Geographic Routing Research Articles

Spatial Connectivity Through Mountains and Deserts Drove South American Scorpions Dispersal

ABSTRACTAimThe aim of this study is to infer the geographic dispersal paths and the environmental conditions that shaped the historical biogeography of Brachistosternus scorpions in South America. We evaluated the role that altitude and aridity had on the geographic distance that each species dispersed from the location of the genus common ancestor. Based on the previous studies, we evaluated the hypothesis postulating that species geographic expansion was promoted by arid conditions in high altitudes.LocationSouth America.TaxonBrachistosternus genus.MethodsWe integrated two methodological approaches in this study, the phylogenetic Geographical model and the Conductance model, considering the uncertainty associated with the phylogenetic relationship and the species classification. The Geo model infers the locations of ancestral species in a phylogenetic tree, assuming a spherical space and using samples of georeferenced locations for every species as input data. It allows us to estimate the species dispersal routes and distances from the location of the genus common ancestor. The Conductance model is based on the circuit theory and infers the geographic route and distance of least resistance between an origin and destination point. We defined the origin as the location of the genus common ancestor obtained from the Geo model and a destination point as the current geographic location of each species. This model infers the geographic routes with the least cost of resistance for dispersal in a landscape of varying altitude and aridity. Finally, we evaluated the correlation between the two dispersal distances each species have moved from the location of the common ancestor, that is, the distance inferred from the Geo model and from the Conductance model.ResultsThe Geo model shows that Brachistosternus's geographical origin was most likely along the coast of south Peru, and central Chile. From this location, extant species dispersed thorough routes ranging from 873 to 2800 km in average. The Conductance model that considers the routes with least resistance to elevation and aridity simulated dispersal distances that are highly correlated with the species dispersal distances obtained from the Geo model.Main ConclusionsWe revealed the geographic dispersal routes, with the least resistance to the pressures imposed by changes in altitude and aridity, that 55 species of scorpions have probably followed in the last 30 million years in South America. These geographic routes that went along the Andean Mountains and the arid zones of South America shaped the current spatial distribution of the genus Brachistosternus.

The flyway construct and assessment of offshore wind farm impacts on migratory marine fauna

Abstract The flyway construct was developed in the 1930s to protect networks of bird stopover habitats along annual migration circuits. Here flyway is conceived as a quantifiable vector, a construct of the seasonal velocity and the geographic route taken among networked habitats, generalizable among marine migratory animals, and a way forward in assessing regional offshore wind (OW) development impacts. OW development in the South and East China Sea, North Sea, and US Mid-Atlantic Bight will result in thousands of offshore wind turbines that transect coastal and shelf flyways, not only for birds but also for fishes, sea turtles, and marine mammals. With increased capacity to fully measure the extent and dynamics of migrations through electronic tracking and observing systems, avian-like behaviors are apparent for whales, turtles, and fishes, such as stopover behaviors, migration synchrony, and partial migration. Modeled northwest Atlantic flyways (migration vectors) for northern gannets, striped bass, and North Atlantic right whale quantified seasonal speed, persistence, and variance during seasonal migration phases. From these flyway models, a series of flyway metrics are proposed that lend themselves to hypothesis-testing, dynamic habitat models, and before–after impact assessment. The flyway approach represents a departure from current impact designs, which are dominated by local OW farm studies and, at regional dimensions, survey-based (Eulerian) data structures (overlap models). As a next step, we encourage colleagues to develop flyway databases and test flyway hypotheses for model species.

An adaptive and multi-path greedy perimeter stateless routing protocol in flying ad hoc networks

In recent years, flying ad hoc networks (FANET), formed from unmanned aerial vehicles (UAVs), have absorbed the attention of academic and industrial research communities due to their many applications in military and civilian fields. FANETs benefit from unique features, including highly moving UAVs and dynamic topological structure. Therefore, most existing routing protocols, such as the greedy perimeter stateless routing (GPSR), are not compatible with the FANET environment and its specific features. To improve the performance of GPSR in FANET, it is important to address several challenges, namely the selection of the right period for broadcasting hello messages in the network, the selection of the right criteria for selecting the next-hop node, and the improvement of reliability in the data transfer process. In this paper, an adaptive and multi-path greedy perimeter stateless routing (AM-GPSR) protocol is suggested in FANETs. It includes two new strategies, namely adaptive hello strategy and multi-path greedy forwarding strategy. The adaptive hello strategy defines a special hello broadcast period for each UAV according to its speed and error between two estimated and actual positions. Furthermore, the greedy forwarding strategy carries out a filtering operation on candidate nodes and eliminates border UAVs and those that are far from the destination. Then, candidate UAVs are prioritized based on the time to reach the destination and buffer capacity, and UAVs with higher priorities are chosen to send data packets. Finally, AM-GPSR applies a greedy multi-path forwarding strategy to increase reliability in the data transmission process. Lastly, the simulation of AM-GPSR is done via the network simulator version 2 (NS2) to evaluate its performance. This evaluation process includes two different scenarios, i.e. change in the speed of UAVs and change in their communication range. In this process, AM-GPSR is compared with three other methods, namely the aerial greedy geographic routing (AGGR) protocol, the geolocation assisted aeronautical routing protocol (AeroRP), and GPSR. This comparison shows the successful performance of AM-GPSR in terms of delivery success rate, throughput, and delay. Although the control overhead of the proposed method is more than that of AGGR.

Fault‐tolerant independent gradient forwarding routing for wireless sensor network

SummaryFor the sensor network, greedy forwarding is thought to be the best energy‐efficient routing method. There are two restrictions on greedy forwarding: a minimum local scenario and an intersection problem in which it is unable to locate an existing route. The proposed independent gradient forward (IGF) proves that the Euclidean equation used by greedy forwarding is less capable of dealing with the problems mentioned above. IGF considers each dimension independently to enhance the efficiency of greedy forwarding, sustaining its simplicity. It contributes to strengthening the suggested IGF's ability to manage holes and get around obstructions in the wireless sensor network. We have demonstrated that it travels a shorter potential path and avoids stuck nodes well in advance. It is more energy‐efficient since it can predict the void node and never visits a stalled node. We evaluated the suggested IGF's performance against other traditional boundary traversal‐based methods on both rare and dense networks. In comparison with other boundary traversal‐based algorithms, the simulation results indicate that it generates a 10% smaller route to bypass. The IGF average energy consumption increases slowly, rising by just 7% and 12.5% for dense and rare networks, respectively, with a fivefold increase in void size (from 100 to 500 m).

SDN-based reliable emergency message routing schema using Digital Twins for adjusting beacon transmission in VANET

Digital Twin (DT) has revolutionized the contextualized digital environment. This advancement enables real-time monitoring and simulation of events, leading to more effective decision-making. In smart transportation, DT plays a crucial role in enhancing various aspects of road decision-making, including optimizing routing decisions for Emergency Message (EM) forwarding in Vehicular Ad hoc Networks (VANETs). In this study, DT has integrated with cutting-edge technologies such as Software-Defined-Network (SDN) and geographically distributed computing (fog computing) to elevate the position-based routing behavior in two directions: selecting the optimal next-hop and taking control of periodic messages (beacon transmission). The nature of routing decisions can be complex due to constant movement and frequent position changes of vehicles in the VANET system. Merely selecting the next-hop vehicle without considering its stability and predictability can result in suboptimal routing choices. To this end, SDN based on Reliable Emergency Message Routing Schema (SDN-REMR) is introduced. This schema utilizes DT and aims to leverage the behaviors of moving vehicles to enhance the delivery of EM by providing a reliable routing mechanism. To eliminate unstable neighbors from the list of potential next-hops, SDN-REMR forecasts the relative positions of surrounding vehicles using Euclidean distance and position information. Moreover, SDN-REMR uses the movement information collected from vehicles (such as position, speed, and power consumption) to reduce the possibility of a link disruption and select the best next-hop for reliable communication. SDN-REMR enables the Store-Carry-Forward (SCF) approach to ensure packets are successfully forwarded to the destination, even in challenging network conditions. This control strategy aims to strike a balance between maintaining reliable communication and minimizing congestion in the network. The experimental results prove that there are 42.6%, 76.22%, 76.22%, and 22.2% enhancement in terms of network throughput, total delivered packets, number of dropped packets, and routing overhead.

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).

Energy-Efficient and Highly Reliable Geographic Routing Based on Link Detection and Node Collaborative Scheduling in WSN.

Energy efficiency and data reliability are important indicators to measure network performance in wireless sensor networks. In existing research schemes of routing protocols, the impact of node coverage on the network is often ignored, and the possibility that multiple sensor nodes may sense the same spatial point is not taken into account, which results in a waste of network resources, especially in large-scale networks. Apart from that, the blindness of geographic routing in data transmission has been troubling researchers, which means that the nodes are unable to determine the validity of data transmission. In order to solve the above problems, this paper innovatively combines the routing protocol with the coverage control technique and proposes the node collaborative scheduling algorithm, which fully considers the correlation characteristics between sensor nodes to reduce the number of active working nodes and the number of packets generated, to further reduce energy consumption and network delay and improve packet delivery rate. In order to solve the problem of unreliability of geographic routing, a highly reliable link detection and repair scheme is proposed to check the communication link status and repair the invalid link, which can greatly improve the packet delivery rate and throughput of the network, and has good robustness. A large number of experiments demonstrate the effectiveness and superiority of our proposed scheme and algorithm.

Connected metric dimension of the class of ladder graphs

Numerous applications, like robot navigation, network verification and discovery, geographical routing protocols, and combinatorial optimization, make use of the metric dimension and connected metric dimension of graphs. In this work, the connected metric dimension types of ladder graphs, namely, ladder, circular, open, and triangular ladder graphs, as well as open diagonal and slanting ladder graphs, are studied.

The Secure Metric Dimension of the Globe Graph and the Flag Graph

Let G = (V, E) be a connected, basic, and finite graph. A subset T=u1,u2,…,uk of V(G) is said to be a resolving set if for any y ∈ V(G), the code of y with regards to T, represented by CTy, which is defined as CTy=du1,y,du2,y,…,duk,y, is different for various y. The dimension of G is the smallest cardinality of a resolving set and is denoted by dim(G). If, for any t ∈ V – S, there exists r ∈ S such that S–r∪t is a resolving set, then the resolving set S is secure. The secure metric dimension of 𝐺 is the cardinal number of the minimum secure resolving set. Determining the secure metric dimension of any given graph is an NP-complete problem. In addition, there are several uses for the metric dimension in a variety of fields, including image processing, pattern recognition, network discovery and verification, geographic routing protocols, and combinatorial optimization. In this paper, we determine the secure metric dimension of special graphs such as a globe graph Gln, flag graph Fln, H- graph of path Pn, a bistar graph Bn,n2, and tadpole graph T3,m. Finally, we derive the explicit formulas for the secure metric dimension of tadpole graph Tn,m, subdivision of tadpole graph ST3,m, and subdivision of tadpole graph STn,m.

The Bus Rapid Transit investment problem

Bus Rapid Transit (BRT) systems can provide a fast and reliable service to passengers at low investment costs compared to tram, metro and train systems. Therefore, they can be of great value to attract more passengers to use public transport. This paper thus focuses on the BRT investment problem: Which segments of a single bus line should be upgraded such that the number of newly attracted passengers is maximized? Motivated by the construction of a new BRT line around Copenhagen, we consider a setting in which multiple parties are responsible for the financing of different segments of the line. As each party has a limited willingness to invest, we solve a bi-objective problem to quantify the trade-off between the number of attracted passengers and the investment budget. We model different problem variants: First, we consider two potential passenger responses to upgrades on the line. Second, to prevent scattered upgrades along the line, we consider different restrictions on the number of upgraded connected components on the line. We propose an epsilon-constraint-based algorithm to enumerate the complete set of non-dominated points and investigate the complexity of this problem. Moreover, we perform extensive numerical experiments on artificial instances and a case study based on the BRT line around Copenhagen. Our results show that we can generate the full Pareto front for real-life instances and that the resulting trade-off between investment budget and attracted passengers depends both on the origin–destination demand and on the passenger response to upgrades. Moreover, we illustrate how the generated Pareto plots can assist decision makers in selecting from a set of geographical route alternatives in our case study.

Evaluation of Greedy and CBF for ETSI non-area GeoNetworking: The impact of DCC

This paper evaluates the performance of the two ETSI non-area forwarding algorithms in the GeoNetworking specification: Greedy Forwarding and Non-Area Contention-Based Forwarding (CBF). Non-area forwarding occurs when a packet is sent to a geographical Destination Area from a node located outside of this area, e.g., when a vehicle wants to alert of hazardous events to other vehicles located in a distant geographical area. The evaluation has been carried out both in urban and highway scenarios and takes into account the complete ETSI Architecture, including the interaction with the Decentralized Congestion Control (DCC) mechanism. We have also compared ETSI-defined mechanisms with optimizations found in the literature. Our main findings are that Greedy Forwarding, when combined with DCC, is extremely ineffective even with optimizations, and Non-Area CBFs (both ETSI CBF and an optimized version called S-FoT+) outperform Greedy Forwarding both in highway and urban scenarios.

RC-LAHR: Road-Side-Unit-Assisted Cloud-Based Location-Aware Hybrid Routing for Software-Defined Vehicular Ad Hoc Networks.

The reliability of the communication link is quite common and challenging to handle as the topology changes frequently in vehicular ad hoc networks (VANETs). Another problem with VANETs is that the vehicles are from different manufacturers. Hence, the heterogeneity of hardware is obvious. These heterogeneity and reliability problems affect the message dissemination in VANETs. This paper aims to address these challenges by proposing a robust routing protocol capable of ensuring reliable, scalable, and heterogeneity-tolerant message dissemination in VANETs. We first introduced a hybrid hierarchical architecture based on software-defined networking (SDN) principles for VANETs, leveraging SDN's inherent scalability and adaptability to heterogeneity. Further, a road-side unit (RSU)-assisted cloud-based location-aware hybrid routing for software-defined VANETs (SD-VANETs) that we call RC-LAHR was proposed. RC-LAHR was rigorously tested and analyzed for its performance in terms of packet delivery ratio (PDR) and end-to-end delay (EED), along with a comprehensive assessment of network traffic and load impacts on cloud infrastructure and RSUs. The routing protocol is compared with state-of-the-art protocols, Greedy Perimeter Stateless Routing (GPSR) and Opportunistic and Position-Based Routing (OPBR). The proposed routing protocol performs well as compared to GPSR and OPBR. The result shows that the EED is reduced to 20% and the PDR is increased to 30%. The network reliability is also increased up to 5% as compared to the OPBR and GPSR.

A Survey on Comprehensive Exploration of Modern Perception System

This review paper offers an in-depth analysis of recent advancements in image-based intelligent transportation systems. It provides insights into various approaches employed for detecting false images, license plate recognition, distributing emergency messages, as well as detecting and classifying vehicle accidents and road accidents. The research critically evaluates the pros and cons of deep learning techniques, including position-based routing, enhanced YOLOv8, optimal K-means with a convolutional neural network, and gated hierarchical multi-task learning. The investigation of an Internet of Things-based vehicle accident detection and classification system highlights the benefits of sensor fusion for precise accident detection; nonetheless, the evaluation raises questions regarding the scalability and reliability of smartphone sensors. The utility of a deep learning system for road accident identification utilizing artificially created multi-perspective movies is emphasized, along with warnings about possible limits in capturing real-world circumstances

Hyperbolic-Embedding-Aided Geographic Routing in Intelligent Vehicular Networks

Intelligent vehicular networks can not only connect various smart terminals to manned or unmanned vehicles but also to roads and people’s hands. In order to support diverse vehicle-to-everything (V2X) applications in dynamic, intelligent vehicular networks, efficient and flexible routing is fundamental but challenging. Aimed to eliminate routing voids in traditional Euclidean geographic greedy routing strategies, we propose a hyperbolic-embedding-aided geographic routing strategy (HGR) in this paper. By embedding the network topology into a two-dimensional Poincaré hyperbolic disk, greedy forwarding is performed according to nodes’ hyperbolic coordinates. Simulation results demonstrated that the proposed HGR strategy can greatly enhance the routing success rate through a smaller stretch of the routing paths, with little sacrifice of routing computation time.

Evaluation of the performance of the vehicular ad hoc network protocols in the case of V2I and EV2I communications

Vehicular ad hoc network (VANET) is an intelligent technology that enables efficient communication, secure data transmission, and traffic management. The purpose of routing protocols in the VANET network is to route data between vehicles (V2V) and vehicles-to-infrastructure (V2I). Recently, researchers have shown interest in designing effective routing protocols for the VANET network, as not all existing protocols are suitable for all traffic scenarios. Electric vehicles (EVs) are increasingly being adopted and integrated into intelligent transportation systems (ITS). Developed countries are actively promoting sustainable transportation solutions to increase energy efficiency and reduce carbon emissions. Therefore, this research presents an EV charging station (CS) management scheme based on communication between EVs and RSUs, with performance evaluation simulated using VANET network protocols. In this study, the G-MDORA, MDORA, and geographical routing protocol (GRP) protocols were modified to accommodate V2I communication, and RSUs were distributed along the roadmap. Additionally, a scheme for managing electric vehicle CS was presented, focusing on the communication between electric vehicles and RSUs in the EV2I context. Performance was evaluated using the G-MDORA, GRP, and MDORA protocols, considering factors such as throughput, communication overhead, packet delivery ratio, and end to end delay.

MF‐DLB: Multimetric forwarding and directed acyclic graph‐based load balancing for geographic routing in FANETs

SummaryFlying ad hoc network (FANET) comprising unmanned aerial vehicles (UAVs) emerges as a promising solution for numerous military and civil applications. Transferring data collected from the environment to the ground station (GS) is a primary concern for meeting the communication demands of most of these applications. However, the highly mobile UAVs with limited communication range, resulting in frequent topology change and intermittent connectivity, make data routing challenging. In such scenarios, geographic routing is a viable solution due to its scalability and robustness. However, the basic forwarding mechanism of geographic routing favors the neighboring UAV nearest to the destination, impacted substantially by link failures and routing holes in a dynamic environment. Additionally, routing decisions ignoring the current load over UAVs contribute to performance degradation due to the high concentration of data traffic near the GS. Thus, to address these issues, a geographic routing protocol named MF‐DLB comprising multimetric forwarding (MF) and a directed acyclic graph‐based load balancing (DLB) scheme is proposed to enhance packet forwarding in FANETs. MF takes account of multiple metrics related to connectivity, geographic progress, link lifetime, and residual energy to select the next hop with a stable communication link while effectively bypassing the routing holes. The second scheme, DLB, focuses on proactively maintaining routing paths near GS for load distribution among underutilized nodes to address the congestion problem. Simulations performed in network simulator ns‐3 confirm the outperformance of MF‐DLB over other related routing schemes in terms of different performance metrics.

Implementation of Network Security for Intrusion Detection & Prevention System in IoT Networks: Challenges & Approach

EPSO-GPSR (Enhanced Particle Swarm Optimization-based Load Balancing with Geographic Routing using Greedy Perimeter Stateless Routing), a unique strategy designed specifically for WSNs, is presented in this work. Using Enhanced Particle Swarm Optimization (EPSO) to provide load balancing across sensor nodes, the proposed EPSO-GPSR technique reduces energy disparities and increases the operational lifetime of the network. Additionally, it interfaces with the geographic routing protocol Greedy Perimeter Stateless Routing (GPSR) to enable effective data forwarding based on geographic locations, minimizing communication overhead and improving scalability. EPSO-GPSR's efficacy is shown against traditional load balancing and routing methods via comprehensive simulations and performance assessments. The network lifetime, energy efficiency, throughput, packet delivery ratio, and delay have all significantly showed better performance, according to the results. Additionally, the EPSO-GPSR algorithm demonstrates robustness against node failures and issues related to scalability, indicating a significant potential for real-world implementation in various WSN scenarios.

A Q-learning-based smart clustering routing method in flying Ad Hoc networks

Flying ad hoc networks (FANETs) have particular importance in various military and civilian applications due to their specific features, including frequent topological changes, the movement of drones in a three-dimensional space, and their restricted energy. These features have created challenges for designing cluster-based routing protocols. In this paper, a Q-learning-based smart clustering routing method (QSCR) is suggested in FANETs. In QSCR, each node discovers its neighbors through the periodic exchange of hello messages. The hello time interval is different in each cluster, and cluster leaders determine this interval based on the average speed similarity. Next, an adaptive clustering process is presented for categorizing drones in the clusters. In this step, the cluster leader is selected based on a new parameter called merit value, which includes residual energy, centrality, neighbor degree, speed similarity, and link validity time. Then, a centralized Q-learning model is presented to tune weight coefficients related to merit parameters dynamically. In the last step, the routing process is done using a greedy forwarding technique. Finally, QSCR is run on NS2, and the simulation results of QSCR are compared with those of ICRA, WCA, and DCA. These results show that QSCR carries out the clustering process rapidly but has less cluster stability than ICRA. QSCR gets energy efficiency and improves network lifetime. In the routing process, QSCR has a high packet delivery rate compared to other schemes. Also, the number of isolated clusters created in QSCR is less than other clustering methods. However, the proposed scheme has a higher end-to-end delay than ICRA. Also, this scheme experiences more communication overhead than ICRA slightly.

High-Reliability Low-Latency Intelligent Geographic Routing Protocol for Vehicle Road Cooperation System

High-Reliability Low-Latency Intelligent Geographic Routing Protocol for Vehicle Road Cooperation System

Learning-Based Geographic Routing for Delay-Limited Multi-Hop Wireless Networks

Learning-Based Geographic Routing for Delay-Limited Multi-Hop Wireless Networks