Multipath Routing Research Articles

Blockchain-Empowered Efficient Data Sharing in Internet of Things Settings

Sharing data across various Internet of Things (IoT) devices has been a common challenge due to efficiency, security, and stability issues. Blockchain, with security features, is considered to be a potential solution for data sharing in IoT settings. However, traditional blockchain-based solutions cannot satisfy the efficiency requirement of high-frequency data sharing among IoT devices. In this paper, we propose an efficient IoT data sharing approach by adopting the Payment Channel Network (PCN)-extended blockchain. Besides, we develop a homomorphic hashing-based transaction segmentation scheme to solve the issue of low transaction success ratio caused by channel deposit restrictions in PCN. In addition, a Multi-point Relay (MPR)-based multi-path routing scheme has been developed to ensure high-frequency transaction forwarding. The communication overhead of maintaining the routing table is reduced by our proposed Multi-point Relay Selection algorithm, and multiple alternate paths generated by Multiple Routing Path algorithm can improve the transaction success rate. Experiment evaluations have demonstrated that that our proposed approach outperforms the baseline approaches in terms of the transaction efficiency and success ratio.

A lightweight deployment of TD routing based on SD-WANs

Multipath routing conforms to the evolution principle of network development and is a trend of routing architectures. It can not only meet the needs but also enhance the performance and security of a network. However, deploying multipath routing further increases the scale of the forwarding information base (FIB) and the cost of forwarding devices. Therefore, to realize the lightweight deployment of multipath routing with a distributed architecture, this paper takes two-dimensional routing (TD routing) as an example to provide a solution. We propose a distributed storage mechanism of TD routing in combination with SRv6 (TDSR) and the corresponding SRv6 header (SRH) compression (CARD) method. The main methods are as follows: Inspired by a software-defined wide area network (SD-WAN), this paper introduces segment routing (SR) in the data plane, which can disperse TD-FIBs in different ingress nodes. The ingress routers push path information into the stack, and the intermediate routers forward the packets according to the SRH. Second, for the bandwidth waste of the SRH, compression is attempted by comparing the difference between the the shortest path first (SPF) path and the SRv6 path. Only a few hops in the TD path that are different from those in the one-dimensional (OD) path are kept. Finally, we sort out several typical application scenes of multipath routing and discuss several simplification algorithms. The experimental results show that the TDSR can reduce TD entries by 69%, and the average compression rate of CARD can reach 70%. In addition, CARD can be combined with existing methods to improve their effect.

Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring.

Mobility and low energy consumption are considered the main requirements for wireless body area sensor networks (WBASN) used in healthcare monitoring systems (HMS). In HMS, battery-powered sensor nodes with limited energy are used to obtain vital statistics about the body. Hence, energy-efficient schemes are desired to maintain long-term and steady connectivity of the sensor nodes. A sheer amount of energy is consumed in activities such as idle listening, excessive transmission and reception of control messages, packet collisions and retransmission of packets, and poor path selection, that may lead to more energy consumption. A combination of adaptive scheduling with an energy-efficient protocol can help select an appropriate path at a suitable time to minimize the control overhead, energy consumption, packet collision, and excessive idle listening. This paper proposes a region-based energy-efficient multipath routing (REMR) approach that divides the entire sensor network into clusters with preferably multiple candidates to represent each cluster. The cluster representatives (CRs) route packets through various clusters. For routing, the energy requirement of each route is considered, and the path with minimum energy requirements is selected. Similarly, end-to-end delay, higher throughput, and packet-delivery ratio are considered for packet routing.

An Adaptive Multipath Routing Method Based on Improved GA and Information Entropy

With the development of artificial intelligence technology, some emerging applications (such as computation offloading and digital twin) require higher transmission quantity and quality for wireless sensor networks (WSNs). The increasing amount of data aggravates the imbalance of node energy consumption. In the single-path routing, nodes on the optimal path consume too much energy, which leads to the premature failure of the network. In addition, the transmission protocols based on the acknowledge character (ACK) mechanism ensure the quality of service, but bring additional energy overhead. In this article, an adaptive multipath routing method based on balanced energy consumption (AMRBEC) is proposed for WSNs that significantly reduce energy consumption and prolong network lifetime. We design a new multidimensional fitness function based on the path energy, distance, hops and the lowest energy node, which servers to an improved genetic algorithm (GA) to select the multiple optimal paths. The packet loss rate is predicted by random forest to realize the non-feedback transmission. According to the residual energy and the information entropy of each path, the amount of data transmitted is dynamically adjusted, and the optimal paths are updated to balance the energy consumption of the whole network. A simulation analysis shows that the proposed AMRBEC performs better than other algorithms from the literature by improving the lifetime by 20% and reducing energy consumption by more than 20%.

Survey on Multi-Path Routing Protocols of Underwater Wireless Sensor Networks: Advancement and Applications

Underwater wireless sensor networks (UWSNs) are a prominent research topic in academia and industry, with many applications such as ocean, seismic, environmental, and seabed explorations. The main challenges in deploying UWSN are high ocean interference and noise, which results in longer propagation time, low bandwidth, and changes in network topology. To mitigate these problems, routing protocols have been identified as an efficient solution. Over the years, several protocols have been proposed in this direction and among them, the most popular are the ones that use multi-path propagation. However, there is a lack of compilation of studies that highlight the advancement of multi-path routing protocols of UWSN through the years. Hence, getting a heuristic idea of the existing protocols is crucial. In this study, we present a comprehensive survey of UWSNs multi-path routing protocols and categorize them into three main categories; energy-based routing protocols, geographic information-based routing protocols, and data-based routing protocols. Furthermore, we sub-classify them into several categories and identify their advantages and disadvantages. In addition, we identify the application of UWSN, open challenges and compare the protocols. The findings of our study will allow researchers to better understand different categories of UWSN multi-path routing protocols in terms of their scope, advantages, and limitations.

Sustainable Multipath Routing for Improving Cross-Layer Performance in MANET Using an Energy Centric Tunicate Swarm Algorithm

Generally, battery power is a valuable resource for mobile devices in a Mobile Ad Hoc Network (MANET). Therefore, energy efficiency and network lifetime should be taken into account when developing control strategies. However, designing an energy-efficient routing mechanism necessitates consideration of many nodes from many layers, such as remaining energy, overall traffic load, and channel assumptions. The traditional layered strategy is unsuccessful in dealing with power-related issues that might affect all layers of the stack. In this paper, the Energy Centric Tunicate Swarm Algorithm (ECTSA) is proposed to perform the cross-layer routing over MANET. The fitness metrics considered in the ECTSA to improve the cross-layer routing are residual energy, communication cost, Data Success Rate (DSR), and mobility. Additionally, an Adaptive Competition Window (ACW) adjustment is used for minimizing the energy consumption caused by the contentions. The performance of the proposed ECTSA is analyzed by means of energy consumption, Packet Delivery Ratio (PDR), End-to-End Delay (EED), and routing overhead. Next, the existing techniques, namely, CEELBRP and EECRP-PSO, are used to evaluate the efficiency of the ECTSA method. The energy consumption of the ECTSA is 7.1 joules and prolongs the network lifetime up to 1603 s for 50 nodes, which is better when compared to the existing CEELBRP and EECRP-PSO techniques.

An optimal method for resource allocation in SDN optical networks

The proliferation of cloud services and the massive volume of traffic provided by content delivery networks are driving the present rapid growth of Internet traffic. This obviously exacerbates the congestion concerns in communication networks, with a focus on the core and backbone components in particular. In this paper, a multi-path routing traffic grooming and adaptive modulation are combined to establish an integer linear programming model with the optimization objective of minimizing the spectrum resource occupation, and a novel algorithm is proposed to solve the problem of bandwidth allocation and holding-time-aware routing in software defined networks (SDN) integrated with elastic optical networks. A single path single service allocation approach is utilized to establish the service connection for both instant reservation allocation and advance reservation allocation. If the single path single service allocation technique fails, the single path multi sub service allocation method is applied. If it fails, the multipath multi sub service allocation method is attempted. When establishing a service connection, the allocation method with the least spectrum resource is recommended in order to reduce spectrum resource occupancy and release spectrum resource as soon as feasible. When there are no differences in spectrum resources, the allocation technique with the fewest total occupied time slots is selected. The results of simulations suggest that this approach may minimize blocking rates while also increasing spectrum usage.

Intelligent Multipath Routing in Software Defined Networks Based on Ant Colony Optimization Algorithms

The software defined networks (SDN) are a modern trend in building of new generation computer networks. The SDN provide flexibility and control in management of data flows in the network, as well as requirement level of quality of service for applications. An approach to intelligent multipath routing in SDN based on ant colony optimization algorithms is proposed in this paper. Research and analysis of two types of ant colony optimization algorithms was carried out: ant system (AS) and ant colony system (ACS). The visual software system SDNLoadBalancer was developed and the experimental SDN topology was designed. They make it possible to research in detail the processes of multipath routing in SDN based on the proposed approach. Also comparative analysis of the proposed approach, results of the Yen algorithm and the genetic algorithm was carried out. The results of experimental research show the effectiveness of the use of ant colony optimization algorithms in solving the problem of multipath routing in SDN, allow to obtain results close to optimal in an acceptable time, and also reduce the transmission delay jitter in the network.

An optimized ticket manager based energy‐aware multipath routing protocol design for IoT based wireless sensor networks

SummaryRecently, wireless sensor networks (WSNs) and Internet of Things (IoTs) have emanated as an indispensable assets that play a critical role in revolutionizing the field of data communication. Owing to the evolution of communication standards, research trends in IoT based wireless sensor networks have been rapidly progressing toward achieving effective data routing with a prolonged network lifetime and minimized energy consumption. In this article, an optimized ticket manager based energy‐aware multipath routing protocol (TMERP) is proposed. The proposed protocol design comprises three important functional entities: ticket manager (TM), routing planner (RP), and backup node (BN). The TM is responsible for controlling and monitoring all the constraints related to networking. Then, the RP minimizes the overall complexity of the optimal resource allocation by avoiding an end‐to‐end delay. Finally, the BN facilitates efficient data routing through the optimal selection of routing paths using the node trust evaluation and backup process to minimize data loss. Hence, the proposed multipath routing system has a distinct advantage in enhancing the network lifetime constraint with minimal energy consumption owing to the collective performance of its functional entities. The simulation results of the experimental studies show that the proposed protocol design achieved an improved performance in terms of network energy, throughput, and network operational lifetime by 39.3%, 47.9%, and 10.5%, respectively when compared with similar existing protocols.

A Multipath Routing for Payment Channel Networks for Internet of Things Microtransactions

The blockchain with a distributed network structure can provide a reliable and secure environment for Internet of Things (IoT) transactions, which also suffers from low throughput, high computation overhead, and large transaction fee. Payment channel networks (PCNs) are developed to address the scalability issue of blockchain. A key enabler of PCNs is the path-finding services. Most of existing routing algorithms target at finding a single feasible path, which may lead to failure of large payments. Moreover, previous solutions did not consider the transaction fee of the chosen path, which is extremely important to cost-sensitive users. In this work, we design a new multipath routing algorithm for PCNs that aims at minimizing transaction fees. Together considering the determination of the optimal number of paths, the optimal path routes and the optimal allocation leads to difficulty of the problem. In addition, the transaction fees along the path are closely related to the amount of payment, and the capacity of a payment channel limits the payment that can be carried. To address these challenges, we propose MILPA-PCN, a cost-effective multipath routing framework for PCNs. We develop a genetic algorithm-based routing determination algorithm with carefully designed genetic operations. We evaluate MILPA-PCN based on the real trace of the lightning network, and verify that MILPA-PCN can reduce the transaction fee by 33.56% and improve the payment success rate by 14.45%.

Efficient resource provisioning using traffic balancing and crosstalk consideration in multi-core fiber based EONs

Elastic optical networks technology in combination with space division multiplexing is playing a major role to fulfil the need of increasing internet traffic. However, it suffers from resource sub-utilization due to the traffic heterogeneity in time and bandwidth domains, and the crosstalk between the adjacent cores in a multi-core fiber link. In this paper, we propose time and bandwidth aware traffic balancing strategies with crosstalk consideration to efficiently utilize the spectral resources in MCF based EONs. To this end, we utilize network features and SLA conditions to balance the traffic, in both, the time and the bandwidth domains. In bandwidth domain, we utilize squeezing and splitting with multipath routing, and in time domain, we use sliding and shifting. To further enhance the performance, we use a MCF structure dependent prioritized core allocation based RMCSA strategy. Simulation results indicate superiority of the proposed strategies in terms of different parameters of interest as compared to the benchmark strategies.

Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning.

Multi-path transmission can well solve the data transmission reliability problems and life cycle problems caused by single-path transmission. However, the accuracy of the routing scheme generated by the existing multi-path routing algorithms is difficult to guarantee. In order to improve the accuracy of the multi-path routing scheme, this paper innovatively proposes a multi-path routing algorithm for a wireless sensor network (WSN) based on the evaluation. First, we design and implement the real-time evaluation algorithm based on semi-supervised learning (RESL). We prove that RESL is better in evaluation time and evaluation accuracy through comparative experiments. Then, we combine RESL to design and implement the multi-path routing algorithm for wireless sensor networks based on semi-supervised learning (MRSSL). Then, we prove that MRSSL has advantages in improving the accuracy of the multi-path routing scheme through comparative experiments.

Energy-efficient proactive caching with multipath routing

The ever-continuing explosive growth of on-demand content distribution has imposed great pressure on mobile/wireless network infrastructures. To ease congestion in the network and to increase perceived user experience, caching of popular content closer to the end-users can play a significant role and as such this issue has received significant attention over the last few years. Additionally, energy saving is treated as a fundamental requirement in the design of next-generation mobile networks. However, there has been little attention to the overlapping area between energy saving and network caching especially when considering multipath routing. To this end, this paper proposes an energy-efficient caching with multipath routing support. The proposed scheme provides a joint anchoring of popular content into a set of potential caching nodes with optimized multipath support whilst ensuring a balance between transmission and caching energy cost. The proposed model also considers different content delivery modes, such as multicast and unicast . Two separated Integer-Linear Programming (ILP) models are formulated for each delivery mode. To tackle the curse of dimensionality we then provide a greedy simulated annealing algorithm , which not only reduces the time complexity but also provides a competitive performance. A wide set of numerical investigations reveal that the proposed scheme reduces the energy consumption up to 75% compared with other widely used caching approaches under the premise of network resource limitation. Sensitivity analysis to different parameters is also meticulously discussed in this paper.

Multiagent Meta-Reinforcement Learning for Adaptive Multipath Routing Optimization.

In this article, we investigate the routing problem of packet networks through multiagent reinforcement learning (RL), which is a very challenging topic in distributed and autonomous networked systems. In specific, the routing problem is modeled as a networked multiagent partially observable Markov decision process (MDP). Since the MDP of a network node is not only affected by its neighboring nodes' policies but also the network traffic demand, it becomes a multitask learning problem. Inspired by recent success of RL and metalearning, we propose two novel model-free multiagent RL algorithms, named multiagent proximal policy optimization (MAPPO) and multiagent metaproximal policy optimization (meta-MAPPO), to optimize the network performances under fixed and time-varying traffic demand, respectively. A practicable distributed implementation framework is designed based on the separability of exploration and exploitation in training MAPPO. Compared with the existing routing optimization policies, our simulation results demonstrate the excellent performances of the proposed algorithms.

Resource Reservation in Backhaul and Radio Access Network With Uncertain User Demands

Resource reservation is an essential step to enable wireless data networks to support a wide range of user demands. In this paper, we consider the problem of joint resource reservation in the backhaul and Radio Access Network (RAN) based on the statistics of user demands and channel states, and also network availability. The goal is to maximize the sum of expected traffic flow rates, subject to link and access point budget constraints, while minimizing the expected outage of downlinks. The formulated problem turns out to be non-convex and difficult to solve to global optimality. We propose an efficient Block Coordinate Descent (BCD) algorithm to approximately solve the problem. The proposed BCD algorithm optimizes the link capacity reservation in the backhaul using a novel multi-path routing algorithm that decomposes the problem down to link-level and parallelizes the computation across backhaul links, while the reservation of transmission resources in RAN is carried out via a novel scalable and distributed algorithm based on Block Successive Upper-bound Minimization (BSUM). We prove that the proposed BCD algorithm converges to a Karush-Kuhn-Tucker (KKT) solution. Simulation results verify the efficiency and the efficacy of our BCD approach against two heuristic algorithms.

Hierarchical autoregressive bidirectional least-mean-square algorithm for data aggregation in WSN based IoT network

The Internet of Things (IoT) is emerging that acquired the interest of several researchers, as it inspires each object to offer services and data to users by collaborating with each other. The Wireless sensor network (WSN) is a major constituent of IoT in transmitting data. Various wireless sensor-based protocols are developed for path selection. This paper develops a data aggregation technique using an optimization model for WSN-based IoT network. The overall procedure involves simulation of WSN-based IoT, multipath routing and finally, data aggregation. At first, the WSN-based IoT nodes are simulated, which are subjected to a multipath routing process. The multipath routing is done using DEMFO, which is devised by combining Dolphin Echolation (DE) and Moth fly optimization technique (MFO). From the obtained paths, the data routing is performed wherein data are subjected to the base station. In the base station, the data aggregation is done using the proposed Hierarchical autoregressive Bidirectional Least-Mean-Square algorithm (HABLMS). The proposed HABLMS is devised by combining Hierarchical autoregressive (HA) using the Caviar model and Bidirectional Least-Mean-Square algorithm (BLMS). Thus, the weights are updated in an optimal manner. Thus, the proposed HABLMS algorithm helps to adapt optimal aggregation of data in a WSN-based IoT network. The developed DECSA+HABLMS offered superior performance with minimal prediction error of 0.0147.

Multipath Routing in Cloud Computing using Fuzzy based Multi-Objective Optimization System in Autonomous Networks

Intelligent houses and buildings, autonomous automobiles, drones, robots, and other items that are successfully incorporated into daily life are examples of autonomous systems and the Internet of Things (IoT) that have advanced as research areas. Secured data transfer in untrusted cloud applications has been one of the most significant requirements in the cloud in recent times. In order to safeguard user data from unauthorised users, encrypted data is stored on cloud servers. Existing techniques offer either security or efficiency for data transformation. They fail to retain complete security while undergoing significant changes. This research proposes novel technique in multipath routing based energy optimization of autonomous networks. The main goal of this research is to enhance the secure data transmission in cloud computing with network energy optimization. The secure data transmission is carried out using multi-authentication attribute based encryption with multipath routing protocol. Then the network energy has been optimized using multi-objective fuzzy based reinforcement learning. The experimental analysis has been carried out based on secure data transmission and energy optimization of the network. The parameters analysed in terms of scalability of 79%, QoS of 75%, encryption time of 42%, latency of 96%, energy efficiency of 98%, end-end delay of 45%.

Improving Source location privacy in social Internet of Things using a hybrid phantom routing technique

The amalgamation of Smart IoT and Machine learning is an emerging research area. In this context, a new trend in IoT called Social IoT has been considered for this study. The Social IoT has benefits of connectivity exhibited within the network of connected objects through the Internet of Things (IoT). It covers the entire world and provides innovative services to improve life standards, establishes novel businesses, and makes buildings and cities. Certain smart things allow the collection of ubiquitous data or traffic, which pose a threat to source location privacy. Therefore, it limits the source of the Internet of Things vision if implemented wrongly. These threats come along with some challenges, adversary profiles, and the location privacy of personal data. When they are used to monitor important assets, the attacker can easily hunt the location of these assets. However, the source location constitutes a way to prevent the adversary from finding the location of the source. This research has used a hybrid phantom method by combining the phantom node and multi-path route that improves privacy and reduces the consumption of energy. The Analytic Hierarchal Process (AHP) is used for phantom node selection, based on parameters such as energy, distance, heterogeneity, and neighbor list. The result shows the average consistency value of the parameters is 4.2 and the consistency index value is 0.066. The overall priority of the alternative node is 2.089 as compared to other nodes. The sum of the vector weight value is obtained as 4.845. The total average energy consumption is 1.211 J and the average safety period capture ratio is 59.41%. The proposed techniques overwhelmed the deficiencies in existing techniques, reduces energy consumption improves the safety period and increases the network lifetime.

BGP-Multipath Routing in the Internet

BGP-Multipath (BGP-M) is a multipath routing technique for load balancing. Distinct from other techniques deployed at a router inside an Autonomous System (AS), BGP-M is deployed at a border router that has installed multiple inter-domain border links to a neighbor AS. It uses the equal-cost multi-path (ECMP) function of a border router to share traffic to a destination prefix on different border links. Despite recent research interests in multipath routing, there is little study on BGP-M. Here we provide the first measurement and a comprehensive analysis of BGP-M routing in the Internet. We extracted information on BGP-M from query data collected from Looking Glass (LG) servers. We revealed that BGP-M has already been extensively deployed and used in the Internet. A particular example is Hurricane Electric (AS6939), a Tier-1 network operator, which has implemented >1,000 cases of BGP-M at 69 of its border routers to prefixes in 611 of its neighbor ASes, including many hyper-giant ASes and large content providers, on both IPv4 and IPv6 Internet. We examined the distribution and operation of BGP-M. We also ran traceroute using RIPE Atlas to infer the routing paths, the schemes of traffic allocation, and the delay on border links. This study provided the state-of-the-art knowledge on BGP-M with novel insights into the unique features and the distinct advantages of BGP-M as an effective and readily available technique for load balancing.

CDMR: Effective Computing-Dependent Multi-Path Routing Strategies in Satellite and Terrestrial Integrated Networks

With the development of science and technology, Satellite and Terrestrial Integrated Network (STIN) can effectively make up for the defects in the terrestrial network coverage and provide robust end-to-end transmission and computing supports for users in many communication scenarios such as emergency rescue, space exploration, and so on. However, many researchers only studied transmission or computing, ignoring that the task can be computed in task transmission. And limited by the satellite's battery resources and high dynamics, it is challenging to realize the fusion of communication and computing with the constraint of energy consumption in the high dynamic satellite network. Therefore, this paper proposes a Computing-Dependent Multi-path Routing (CDMR) paradigm to address such a challenge. In CDMR, the task is mapped to the multi-path route in STIN, and the energy consumption is taken as the optimization goal. Furthermore, the processing latency is the constraint to ensure that the optimal CDMR strategy is under latency constraint. Finally, the simulation results show that the CDMR proposed in this paper can effectively compute the task during the end-to-end transmission. Compared with other computing models, CDMR has more evident advantages in energy saving.