Stateless Routing Protocol Research Articles

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.

Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters

Unmanned aerial vehicle (UAV) clusters offer significant potential in civil, military, and commercial fields due to their flexibility and cooperative capabilities. However, characteristics such as dynamic topology and limited energy storage bring challenges to the design of routing protocols for UAV networks. This study leverages the Deep Double Q-Learning Network (DDQN) algorithm to optimize the traditional Greedy Perimeter Stateless Routing (GPSR) protocol, resulting in a multi-objective optimized GPSR routing protocol (DDQN-MTGPSR). By constructing a multi-objective routing optimization model through cross-layer data fusion, the proposed approach aims to enhance UAV network communication performance comprehensively. In addition, this study develops the above DDQN-MTGPSR intelligent routing algorithm based on the NS-3 platform and uses an artificial intelligence framework. In order to verify the effectiveness of the DDQN-MTGPSR algorithm, it is simulated and compared with the traditional ad hoc routing protocols, and the experimental results show that compared with the GPSR protocol, the DDQN-MTGPSR has achieved significant optimization in the key metrics such as the average end-to-end delay, packet delivery rate, node average residual energy variance and percentage of node average residual energy. In high dynamic scenarios, the above indicators were optimized by 20.05%, 12.72%, 0.47%, and 50.15%, respectively, while optimizing 36.31%, 26.26%, 8.709%, and 69.3% in large-scale scenarios, respectively.

SRP-HEE: A Modified Stateless Routing Protocol based on Homomorphic Energy based Encryption for Wireless Sensor Network

Due to the wireless nature, the sensors node data are prone to location privacy of source and classification of the packet by unauthorized parties. Data encryption is one of the most effective ways to thwart unauthorized access to the data and trace information. Traditional wireless network security solutions are not viable for WSNs In this paper, a novel distributed forward aware factor based heuristics towards generating greedy routing using stateless routing is SRP-HEE for wireless sensor network. The model employs the homomorphic Energy based encryption technique. Energy based Encryption model is devoted as homomorphic mechanism due to their less computational complexity. Additionally, privacy constraint becoming a critical issue in the wireless sensor networks (WSNs) because sensor nodes are generally prone to attacks which deplete energy quickly as it is exposed to mobile sink frequently for data transmission. Through inclusion of the Forward aware factor on the Greedy routing strategies, it is possible to eliminate the attacking node which is depleting the energy of the source node. Heuristic conditions are used for optimizing the sampling rate and battery level for tackling the battery capacity constraints of the wireless sensor nodes. The Node characteristics of the propagating node have been analysed utilizing kalman filter and linear regression. The cooperative caching of the network information will enable to handle the fault condition by changing the privacy level of the network. The Simulation results demonstrate that SRP-HEE model outperforms existing technique on basis of Latency, Packet Delivery Ratio, Network Overhead, and Energy Utilization of nodes.

AFB-GPSR: Adaptive Beaconing Strategy Based on Fuzzy Logic Scheme for Geographical Routing in a Mobile Ad Hoc Network (MANET)

In mobile ad hoc networks (MANETs), geographical routing provides a robust and scalable solution for the randomly distributed and unrestricted movement of nodes. Each node broadcasts beacon packets periodically to exchange its position with neighboring nodes. However, reliable beacons can negatively affect routing performance in dynamic environments, particularly when there is a sudden and rapid change in the nodes’ mobility. Therefore, this paper suggests an improved Greedy Perimeter Stateless Routing Protocol, namely AFB-GPSR, to reduce routing overhead and increase network reliability by maintaining correct route selection. To this end, an adaptive beaconing strategy based on a fuzzy logic scheme (AFB) is utilized to choose more optimal routes for data forwarding. Instead of constant periodic beaconing, the AFB strategy can dynamically adjust beacon interval time with the variation of three network parameters: node speed, one-hop neighbors’ density, and link quality of nodes. The routing evaluation of the proposed protocol is carried out using OMNeT++ simulation experiments. The results show that the AFB strategy within the GPSR protocol can effectively reduce the routing overhead and improve the packet-delivery ratio, throughput, average end-to-end delay, and normalized routing load as compared to traditional routing protocols (AODV and GPSR with fixed beaconing). An enhancement of the packet-delivery ratio of up to 14% is achieved, and the routing cost is reduced by 35%. Moreover, the AFB-GPSR protocol exhibits good performance versus the state-of-the-art protocols in MANET.

Weight-Based PA-GPSR Protocol Improvement Method in VANET.

Vehicle Ad-hoc network (VANET) can provide technical support and solutions for the construction of intelligent and efficient transportation systems, and the routing protocol directly affects the efficiency of VANET. The rapid movement of nodes and uneven density distribution affect the routing stability and data transmission efficiency in VANET. To improve the local optimality and routing loops of the path-aware greedy perimeter stateless routing protocol (PA-GPSR) in urban sparse networks, a weight-based path-aware greedy perimeter stateless routing protocol (W-PAGPSR) is proposed. The protocol is divided into two stages. Firstly, in the routing establishment stage, the node distance, reliable node density, cumulative communication duration, and node movement direction are integrated to indicate the communication reliability of the node, and the next hop node is selected using the weight greedy forwarding strategy to achieve reliable transmission of data packets. Secondly, in the routing maintenance stage, based on the data packet delivery angle and reliable node density, the next hop node is selected for forwarding using the weight perimeter forwarding strategy to achieve routing repair. The simulation results show that compared to the greedy peripheral stateless routing protocol (GPSR), for the maximum distance-minimum angle greedy peripheral stateless routing (MM-GPSR) and PA-GPSR protocols, the packet loss rate of the protocol is reduced by an average of 24.47%, 25.02%, and 14.12%, respectively; the average end-to-end delay is reduced by an average of 48.34%, 79.96%, and 21.45%, respectively; and the network throughput is increased by an average of 47.68%, 58.39%, and 20.33%, respectively. This protocol improves network throughput while reducing the average end-to-end delay and packet loss rate.

UF‐GPSR: Modified geographical routing protocol for flying ad‐hoc networks

AbstractFlying Ad‐hoc Networks (FANETs) has recently gained significant exposure for their emerging military, civil, and commercial applications. In FANETs, all Unmanned Aerial Vehicles (UAVs) can communicate within a restricted wireless communication range without fixed infrastructure. However, the highly dynamic nature of UAVs imposes a significant issue in FANET's communications. Therefore, designing an effective routing strategy is always necessary for sustaining appropriate network performance in FANETs. This paper is interested in modifying Greedy Perimeter Stateless Routing (GPSR) protocol named Utility Function based Greedy Perimeter Stateless Routing (UF‐GPSR) protocol for FANETs. The proposed approach optimizes the greedy forwarding strategy by considering multiple vital parameters of UAVs: residual energy ratio, distance degree, movement direction, link risk degree, and speed, respectively. The proposed UF‐GPSR applies the utility function on these parameters to enhance the routing performance by selecting optimal next‐hop within the communication range. The effectiveness of the proposed work is mathematically evaluated and then simulated through the NS‐3.23 simulator. The experimental outcomes verify that the proposed routing approach enhances the packet delivery ratio and throughput while decreasing packet drop ratio and delay compared to the other routing approaches: GPSR and GPSR‐PPU.

Multi-index joint node selection protocol in UAV ad hoc network

In the application scenario of a UAV ad hoc network, aiming at the problems of low packet forwarding success rate and premature death of some nodes in greedy perimeter stateless routing (GPSR) protocol, a multi-index joint node selection-GPSR protocol (MT-GPSR) is proposed. Based on greedy forwarding, MT-GPSR protocol comprehensively considers the link holding time of adjacent nodes, the residual energy of adjacent nodes, and the node degree to jointly select the next hop node, and proposes an AHP maximum entropy criterion weighting algorithm, which reasonably weights the node index value, adds the weighted index values to obtain the selection priority value, and selects the neighbor node whose priority value is the best under the weighting condition as the next hop node for data forwarding, so as to improve the efficiency of data forwarding. At the same time, the network life is prolonged. The simulation results show that compared with the GPSR algorithm and GPSR-NMP algorithm, this algorithm ensures stable communication between UAVs, improves the network communication quality, and improves the network lifetime.

SPEED-UP, and energy-efficient GPSR protocol for WSNs using IOT

The development of new applications for intelligent cities, medicine, agriculture and energy harvesting would be a promising concept using wireless sensor networks based on the Internet of Things (IoT-WSNs). At the moment, the research was aimed at reducing the limited energy supply of IoT devices, which has a serious impact on their architecture. To ensure rapid and stable connection, new and efficient methods of energy systems must be designed. To handle data transfer in WSNs and to handle the problem of detector energy supply, the authors presented an improved Speed Up-Greedy Perimeter Stateless Routing Protocol for Wireless Sensor Networks (SU-GPSR) and Energy Harvest Greedy Perimeters Stateless Routing Protocol (EH-GPSR). SU-GPSR, unlike other variations of GPSR algorithms, provides complete energy consumption while assessing the next jump for local nodes and mobile nodes in an integrated framework. Additionally, established measurements are recorded using the SU-GPSR skip selection method. To calculate the amount of energy collected, EH-GPSR uses an EH rate that is generated by Mobilized Minimum Path Recovery Time (MMRT) method, as it improves the method of greedy routing. To incrementally choose the next hop, it particularly uses a weighted method that merges the node's data cost with the EH rate. In both packed and systems, the simulation study infers that SU-GPSR performs better than EH-GPSR and FA-GPSR, whereas EH-GPSR increases the node life with a speed by 20% when contrasted to the other GPSR method.

Research on optimal intelligent routing algorithm for IoV with machine learning and smart contract

The huge increase in the communication network rate has made the application fields and scenarios for vehicular ad hoc networks more abundant and diversified and proposed more requirements for the efficiency and quality of data transmission. To improve the limited communication distance and poor communication quality of the Internet of Vehicles (IoV), an optimal intelligent routing algorithm is proposed in this paper. Combined multi-weight decision algorithm with the greedy perimeter stateless routing protocol, designed and evaluated standardized function for link stability. Linear additive weighting is used to optimize link stability and distance to improve the packet delivery rate of the IoV. The blockchain system is used as the storage structure for relay data, and the smart contract incentive algorithm based on machine learning is used to encourage relay vehicles to provide more communication bandwidth for data packet transmission. The proposed scheme is simulated and analyzed under different scenarios and different parameters. The experimental results demonstrate that the proposed scheme can effectively reduce the packet loss rate and improve system performance.

LDAB-GPSR: Location PreDiction with Adaptive Beaconing – Greedy Perimeter Stateless Routing Protocol for Mobile Ad Hoc Networks

In mobile ad-hoc networks (MANET), nodes are randomly distributed and move freely, and hence the network may face rapid and unexpected topological changes. In this paper, an improved greedy perimeter stateless routing protocol, called “LDAB-GPSR”, is proposed. LDAB-GPSR mainly focuses on maximizing the packet delivery ratio while minimizing the control overhead. In order to accomplish this, two techniques are introduced, the first one is the location prediction technique in which the greedy forwarding strategy is improved by choosing more stable routes for data forwarding. The second one is the adaptive beaconing technique in which the slow start algorithm is employed to adapt the beacon packet interval time based on the mobility of nodes and the data traffic load instead of using the periodic beaconing strategy. These two strategies together improve the overall performance of the GPSR routing protocol. The performance of the new proposed protocol is evaluated by carrying out several NS-2.35 simulation experiments. The simulation results show that LDAB-GPSR protocol outperforms the GPSR+Predict protocol in terms of packet delivery ratio, control traffic overhead, end to end delay, and throughput. The ratios of enhancement approaches 40%.

Evaluation of VANETs Routing Protocols for Data-Based Smart Health Monitoring in Intelligent Transportation Systems

Vehicular Ad-hoc Network (VANET) is an essential part of futuristic Intelligent Transportation Systems. VANET can improve the overall traffic control system and reduce road accident deaths by providing remote health monitoring in hazardous conditions to outdoor patients. Nowadays, vehicles have become so intelligent that they can sense patient health data and transmit it to a nearby ambulance or hospital in emergency or road accident situations. Health professionals can provide appropriate treatment without wasting critical time in further testing. Developing an efficient and reliable routing solution is a significant research problem for VANET based health monitoring applications because of time-sensitives. Routing approaches to reduce the transmission delay for critical applications are based on topological, geographical, clustering, and flooding techniques. This article has evaluated and compared widely used topological and geographical routing protocols for data-based VANETs health monitoring applications. A comprehensive analysis is performed on Ad hoc On-Demand Distance Vector (AODV), Destination-Sequenced Distance-Vector (DSDV), Optimized Link State Routing (OLSR), Greedy Perimeter Stateless Routing (GPSR), Greedy Perimeter Stateless Routing-Modified (GPSR-M), and Max duration-Minangle Greedy Perimeter Stateless Routing (MM-GPSR) protocols with different numbers of nodes, CBR connections, communication range and packet size on Network Simulator (NS-3.23) and Simulation of Urban Mobility (SUMO) platforms. Experimental results give useful knowledge in analyzing routing protocols for VANET's data-based smart health monitoring applications.

A Deep Reinforcement Learning-Based Geographic Packet Routing Optimization

Packet routing is a fundamental problem in wireless networks in which routers decide the next hop for each packet in order to deliver it to its destination as quickly as possible. In order to overcome the shortcoming of non optimal forwarding path caused by fixed forwarding mode in geographic location-based routing algorithm, we investigated a new efficient packet routing strategy which combined with Deep Reinforcement Learning (DRL) algorithm (Proximal Policy Optimization: PPO) to minimize the hops and reduce the probability of encountering “routing hole” during the forwarding of the packets in complex networks. Each node in our network can make routing decisions to learn a routing policy that can not only use the greedy mode to maintain the efficiency of routing policy but also reduce the use of peripheral forwarding mode. The learning process of the DRL agent is based on the status information of the packet being transmitted and information of neighbor nodes within the communication range. Extensive simulations over dynamic network topologies and the number of nodes have shown that our packets routing agent can learn the optimal policy in terms of average packets delivery rate and the number of hops compared with Greedy Perimeter Stateless Routing (GPSR) protocol. The performance of PPO in large-scale action space is also verified, which provides a basis for the future researches to combine PPO technique with packet routing optimization.

WA-GPSR: Weight-Aware GPSR-Based Routing Protocol for VANET

The extremely fast topology has created new requirements for the geographic routing protocol, which has been the most efficient solution for Vehicular Ad-hoc Networks (VANETs). The frequent disconnection of links makes the choice of the next routing node extremely difficult. Hence, an efficient routing algorithm needs to deliver the appropriate path to transfer the data packets with the most relevant quality of service (QoS). In this work, the weight-aware greedy perimeter stateless (WA-GPSR) routing protocol is presented. The enhanced GPSR protocol computes the reliable communication area and selects the next forwarding vehicle based on several routing criteria. The proposal has been evaluated and compared to Maxduration-Minangle GPSR (MM-GPSR) and traditional GPSR using strict metric analysis. Our experimental results using NS-2 and VanetMobiSim, have demonstrated that WA-GPSR has the ability to enhance network performance.

Performance Analysis of GreedLea Routing Protocol in Internet of Vehicle (IoV) Network

The Internet of Vehicles (IoV) network transforms smart life on the wheels through several connections between vehicles, highways, people and networks, providing a safer, more effective and more energy-efficient driving experience. In a specific field, the reliable arrival of independent vehicles and the typical enhancement of traffic safety change through a fast and consistent distribution of messages. It is important to disseminate messages between vehicles that make up the IoV network and to be exploit of the quick and effective transmission of multi-hop communication for the information broadcasting. This study introduces the standardization method and summarizes the primary technologies of IoV network. This study provides a set of traditional research developments, analyses key innovations to date and, eventually, proposes solutions to common use cases that could provide valuable references for the development and implementation of potential IoVs network. The simulation has been done using OMNET++ platform to evaluate the GreedLea routing protocol with the standard Greedy Perimeter Stateless Routing (GPSR) and Ad-hoc On-demand Distance Vector (AODV) routing protocol in IoV network scenario. In the performance analysis varied parameters for example direction, node and speed has been take into account. This study also proposed to evaluate GreedLea in a crowded city situation and in a highway situation to provide further realistic simulations. From the simulation results, it shown that the GreedLea presented better performance compared to the traditional GPSR and AODV in term of end-to-end latency, packet loss rate and path loss.

Transmission of Emergency Data Packets in Vehicular Ad-Hoc Network

The Vehicular Ad-Hoc network is a new technology in the internet of things (IoT). It is broadly used in the intelligent transport system. VANET provides V2V communication and V2R communication in real-time communication. The intelligent transport system is used for road safety, information service, and traffic efficiency. The Greedy Perimeter Stateless Routing Protocol (GPSR) and Greedy Perimeter Coordinate Routing Protocol (GPCR) are proposed however, transmission of emergency data packets can be risky since packet delivery ratio is not guaranteed as GPCR link connectivity is not considered, hence they are not suitable for emergency data transmission. The algorithm called Destination Sequenced Distance Vector (DSDV) and Ad-hoc On-demand Distance Vector (AODV) are proposed to resolve the problems like End-to-End delay, average packet delivery ratio, and routing overhead. It is observed that the AODV algorithm has improved the throughput, PDR and has reduced the End-to-End delay in transmission of emergency data packets.

Link Failure Tolerant GPSR Protocol

Link failure cause packet loss, increased delays, disconnection from the network or even the shutdown of the inter-vehicle communication system vehicle-to-vehicle. Routing protocols suffer from this situation due to the lack of fault tolerance mechanisms that are intended to make the vehicular ad hoc network capable of remaining functional over time with low response time and/or high throughput; despite failures. Through this paper, we propose a version of the Greedy Perimeter Stateless Routing (GPSR) protocol that is tolerant to link failures based on the prediction of the future position of each vehicle (position prediction-GPSR) and a minimum threshold of the vehicle coverage area (position predictive with threshold-GPSR) to elect the next hops. Simulation results show that the proposed approach offers good performance.

GPSR Routing Performances Enhancement for VANET networks with Taguchi Optimization Mechanism

Routing mechanism plays an important role in the performances of Vehicular Ad Hoc Networks (VANET). Hence, various routing mechanisms are proposed to enhance VANET performances, however few researches are dedicated to optimize these routing mechanisms. In this paper an optimization mechanism is proposed to improve the performances of Greedy Perimeter stateless Routing (GPSR) protocol. Design of Experiments is used along with Taguchi Optimization method to fine tune GPSR internal routing parameters against VANET network scenarios. The target of optimization in this work is set to network performances including network throughput, delay and packet delivery ration (PDR). These targets are mathematically combined to form a single optimization target. A simulation experiments are performed to evaluate VANET performances. Obtained results showed that the proposed optimization improves the VANET performances in terms of throughput, PDR and delay. Further real-time integration of Optimization and routing mechanism can improve network performances.

Link risk degree aided routing protocol based on weight gradient for health monitoring applications in vehicular ad-hoc networks

Recently Vehicular Ad-hoc Network (VANET) has gained great exposure for its emerging applications and becoming an essential part of human daily life. VANET can broadcast safety and control messages to neighbors and intended destinations on time. But due to the frequent topology change and high mobility of vehicles imposes a major challenge in VANET communications. Designing an efficient routing algorithm for maintaining adequate network performance in health monitoring applications is the necessity of today’s life. Wireless technology and positioning system make geographic routing protocol a more suitable and effective solution for VANET applications. High-quality medical facilities and respond promptly needed to the patient in an emergency or disaster situation. Therefore, using efficient routing, a patient’s health can be monitored through communication between vehicles and the patient. Greedy Perimeter Stateless Routing protocol can be a suitable routing protocol that can help in best route selection in an emergency. The work presented in this paper analyzes the advantages and shortcomings of GPSR and proposes an improved link risk degree aided Greedy Perimeter Stateless Routing protocol based on Weight Gradient (GPSR-WG) protocol. GPSR-WG improves the greedy forwarding by considering multiple routing criteria such as direction, distance degree, link risk degree, and normalized speed factor. GPSR-WG implements the weight gradient on these criteria for selecting the next-hop node within the communication range to maximize the routing performance. We have evaluated the work mathematically and simulated it using the NS-3 network simulator. Simulation results have proved that the GPSR-WG protocol performs better when compared with GPSR, GPSR-M, and MM-GPSR in an urban vehicular environment.

Greedy Intersection-Mode Routing Strategy Protocol for Vehicular Networks

The advantages of vehicular ad hoc networks (VANETs) have been acknowledged, particularly during the last decade. Furthermore, VANET-related issues have been addressed by different researchers. Forwarding information professionally in a VANET is considered a challenging task precisely at the intersections where forwarding the information turns out to be extremely problematic. To elucidate this problem, many researchers have established routing protocols. The improved greedy perimeter stateless routing protocol (IGPSR) has been suggested, in the direction of employing greedy-mode proceeding traditional transportation's streets as well as to employ intersection-method at the joints. In view of greedy mode, the selection of the following stage is as in GPSR. By contrast, in the mode at an intersection, we would expect the vehicle guidelines to govern the following stage. The recreated consequences expose the algorithm, which is anticipated to undeniably demonstrate its competency.

3DEOR: an opportunity routing protocol using evidence theory appropriate for 3D urban environments in VANETs

The vehicular ad hoc network (VANET) is a promising technology to improve the comfort and safety of passengers, roads and urban traffic. Applications applied to VANETs require efficient routing protocols. Urban environments include tunnels, subways, overpasses, and multilevel highways, which indicate the multilevel information environment and specific conditions of radio channel dissemination. In this paper proposed a three-dimensional (3D) evidence theory based, opportunistic routing protocol, called 3DEOR, which address the above issues using a new hybrid criterion called PAL. The PAL criterion includes three metrics of packet delivery probability to compensate for unreliable dissemination environments, packet advancement appropriated to 3D environments to reduce the number of hop counts and level to improve link connectivity. Prioritise the relay set members based on the new criteria to select the best relay node. In cases where there is no certainty, the evidence theory can be a good choice for combining the three metrics of the PAL criterion. The simulation results show the superiority of the proposed protocol over the 3D link state aware geographic opportunistic (3DLSGO) and 3D greedy perimeter stateless routing (3DGPSR) protocols in network performance indices such as packet delivery rate, end-to-end delay and average hop count.