Proposed Routing Algorithm Research Articles

An efficient adaptive reconfigurable routing protocol for optimized data packet distribution in network on chips

The deadlock-free and live lock-free routing at the same time is minimized in the network on chip (NoC) using the proposed adoptive reconfigurable routing protocol (ARRP). Congestion condition emergencies are avoided using the proposed algorithm. The input packet distribution process is improved among all its shortest paths of output points. The performance analysis has been initiated by considering different configuration (N*N) mesh networks, by sending various ranges of data packets to the network on chip. The average and maximum power dissipation of XY, odd-even, Dy-XY algorithm, and proposed algorithm are determined. In this paper, an analysis of gate utilization during data packet transfer in various mesh configurations is carried out. The number of cycles required for each message injection in different mesh configurations is analyzed. The proposed routing algorithm is implemented and compared with conventional algorithms. The simulation has been carried out using reconfigurable two-dimensional mesh for the NoC. The proposed algorithm has been implemented considering array size, the routing operating frequency, link width length, value of probability, and traffic types. The proposed ARRP algorithm reduces the average latency, avoids routing congestion, and is more feasible for NoC compared to conventional methods.

A public static agreement key based cryptography for secure data transmission in WSN based smart environment application

Wireless Sensor Networks (WSN) is an emerging networking technology that allows for the low-cost, unattended monitoring of a variety of environments, thus WSN technologies have become increasingly important. Simultaneously, lack of security and poor energy efficiency is considered the major concerns in many routing protocols in various existing WSN protocols. Security and energy efficiency concerns are mainly due to the emergence of various kinds of malicious attacks because of advancements in networking technology. To mitigate these issues secured transmission is essential in any routing protocol. In order to provide safe data transfer in WSN-based smart environment applications, the TABEESR protocol (Trust Analysis Based Energy Efficient and Safe Routing) was developed. The proposed routing protocol encompasses four phases such as network creation and node deployment, grading for trust evaluation, route identification process and data transmission. The trust evaluation is performed with the aid of sensor packet features to categorize the positive nodes and negative nodes. The path identification process includes two processes such as cluster head selection through red fox optimization and relay node selection using a fuzzy interference system for energy efficient and secure path selection. A public static agreement key based cryptography technique is introduced for secure data transmission. The proposed TABEESR is tested with MATLAB software to validate its performance. SMEER, SHER, SRPA and AFSA are the prior routing protocols in WSN that are considered to compare the performance of the proposed routing protocol. The proposed TABEESR algorithm attained 95(%) of Average Residual Energy, 91 (%) of packet delivery ratio, 1350 (sec) network lifetime, 0.131 (Mbps) of throughput and 7(%) Packet Loss. Moreover, the proposed PSAKC algorithm attained 5.8 (%) information loss, 3.8 (sec) encryption time, 2.3 (sec) decryption time, 6 (sec) execution time, and 6.5 (J) energy consumption which is better than the prior methods. The proposed routing algorithm is applicable for providing security in both server to device and device to device in various smart applications.

Load-balancing routing algorithms for service congestion avoidance in LEO optical satellite networks

The low-Earth-orbit optical satellite network (LOSN) is an attractive solution to realize the end-to-end service quality of large-scale services, and it is the basis of establishing the 6G mobile network with high throughput and high reliability. However, the current LOSN is practically unable to achieve such high throughput for global service access. First, the centralized deployment of ground gateways will cause a heavy traffic load in the space segment of the LOSN, which becomes the bottleneck constraining the further growth of network throughput. Second, the high-speed movement of LEO satellites will cause continuous movement of traffic load, which will affect the scalability of routing computing. In this paper, two routing algorithms are proposed to increase the throughput in the dynamic LOSN topology. The congestion-aware load balancing (CALB) algorithm is proposed to address the inter-satellite link congestion in the dynamic LOSN topology. Then, a load-balancing routing algorithm based on satellite–ground cooperation (SGC-LB) is proposed to further reduce the impact of the network bottleneck. To evaluate the performance of the proposed routing algorithm, extensive simulations were performed on a 288-satellite Walker-Star constellation with inter-satellite links. The service blockage rate and network bandwidth utilization rate are evaluated showing the effectiveness of the proposed routing algorithm. The network using the SGC-LB algorithm can accommodate 60.00%, 56.00%, 42.22%, and 33.33% more services than using the Shortest Path algorithm, Sway algorithm, CALB algorithm, and Anycast algorithm, respectively, with zero service congestion during the simulation. The SGC-LB also gains a 6.04%, 5.00%, 5.04%, and 0.77% higher network utilization rate than the Shortest Path, Sway, CALB, and Anycast algorithms, respectively.

Exploiting Network Structure in Multi-criteria Distributed and Competitive Stationary-resource Searching

Transportation between satellite cities or inside the city center has always been a crucial factor in contributing to a better quality of life. This article focuses on multi-criteria distributed and competitive route planning for stationary resources in regions where neither real-time nor historical availability of the targeted resource is accessible. We propose an inference-than-planning approach, with an availability inference for stationary resources in areas with no sensor coverage and a distributed routing where no information is shared among agents. We leverage the inferred availability and network structure in the searching space to suggest a two-stage algorithm with three relaxing policies: adjacent cruising, on-orbital annealing, and orbital transitioning. We take two publicly accessible parking-slot datasets from San Francisco and Melbourne for evaluation. Overall results show that the proposed availability inference model can retain decent performance. Furthermore, our proposed routing algorithm maintains the quality of solutions by achieving the Pareto-optimal between searching experience and resource utilization among baseline and state-of-the-art methods under various circumstances.

Rough Set Techniques in Wireless Sensor Networks using Membership Function and Rough Labelling Graphs for Energy Aware Routing

Rough set theory and rough graphs are employed for data analysis. Rough graphs utilize approximations, and this paper presents a method for implementing rough graphs using rough membership functions and graph labeling for data structures and reduction. This paper proposes a rough graph labeling method, termed rough -labeling similarity graph, that utilizes a similarity measure for vertex and edge labeling. This method aims to minimize boundary regions in rough graphs and is applicable to wireless sensor networks (WSNs). In WSN, the proposed algorithms such as PSO-LSTM, COA-LSTM, LOA-LSTM integrates with rough set theory based rough -labeling for boundary region identification. The proposed method incorporates the membership functions encapsulates the rough labeling graphs for cluster boundary region identification for WSN. The cluster boundary for WSN is based on rough set membership and rough labelling is termed as rough set membership boundary region (RMB). RMB in WSN and implementation of proposed routing algorithm such as LOA-LSTM provides high throughput and energy saving when compared to existing cluster boundary structure methods such as Voronoi, Spectrum and Chain.

Time-Varying Graph and Binary Tree Search Based Routing Algorithm for LEO Satellite Networks

Though low earth orbit (LEO) satellite networks have superiorities in short delay, wide coverage and flexible networking, the end-to-end (E2E) transmission has been confronted with severe challenges due to frequent changes of network topology. In this paper, a time-varying graph and binary tree search based routing algorithm for LEO satellite networks is proposed to reduce the E2E transmission delay and improve the network quality of service. Firstly, a coordinate graph (CG) model is adopted to characterize the topology structure and resource distribution of LEO satellite networks. Then, an E2E minimum-hop area is determined based on CG model, so that a minimum-hop binary tree (MHBT) is constructed. Finally, the pruning traversal of MHBT is performed to obtain all minimum-hop paths, based on which the E2E path with the shortest delay is determined. Simulation results show that the proposed routing algorithm not only reduces the path delay, but also improves the file delivery ratio.

Graph based routing algorithm for torus topology and its evaluation for the Angara interconnect

Several approaches and techniques exist to resolve load balancing problem in general and torus topology networks. Graph methods are natural ways to perform balancing of routing paths. A routing balancing algorithm must operate within the constraints of the underlying network architecture that limits several parameters, such as the number of logical paths in the network. In this paper, we consider a torus topology that is one of the common topologies that are used in high performance computing systems. We introduce a routing graph that corresponds to a interconnect topology, abstracts interconnect routing rules, providing one-to-one correspondence of network routes and graph paths. We propose a deadlock-free routing algorithm based on a fast single-source shortest path algorithm in a routing graph for the deterministic routing of a torus topology interconnect with a single virtual channel. The proposed algorithm is a tradeoff between two existing routing algorithms, also the algorithm is optimized for multidimensional torus topology and can be applied to any torus topology HPC system. We present a complete description of a routing graph that abstracts the Angara interconnect with 4D torus topology. We evaluated the proposed routing algorithm and benchmarked the maximum performance improvement of 71% for the Alltoall pattern for torus topology systems up to 432 nodes on the Angara interconnect simulator. The performance improvement of more than 5% was obtained for the NPB FT and IS application kernels on a 32-node supercomputer.

Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning.

In high-speed railway operational monitoring network systems targeting railway infrastructure as its monitoring objective, there is a wide variety of sensor types with diverse operational requirements. These systems have varying demands on data transmission latency and network lifespan. Most of the previous research focuses only on prolonging network lifetime or reducing data transmission delays when designing or optimizing routing protocols, without co-designing the two. In addition, due to the harsh operating environment of high-speed railways, when the network changes dynamically, the traditional routing algorithm generates unnecessary redesigns and leads to high overhead. Based on the actual needs of high-speed railway operation environment monitoring, this paper proposes a novel Double Q-values adaptive model combined with the existing reinforcement learning method, which considers the energy balance of the network and real-time data transmission, and constructs energy saving and delay. The two-dimensional reward avoids the extra overhead of maintaining a global routing table while capturing network dynamics. In addition, the adaptive weight coefficient is used to ensure the adaptability of the model to each business of the high-speed railway operation environment monitoring system. Finally, simulations and performance evaluations are carried out and compared with previous studies. The results show that the proposed routing algorithm extends the network lifecycle by 33% compared to the comparison algorithm and achieves good real-time data performance. It also saves energy and has fewer delays than the other three routing protocols in different situations.

Computing Power Network: Multi-Objective Optimization-Based Routing.

This paper presents a novel routing planning method based on multi-objective optimization to tackle the routing problem in computing power networks. The proposed method aims to improve the performance and efficiency of routing by considering multiple objectives. In this study, we first model the computing power network and formulate the routing problem as a multi-objective optimization problem. To address this problem, we introduce a non-dominated sorting genetic algorithm incorporating a ratio parameter adjustment strategy based on reinforcement learning. Extensive simulations are conducted to evaluate the performance of the proposed routing algorithm. The results demonstrate significant client latency and cost reductions, highlighting the algorithm's effectiveness. By providing a comprehensive solution to the routing problem in computing power networks, this work contributes to the field by offering improved performance and efficiency. The proposed method's ability to optimize multiple objectives sets it apart from existing approaches, making it a valuable contribution to the research community.

New Measure Routing Algorithm for PEGASIS Wireless Sensor Network

The sensor node spends the maximum energy in data transmission for maintaining this issue we proposed a new routing protocol for sensor nodes in WSN. A sensor network is organized into many sets and all sets collect the data with minimum edge weight in a parallel way so doing this all sets create a minimum edge weight chain. Further, the sets make a head node that is near the base station. All sets have a unique chain in this round after that all set again to select a head node on the basis of high residual energy, less mobility, and minimum distance to the base station that the head node sends the data to the base station. Further, our proposed routing algorithms save energy. We simulated the proposed model in MATLAB all simulation result is better than both routing algorithms.

Simultaneous Connections Routing in Wavelength–Space–Wavelength Elastic Optical Switches

In this paper, we investigate the three-stage, wavelength-space-wavelength switching fabric architecture for nodes in elastic optical networks. In general, this switching fabric has r input and output switches with wavelength-converting capabilities and one center-stage space switch that does not change the spectrum used by a connection. This architecture is most commonly denoted by the WSW1 (r, n, k) switching network. We focus on this switching fabric serving simultaneous connection routing. Such routing takes place mostly in synchronous packet networks, where packets for switching arrive at the inputs of a switching network at the same time. Until now, only switching fabrics with up to three inputs and outputs have been extensively investigated. Routing in switching fabrics of greater capacity is estimated based on routing in switches with two or three inputs and outputs. We now improve the results for the switching fabrics with four inputs and outputs and use these results to estimate routing in the switching fabric with an arbitrary number of inputs and outputs. We propose six routing algorithms based on matrix decomposition for simultaneous connection routing. For the proposed routing algorithms, we derive criteria under which they always succeed. The proposed routing algorithms allow the construction of nonblocking switching fabrics with a lower number of wavelength converters and the reduction of the overall switching fabric cost.

Improved Quality of Service by canine olfactory route finding algorithm for Vehicular Ad Hoc Network

AbstractQoS aware vehicular ad‐hoc network (VANET) routing protocols address the increasing demand for delay‐sensitive vehicular applications to establish intelligent transportation. Primary challenge with ad‐hoc nature of VANET is that the communication between vehicle‐to‐vehicle and vehicle‐to‐infrastructure is prone to link failure. Bio‐inspired algorithms offer robust solutions to secure VANET links. Accordingly in this article, novel canine olfactory route‐finding algorithm (CORFA) is proposed for VANET to achieve the best possible route with the minimum transmission of packets, which ensures enhanced QoS. The underlying architecture utilizes the exceptional ability of canines to evaluate and memorize the environment and pass the message to neighbors. It makes canines a great choice to model their behavior for VANET routing. RSUs are fundamental components of VANET that supply contents to the proceeding vehicles from their cache. As RSUs carry out information dissemination tasks efficiently, the already traversed and discovered routing paths in the recent past can be cached in RSU's storage and through back‐haul can be circulated to neighboring RSUs. Vehicles request the pre‐cached route and use them for data transmission. Thereby avoiding the route discovery and maintenance procedure reduces the routing overhead. In absence of a pre‐cached route, the fittest vehicle from each hop is selected as next forwarder for packet transmission. The proposed routing algorithm forms multiple routes during route discovery to handle link failure scenario. Traffic simulator, SUMO is used to generate the mobility model and integrated with MATLAB for analysing the performance of CORFA with related geographical and topological routing protocols that confirm the performance superiority of CORFA under several metrics such as packet delivery ratio, network latency, throughput, and overhead.

Time-Varying Topology Model for Dynamic Routing in LEO Satellite Constellation Networks

With the characteristics of low-latency, seamless coverage and high bandwidth, the Low Earth Orbit (LEO) satellite network has been the promising technology for the sixth-generation mobile communication (6G) networks, especially, the inter-satellite link can improve the flexibility of inter-satellite networking and routing. However, in the existing works, since the impact of link attributes on the satellite topology has not been well investigated, it is difficult to avoid the loss of topology information for the transmission path selection, which may aggravate the unreliability of routing path. In this paper, we propose a novel time-varying topology model for the LEO satellite network, to improve the adaptability of the satellite routing. Firstly, the weighted time-space evolution graph based on the link attributes is established to construct the time-varying topology model of LEO satellite networks. Then, the utility function of the link attributes is modelled and the multi-attribute decision-making is introduced to calculate the weight of each link attribute for the quantification of the link utility. Finally, based on the topology model, the inter-satellite link utility-based dynamic routing algorithm is proposed to improve the adaptability of satellite routing. The simulation results demonstrate that the proposed routing algorithm outperforms the existing routing algorithms in terms of packet drop rate, end-to-end delay and throughput.

LEAF: Let’s Efficiently Make Adaptive Forwarding in Payment Channel Networks

Blockchain technology is widely applicable to modern payment systems but has inherent throughput limitations. Off-chain networks are proposed to solve scalability issues, which allows parties to efficiently perform micropayments without committing all of the payments to the blockchain. Off-chain payments with security and privacy protection requirements use the smart contract to ensure security and reduce the risk of sensitive information leakage. Although off-chain payments avoid expensive on-chain operations, it raises many concerns, such as the capacity limitation of payment channels and highly dynamic channel status, lowering the throughput of payment channel networks (PCNs). In our work, we explore the path overlap issues in PCNs and propose a decentralized payment routing scheme to improve the network throughput and reduce the redundant traffic overhead of PCNs, thereby guaranteeing efficient payments. The simulation results indicate that the proposed routing algorithm can achieve higher throughput than other routing schemes while guaranteeing short payment times.

Link-State Aware Hybrid Routing in the Terrestrial–Satellite Integrated Network

In this paper, we study data transmission in the Terrestrial-Satellite Integrated Network (TSIN), where terrestrial networks and satellites are combined together to provide seamless global network services for ground users. However, efficiency of the data transmission is limited by the time-varying inter-satellite link connection and intermittent terrestrial-satellite link connection. Therefore, we propose a link-state aware hybrid routing algorithm, which selects the integrated data transmission path adaptively in this paper. First of all, a space-time topology model is constructed to represent the dynamic link connections in TSIN. Thus, the transmission delay can be analyzed accordingly, and the data transmission problem can then be formulated. To balance the effectiveness and accuracy of searching a hybrid path, we carefully discuss the optimization of space-time topology updating, and propose an inter-satellite link selection algorithm. For the terrestrial-satellite link in hybrid routing, the data transmission problem is transformed into a weighted bipartite graph matching problem and solved with a Kuhn-Munkres-based link selection algorithm. To verify the effectiveness of our proposed routing algorithm, extensive simulations are conducted based on a realistic Hongyun constellation project. Results show that the network performance is improved with respect to data transmission delay, packet loss rate, and throughput.

TTQR: A Traffic- and Thermal-Aware Q-Routing for 3D Network-on-Chip.

The die-stacking structure of 3D network-on-chips (3D NoC) leads to high power density and unequal thermal conductance between different layers, which results in low reliability and performance degradation of 3D NoCs. Congestion-aware adaptive routing, which is capable of balancing the network's traffic load, can alleviate congestion and thermal problems so as to improve the performance of the network. In this study, we propose a traffic- and thermal-aware Q-routing algorithm (TTQR) based on Q-learning, a reinforcement learning method. The proposed algorithm saves the local traffic status and the global temperature information to the Q1-table and Q2-table, respectively. The values of two tables are updated by the packet header and saved in a small size, which saves the hardware overhead. Based on the ratio of the Q1-value to the Q2-value corresponding to each direction, the packet's output port is selected. As a result, packets are transferred to the chosen path to alleviate thermal problems and achieve more balanced inter-layer traffic. Through the Access Noxim simulation platform, we compare the proposed routing algorithm with the TAAR routing algorithm. According to experimental results using synthetic traffic patterns, our proposed methods outperform the TAAR routing algorithm by an average of 63.6% and 41.4% in average latency and throughput, respectively.

A Data-Driven Packet Routing Algorithm for an Unmanned Aerial Vehicle Swarm: A Multi-Agent Reinforcement Learning Approach

Routing decisions made by unmanned aerial vehicle (UAV) swarms are affected by complex and dynamically changing topologies. A centralized routing algorithm imposes the entire computational burden on one module, and the high data dimensionality renders computation burdensome. In this letter, we develop a multi-agent reinforcement learning-based routing algorithm for a UAV swarm. The UAVs are trained in a data-driven manner to make distributed routing decisions. Factors that include channel quality, UAV movement, UAV overhead, and the extent of neighbor variation are incorporated into link quality assessment. Long short-term memory is used to improve the Actor and Critic networks, and more information on temporal continuity is added to facilitate adaptation to the dynamically changing environment. Simulations show that the proposed routing algorithm reduces data transmission delay and enhances the transmission rate compared with traditional routing algorithms.

Low‐cost regional‐based congestion‐aware routing algorithm for 2D mesh NoC

SummaryGiven the advantages of network‐on‐chips (NoCs), they are rapidly improving to replace other forms of System‐on‐Chip (SoC) designs. Although various factors improve the NoC's performance, many challenges should be addressed when designing an NoC, one of which is congestion and its impacts on performance and efficiency. Hence, numerous routing algorithms have been proposed that contemplate the congestion influences to deal with its complexities. Nevertheless, given the significant impacts of overheads on performance and efficiency, it is crucial to consider them when designing an enhanced NoC. The proposed routing algorithm employs regional traffic information within multiple clusters and has a decent view of the traffic condition when choosing the path to the destination. Each node generates one bit of traffic information and propagates it only when the node is congested, thus preventing the information overhead. Finally, the path diversity parameter is utilized to identify the best route from the source to the destination. The proposed algorithm's results show that the number of received packets, average latency, average throughput, maximum latency, and energy consumption while using different traffic patterns are improved by 10.9%, 35.3%, 15.8%, 43%, and 15.3%, respectively.

Secure Energy-Efficient Load Balancing and Routing in Wireless Sensor Networks With Mediative Micro-ANN Fuzzy Logic

The security and life time of network are most import factors in wireless sensor networks (WSN). In this research work Mediative Micro ANN Fuzzy logic(MMAFL) has been proposed, along with load-balance routing algorithm. The designed method can increase WSN’s additional feature like securing packet from message capture and tampering attacks. The aim of the proposed routing algorithm is reduce the overall energy consumption and ensure fair use of node energy, so that lifetime of the network has been automatically increased. Experimentation of the proposed Mediative Micro ANN Fuzzy logic is implemented in MATLAB 2018b software and compared with the existing techniques to prove the effectiveness of the approach. The MMAFL model is most useful for medical, Mobile, and industrial applications for data transferring and network security. The performance measures like accuracy 97.45%, F measure 97.34%, overhead 11.10%, delay 8.96ms, throughput 90%, lifetime 27%, and packet-delivery ration 86.11% had been attained, which are most prominent compared to earlier models.

Energy Efficient Ant Colony System for Packet Routing in Wireless Sensor Network

Routing packets in Wireless Sensor Network (WSN) is challenging due to the distribution of sensor nodes with different ability. Inefficient routing may lead to higher failure rate, higher latency and higher energy consumption. One of the common approaches to solve this problem is by using bio-inspired routing algorithms due to their abilities to adapt with dynamic environment. This paper proposed an improved ant colony system for packing routing in WSN that focuses on exploration and exploitation techniques. In the proposed routing algorithm, the best path to be used for packet transmission will be determined by considering the remaining energy of each sensor node to reduce the hotspot problem. Local pheromone update and global pheromone update are used with the aim to prevent imbalanced energy depletion of sensor nodes and to balance the packet distribution. The proposed routing algorithm was validated against several bio-inspired routing algorithms in medium and large sized networks. The results suggested that it has outperformed in terms of success rate, packet loss rate, latency and energy efficiency.