Distributed Routing Algorithm Research Articles

Energy Efficient Region based Source Distributed Routing Algorithm for Sink Mobility in Underwater Sensor Network

The residual nodes energy consumption is a primary factor to extend the lifespan of network. Utilizing a sensor node's very low-powerbattery in Underwater Wireless Sensor Networks (UWSNs) is still a difficult task. To increase the network lifespan, this paper proposes an Energy Efficient Region based Source Distributed Routing Algorithm for Sink Mobility in Underwater Sensor Network (EERSDRA-GCOP-UWSN). The proposed work consists of three Phases that are initialization phase, Routing Phase and Data transmission. For initialization phase, sinks and the sensor nodes are initialized. For Routing phase, Energy EERSDRA with sink mobility is employed to identify the region of sensor nodes. For data transmission phase, Geographic and Cooperative Opportunistic Protocol is proposed to select the best relay node on the basis of depth fitness factor. The proposed EERSDRA-GCOP-UWSN is evaluated with the performance metrics. Then performance of the EERSDRA-GCOP-UWSN proposed method provides 18.409%, 13.843% and 23.768% high Network lifetime, 27.744%, 24.881% and 19.703% low energy consumption, 17.346%, 20.852% and 26.347% high throughput and 16.385%, 20.416% and 22.907% high PDR when compared to the existing methods such as, Energy-efficient regional based cooperative routing protocol with sink mobility for UWSN (EERCRP-UWSN), a safe energy efficient and cooperative routing protocol for UWSN (SEECRP-UWSN), geo-opportunistic routing protocols for improving the residual energy range (EEGORP-UWSN) methods respectively. Finally, the performance of the proposed method achieves better result while compared to the existing methods.

Dynamic Parallel Flow Algorithms With Centralized Scheduling for Load Balancing in Cloud Data Center Networks

BCube is a well-known recursively defined network structure. It provides multiple low-diameter paths and good fault-tolerance for data center networks (DCNs). Its distributed routing algorithm, BCube Source Routing (BSR), can be deployed rapidly and conveniently to build multiple parallel path sets. But in the worst case, BSR may suffer from flow collisions and waste $50\%$ of the capacity of each BCube link. In this paper, to fully use those redundant links and to improve BCube load balancing, we supplement the BCube topology with a central master computer and we design two centralized dynamic parallel flow scheduling algorithms: CDPFS and CDPFSMP, for single-path and multi-path respectively. We focus on finding the least congested path for each flow by analyzing the information about the state of the global network. Furthermore, we allocate those paths to each flow in parallel. The simulation result shows that our proposed algorithms take advantage of BCube structure and deliver high-performance solutions for load balancing problems.

Solving Adaptive Distributed Routing Algorithm Using Crow Search Algorithm

Crow Search Algorithm (CSA) can be defined as one of the new swarm intelligence algorithms that has been developed lately, simulating the behavior of a crow in a storage place and the retrieval of the additional food when required. In the theory of the optimization, a crow represents a searcher, the surrounding environment represents the search space, and the random storage of food location represents a feasible solution. Amongst all the food locations, the one where the maximum amount of the food is stored is considered as the global optimum solution, and objective function represents the food amount. Through the simulation of crows’ intelligent behavior, the CSA attempts to find the optimum solutions to a variety of the problems that are related to the optimization. This study presents a new adaptive distributed algorithm of routing on CSA. Because the search space may be modified according to the size and kind of the network, the algorithm can be easily customized to the issue space. In contrast to population-based algorithms that have a broad and time-consuming search space. For ten networks of various sizes, the technique was used to solve the shortest path issue. And its capability for solving the problem of the routing in the switched networks is examined: detecting the shortest path in the process of a data packet transfer amongst the networks. The suggested method was compared with four common metaheuristic algorithms (which are: ACO, AHA, PSO and GA) on 10 datasets (integer, weighted, and not negative graphs) with a variety of the node sizes (10 - 297 nodes). The results have proven that the efficiency of the suggested methods is promising as well as competing with other approaches.

Spatial Location Aided Fully-Distributed Dynamic Routing for Large-Scale LEO Satellite Networks

Due to the explosive growth in the scale of Low Earth Orbit (LEO) satellite networks, designing dynamic routing has become a promising way to improving satellite communication performance. Most of the existing approaches derive routing policies in a centralized paradigm, which often suffer from the high cost of collecting global routing information and the high computational complexity in large-scale networks. Therefore, this letter proposes a spatial location aided fully distributed routing algorithm for large-scale satellite network with minimizing the average delivery time. Based on this, a novel Fully Distributed dynamic Routing algorithm based on Multi-Agent deep Reinforcement Learning (FDR-MARL) is proposed to derive the optimal routing strategy. Extensive experiments are carried out to verify the effectiveness and advantages of our proposed approach under large-scale LEO satellite networks.

Comparative Analysis of Routing Schemes Based on Machine Learning

Machine learning-based distributed routing algorithms, in contrast to traditional mathematical model-driven distributed routing algorithms, are typically data-driven, allowing them to adapt to dynamically changing network environments and various performance evaluation index optimization requirements. It is quite likely that it will become a key part of the next-generation Internet in the future. However, current intelligent routing research is still in its early stages. This article provides a comprehensive review of the state-of-the-art routing algorithms based on machine learning. First, important research on existing data-driven intelligent routing algorithms is presented with the key concepts and applications of these systems demonstrated. To enable intelligent routing algorithms to be deployed in real scenarios with cheap cost and high reliability, two appropriate training deployment frameworks and intelligent routing algorithm training and deployment strategies are given. Finally, the future development of machine learning-based intelligent routing systems is examined. The opportunities and problems that have been encountered, as well as prospective research directions, are discussed.

A Scalable Multicast Routing Protocol for Mobile Ad-Hoc Networks

The multicasting technique supports a variety of applications that require data to be instantaneously transmitted to a set of destination nodes. In environments with continuously moving nodes, such as mobile ad-hoc networks, the search for efficient routes from sources to the projected destinations is a common issue. Proposed Windmill protocol provides a scalable multicast solution for mobile ad-hoc networks. Windmill aims to improve routing protocol’s performance by introducing a hierarchal distributed routing algorithm and dividing the area into zones. Additionally, it attempts to demonstrate better scalability, performance and robustness when faced with frequent topology changes, by utilizing restricted directional flooding. A detailed and extensive simulated performance evaluation has been conducted to assess Windmill and compare it with multicast ad-hoc on-demand distance vector (MAODV) and on-demand multicast routing protocols (ODMRP). Simulation results show that the three protocols achieved high packet delivery rates in most scenarios. Results also show that Windmill is capable of achieving scalability by maintaining the minimum packet routing load, even upon increasing the nodes’ speed, the number of sources, the number of group members and the size of the simulated network. The results also indicate that it offers superior performance and is well suited for ad-hoc wireless networks with mobile hosts. The trade-off of using Windmill consists in slightly longer paths – a characteristic that makes it a good choice for applications that require simultaneous data transmission to a large set of nodes.

Extending lifetime of Wireless Nano-Sensor Networks: An energy efficient distributed routing algorithm for Internet of Nano-Things

Recent improvements in nanotechnology and Internet of Things (IoT) have led to the emergence of the concept of Internet of Nano-Things (IoNT). This concept provided ease of access and communication facilities for the nano-devices in applications of different fields operating at the molecular level such as health, industry, agriculture, robotics and military. These distributed devices interconnect over the Wireless Nano-Sensor Networks (WNSNs) in nano-scale which have limited energy and capacity. Therefore, these devices need to use their energy efficiently when communicating with each other to ensure network continuity and maintain all processes of IoNT applications. In this paper, a novel energy-efficient distributed routing algorithm, namely DEEPNT, is proposed in order to extend the lifetime of WNSNs in IoNT applications. The communication backbone of WNSNs constructed by DEEPNT by selecting cluster head nodes can be used in all types of IoNT applications in nano-scale in order to provide continuous data flow in critical areas such as healthcare running in vivo. This novel energy-efficient distributed protocol is compared with the traditional flooding based method and with an energy aware routing algorithm. According to the results, DEEPNT prolongs the WNSNs average lifetime respectively up to 10.78 and 5.95 times compared to mentioned algorithms despite the larger network sizes in nano-scale.

On oriented diameter of [formula omitted]-star graphs

Assignment of one of two possible directions to every edge of an undirected graph G=(V,E) is called an orientation of G. The resulting directed graph is denoted by G⃗. A strong orientation is one in which every vertex is reachable from every other vertex via a directed path in G⃗. The diameter of G⃗, i.e., the maximum distance from any vertex to any other vertex, depends on the particular orientation. The minimum diameter among all possible orientations of G is called the oriented diameterdiam⃗(G) of G. Let n,k be two integers such that 1≤k<n. In the realm of interconnection networks of processing elements, an (n,k)-star graph Sn,k offers a topology that permits to circumvent the lack of scalability of n-star graphs Sn. The oriented diameter quantifies an upper limit on the delay in communication over interconnection networks. In this paper, we present a strong orientation scheme for Sn,k that combines approaches suggested by Cheng and Lipman (2002) for Sn,k with the one proposed by Fujita (2013) for Sn, reaping benefits from both worlds. Next, we propose a distributed routing algorithm for Sn,k⃗ inspired by an algorithm proposed in Kumar et al. (2021) for Sn⃗. With the aid of both the orientation scheme and the routing algorithm, we show that diam⃗(Sn,k)≤⌊n+k2⌋+2k+6−δ(n,k) where δ(n,k) is a non-negative function. The function δ(n,k) takes on values 2k−n, 0, and n−3k2 respectively for three disjoint intervals k>n2, n3<k≤n2 and k≤n3. For every value of n, k, our upper bound performs better than all known bounds in literature.

A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks

The low earth orbit (LEO) constellation network has become a promising approach to provide global communication services, due to its advantages in wide global coverage, low transmission delay, and convenient networking. However, the instability of the intersatellite laser terminal and the high relative speed between adjacent satellites cause frequent network topology changing problems for data routing. In this paper, a disruption tolerant distributed routing algorithm (DTDR) is proposed, where the satellites calculate the alternate path for transmission when the network topology changes, which improves the performance of packet loss. Specifically, each satellite maintains the intersatellite link (ISL) information within a specified number of hops. When an ISL state changes within the specified number of hops, the corresponding satellite calculates and switches to the detour path. Furthermore, the traffic is balanced through the detour process. Various simulations were constructed and show that the proposed algorithm outperforms the existing algorithm in terms of packet loss ratio and transmission delay.

An energy balanced and nodes aware routing protocol for energy harvesting wireless sensor networks

The lifetime of Wireless Sensor Networks (WSN) is a significant constraint since they are powered by non-rechargeable batteries with limited capacity. A promising solution to the energy issue is energy harvesting (EH). One of the most popular hierarchical routing protocols (RP) is the low-energy adaptive clustering hierarchy (LEACH). Most of the available RPs based on LEACH for EH-WSNs have employed the traditional cluster head (CH) selection based on taking turns, which is unsuitable. Moreover, most of them have not considered the remaining energy and harvested energy, nor investigated the weightages of these energies in selecting the CH. This work proposes the energy balanced and nodes aware (EBNA) routing protocol for EH-WSNs. It considers both the remaining energy and harvested energy along with the number of active nodes in selecting the CH which improves throughput. In addition, the weightages of the energies are investigated. EBNA is evaluated using the network simulator, GreenCastalia in OMNET. It uses the actual solar irradiance data with a resolution of 1 s. The performance is compared with energy-aware distributed clustering (EADC) and clustering routing algorithm of self-energized (CRAS) for EH-WSNs. The results show that EBNA outperforms EADC and CRAS in throughput by up to 58% and 113% and by number of CHs by up to 148% and 541%, respectively, during the high irradiance scenarios. In the low irradiance scenario, the improvement in throughput is up to 52% and 98%, and the number of CHs is up to 146% and 569%, as compared to EADC and CRAS, respectively.

Обеспечение безопасной маршрутизации в беспроводных самоорганизующихся сетях

Purpose of the study: development of a distributed routing algorithm to ensure security in wireless self-organizing networks from intruder attacks on the network. Methods: application of algorithms, graph theory, discrete optimization and heuristic approaches based on the results of numerical experiments. Results: the paper proposes a modified distributed routing algorithm for self-organizing networks. The algorithm, presented in this paper, is based on a distributed version of Dijkstra’s algorithm, designed to detect the shortest paths without loops on a graph under conditions of changing the weight of its edges. The loop freedom in this case is achieved by storing at each node an additional table containing the penultimate nodes on the shortest routes to all nodes, which allows the node to build a tree of shortest routes with itself as a root. In the modification of the algorithm, these tables are used by the nodes to check the correspondence of the declared route and the return route, which makes it possible to recognize and exclude from the network an intruder who carries out attacks to disrupt the correct routing mechanism. The effectiveness of the proposed algorithm for protection against routing attacks, in particular, black hole attacks, is confirmed by the results of testing on a program model.

Novel Concentric Hexagonal-Shaped RFID Tag Antenna With T-Shaped Stub Matching

In Mobile Wireless Sensor Networks there could be scenarios where absolutely all network nodes (including the base station) are mobile, becoming a very hard task to find a communication path between a sensor node and the base station due to many network variables are changing at each moment. In addition, there are delay-sensitive applications that require establishing communication paths as soon as possible to mitigate low network performance in terms of end-to-end delay, reducing, at the same time, the energy consumption of the network. For this reason, we propose a multiobjective mathematical optimization model for finding the optimal communication path between a source node and a sink (base station) considering hard scenarios where all network nodes are mobile and minimizing end-to-end delay and energy consumption. This mathematical model would offer significant advantages to evaluate new algorithms due to we could know how far or close are the algorithm results from the optimal values given by the mathematical model. In addition, we propose a prediction distributed routing algorithm based on Markov Chains that takes into account the network mobility in order to find as fast as possible a communication path between a source node and a sink with minimal energy consumption. We also propose a deep learning approach to predict future nodes’ distances in a mobile network to determine if future movements of nodes will cause communication disruptions in paths. Significant findings were obtained when the Markov Chains and Deep Learning approaches were compared in terms of predicting nodes mobility and reducing the delay and the energy consumption in the network. The performance of our prediction algorithms (Markov Chains and Deep Learning approaches) is evaluated against the mathematical model to determine how good it is. Finally, to analyze our prediction algorithms considering real online scenarios, we compared it against typical routing algorithms, obtaining promising results in terms of delay and energy consumption in all mobile node scenarios.

Research on financial information disclosure risk management method based on internet of things

In order to overcome the problem that the traditional risk management method has difficulty achieving the expected effect due to the careless classification of risk levels, this paper designs the financial information disclosure risk management method based on the internet of things. Based on the network financial information disclosure traceability model, the risk assessment index system is built, and the risk events are classified by ranking the risk events. The evaluation result is divided into five categories and the risk warning level is set. Then, the wireless sensor technology in the internet of things is used to design data fusion algorithm and distributed routing algorithm to achieve risk management. Simulation results show that the recall rate of the risk assessment index is above 94%, and the time to issue risk warning is always less than 6 min, which fully proves the effectiveness of the method.

Oriented diameter of star graphs

An orientation of an undirected graph G is an assignment of exactly one direction to each edge of G . Converting two-way traffic networks to one-way traffic networks and bidirectional communication networks to unidirectional communication networks are practical instances of graph orientations. In these contexts minimising the diameter of the resulting oriented graph is of prime interest. The n -star network topology was proposed as an alternative to the hypercube network topology for multiprocessor systems by Akers and Krishnamurthy (1989). The n -star graph S n consists of n ! vertices, each labelled with a distinct permutation of [ n ] . Two vertices are adjacent if their labels differ exactly in the first and one other position. S n is an ( n − 1 ) -regular, vertex-transitive graph with diameter ⌊ 3 ( n − 1 ) / 2 ⌋ . Orientations of S n , called unidirectional star graphs and distributed routing protocols over them were studied by Day and Tripathi (1993) and Fujita (2013). Fujita showed that the (directed) diameter of this unidirectional star graph S n ⃗ is at most ⌈ 5 n / 2 ⌉ + 2 . In this paper, we propose a new distributed routing algorithm for the same S n ⃗ analysed by Fujita, which routes a packet from any node s to any node t at an undirected distance d from s using at most min { 4 d + 4 , 2 n + 4 } hops. This shows that the (directed) diameter of S n ⃗ is at most 2 n + 4 . We also show that the diameter of S n ⃗ is at least 2 n when n ≥ 7 , thereby showing that our upper bound is tight up to an additive factor.

Optimization of E-Commerce Logistics Distribution Path Algorithm under the Background of Big Data

In recent years, online shopping has become a major development trend of shopping. The traditional e-commerce logistics distribution mode has played e-commerce by taking advantage of the advantages of the distribution path algorithm. However, with the increase of the order quantity, the previous path optimization algorithm is no longer efficient. This paper makes an in-depth investigation on the main problems existing in China’s e-commerce logistics. Through the analysis of the survey results, we can see that there are mainly problems such as low degree of data application and imperfect function of logistics information platform. Aiming at these problems, this paper optimizes the logistics distribution routing algorithm according to the actual demand. Through the establishment of three-dimensional constraint model, the problem of low efficiency in the traditional model is effectively improved. In contrast with the genetic algorithm, the distribution mileage of the optimization algorithm is reduced by more than 25% compared with the genetic algorithm, which improves the comprehensive performance of the model. Constraint model established in this paper makes up for the shortcomings of the current e-commerce logistics distribution path algorithm to a certain extent, and makes a contribution to the development of China’s e-commerce logistics distribution field.

Energy balanced, delay aware multi-path routing using particle swarm optimisation in wireless sensor networks

World-wide use of wireless sensor networks (WSNs) has urged the need for energy-efficient, distributed routing algorithms. The interest of researchers from the past decade is in energy-efficient routing. In this paper, sequential quadratic programming (SQP) based multi-path routing formulation focusing on improving lifetime and delay is represented, namely EBDA-DEFL. This SQP based formulation is solved using the optimisation tool after that same formulation is solved using particle swarm optimisation (PSO). Also, a quota strategy for traffic load distribution is also introduced to mitigate the negative effects of multi-path routing. The proposed work is experimented and compared with existing algorithms to analyse its quality over previous work. Comparison has been done in terms of first node death, half node death, last node death, delay, and time consumed by Fminimax and PSO. Simulation results confirm the supremacy of proposed work over the existing ones.

Multi-Agent Deep Reinforcement Learning-Based Algorithm For Fast Generalization On Routing Problems

We propose a fast generalization method for DQN-Routing, an algorithm based on multi-agent deep reinforcement learning that suffers from generalization problem when introduced to new topologies even if it was trained on a similar topology. The proposed method is based on the wisdom of crowds and allows the distributed routing algorithm, DQN-Routing, to generalize better to new topologies that were not seen before during training. The proposed method also aims to decrease the solution search time as the original DQN-Routing algorithm takes a long time to converge, and to increase the overall performance by minimizing the mean delivery time and total power consumption and the number of collisions. The experimental evaluation of our method proved that is capable to generalize to new topologies and outperform the DQN-Routing algorithm.

Energy balanced, delay aware multi-path routing using particle swarm optimisation in wireless sensor networks

World-wide use of wireless sensor networks (WSNs) has urged the need for energy-efficient, distributed routing algorithms. The interest of researchers from the past decade is in energy-efficient routing. In this paper, sequential quadratic programming (SQP) based multi-path routing formulation focusing on improving lifetime and delay is represented, namely EBDA-DEFL. This SQP based formulation is solved using the optimisation tool after that same formulation is solved using particle swarm optimisation (PSO). Also, a quota strategy for traffic load distribution is also introduced to mitigate the negative effects of multi-path routing. The proposed work is experimented and compared with existing algorithms to analyse its quality over previous work. Comparison has been done in terms of first node death, half node death, last node death, delay, and time consumed by Fminimax and PSO. Simulation results confirm the supremacy of proposed work over the existing ones.

Solving Routing Problem Using Improved Camel Herds Algorithm

This paper introduces a new adaptive, distributed routing algorithm based on the Improved Camel Herds Algorithm (CHA). It is an intelligent, multi-agent optimization algorithm that is inspired by the behavior of camels and how they search for food in their desert environment. We examine its ability to solve the routing problem in switched networks: finding the shortest path in the process of transferring data packets between networks. Many meta-heuristic algorithms have been previously proposed to address the routing problem, and this proposed approach is compared with three well-known algorithms (ACO, GA, PSO) on ten graphs (weighted, integer, and not negative) and datasets with various size of nodes (from 10 nodes to 297 nodes). Three performance criteria were used to evaluate the performance of the algorithms (mean relative error, standard deviation, and number of function evaluations). The results proved that the performance of the proposed algorithm is both promising and competitive with other algorithms.

Social characteristics-based routing algorithm for a mobile social network

Utilization of social characteristics presents a modern approach in the design of routing protocols for Mobile Social Networks. In this paper, we present a novel routing algorithm called the Social Characteristics-based Distributed Routing Algorithm (SCDRA) for a Mobile Social Network. SCDRA explores community and friendship as social features to effectively route messages. In SCDRA, nodes develop their community in a distributed way based on the concept of a familiar set. Merging of communities of nodes is governed by certain rules. Friendship in SCDRA evolves based on various factors like inter-contact duration, frequency of encounters and successful delivery of messages forwarded in a few past time intervals between the nodes. Routing of messages is accomplished by utilizing community and friendship as social characteristics. Extensive simulation studies performed show that the proposed algorithm performs better when compared to existing algorithms such as Bubble rap, SMART and EpSoc for performance metrics like delivery ratio, overhead ratio and average delivery latency in various scenarios.