study revealed that stress levels can negatively impact performance in maritime navigation and sailing route reliability. These findings provide insights into how to improve the quality and effectiveness of maritime training programs and ultimately enhance safety at sea.

Versatile Nanoparticulate Systems as a Prosperous Platform for Targeted Nose-Brain Drug Delivery.

The intranasal route has proven to be a reliable and promising route for delivering therapeutics to the central nervous system (CNS), averting the blood-brain barrier (BBB) and avoiding extensive first-pass metabolism of some drugs, with minimal systemic exposure. This is considered to be the main problem associated with other routes of drug delivery such as oral, parenteral, and transdermal, among other administration methods. The intranasal route maximizes drug bioavailability, particularly those susceptible to enzymatic degradation such as peptides and proteins. This review will stipulate an overview of the intranasal route as a channel for drug delivery, including its benefits and drawbacks, as well as different mechanisms of CNS drug targeting using nanoparticulate drug delivery systems devices; it also focuses on pharmaceutical dosage forms such as drops, sprays, or gels via the nasal route comprising different polymers, absorption promoters, CNS ligands, and permeation enhancers.

Reliable Routing Strategies on Urban Transportation Networks

The problem of finding the most reliable routing strategy on urban transportation networks refers to determining the time-adaptive routing policy that maximizes the probability of on-time arrival at a destination given an arrival time threshold. The problem is defined on a stochastic and time-dependent network that captures real-world transportation systems’ inherent uncertainty and dynamism. To solve this problem, we present a dynamic programming–based algorithm that benefits from a node-time pairs queue implementation. In addition to improving the computational running time in most cases, this implementation supports different queue disciplines, leading to different algorithmic approaches: label-correcting and label-setting methods. We prove the correctness of the algorithm and derive its worst case time complexity. We present computational experiments over real-world, large-scale transportation networks with up to [Formula: see text] nodes, showing that the algorithm is a viable alternative to existing state-of-the-art methods. It can be four times faster for relatively tight arrival time thresholds and is competitive for looser ones. We also present experiments assessing the different queue disciplines used within the algorithm, the gains of the node–time pairs queue implementation, and comparing optimal strategies obtained from reliability and travel time objectives.

AFHO‐based link reliable routing and power‐assisted channel allocation protocol in cognitive radio networks for healthcare applications

AbstractIn the realm of cognitive networks, accommodating the increasing need for radio spectrum to facilitate both secondary and primary users stands as an intricate and demanding task. This study introduces a tailored framework for healthcare applications in Cognitive Radio (CR) networks for link reliable routing and power‐assisted channel allocation. Through the integration of Adaptive Fire Hawk Optimization for routing, the strategy addresses optimization complexities while prioritizing robust link quality via optimal hop selection for effective communication. A new routing optimization challenge is formulated, aiming to establish reliable communication pathways and extend link reliability. Moreover, the framework incorporates power‐assisted channel allocation to strategically enhance resource management and overall network performance. The rigorous evaluation demonstrates significant enhancements across key performance metrics, which showcase percentage improvements of 9.86%, 14.56%, 3.85%, 5.06%, 6.94%, and 17.80% for active nodes, delay, Packet Delivery Ratio (PDR), residual energy, and throughput, respectively. These advancements underscore the proposed approach's efficacy in enhancing CR network performance, particularly for healthcare applications.

Link prediction in multilayer networks using weighted reliable local random walk algorithm

The expansion of online social networks has led to attracting a significant number of users. Meanwhile, the analysis of these networks has raised new perspectives and challenges due to the large amount of information. One of these challenges in the analysis of social networks is the problem of link prediction. The purpose of this problem is to find links that have not yet been observed, but may exist in the future. There are many solutions for link prediction on monoplex networks. However, many real social networks model communication in multiple layers. In recent years, link prediction in multilayer social networks has attracted the attention of many researchers. A solution for multilayer networks involves taking into account the information of all layers to make predictions for a target layer. Among the link prediction techniques based on monoplex networks, local random walk is very popular. This paper presents an extended version of this technique for multilayer social networks considering reliable routes and weighted links. Our proposed algorithm as Weighted Reliable Local Random Walk (WRLRW) simultaneously uses interlayer and intralayer information to predict links in a target layer of a multilayer network. WRLRW adjusts link weights based on multimodal and topological features and forms reliable routes between users by multiplying link weights. We demonstrated the effectiveness of WRLRW to address the link prediction problem on real-world multilayer networks through numerical simulations. WRLRW has better performance compared to link prediction techniques based on monoplex networks as well as state-of-the-art methods based on multilayer networks. According to the results of the experiments, WRLRW obtains 3.2% and 2.5% better average f-measure compared to SEM-Path (Structural, Ego-Paths and Meta-Paths features) and MLRW (Multiplex Local Random Walk), respectively.

A Survey on Routing Solutions for Low-Power and Lossy Networks: Toward a Reliable Path-Finding Approach

Low-Power and Lossy Networks (LLNs) have grown rapidly in recent years owing to the increased adoption of Internet of Things (IoT) and Machine-to-Machine (M2M) applications across various industries, including smart homes, industrial automation, healthcare, and smart cities. Owing to the characteristics of LLNs, such as Lossy channels and limited power, generic routing solutions designed for non-LLNs may not be adequate in terms of delivery reliability and routing efficiency. Consequently, a routing protocol for LLNs (RPL) was designed. Several RPL objective functions have been proposed to enhance the routing reliability in LLNs. This paper analyses these solutions against performance and security requirements to identify their limitations. Firstly, it discusses the characteristics and security issues of LLN and their impact on packet delivery reliability and routing efficiency. Secondly, it provides a comprehensive analysis of routing solutions and identifies existing limitations. Thirdly, based on these limitations, this paper highlights the need for a reliable and efficient path-finding solution for LLNs.

High-performance lithium storage in Li2SnO3-graphite composite enabled by Ag3Sn nanoparticles and oxygen vacancies

With high capacity (653 mAh g−1) and low voltage (0.6 V), Li2SnO3 is considered as a prospective anode material for Li-ion batteries (LIBs). Nevertheless, its practical uses are restricted by inferior electrochemical kinetics and fast capacity decay. Herein, a novel ternary composite of Ag3Sn alloy-lithium stannate-graphite (denoted as Ag3Sn-Li2SnO3-x-G) is designed and fabricated by ball milling and vacuum annealing. In this ternary composite, oxygen vacancy-containing Li2SnO3 and Ag3Sn alloy nanoparticles are homogeneously dispersed in the exfoliated graphite nanosheets. The introduction of Ag3Sn nanoparticles and oxygen vacancies into Li2SnO3-graphite can significantly enhance charge transport kinetics and structure stability of composite during the lithiation and delithiation processes. Benefiting from these merits, the Ag3Sn-Li2SnO3-x-G composite presents excellent cycling stability (782.4 mAh g−1 at 0.2 A g−1 after 200 cycles) and impressive rate capability (251.5 mAh g−1 at 2 A g−1). Broadly, this design strategy can provide an effective and reliable route for constructing advanced metal oxide-based anode materials for high-energy LIBs.

Retracted: Enhancing Vehicular Ad Hoc Networks’ Dynamic Behavior by Integrating Game Theory and Machine Learning Techniques for Reliable and Stable Routing

Retracted: Enhancing Vehicular Ad Hoc Networks’ Dynamic Behavior by Integrating Game Theory and Machine Learning Techniques for Reliable and Stable Routing

Deep neural network with fuzzy algorithm to improve power and traffic-aware reliable reactive routing

<span>In wireless networks, link breaks, and restricted resources create fundamental challenges for maintaining network applications. Several wireless network routing techniques concentrate on power efficiency to expand the network lifetime, but the traffic and reliability parameters are not the primary concern. Though, these techniques are not capable of dealing with the wireless network. Hence, this paper proposes deep neural network (DNN) with a fuzzy algorithm to improve power and traffic-aware reliable reactive routing (PTAR) in wireless networks. The wireless network is formed by clustering by the node power and selects the cluster head (CH) based on a fuzzy algorithm. The wireless node power level, node buffer space, and node reliability to consider the input parameters of the fuzzy system. Then thefuzzy algorithm gives the output for CH round length. This selected CH improves the node reliability, power efficiency with minimized network congestion. Then we use a DNN algorithm to choose an optimal relay by applying an adaptive load balance factor in the network. DNN is a machine learning algorithm, and it provides high accuracy. From the simulation results, the PTAR approach improves the network performance, such as packet received ratio, delay, residual energy, and routing overhead.</span>

Secure and Reliable Routing in the Internet of Vehicles Network: AODV-RL with BHA Attack Defense

Secure and Reliable Routing in the Internet of Vehicles Network: AODV-RL with BHA Attack Defense