Proposed Routing Approach Research Articles

Fault-aware routing approach for mesh-based Network-on-Chip architecture

Aggressive communication among cores in multi-core architectures leads to excessive workload on the components which often degrades the normal functionality and induces faults. Network-on-Chip (NoC) also inherits the phenomenon of fault occurrence in multi-core architectures that affect the links and routers of the communication infrastructure. In this work, we emphasize on faults that occur in NoC routers and propose a fault-aware routing approach to mitigate them. The proposed routing approach selects the routing path for flits depending on the health status of the neighboring routers. Furthermore, a router cool-off mechanism is introduced so that the routers that have reached the workload threshold can be reused after some cool-off period. A router micro-architecture is also presented to support the fault-aware routing approach. While comparing with the state-of-art works, the proposed methodology achieves both zero fault occurrence and significant improvement in throughput with negligible delay overhead.

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.

An Intersection-Based Routing Scheme Using Q-Learning in Vehicular Ad Hoc Networks for Traffic Management in the Intelligent Transportation System

Vehicular ad hoc networks (VANETs) create an advanced framework to support the intelligent transportation system and increase road safety by managing traffic flow and avoiding accidents. These networks have specific characteristics, including the high mobility of vehicles, dynamic topology, and frequent link failures. For this reason, providing an efficient and stable routing approach for VANET is a challenging issue. Reinforcement learning (RL) can solve the various challenges and issues of vehicular ad hoc networks, including routing. Most of the existing reinforcement learning-based routing methods are incompatible with the dynamic network environment and cannot prevent congestion in the network. Network congestion can be controlled by managing traffic flow. For this purpose, roadside units (RSUs) must monitor the road status to be informed about traffic conditions. In this paper, an intersection-based routing method using Q-learning (IRQ) is presented for VANETs. IRQ uses both global and local views in the routing process. For this reason, a dissemination mechanism of traffic information is introduced to create these global and local views. According to the global view, a Q-learning-based routing technique is designed for discovering the best routes between intersections. The central server continuously evaluates the created paths between intersections to penalize road segments with high congestion and improve the packet delivery rate. Finally, IRQ uses a greedy strategy based on a local view to find the best next-hop node in each road segment. NS2 software is used for analyzing the performance of the proposed routing approach. Then, IRQ is compared with three methods, including IV2XQ, QGrid, and GPSR. The simulation results demonstrate that IRQ has an acceptable performance in terms of packet delivery rate and delay. However, its communication overhead is higher than IV2XQ.

A novel extended Kalman filter-based optimized routing approach for IoV environment

The development of intelligent transportation system has divulged towards traffic management, planning and control that requires precise neighboring vehicle location anticipation for information transmission. The crucial problem is to maintain QoS parameters in high speed and varying vehicular topology environment. The paper presents a novel EK-PGRP (Extended Kalman filter- Predictive Geographic Routing Protocol) routing approach to anticipate neighbor location and to select the propitious neighbor for advancing packets from source to destination vehicle using extended Kalman filter for real-time V2V communication in both urban and highway vehicular environment. This is acquired according to spatial and temporal movement attributes; every vehicle has an anticipation model to anticipate its own and neighboring vehicle mobility. Moreover, if LMP (Local Maximum Problem) state is encountered, i.e., where a vehicle is unable to locate any neighbor nearer to destination than itself to forward an information packet; then it uses predictive prediction algorithm to overcome that state. The precision, robustness and coherence attributes of the proposed routing approach are illustrated via extensive simulations. EK-PGRP is contrasted with K-PGRP (Kalman filter- Predictive Geographic Routing Protocol), PGRP (Predictive Geographic Routing Protocol) and GPSR (Greedy Perimeter Stateless Routing) routing protocols and results demonstrate that EK-PGRP outperformed most of the simulation cases and attained the minimum location error while ameliorating prediction accuracy of the vehicles in vehicular environment. The simulations were performed on MATLAB R2018a along with traffic simulator SUMO.

Graph Theory Algorithms of Hamiltonian Cycle from Quasi‐Spanning Tree and Domination Based on Vizing Conjecture

In this study, from a tree with a quasi‐spanning face, the algorithm will route Hamiltonian cycles. Goodey pioneered the idea of holding facing 4 to 6 sides of a graph concurrently. Similarly, in the three connected cubic planar graphs with two‐colored faces, the vertex is incident to one blue and two red faces. As a result, all red‐colored faces must gain 4 to 6 sides, while all obscure‐colored faces must consume 3 to 5 sides. The proposed routing approach reduces the constriction of all vertex colors and the suitable quasi‐spanning tree of faces. The presented algorithm demonstrates that the spanning tree parity will determine the arbitrary face based on an even degree. As a result, when the Lemmas 1 and 2 theorems are compared, the greedy routing method of Hamiltonian cycle faces generates valuable output from a quasi‐spanning tree. In graph idea, a dominating set for a graph S = (V, E) is a subset D of V. The range of vertices in the smallest dominating set for S is the domination number (S). Vizing’s conjecture from 1968 proves that the Cartesian fabricated from graphs domination variety is at least as big as their domination numbers production. Proceeding this work, the Vizing’s conjecture states that for each pair of graphs S, L.

Implementation of ACO in Vanet with Detection of Faulty Node

Objective: To propose an Ant Colony Optimization (ACO) based routing approach for estimating the reliability parameter of backward and forward ants. Methods: The ACO-based proposed methodology is used to predict path or route discovery in VANET. This study has discovered the best optimal path for data transmission. The forward ant movement has a minimum hop count of four for vehicle number 6. The vehicle 6 is a source vehicle, and it starts data transmission. The pheromone concentration of the entire vehicle defers and, after updating, the performance was analyzed. The best path is selected based on hop count, then a path with maximum pheromone concentration is selected. The pheromone evaporation factor is updated after a certain time interval. Findings: We have checked the reliability of the path, considering pheromone evaporation as a factor, the pheromone table is updated at a regular time interval, and the reliability parameter is also proposed for performing a task without failure in an efficient manner. We have arranged a routing table in descending order of hop count and then select the best of three paths with a minimum hop count. We search for hop count less than or equal to 5 and total pheromone greater than or equal to 17.55. We find that Ant 3 having a hop count of 5 and total pheromone concentration of 19.37. In this work, we have taken the evaporation factor as 0.5. If the transmission of a message is successful, then the reliability assigned to the edge will be 100, and for unsuccessful transmission, reliability is taken down by 20. If the reliability of the path will become 40, then the vehicle has to search for a new path. In this way, we eliminate malicious nodes for efficient transmission of message. Novelty: The performance of VANET is improved using ACO based proposed routing approach. The optimal path is selected with a minimum hop count and reliability parameter to identify the successful and unsuccessful path. Keywords: Network security; VANET; Sybil; ACO; Pheromone Evaporation

EMBLR: A High-Performance Optimal Routing Approach for D2D Communications in Large-scale IoT 5G Network

Coping with the skyrocketing needs for massive amounts of data for the future Fifth Generation (5G) network, Device-to-Device (D2D) communications technology will provide seamless connectivity, high data rates, extended network coverage, and spectral efficiency. The D2D communications are a prevalent emerging technology to achieve the vision of symmetry in the Internet of Things (IoT) services. However, energy resource constraints, network stability, traffic congestion, and link failure of the devices are the crucial impediments to establish an optimal route in the D2D communications based IoT 5G network. These obstacles induced packet drop, rapid energy depletion, higher end-to-end delay, and unfairness across the network, leading to significant route and network performance degradation. Therefore, in this paper, an energy, mobility, queue length, and link quality-aware routing (EMBLR) approach is proposed to overcome the challenges and boost network performance. Moreover, a multicriteria decision making (MCDM) technique is utilized for the selection of the intermediate device in an optimal route. Extensive simulation has been conducted and proven that the proposed routing approach significantly enhances network performance. Overall, results have been carried out in Quality of Service (QoS) performance metrics and compared with other well-known routing approaches.

PORA: A Physarum-inspired obstacle-avoiding routing algorithm for integrated circuit design

The plasmodium of Physarum polycephalum, a large, amoeboid cell, has attracted much attention recently due to its intelligent behaviors in pathfinding, danger avoidance, and network construction. Inspired by the biological behaviors of this primitive organism, in this study, we explore the optimization capability of Physarum polycephalum systematically and present the first Physarum-inspired obstacle-avoiding routing algorithm for the physical design of integrated circuits. We simulate the foraging behaviors of Physarum polycephalum using a novel nutrition absorption/consumption mathematical model, thereby presenting an efficient routing tool called Physarum router. With the proposed routing approach, for a given set of pin vertices and a given set of on-chip functional modules, a rectilinear Steiner minimal tree connecting all the pin vertices while avoiding the blockage of functional modules can be constructed automatically. Furthermore, several heuristics including a divide-and-conquer strategy, a non-pin leaf node pruning strategy, a dynamic parameter strategy, etc., are integrated into the proposed algorithm to fundamentally improve the performance of the Physarum router. Simulation results on multiple benchmarks confirm that the proposed algorithm leads to shorter wirelength compared with several state-of-the-art methods.

Quality of Service Aware Routing Protocol in Software-Defined Internet of Vehicles

Software-defined Internet of Vehicles (SDIoV) has emerged as a promising field of study as it could overcome the shortcomings of traditional vehicular networks, such as offering efficient data transmission and traffic shaping in different vehicular scenarios to satisfy all the requirements of applications on the fly. Although routing solutions are lightly addressed for SDIoV, there are many limitations of routing protocols unaddressed in such environment. More precisely, shortest path routing algorithms are mostly focused in the state of the arts. This paper presents quality of service aware routing algorithm that forwards packets toward the most reliable and connected path to the destination. Particularly, candidate routes should satisfy metrics, such as signal to interference and noise ratio (SINR) constraint and have the highest probability of connectivity. To address these issues, we have formulated a discrete optimization problem to favor the best route among candidate paths and proposed the modified laying chicken algorithm (LCA) that results better results than the traditional approaches. We have mathematically analyzed the probability of connectivity along with the SINR metric. Moreover, a multiscore function based on traffic density and greediness factor is proposed to make intelligent decision at the intersections. Simulation results are used to validate the superiority of the proposed routing approach over the existing solutions.

Efficient Routing Protocol Based on Reinforcement Learning for Magnetic Induction Underwater Sensor Networks

In recent research on 3D underwater wireless sensor network (UWSN), magnetic induction communication is a promising candidate, thanks to several unique features, such as small transmission delay, constant channel behavior, and adequate long communication range. However, designing a routing protocol that prolongs the network lifetime and reduces the transmission delay has been still a challenge for a 3D UWSN. In this paper, we propose an efficient routing protocol based on reinforcement learning, in particular, the Q-learning that aims to investigate the resource management in the hierarchical networks. Through defining the single hopping bonus metrics of distance and energy, we deduce the updating formula of the routing algorithm and derive the relationship between energy priority and distance priority. In addition, we set up a regulatory factor to adjust the proportion between energy saving and low delay, and thus, it can meet different needs. The simulation results show that the proposed routing approach outperforms the conventional protocol in extending the network lifetime and reducing the transmission delay.

A Reinforcement Learning Based Routing in Cognitive Radio Networks for Primary Users with Multi-stage Periodicity

Designing an efficient routing protocol for cognitive radio networks is critical due to the dynamic behavior of the primary users. Based on empirical studies, the primary users activity on the licensed channels has periodicity comprised of several stages, and that the model of primary users activity may change during different stages. This paper has identified two main challenges facing designers: how to transmit packets via a stable route, and how to ensure imposing of minimal interference on the primary users. To address these, we propose a routing protocol which is based on a generalized version of Q-learning and which exploits the said model of primary users behavior. We divide the infinite time horizon into cycles (corresponding to periods), then break each cycle into several sub-cycles (corresponding to stages), making an assumption that the statistical model parameters of the primary users activity will not change during a sub-cycle. The extensive simulations confirm that our proposed routing approach outperforms existing schemes in terms of throughput and minimal interference. The paper also verifies that imposing of significant interference on the primary users and degradation of QoS of secondary users stem from lack of attention to the multi-stage periodic behavior of primary users.

Detection of denial of service attacks by domination graph application in wireless sensor networks

In many web services systems, need extended confirmation of confidential information including access control policies, which are cryptographically certified for secure data exchanges in wireless sensor networks. The domination set in graph theory enriches many applications in the field of networks. One of the application with the novel procedure named school bus routing approach is proposed, which can coordinate the characteristics of the system, with the goal of detection of denial of service attacks. This strategy is employed that is most powerful adequate to schedule the sensor nodes in such a route, to the point that it monitors the network traffic for identification of source of attack and for the denial of service provision to the users. The throughput, packet loss ratio and packet delivery results were simulated and analyzed in the network based on the implementation of proposed routing approach. Our approach is more resilient for incorporation environment.

A JOINT DUTY CYCLE SCHEDULING AND ENERGY AWARE ROUTING APPROACH BASED ON EVOLUTIONARY GAME FOR WIRELESS SENSOR NETWORKS

Network throughput and energy conservation are two conflicting important performance metrics for wireless sensor networks. Since these two objectives are in conflict with each other, it is difficult to achieve them simultaneously. In this paper, a joint duty cycle scheduling and energy aware routing approach is proposed based on evolutionary game theory which is called DREG. Making a trade-off between energy conservation and network throughput, the proposed approach prolongs the network lifetime. The paper is divided into the following sections: Initially, the discussion is presented on how the sensor nodes can be scheduled to sleep or wake up in order to reduce energy consumption in idle listening. The sensor wakeup/sleep scheduling problem with multiple objectives is formulated as an evolutionary game theory. Then, the evolutionary game theory is applied to find an optimal wakeup/sleep scheduling policy, based on a trade-off between network throughput and energy efficiency for each sensor. The evolutionary equilibrium is proposed as a solution for this game. In addition, a routing approach is adopted to propose an energy aware fuzzy logic in order to prolong the network lifetime. The results show that the proposed routing approach balances energy consumption among the sensor nodes in the network, avoiding rapid energy depletion of sensors that have less energy. The proposed simulation study shows the more efficient performance of the proposed system than other methods in term of network lifetime and throughput.

Efficient routing for middlebox policy enforcement in software-defined networking

Network applications require traffic to sequence through multiple types of middleboxes to enhance network functions, e.g., providing security and guaranteeing performance. Sequenced-middlebox policy routing on top of regular layer 2/3 flow routing is challenging to be flexibly managed by network administrators. In addition, various types of middlebox resources concurrently obtained by numerous applications complicate network-resource management. Furthermore, middlebox failures can lead to a lack of security and the malfunction of entire network. In this paper, we formulate a mixed-integer linear programming problem to achieve a network load-balancing objective in the context of sequenced-middlebox policy routing. Our global routing approach manages network resources efficiently by simplifying candidate-path selections, balancing the entire network and using the simulated annealing algorithm. Moreover, in case of middlebox failures, we design a fast recovery mechanism by exploiting the remaining link and middlebox resources locally. To the best of our knowledge, this is the first work to handle failures in sequenced-middlebox scenarios using OpenFlow. Finally, we implement proposed routing approaches on Mininet testbed and evaluate experiments’ scalability, assessing the effectiveness of the approaches. Results of the optimization on a test topology include an increase up to 26.4% of the throughput with respect to a sequenced shortest-path routing.

TGRP: A New Hybrid Grid-based Routing Approach for Manets

Most existing grid-based routing protocols use reactive mechanisms to build routing paths. In this paper, we propose a new hybrid approach for grid-based routing in MANETs which uses a combination of reactive and proactive mechanisms. The proposed routing approach uses shortest-path trees to build the routing paths between source and destination nodes. We design a new protocol based on this approach called the Tree-based Grid Routing Protocol (TGRP). The main advantage of the new approach is the high routing path stability due to availability of readily constructed alternative paths. Our simulation results show that the stability of the TGRP paths results in a substantially higher performance compared to other protocols in terms of lower end-to-end delay, higher delivery ratio and reduced control overhead.

Routing in Wireless Mesh Networks: Three New Nature Inspired Approaches

Multi radio, multi hop, self organizing and self configuring wireless technology are the characteristic features of wireless mesh networks (WMNs) to offer last mile access to end users. The emergence of stochastically varying network environments critically affects routing in WMNs. Any routing policy meant for WMNs must be quickly adaptive and evolve in a decentralized self organizing and self configuring manner. This paper firstly proposes formulation of a soft computing i.e. fuzzy logic based hybrid performance metric which includes per flow (throughput, delay and jitter) as well as per node (residual energy of the node) parameters. This fuzzy logic based hybrid performance metric enumerates the integrated link cost (ILC) which is used as distance measure between two adjacent nodes. The paper further proposes three routing algorithms based upon nature inspired computing approaches namely firefly algorithm, Big Bang Big Crunch and Ant Colony Optimization. The proposed routing approaches aim at finding the minimal ILC path within a stipulated time constraint. The time constraint is governed by the mobility of network nodes. Extensive simulations were conducted for various WMN topologies. The results of the proposed approaches have been compared with two commonly used conventional approaches and were found to be far more superior. It was also observed that the self organizing capability of the proposed nature inspired routing approaches effectively reduces the complexity and makes a network quite adaptive to the dynamic network behavior found in WMNs.

Efficient communication approach in Vehicular PKI

In recent era, we know transport industry tend to incorporates many facilities to the vehicle users which not only helps people to have safe journey but also smooth in nature. Intelligent transportation system (ITS) is the need for today's luxurious life. In this paper, it is proposed that communication among vehicles is performed using efficient routing where as secure environment is established using PKI(public key environment). It can be said that proposed Routing approach have better capabilities than other existing routing protocols it means communication where emergency messages will be disseminated efficiently and as early as possible. When VANET is to be protected from malicious access or to avoid the problem of vehicle tracking, one of the security features is called as location privacy is not mainly offered by typical PKI. Here efficient routing approach will be blended with security feature such as vehicles 's location privacy feature. which is definitely will be used to enhance typical PKI security.

Filo Büyüklüğü ve Karma Araç Rotalama Problemleri İçin Yeni Bir Yapısal Rotalama Algoritması

In this study, a new constructive routing algorithm for fleet size and mix vehicle routing problem is proposed in which residual costs rather than vehicle types are considered for route selection. The algorithm of the proposed routing approach is given and then the solution phases of a sample problem are shown by using the given algorithm. In order to highlight the performance of the routing approach, Golden's 12 test problems (Fleet Size and Mix Vehicle Routing Problem with Fixed Cost) are used. It is seen that the proposed method has better average time complexity and cost performances than Ochi's routing approach. Therefore, the solutions of the proposed method that uses vehicle type information are better than those of the methods that use residual cost based on the vehicle type information.

Nexthop selection mechanism for nodes with heterogeneous transmission range in VANETs

The frequent relocating of vehicles will significantly degrade the routing performance of a VANET. One of the proposed routing approaches to relieve this drawback adopts a greedy fashion to relay the packets hop-by-hop by selecting the nearest neighboring vehicle to the destination as the nexthop forwarder. It is an efficient method of packet routing when the transmission range of each vehicle in the VANET is of the same fixed length. Recently, as heterogeneous wireless networks have become popular, wireless terminals are generally equipped with multiple wireless access network interfaces to improve the quality of transmission and to gain a good signal while relocating. However, the greedy approach of nexthop forwarder selection will not always use the minimum hop counts of the routing path for VANET heterogeneous transmission range nodes. In this paper, we propose a nexthop selection mechanism for VANETs which takes the heterogeneous environment into consideration. A minimum hop count prediction method is firstly proposed to help the current packet-carrying vehicle node to estimate the minimum hop counts required from each neighbor to the destination. Subsequently, based on the estimated values, the routing decision can be made. The simulation results show that our proposed method exhibits better performance in terms of the average end-to-end delay and the packet delivery ratio comparisons than the compared methods.

A New Hybrid Grid-based Routing Approach for Manets

Abstract Most previous grid-based routing protocols use reactive mechanisms to build routing paths. In this paper, we propose a new hybrid approach for grid-based routing in MANETs which uses a combination of reactive and proactive mechanisms. The proposed routing approach, referred to as the Tree-based Grid Routing Protocol (TGRP), uses shortest-path trees to build the routing paths between source and destination nodes. Combining the desirable features of the reactive and proactive mechanisms allows for more stable routing paths by providing readily-constructed alternative paths. The stability of the TGRP paths lead to a substantial system performance compared to other protocols in terms of end-to-end delay and delivery ratio. Extensive simulation results reveal that the new TGRP outperforms the existing well-known GRID in terms of these measures