Routing Scheme Research Articles

Dodecagonal Parameter-Based Energy-Efficient Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are composed of several sensor nodes (SN) that are dispersed geographically and interact with one another over wireless media in order to track and log physical data from their environment. At the moment, WSNs frequently use routing and clustering strategies to extend the life of their networks. This paper proposes a DODECAGONAL parameter-based Energy-Efficient Routing in WSN (DOPE-WSN) for improvement of network lifetime and saving the energy consumption. Initially the cluster is selected using Agglomerative clustering. In the second phase, Pelican optimization (PELO) based Cluster head selection (CHs) meant for considering Congestion, Node Degree, Node Density, Network area, Network Coverage, Number of clusters, Number of nodes, Communication cost, Distance, Residual Energy, Distance to neighbor, Node Centrality. Moreover, the Sooty Tern Optimization (STO) model are utilized for the determination of the best routing path for the cluster heads. Taking into account node degree, residual energy, and distance, the STO maximizes network performance. The suggested approach has undergone thorough testing for ensuring network durability and energy efficiency. The proposed model achieved a maximum 97% Packet Delivery Ratio (PDR) with 900 nodes in comparison with 91%, 89%, 83%, and 82% for CRPSH, HQCA, EACRLEACH, and BWO-IACO algorithms respectively.

Energy-efficient hierarchical cluster-based routing strategies for Internet of Nano-Things: Algorithms design and experimental evaluations

Nanodevices (NDs), which are only a few nanometers (nm) in size, need to communicate with each other to perform complex operations. In nanonetworks, this communication typically involves multiple hops, requiring efficient routing protocols. Existing protocols are not well suited for nanonetworks due to their high resource consumption and setup overhead. In this paper, we propose three novel routing protocols for nanodevices. Non-Back Flooding Routing (NBFR) and Layer-Based Flooding Routing (LBFR) aim to reduce unnecessary packet transmission by utilizing distance and layer information based on received signal power. On the other hand, Tree-Based Forwarding Routing (TBFR) is a unicast-based approach that aims to transmit the packet to the destination using the shortest and most reliable path possible through a tree structure. The performance of these proposed methods is compared to well-known methods in terms of packet transmission, energy consumption, end-to-end delay, and setup overhead. TBFR achieved a packet transmission success of 92.95% in topology with the highest density of nanorouters (NRs), while it reached up to 99.57% for fewer nanorouters. Moreover, its end-to-end delay values are much lower than those of multi-path routing protocols. It also consumed one-fifth of the energy compared to its most challenging multi-path competitor, NBFR, regarding packet transmission success. However, for dense nanosensor (NS) topologies, NBFR and LBFR achieved higher packet transmission rates of 87.04% and 86.66%, respectively. Furthermore, in addition to achieving low end-to-end delays, the energy consumption of NBFR is very close to that of TBFR. In summary, the tests show that TBFR is more suitable for communication among nanorouters due to the requirement of building the tree structure, which results in a slightly higher setup overhead. In contrast, NBFR and LBFR are more suitable for communication between nanosensors because of their simplicity and low setup overhead. But, it should be noted that NBFR requires a larger header than the other alternatives.

Improved local routing strategy for enhancing the traffic capacity of scale-free networks

The study of network traffic capacity is of significant importance, and it has received increasing attention across various scientific disciplines. In this paper, the improved local routing (ILR) strategy is utilized to enhance network traffic capacity. In the context of current routing strategies and network structures, packets can be transmitted along either the most efficient paths or the shortest paths. By optimizing the combination of nodes' degree centrality and betweenness centrality to bypass hub nodes, the ILR strategy can reasonably distribute the traffic load. According to simulation results, the ILR strategy significantly enhances network traffic capacity and shortens average path length, surpassing the performance of the effective routing strategy (ERS). Furthermore, the findings indicate that networks employing the ILR strategy exhibit strong robustness against both random and deliberate attacks.

Enhancing Urban Safety: Optimal Patrol Route Strategies for Volunteer Security Squads Based on Integrated BIM-GIS Data

Enhancing Urban Safety: Optimal Patrol Route Strategies for Volunteer Security Squads Based on Integrated BIM-GIS Data

Solving the Robust Shortest Path Problem with Multimodal Transportation

This paper explores the challenges of finding robust shortest paths in multimodal transportation networks. With the increasing complexity and uncertainties in modern transportation systems, developing efficient and reliable routing strategies that can adapt to various disruptions and modal changes is essential. By incorporating practical constraints in parameter uncertainty, this paper establishes a robust shortest path mixed-integer programming model based on a multimodal transportation network under transportation time uncertainty. To solve robust shortest path problems with multimodal transportation, we propose a modified Dijkstra algorithm that integrates parameter uncertainty with multimodal transportation. The effectiveness of the proposed multimodal transportation shortest path algorithm is verified using empirical experiments on test sets of different scales and a comparison of the runtime using a commercial solver. The experimental results on the multimodal transportation networks demonstrate the effectiveness of our approach in providing robust and efficient routing solutions. The results demonstrate that the proposed method can generate optimal solutions to the robust shortest path problem in multimodal transportation under time uncertainty and has practical significance.

Evaluation of electric vehicle power routing and charging analysis using artificial neural network synchronized with 5G-network

To motivate the advanced National Clean Air Program (NCAP) strategy announced by the Indian Government in 2017, automizing the routing control on electric vehicle (EV) is a challenging area. According to the public authority guideline, the air contamination issues over the nation focus on accomplishing a 20% to 30% decrease in vehicular outflows in 2024 by enhancing EV mobility. However electric vehicle's limitations on battery capacity and route strategy to charging stations to preserve an adequate level of battery charge still need automized communication strategy. Fixed route solutions might not always be executable because battery consumption varies on several external factors. To better fit the real situation, this study proposes the notion of breaking down the self-adaptive online routing by encountering automatic path detection concerning battery efficiency with adaptive charging decisions. An inventory control-style charging technique is suggested to account for stochastic battery use. In the proposed work, by using an artificial neural network, the power demand and energy consumption of the EV world will be monitored and controlled by using 5G coverage on the smart city evolution. The obtained simulation analysis confirms that in comparison to current conventional and fixed tariff schemes, 5G accompanied EV accomplish an examined rate of SoC charge around 19.95 MW holding a peak range of 108.96 MW. Also, an evaluated typical power loss rate of 2.873 kWH which is less than 11.95% through frequency band analysis with signal-to-noise ratio and downlink power utilization.

On the aggregation of FIBs at ICN routers using routing strategy

Utilizing in-network caching is essential for the current communication network. In the last decade, ICN (Information-Centric Networking) has been under the spotlight as a network that mainly focuses on transmitted and received data rather than on hosts that transmit and receive data. In ICNs, to appropriately forward request packets, a router maintains a routing table called FIB (Forwarding Information Base). However, it is unsuitable for us to assume that FIB can store entries of all contents within a network. This is mainly because the FIB memory is not large enough to store the prefix of all contents. Thus, for realizing global-scale ICNs, it is crucial to develop an effective technique to reduce the size of FIB. In this paper, to tackle the reduction in the FIB size with the aggregation, we propose a routing strategy called constrained shortest-path tree (CSPT) routing. The fundamental idea of our CSPT routing is to combine shortest-paths on the network and that on a shortest-path tree of the network, which is intended to enhance the effect of FIB aggregation. Furthermore, we extensively investigate the relationship, i.e., trade-off, between the FIB aggregation and the communication performance of ICN using CSPT routing. Consequently, we reveal that our CSPT routing can dramatically reduce the number of FIB entries while suppressing the increase in the number of hops required to deliver request packets.

A novel fractional rat hawk optimization–enabled routing with deep learning–based energy prediction in wireless sensor networks

SummaryWireless sensor networks (WSNs) contain different sensors, which collect various data in the monitoring area. In general, one of the significant resources in WSNs is energy, which prolongs the network's lifetime. The energy‐efficient routing algorithms reduce energy consumption and enhance the survival cycle of WSNs. Thus, this work developed the optimization‐based WSN routing and deep learning (DL)–enabled energy prediction scheme for efficient routing in WSNs. Initially, the WSN simulation is carried out, and then, the node with minimum energy consumption is chosen as the cluster head (CH). Here, the proposed rat hawk optimization (RHO) algorithm is established for finding the best CH, and the RHO is the integration of rat swarm optimization (RSO) and fire hawk optimization (FHO). Furthermore, the routing is accomplished by the developed fractional rat hawk optimization (FRHO) using the fitness function includes delay, distance, link lifetime, and predicted energy of a network for predicting the finest route. Here, the fractional calculus (FC) is incorporated with the RHO to form the FRHO. The energy prediction is achieved by deep recurrent neural network (DRNN). The energy, delay, and throughput evaluation metrics are considered for revealing the efficiency of the proposed system, and the proposed system achieves the best results of 0.246 J, 0.190 s, and 67.13 Mbps, respectively.

Optimization for route selection under the integration of dispatching and control at the railway station: A 0‐1 programming model and a two‐stage solution algorithm

AbstractAt present, the mainstream studies on route selection optimization at the railway station rarely considered the overall punctuality of the operation plans and the seizing route resource between shunting operation and train running, which can endanger the running safety and reduce the efficiency at the station. Therefore, this paper proposes an optimization method for the route selection under the integration of dispatching and control at the railway station. Firstly, the station‐type data structure, the route occupation conflict, and the operation task order were defined. Then, a 0‐1 programming model was constructed to minimize the total delay time and shorten the total travel time of all operations. Finally, a two‐stage solution algorithm based on depth‐first search algorithm and genetic algorithm was designed, and two actual cases of a technical station in China were designed. The instance verification results show that the algorithm can find the satisfactory route scheme in 250 iterations; different delay factors and travel coefficients will get different route schemes, which can provide decision support for dispatchers and operators to select routes. Through comparative analysis of algorithms, it is found that the two‐stage algorithm has higher solving efficiency than the individual depth‐first search algorithm and individual genetic algorithm.

Collaborative last-hop scheduling strategy for large-scale LEO constellation routing

The transmission of data packets from satellites to Earth Stations (ESs) is the last hop of satellite network routing. As the final stage of packet transmission, the use of different scheduling strategies directly affects the throughput of the satellite network. Traditionally, researchers have attempted to enhance network performance by investigating inter-satellite routing protocols or inter-satellite data offloading strategies. However, these approaches have failed to address scheduling issues in the last hop of large-scale Low Earth Orbit (LEO) constellations. In this paper, we present the first Cooperative Last-Hop Scheduling (CLHS) strategy for routing in large-scale LEO constellations based on a bidirectional communication domain. In this strategy, we first utilize the bidirectional communication domain to determine the communication ranges of satellites and ESs. Subsequently, an information flow is established to interact with ESs and satellites. The ESs receive and reconstruct the Information Matrix (IM) from the information flow. Moreover, we propose the Maximum Decision Value Priority (MDVP) algorithm, which takes the reconstructed IM as input and computes the scheduling commands for satellites within the communication range of the ESs. To address the issue of multiple ESs simultaneously scheduling the same satellite, we introduce the Collision Avoidance Algorithm (CAA). Finally, to enhance the data packet transmission efficiency of the scheduled satellites, we propose the Weighted First-In-First-Out (WFIFO) algorithm, which is specifically designed for satellite packet dequeuing. We validate the CLHS through simulations on two satellite constellations: the first-generation Starlink constellation with 4409 satellites and the GW-2 constellation with 6912 satellites. The results show that CLHS can achieve better network throughput than traditional strategies. CLHS provides a novel method of scheduling the last hop in large-scale satellite constellations.

PtrNet-RSA: A Pointer Network-Based QoT-Aware Routing and Spectrum Assignment Scheme in Elastic Optical Networks

PtrNet-RSA: A Pointer Network-Based QoT-Aware Routing and Spectrum Assignment Scheme in Elastic Optical Networks

Fake Base Station Detection and Link Routing Defense

Fake base stations comprise a critical security issue in mobile networking. A fake base station exploits vulnerabilities in the broadcast message announcing a base station’s presence, which is called SIB1 in 4G LTE and 5G NR, to get user equipment to connect to the fake base station. Once connected, the fake base station can deprive the user of connectivity and access to the Internet/cloud. We discovered that a fake base station can disable the victim user equipment’s connectivity for an indefinite period of time, which we validated using our threat prototype against current 4G/5G practices. We designed and built a defense scheme which detects and blacklists a fake base station and then, informed by the detection, avoids it through link routing for connectivity availability. For detection and blacklisting, our scheme uses the real-time information of both the time duration and the number of request transmissions, the features of which are directly impacted by the fake base station’s threat and which have not been studied in previous research. Upon detection, our scheme takes an active measure called link routing, which is a novel concept in mobile/4G/5G networking, where the user equipment routes the connectivity request to another base station. To defend against a Sybil-capable fake base station, we use a history–reputation-based link routing scheme for routing and base station selection. We implemented both the base station and the user on software-defined radios using open-source 5G software (srsRAN v23.10 and Open5GS v2.6.6) for validation. We varied the base station implementation to simulate legitimate vs. faulty but legitimate vs. fake and malicious base stations, where a faulty base station notifies the user of the connectivity disruption and releases the session, while a fake base station continues to hold the session. We empirically analyzed the detection and identification thresholds, which vary with the fake base station’s power and the channel condition. By strategically selecting the threshold parameters, our scheme provides zero errors, including zero false positives, to avoid blacklisting a temporarily faulty base station that cannot provide connectivity at the time. Furthermore, our link routing scheme enables the base station to switch in order to restore the connectivity availability and limit the threat impact. We also discuss future directions to facilitate and encourage R&D in securing telecommunications and base station security.

Optimization of Traffic Flow Using Computational Fluid Dynamics and AI Algorithms

The effective administration of urban activity may be a basic challenge in contemporary city arranging, driven by the got to diminish blockage, minimize travel time, and lower natural affect. This paper presents a novel approach to optimizing activity stream through the integration of Computational Fluid Dynamics (CFD) and Artificial Insights (AI) calculations. By leveraging CFD, we demonstrate activity as a liquid, permitting for the exact reenactment of activity elements beneath different conditions. This fluid-based modeling approach captures the complexities of vehicular development and intuitive in a more nuanced way than conventional activity recreation strategies. To upgrade the viability of our CFD models, we consolidate AI calculations, counting machine learning procedures such as fortification learning and neural systems. These AI calculations analyze expansive datasets of activity designs, anticipate blockage focuses, and propose ideal activity flag timings and routing strategies. The cooperative energy between CFD and AI encourages real-time versatile activity administration, empowering the framework to reply powerfully to changing activity conditions. Our approach is approved through broad recreations and real-world case thinks about in a major metropolitan zone. The comes about illustrate noteworthy enhancements in activity stream, decreased blockage, and lower emanations. Also, the versatile nature of the AI calculations guarantees persistent optimization as activity patterns evolve over time. This investigate contributes to the developing body of information in cleverly transportation frameworks, advertising a adaptable and vigorous arrangement for urban activity administration. The integration of CFD and AI presents a effective apparatus for city organizers and activity engineers, clearing the way for more brilliant, more maintainable urban portability arrangements. Future work will investigate the integration of extra information sources, such as IoT sensors and vehicle-to-infrastructure communications, to advance upgrade the system's prescient capabilities and responsiveness.

Datadog with Zigbee Wireless Communication Network Protocol for an Internal Implementation in an Educational Institution

Wireless Sensor Networks are the most powerful technique for monitoring Environmental factors. As the role of WSN relies totally on the service life of the sensor nodes, it is necessary to have complete monitoring of networks. Energy efficiency has always been a key concern for Wireless sensor networks. This paper presents a detailed literature review that examines how an artificial intelligence networking tool(datalog)can be used with ZIGBee-based network protocol for continuous Real-time data quality monitoring to detect bad data quality issues in an educational organization It also Tracks and improves application speed by following requests from beginning to end and monitoring application performance. By using AI algorithms for routing choices and optimization methods, the study aims to improve network performance, energy efficiency, and system scalability. The analysis of different routing strategies with AI implementations will be covered, emphasizing the potential advantages and challenges of this innovative approach in an organization.

Optimise Energy Consumption of Wireless Sensor Networks by using modified Ant Colony Optimization

Routing represents a pivotal concern in the context of Wireless Sensor Networks (WSN) owing to its divergence from traditional network routing paradigms. The inherent dynamism of the WSN environment, coupled with the scarcity of available resources, engenders considerable challenges for industry and academia alike in devising efficient routing strategies. Addressing these challenges, a viable recourse is applying heuristic search methodologies to ascertain the best path in WSNs. Ant Colony Optimization (ACO) is a well-established heuristic algorithm that has demonstrated notable advancements in routing contexts. This paper presents an altered routing protocol that is based on ant colony optimization. In this protocol, we incorporate the inverse of the distance between nodes and their neighbours in the probability equations of ACO, along with considering pheromone levels and residual energy. These formulation modifications facilitate the selection of the most suitable candidate for the subsequent hop, effectively minimising the average energy consumption across all nodes in each iteration. Furthermore, in this protocol, we iteratively fine-tune ACO's parameter values based on the outcomes of several experimental trials. The experimental analysis is conducted through a diverse set of network topologies, and the results are compared against well-established ACO algorithms and routing protocols. The efficacy of the proposed protocol is assessed based on various performance metrics, encompassing throughput, energy consumption, network lifetime, energy consumption, the extent of data transferred over the network, and the length of paths traversed by packets. These metrics collectively provide a comprehensive evaluation of the performance attainments of the routing protocols.

ATENA: Adaptive TEchniques for Network Area Coverage and Routing in IoT-Based Edge Computing

The Internet of Things (IoT) and Edge Computing (EC) are now pervasive. IoT networks are made up of several objects, deployed in an area of interest (AoI), that can communicate with each other and with a remote computing centre for decision-making. EC reduces latency and data congestion by bringing data processing closer to the source. In this paper, we address the problems of network coverage and data collection in IoT-based EC networks. Several solutions exist designed to solve these problems unfortunately, they are either not energy-efficient or do not consider connectivity and they do not cover AoI. The proposed routing mechanisms are often not suited for AoI coverage schemes and lead to poor data routing delay or high packet losses. To address these shortcomings, we propose ATENA, a periodic, lightweight and energy-efficient protocol that aims to improve network coverage based on the two new schemes used to define a few number of objects to be kept awake at each period it also uses an adaptive routing scheme to send the collected data to the computing centre. This protocol is designed to take into account the limited resources of objects and ensures a longer network lifetime. A comparison of ATENA’s simulation results with recent existing protocols shows that it significantly improves network coverage, network lifetime and end-to-end delay to the computing centre.

Accounting for taxi service conditions in estimating route travel time from floating car data using Markov chain model

Recognising the variations in driving behaviour between taxis in the empty and carry conditions is pivotal for enhancing the accuracy of route travel time estimations using floating car data. However, existing methods largely overlook this distinction. In light of this, this study aims to harness these variations for more precise estimations. Utilising taxi data, we segmented the information by service conditions and executed distinct estimations for each segment. The route travel time was deduced through convolutional operation, complemented by a Markov chain model to discern correlations between travel times across various links. Our innovative approach realised a substantial enhancement in accuracy. Notably, when accounting for distinct service conditions, there was a reduction of 51.44% in mean absolute error and a 46.83% decline in maximum percentage error. By providing more accurate and reliable travel time predictions, our methodology enables better-informed traffic management decisions. Accurate travel time estimations are essential for optimising traffic signal timings, planning efficient routing strategies, and managing road network usage. These improvements in traffic management can lead to smoother traffic flow, reduced travel times, and ultimately, diminished congestion in urban areas.

An expandable and cost-effective data center network

With the rapid growth of data volume, the escalating complexity of data businesses, and the increasing reliance on the Internet for daily life and production, the scale of data centers is constantly expanding. The data center network (DCN) is a bridge connecting large-scale servers in data centers for large-scale distributed computing. How to build a DCN structure that is flexible and cost-effective, while maintaining its topological properties unchanged during network expansion has become a challenging issue. In this paper, we propose an expandable and cost-effective DCN, namely HHCube, which is based on the half hypercube structure. Further, we analyze some characteristics of HHCube, including connectivity, diameter, and bisection bandwidth of the HHCube. We also design an efficient algorithm to find the shortest path between any two distinct nodes and present a fault-tolerant routing scheme to obtain a fault-tolerant path between any two distinct fault-free nodes in HHCube. Meanwhile, we present two local diagnosis algorithms to determine the status of nodes in HHCube under the PMC model and MM* model, respectively. Our results demonstrate that despite the presence of up to 25% faulty nodes in HHCube, both algorithms achieve a correct diagnosis rate exceeding 90%. Finally, we compare HHCube with state-of-the-art DCNs including Fat-Tree, DCell, BCube, Ficonn, and HSDC, and the experimental results indicate that the HHCube is an excellent candidate for constructing expandable and cost-effective DCNs.

Optimizing order picking processes in warehouses: strategies for efficient routing and clustering

The study employed a simulation based on a detailed dataset containing over 307,046 unique order identifiers and 1,050 unique product identifiers. This dataset included information such as order placement dates, product codes, quantities, and precise locations within the warehouse, including coordinates. The simulation modeled the order-picking route using the Single-Picker Routing Problem (SPRP) algorithms to minimize distance and travel time. The methods compared various wave-picking strategies and grouping methods (single-line and multi-line) for their effectiveness.The applied method significantly reduced the travel distance required by the order picker in the warehouse. The key to this optimization was consolidating orders into waves of specific sizes, achieving a fourfold distance reduction for the studied dataset. Additionally, the solution proposed grouping products by location within the warehouse, either in a single aisle or across multiple aisles based on proximity. Although this method often enhances efficiency, it did not in this particular case. However, it was included as it may yield better results with different datasets and further reduce travel distances in the warehouse. The research underscored the critical role of efficient routing and grouping strategies in warehouse operations. Although wave picking significantly reduced travel distances, the effectiveness of clustering strategies depended on the characteristics of the specific dataset, suggesting the need for tailored solutions based on the warehouse layout and the features of the orders. Future research could extend to integrating product volume and weight variations, which may further optimize order-picking strategies.

Stable isotopes reveal sex- and context-dependent amino acid routing in green anole lizards (Anolis carolinensis).

Allocation of acquired resources to phenotypic traits is affected by resource availability and current selective context. While differential investment in traits is well documented, the mechanisms driving investment at lower levels of biological organization, which are not directly related to fitness, remain poorly understood. We supplemented adult male and female Anolis carolinensis lizards with an isotopically labelled essential amino acid (13C-leucine) to track routing in four tissues (muscle, liver, gonads, and spleen) under different combinations of resource availability (high and low-calorie diets) and exercise training (sprint training and endurance capacity). We predicted sprint training should drive routing to muscle, and endurance training to liver and spleen, and that investment in gonads should be of lower priority in each of the cases of energetic stress. We found complex interactions between training regime, diet, and tissue type in females, and between tissue type and training and tissue type and diet in males, suggesting that males and females adjust their 13C-leucine routing strategies differently in response to similar environmental challenges. Importantly, our data show evidence of increased 13C-leucine routing in training contexts not to muscle as we expected, but to the spleen, which turns over blood cells, and to the liver, which supports metabolism under differing energetic scenarios. Our results reveal the context-specific nature of long-term tradeoffs associated with increased chronic activity. They also illustrate the importance of considering the costs of locomotion in studies of life history strategies.