Routing Research Articles

A real‐life study on the value of integrated optimization in order picking operations under dynamic order arrivals

AbstractOptimizing the order picking operations is indispensable for warehouses that promise a high customer service level. While many areas for improvement have been identified and studied in the literature, a large gap remains between academia and practice. To help with closing this gap, we perform a case‐study in collaboration with a spare‐parts warehouse in Belgium. In this study, we optimize the order picking operations of the company, using the actual warehouse layout and real order data. A state‐of‐the‐art online integrated order batching, picker routing and batch scheduling algorithm is adapted to consider multiple real‐life constraints. More specifically, the dynamic arrival of new orders is considered, and a capacity constraint on the sorting installation should be respected. Furthermore, a new waiting strategy is studied in which order pickers can temporarily postpone certain orders, as combining them with possible future order arrivals may allow for more efficient overall picking performance. Finally, the performance of the current operating policy is compared with that of both a seed batching heuristic and our metaheuristic algorithm by use of an ANOVA analysis. The results indicate that the number of order pickers can be reduced by 12.5% if the new optimization algorithm is used, accompanied by an improvement in the offered customer service level.

Approximation Algorithms for the Two-Watchman Route in a Simple Polygon

The two-watchman route problem is that of computing a pair of closed tours in an environment so that the two tours together see the whole environment and some length measure on the two tours is minimized. Two standard measures are: the minmax measure, where we want the tours where the longest of them has smallest length, and the minsum measure, where we want the tours for which the sum of their lengths is the smallest. It is known that computing a minmax two-watchman route is NP-hard for simple rectilinear polygons and thus also for simple polygons. Also, any c-approximation algorithm for the minmax two-watchman route is automatically a 2c-approximation algorithm for the minsum two-watchman route. We exhibit two constant factor approximation algorithms for computing minmax two-watchman routes in simple polygons with approximation factors 5.969 and 11.939, having running times O(n8)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^8)$$\\end{document} and O(n4)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^4)$$\\end{document} respectively, where n is the number of vertices of the polygon. We also use the same techniques to obtain a 6.922-approximation for the fixed two-watchman route problem running in O(n2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2)$$\\end{document} time, i.e., when two starting points of the two tours are given as input.

An energy‐efficient method using PSO clustering and fuzzy routing to reduce energy consumption in IoT

SummaryIn “Internet of Things” (IoT) technology, all objects can connect to the Internet with a unique Internet address. The sensors have limited energy resources due to their use of batteries in supplying energy, and since battery replacement in these sensors is not usually feasible, the longevity of wireless sensor networks is limited. Therefore, reducing the energy consumption of the used sensors in IoT networks to increase the network lifetime is one of the crucial challenges and parameters in such networks. These networks are subject to a variety of errors. Errors in wireless communication channels, network dynamics due to the mobility of nodes, and the potential for mistakes in mobile nodes are among the causes of errors in these networks. Despite the above failures, the development of routing algorithms in IoT‐based networks has become a significant challenge for researchers, so it is essential to provide routing algorithms to increase network lifespan and reduce energy consumption. In this study, a two‐dimensional network‐based architecture for the Internet of Things is presented. The proposed algorithm first organizes the nodes into logical cells and selects nodes in each cell as the cluster head. The cluster head is selected based on the binary Particle Swarm Optimization algorithm. Then, each node sends its collected data to the cluster head. The cluster heads first aggregate the received data and then use a fuzzy method to send the data to the base station. The proposed algorithm uses the remaining energy level parameter, cluster density, and location of each cell using fuzzy system capabilities and the particle swarm algorithm to try to route data with less energy consumption and increase network lifespan. The proposed method is simulated in ns2 software, and for a more accurate evaluation, we compare it with LEACH, Ant‐C, PSO, EABC, and SOA‐CH algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan.

Wireless optimization for sensor networks using IoT-based clustering and routing algorithms.

Wireless sensor networks (WSN) are among the most prominent current technologies. Its popularity has skyrocketed because of its capacity to operate in difficult situations. The WSN market encompasses various industries, including building automation, security networks, healthcare systems, logistics, and military operations. Therefore, increasing the energy efficiency of these networks is of utmost importance. Hierarchical topology, which typically uses a clustering methodology, is one of the most well-known methods for WSN energy optimization. To achieve energy efficiency in WSN, hierarchical topology low-energy adaptive clustering hierarchy (LEACH) was first introduced, and this served as the foundation. However, conventional LEACH has several limitations, which have led to extensive research into improving LEACH's efficacy in its current form. The use of particular algorithms and strategies to enhance the functionality of the conventional LEACH protocol forms the basis of ongoing efforts. Utilizing this enhanced LEACH, performance in terms of throughput and network life may be enhanced by concentrating on elements such as cluster head formation and transmission energy consumption. The enhanced LEACH algorithm demonstrates significant improvements in both throughput and network lifetime compared with conventional LEACH. Through rigorous experimentation, it was found that the enhanced algorithm increases the throughput by 25% on average, which is attributed to its dynamic clustering and optimized routing strategies. Furthermore, the network lifetime is extended by approximately 30%, primarily because of enhanced energy efficiency through adaptive clustering and transmission power control.

Development of Models for Ticketing in Public Transportation System in Nigeria

Transportation is a system that humans use to carrying out their day-to-day activities. In the bid to do activities, transportation by any means has to be used. In the public transportation system, they use the manual or traditional method to process ticketing. This causes many flaws such as long queues, communication gap, miscalculation of tickets, improper records of commuters’ information etc. For commuters to gain a convenient platform for bus reservations, real-time information updates, and a smoother experience; and for the companies to benefit from automated reservations, improved communication, and data-driven insights for better decision- making, there is need to develop a model for ticketing in public transportation system. The models for ticketing in urban transportation system have methods that involve pricing algorithm and route algorithm where scaling factors and user capacity planning models are calculated. With the introduction of the ticketing model in transportation system, existing challenges in urban mobility are revolutionized. The intersection of technology and transportation are positioned in such a way that the model seeks to enhance the overall commuter experience seamlessly by integrating reservation processes, real-time updates, amount payable, time and date of travels and communication between branches. The study envisions a shift towards a more accessible, efficient, and user-centred urban transportation system. It represents a transformative catalyst in transportation system especially by land/roads which are being used by automobiles (buses, cars, tricycle etc).

IMPLEMENTATION OF ENERGY EFFICIENT SECURITY MECHANISMS FOR WIRELESS SENSOR NETWORKS

Designing energy-efficient security mechanisms is critical for WSNs due to their limited power resources. Techniques like low-power cryptography, duty cycling, and energy-aware security protocols can help in achieving security without significantly impacting the energy consumption of sensor nodes. In this paper, we propose an energy aware geographical multipath routing scheme for WSNs. The distance to the destination location, remaining battery capacity, and queue size of candidate sensor nodes in the local communication range are taken into consideration for next hop relay node selection, and Analytical Hierarchy Process (AHP) and Geographical Routing Algorithm (GRA) are applied for decision making. Simulation results show that these schemes can extend the network lifetime longer than the original geographical routing scheme which only considers distance to the destination location. KEYWORDS: WSN, linear discriminate packet flow analysis system, optimized rout path switch, Analytical Hierarchy Process (AHP) and Geographical Routing Algorithms(GRA).

MFAE: Multimodal Fusion and Alignment for Entity-level Disinformation Detection

Nowadays, the dissemination of disinformation on social media has evolved from a purely textual form to multiple modalities consisting of both text and images. This further amplifies the misleading and deceptive nature of disinformation. Overcoming the misleading and confusing noise to achieve accurate disinformation detection presents a significant challenge. To address this challenge, we propose a method named Multimodal Fusion and Alignment for Entity-level Disinformation Detection (MFAE). MFAE first uses an improved dynamic routing algorithm to extract more comprehensive semantic visual entity features. Then, a graph matching network is used to capture the correspondences between entities within modalities. The experiment shows that MFAE is capable of capturing textual and visual semantic information more comprehensively. On the TWITTER and WEIBO datasets, MFAE achieves accuracy improvements of approximately 2.0% and 7.5%, respectively, compared to the state-of-the-art methods, resulting in accuracy of 89.5% and 96.7%.

Implementation and testing of a KNS topology in an InfiniBand cluster

The InfiniBand (IB) interconnection technology is widely used in the networks of modern supercomputers and data centers. Among other advantages, the IB-based network devices allow for building multiple network topologies, and the IB control software (subnet manager) supports several routing engines suitable for the most common topologies. However, the implementation of some novel topologies in IB-based networks may be difficult if suitable routing algorithms are not supported, or if the IB switch or NIC architectures are not directly applicable for that topology. This work describes the implementation of the network topology known as KNS in a real HPC cluster using an IB network. As far as we know, this is the first implementation of this topology in an IB-based system. In more detail, we have implemented the KNS routing algorithm in the OpenSM software distribution of the subnet manager, and we have adapted the available IB-based switches to the particular structure of this topology. We have evaluated the correctness of our implementation through experiments in the real cluster, using well-known benchmarks. The obtained results, which match the expected performance for the KNS topology, show that this topology can be implemented in IB-based clusters as an alternative to other interconnection patterns.

Optimizing Spatial Channel Networks (SCNs) in Hierarchical Optical Cross-Connect (HOXC) architectures: Impact of wavelength switching granularity on performance

The increasing demand for network bandwidth highlights the critical need to enhance optical transmission systems. Utilizing the entire C-band for a single optical channel(OCh) eliminates the requirement for wavelength switching, prompting the emergence of spatial channel networks (SCNs). SCNs transform the optical layer into hierarchical spatial division multiplexing (SDM) and wavelength division multiplexing (WDM) layers through the implementation of hierarchical optical cross-connects (HOXCs). A HOXC comprises a spatial channel cross-connect (SXC) for spatial bypass switching and multiple wavelength cross-connects (WXCs) for wavelength channel switching, ensuring efficient optical transmission. Therefore, the design of the HOXC plays a pivotal role in determining both the device cost and network performance in SCNs. This study investigates the impact of wavelength switching granularity on core-selective switch (CSS)-based HOXC architectures. We propose an adaptive dynamic Routing, Spatial Channel, and Spectrum Assignment (RSCSA) algorithm to solve routing and resource allocation problems in SCNs. By examining SCN designs with varying wavelength switching granularities, we assess the overall network deployment cost, considering both network throughput and spectrum resource utilization. Our findings indicate that adjusting the granularity of wavelength switching in SCNs can significantly affect device costs and performance, highlighting the importance of identifying an optimal SCN design that strikes a balance between these factors. These insights offer valuable guidance for the practical planning and management of future SCN deployments.

A multi-scale spatial–temporal capsule network based on sequence encoding for bearing fault diagnosis

The Capsule Network (CapsNet) has been shown to have significant advantages in improving the accuracy of bearing fault identification. Nevertheless, the CapsNet faces challenges in identifying the type of bearing fault under nonstationary and noisy conditions. These challenges arise from the distinctive nature of its dynamic routing algorithm and the use of fixed single-scale kernels. To address these challenges, a multi-scale spatial–temporal capsule network (MSCN) based on sequence encoding is proposed for bearing fault identification under nonstationary and noisy environments. A spatial–temporal sequence encoding module focuses on feature correlations at various times and positions. Dilated convolution-based multiscale capsule layer (MCaps) is designed to capture spatial–temporal features at different scales. MCaps establishes connections between various layers, enhancing the comprehension and interpretation of spatial–temporal features. Furthermore, the Bhattacharyya coefficient is introduced into the dynamic routing to compare the similarity between capsules. The validity of the model is verified through comparative experiments, and the results show that MSCN has significant advantages over traditional methods.

A comparative study of energy routing algorithms to optimize energy transmission in energy internet

A comparative study of energy routing algorithms to optimize energy transmission in energy internet

A distance adaptive dual-hop routing algorithm for underwater optical wireless networks

Three main kinds of underwater wireless communication, which employ acoustic waves, radio frequency and optical waves, have attracted intensive research interests in recently years. Among them, the underwater optical wireless communication (UOWC) is characterized by high propagation speed and large transmission bandwidth. But, the optical waves in underwater environment are significantly affected by absorption and scattering effects, which limit their transmission range. In order to enhance the performance of UOWC, designing a transmission and energy efficiency routing algorithm has become a non-ignorable issue in UOWC. In this paper, a transmission distance adaptive dual-hop (TDAD) routing algorithm is proposed for underwater optical wireless networks (UOWNs) to improve their efficiency in packet-delivery and energy-consumption. Unlike the existing routing algorithms designed for UOWNs, which pre-set the transmission range of network nodes, the proposed TDAD algorithm adaptively selects the transmission range for each node according to the diversity of heterogeneous service requests and employs location and energy information in its dual-hop based routing procedure. Simulation results indicate that the proposed TDAD algorithm remarkably improves packet delivery rate with more balanced energy consumption when compared to the deviation angle-based single-hop (DAS) algorithm and the distributed sector-based (DS) routing algorithm.

A SDN improvement scheme for multi‐path QUIC transmission in satellite networks

AbstractIn recent years, with the development of low‐earth orbit broadband satellites, the combination of multi‐path transmission and software‐defined networking (SDN) for satellite networks has seen rapid advancement. The integration of SDN and multi‐path transmission contributes to improving the efficiency of transmission and reducing network congestion. However, the current SDN controllers do not support the multi‐path QUIC protocol (MPQUIC), and the routing algorithm used in current satellite networks based on minimum hop count struggles to meet the real‐time requirements for some applications. Therefore, this paper designs and implements an SDN controller that supports the MPQUIC protocol and proposes a multi‐objective optimization‐based routing algorithm. This algorithm selects paths with lower propagation delays and higher available bandwidth for subflow transmission to improve transmission throughput. Considering the high‐speed mobility of satellite nodes and frequent link switching, this paper also designs a flow table update algorithm based on the predictability of satellite network topology. It enables proactive rerouting upon link switching, ensuring stable transmission. The performance of the proposed solution is evaluated through satellite network simulation environments. The experimental results highlight that SDN‐MPQUIC significantly improves performance metrics: it reduces average completion time by 37.3% to 59.3% compared to QSMPS and by 52.8% to 72.4% compared to Disjoint for files with different sizes. Additionally, SDN‐MPQUIC achieves an average throughput improvement of 81.4% compared to QSMPS and 147.8% compared to Disjoint, while demonstrating a 26.3% lower retransmission rate than QSMPS.

DRLAR: A deep reinforcement learning-based adaptive routing framework for network-on-chips

Adaptive routing plays a pivotal role in the overall performance of Network-on-Chips (NoCs). However, with many-core architectures supporting complex and constantly changing traffic patterns for emerging applications, this aspect presents significant challenges. Our meticulous examination and analysis of existing heuristic adaptive routing algorithms revealed three key limiting factors: single network status metric, lack of system feedback awareness, and lack of ability to customize. Reinforcement Learning (RL) methods have demonstrated promising opportunities for exploring adaptive routing design. Deep reinforcement learning (DRL) techniques, in particular, enable efficient exploration in adaptive routing design spaces where heuristic strategies may be inadequate. This paper proposes a novel deep reinforcement learning framework for adaptive routing called DRLAR that is suitable for diversified traffic patterns and resolves multi-objective optimization simultaneously. DRLAR formulates routing as an agent, which makes routing decisions based on autonomous learning using multiple network state features and system-level feedback information. We conduct extensive experiments against state-of-the-art routing algorithms to evaluate our design. The results show that DRLAR reduces packet latency by an average of 33.3%, achieving a reduction of 41.6% and 10.5% on average under heavy synthetic traffic and the PARSEC 2.1 benchmark, respectively. We also perform a cost analysis to validate the potential implementation of DRLAR on NoCs with low computational, storage, and power.

THE ENVIROMENTAL AND SOCIAL IMPACT OF CARPOOLING

Abstract: Car pooling System is an automated system which reduces the misery of travelers and makes them find cars in short period of time. Car pooling is an application of finding car in which drivers who are traveling to work alone can ask for fellow passengers through our application. For those who use public-transport system to go to work daily can use this application to find drivers who are traveling to the same destination in a short path. It provides with a simple riding platform between the car owner and car user. This project enables users to access mobility assets own by others exactly when they need. I t shows a medium for available cars to pick up them on the interest of car owner with time and capacity. This project aims at creation of a Car-pooling System. This paper delves into the design and implementation of a novel carpooling project aimed at addressing the challenges of urban transportation sustainability. With burgeoning urban populations, congestion, and environmental concerns, the need for innovative solutions is paramount. Carpooling presents a viable avenue to alleviate these issues by fostering shared mobility, reducing traffic congestion, and cutting down carbon emissions. The project employs a multi-faceted approach, leveraging advancements in mobile technology, data analytics, and user incentives to optimize the carpooling experience. Key components include a user-friendly mobile application for seamless ride matching, dynamic routing algorithms to optimize trip efficiency, and incentive structures to encourage participation and retention. Through a combination of quantitative analysis and qualitative feedback, the efficacy of the carpooling project is evaluated. Metrics such as reduction in vehicle miles traveled, greenhouse gas emissions, and user satisfaction levels are assessed to gauge its impact on urban transportation sustainability. The findings of this research contribute to the growing body of knowledge on sustainable transportation solutions and provide insights for policymakers, urban planners, and transportation stakeholders seeking to promote carpooling initiatives. Ultimately, the project aims to foster a culture of shared mobility and pave the way for more efficient and sustainable urban transportation systems. IndexTerms – HTML, CSS, Javascript. Bootsrap, MySQL, Python.

Energy efficient reliable data transmission for optimizing IoT data transmission in smart city

The rapid proliferation of the internet of things (IoT) technology has significantly transformed urban landscapes, giving rise to smart city frameworks that leverage interconnected devices for enhanced efficiency and functionality. In these environments, vast amounts of data are generated by diverse sensors and devices, necessitating advanced strategies for effective data collection and transmission. This paper introduces a novel approach to address data collection and transmission challenges in IoT-enabled smart city frameworks. The proposed design integrates IoT-Cloud for efficient data collection and employs the energy efficient reliable data transmission (EERDT) model, optimizing IoT data transmission. The enhanced dragonfly routing algorithm, incorporating the firefly algorithm, enhances data routing efficiency. Experimental results demonstrate EERDT's superiority over energy-aware iot-routing (EAIR) and location-centric energy-harvesting aware-routing (LCEHAR), revealing significant improvements in communication overhead, data processing latency, and network lifetime. The EERDT exhibits substantial reductions in communication overhead, enhancing overall network performance. The EERDT model showcases lower data processing latency and energy consumption, highlighting its potential for resource-efficient IoT data transmission. This work contributes an innovative solution for smart city IoT networks, emphasizing performance enhancements and resource efficiency.

Network Coding with Parallel Path Protection for Multiple Link Failure in WOBAN

Background: WOBAN is a high-speed network and hence any kind of failure results in huge data loss. Using the proposed network coding technique with parallel path protection can handle multiple link failures, the performance of the network can be improved. Aims: This study aims to improve network performance using network coding with parallel path protection routing algorithm (NC-PPR) for multiple link failures in WOBAN. Objective: We investigated the multiple link failures in WOBAN by the proposed approach namely Coded Path Protection Algorithm, which enhances the survivability of the WOBAN against multiple link failures in the front end and eliminates the need of backup resources. Methods: Extensive simulation is carried out to implement proposed work. A simulation model and code is developed in MATLAB to get the performance enhancement of WOBAN. Results: We compared the performance of proposed algorithm with existing algorithm. The obtained results show that the proposed algorithm has superior performance than the existing algorithm. Conclusion: In this paper, a new routing approach, which works in three phases namely path finding, encoding, and decoding, using random linear network coding (RLNC) is introduced to address the survivability issue of the WOBAN. The proposed approach also enhances the network performance in terms of PDR, overhead, and delay.

RETRACTED: An improved hybrid network‐on‐chip with flexible topology and frugal routing

AbstractThis study proposes an improved Hybrid Network‐on‐Chip (HNoC) with a flexible topology and frugal routing. The key advantage is in terms of latency and saturation because of the optimal use of RF‐based wireless networks. The performance improvement is achieved by managing the communication resources through a radio bandwidth allocation algorithm and a frugal routing algorithm that optimizes RF consumption by choosing between wired and wireless networks. The architecture of a hybrid network‐on‐chip has been modeled in SystemC by extending IP from the SoCLib library and Noxim simulator. The distinction of the flexible topology is exhibited by an 11% reduced delay compared to the fixed topology and a 31% shorter delay compared to a wired mesh network for lower transaction rates. A 21% increase in saturation has also established the superiority of the frugal routing algorithm. The hybrid design can be useful in addressing the limitations of purely wireless design, and the frugal routing algorithm can be seen as a complex topology for increasing the size of on‐chip networks.

Improving Quality of Service (QoS) in Wireless Multimedia Sensor Networks using Epsilon Greedy Strategy

Abstract Wireless Multimedia Sensor Networks (WMSNs) are networks consisting of sensors that have limitations in terms of memory, computational power, bandwidth and battery life. Multimedia transmission using Wireless Sensor Network (WSN) is a difficult task because certain Quality of Service (QoS) guarantees are required. These guarantees include a large quantity of bandwidth, rigorous latency requirements, improved packet delivery and lower loss ratio. The main area of research would be to investigate the process of greedy techniques that could be modified to guarantee QoS provisioning for multimedia traffic in WSNs. This could include optimization of routing decisions, dynamic allocation of resources and effective congestion management. This study introduces a framework called Epsilon Greedy Strategy based Routing Protocol (EGS-RP) for multimedia content transmission over WSN. The framework focuses on energy efficiency and QoS by using reinforcement learning to optimize rewards. These incentives are determined by a number of variables, including node residual energy, communication energy and the effectiveness of sensor type-dependent data collection. Experimental analysis was conducted to evaluate the effectiveness of the proposed routing strategy and compare it with the performance of standard energy-aware routing algorithms. The proposed EGS-RP achieves a throughput of 217 kbps, a bandwidth of 985 bps, a packet delivery ratio of 94.45% and an energy consumption of 32%.

Fragmentation and ISRS-Aware Survivable Routing, Band, Modulation, and Spectrum Allocation Algorithm in Multi-Band Elastic Optical Networks

Fragmentation and ISRS-Aware Survivable Routing, Band, Modulation, and Spectrum Allocation Algorithm in Multi-Band Elastic Optical Networks