Average Number Of Hops Research Articles

An intelligent fuzzy reinforcement learning-based routing algorithm with guaranteed latency and bandwidth in SDN: Application of video conferencing services

Online video conferencing services connection by multipoint control units leads to communication bottleneck and high latency. Due to the dynamic traffic in routing connection requests to video conferences, the existing routing algorithms face problems such as communication bottlenecks, high latency, congestion, path redundancy, and local optimum. In recent years, the implementation of video conferencing systems using Software-Defined Networking (SDN) provides new opportunities to improve Quality of Service (QoS) with low latency. In this paper, an Intelligent Fuzzy reinforcement learning-based Routing Algorithm is proposed, which simultaneously Guarantees Latency and Bandwidth for online video conferencing services in SDN (named IFRA-GLB). Fuzzy logic mainly performs online routing between an ingress–egress pair. Meanwhile, through ongoing training, reinforcement learning lowers the average number of hops on the path determined by fuzzy model. Enhancements to the convergence capability of IFRA and reduced reliance on network topology are achieved by adjusting link weights using a weighted shortest path algorithm focused on critical nodes. When the network encounters congestion, a deferral module is applied to assign higher priority to requests with lower resource demands. Experimental results demonstrate that IFRA-GLB significantly improves performance and convergence compared to existing solutions, enhancing the QoS for video conferencing services. Specifically, IFRA-GLB increases the average admission rate by 1.96% for MIRA topology and 2.71% for ANSNET topology with 3.5% lower latency.

Optimization analysis of average message delivery time for healthcare monitoring using a developed NB-IoT technology in a smart city

In contrast to prior research that lacked a comprehensive analysis of a fundamental parameter, namely the average message delivery time (AMDT), this study significantly contributes to the academic community by offering cutting-edge insights into AMDT analysis for wireless healthcare monitoring in smart cities. Employing the innovative Narrowband Internet of Things (NB-IoT) technology, this article thoroughly investigates AMDT within wireless healthcare monitoring. The study focuses on analyzing AMDT during the transmission and reception of messages from base stations (BS) to remote healthcare data aggregators (HDAs) serving remote patients (RPs). Various vital parameters are considered, including modulation rate, average message length (AML), the average number of hops per path (H) required to reach remote patients, patient count, sub-carrier spacing (SS), and the modulation type within the communication range of the BS. This proposed assessment of health network performance offers a brief overview based on essential criteria, facilitating swift and precise evaluations of the efficacy of mobile health applications. The results obtained and the in-depth discussion of the outcomes of AMDT analysis provide valuable insights that can be harnessed to enhance the design and operation of wireless healthcare monitoring systems.

Caching Policy in Low Earth Orbit Satellite Mega-Constellation Information-Centric Networking for Internet of Things.

Information-Centric Networking (ICN) is the emerging next-generation internet paradigm. The Low Earth Orbit (LEO) satellite mega-constellation based on ICN can achieve seamless global coverage and provide excellent support for Internet of Things (IoT) services. Additionally, in-network caching, typically characteristic of ICN, plays a paramount role in network performance. Therefore, the in-network caching policy is one of the hotspot problems. Especially, compared to caching traditional internet content, in-networking caching IoT content is more challenging, since the IoT content lifetime is small and transient. In this paper, firstly, the framework of the LEO satellite mega-constellation Information-Centric Networking for IoT (LEO-SMC-ICN-IoT) is proposed. Then, introducing the concept of "viscosity", the proposed Caching Algorithm based on the Random Forest (CARF) policy of satellite nodes combines both content popularity prediction and satellite nodes location prediction, for achieving good cache matching between the satellite nodes and content. And using the matching rule, the Random Forest (RF) algorithm is adopted to predict the matching relationship among satellite nodes and content for guiding the deployment of caches. Especially, the content is cached in advance at the future satellite to maintain communication with the current ground segment at the time of satellite switchover. Additionally, the policy considers both the IoT content lifetime and the freshness. Finally, a simulation platform with LEO satellite mega-constellation based on ICN is developed in Network Simulator 3 (NS-3). The simulation results show that the proposed caching policy compared with the Leave Copy Everywhere (LCE), the opportunistic (OPP), the Leave Copy down (LCD), and the probabilistic algorithm which caches each content with probability 0.5 (prob 0.5) yield a significant performance improvement, such as the average number of hops, i.e., delay, cache hit rate, and throughput.

Topological assessment of recoverability in public transport networks

Reducing the impact of disruptions is essential to provide reliable and attractive public transport. In this work, we introduce a topological approach for evaluating recoverability, i.e., the ability of public transport networks to return to their original performance level after disruptions, which we model as topological perturbations. We assess recoverability properties in 42 graph representations of metro networks and relate these to various topological indicators. Graphs include infrastructure and service characteristics, accounting for in-vehicle travel time, waiting time, and transfers. Results show a high correlation between recoverability and topological indicators, suggesting that more efficient networks (in terms of the average number of hops and the travel time between nodes) and denser networks can better withstand disruptions. In comparison, larger networks that feature more redundancy can rebound faster to normal performance levels. The proposed methodology offers valuable insights for planners when designing new networks or enhancing the recoverability of existing ones.

Multi‐hop anonymous payment channel network based on onion routing

AbstractPayment channel networks (PCNs) are one of the most promising off‐chain solutions to the blockchain scalability problem. However, most current payment channel schemes suffer from overly ideal routing assumptions or fail to provide complete path privacy protection. To address the above problems, the multi‐hop anonymous and privacy‐preserving PCN scheme are analyzed and a multi‐hop anonymous PCN based on onion routing is proposed based on it. The scheme removes the routing assumptions of the original scheme, designs an onion routing‐based payment channel network, and modifies the way relay nodes construct elliptic curve discrete logarithmic problems to resist wormhole attacks. Finally, the security analysis shows that the scheme in this paper has atomicity, relational anonymity and resistance to wormhole attacks. The latest bilinear pairing anonymous multi‐hop payment (EAMHL+) scheme is used to compare communication and computing overhead. The results show that the scheme in this paper requires less communication overhead and is more efficient in terms of computational overhead when the average hop number of the payment channel network is 3 hops and the network diameter is 6 hops.

Arithmetic Study about Efficiency in Network Topologies for Data Centers

Data centers are getting more and more attention due the rapid increase of IoT deployments, which may result in the implementation of smaller facilities being closer to the end users as well as larger facilities up in the cloud. In this paper, an arithmetic study has been carried out in order to measure a coefficient related to both the average number of hops among nodes and the average number of links among devices for a range of typical network topologies fit for data centers. Such topologies are either tree-like or graph-like designs, where this coefficient provides a balance between performance and simplicity, resulting in lower values in the coefficient accounting for a better compromise between both factors in redundant architectures. The motivation of this contribution is to craft a coefficient that is easy to calculate by applying simple arithmetic operations. This coefficient can be seen as another tool to compare network topologies in data centers that could act as a tie-breaker so as to select a given design when other parameters offer contradictory results.

Key physical topology features for optical backbone networks via a multilayer correlation analysis

A communication network is a multilayer network comprising various layered technologies, and the underlying physical topology is an important aspect that determines the upper bound of overall system performance, including total communication capacity, cost, and robustness. We expect that understanding the impact of the physical topology on system performance will lead to better optical communication network design in the future, and we thus focus on clarifying the relationship between physical topology features and system performance. There have been various studies on the relationship between topology features and overall network performance. For example, the average number of hops and the cluster coefficient are well known to change network properties in complex networks. From the perspective of optical communication networks, it is known that the algebraic connectivity and average path length are related to total network capacity, and these findings have been applied in physical topology design models. On the other hand, there have been no quantitative comparisons among various topology features, and the comprehensiveness of the population from which these features are extracted is insufficient. Hence, we have developed a multilayer (physical topology and layer 1) correlation analysis framework that enables a quantitative comparison of topology features. We use this framework to numerically examine the relationships between physical topology features and the total communication capacity, cost, and robustness of optical communication networks, including graph features (especially graph spectra) that have not been investigated. The results show that the Laplacian spectral radius and geodesic distance Laplacian spectral radius are strongly related to system performance, in addition to the conventional average number of hops, cluster coefficient, algebraic connectivity, and average path length. We confirm that these correlations hold for the different network sizes and spatial nonuniformity of real optical backbone networks in different countries and regions. The results show that the average path length and cluster coefficient, or the Laplacian spectral radius and geodesic distance Laplacian spectral radius, are important guidelines for physical topology design of real optical backbone networks.

Multi-Objective Optimization of Transport Processes on Complex Networks

Transport processes are universal in real-world complex networks, such as communication and transportation networks. As the increase of transport demands in these complex networks, the problems of traffic congestion and transport delay become more and more serious, which call for a systematic network transport optimization. However, it is pretty challenging to improve transport capacity and efficiency simultaneously, since they are often contradictory in that improving one degenerates the other. In this paper, we formulate a multi-objective optimization problem including two objectives: maximizing the transport capacity and minimizing the average number of hops. In this problem, we explore the optimal edge weight assignments and the associated routing paths, corresponding to the optimal trade-off between the two objectives. To solve this problem, we provide a multi-objective evolutionary algorithm, namely network centrality guided multi-objective particle swarm optimization (NC-MOPSO). Specifically, within the framework of MOPSO, we propose a hybrid population initialization mechanism and a local search strategy by employing the network centrality theory to enhance the quality of initial solutions and strengthen the exploration of the search space, respectively. Simulation experiments performed on network models and real networks show that our algorithm has better performance than five state-of-the-art alternatives on several most-used metrics.

ICache: An Intelligent Caching Scheme for Dynamic Network Environments in ICN-Based IoT Networks

Advanced network technologies and ubiquitous connected devices are boosting the development of the Internet of Things (IoT) at an unprecedented pace. However, as most of the connected IoT devices are battery powered, the energy consumption issue has become the bottleneck of the IoT’s development. Caching is a promising approach to reducing the energy consumption of the battery-powered devices since the requested data packets can be retrieved from intermediate nodes in the network, e.g., routers, instead of from the remote battery-powered IoT devices, which allows the IoT devices to spend more time in the sleep mode. To realize in-network caching and overcome the IP-based networks’ inefficiency support for IoT, building IoT over information-centric networking (ICN) is a promising approach advocated by researchers. However, existing works in this area assume the network environments are static, which hinders the development of existing approaches in the real dynamic network environments. In this article, we leverage the deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -networks (DQNs) to propose an intelligent caching scheme (named as iCache) that can automatically adjust the caching nodes’ caching parameters to make caching decisions for the dynamic network environments. Extensive evaluations were conducted and the results show that the proposed iCache outperforms the existing approaches in terms of the total energy consumption (e.g., more than 29% reduction compared to the caching transient data (CTD) caching scheme) and the average number of hops (e.g., more than 20% reduction compared to the CTD caching scheme).

Enhancement of the performance of MANET using machine learning approach based on SDNs

Deep learning (DL) is a subdivision of machine learning (ML) that employs numerous algorithms, each of which provides various explanations of the data it consumes; mobile ad-hoc networks (MANET) are growing in prominence. For reasons including node mobility, due to the potential wireless sensor network (WSN) to provide a small-cost solution to real-world contact challenges. But the lifespan in this network is restricted lifespan. Therefore, the wireless sensor network (WSN) is more vulnerable to battery consumption. On the other hand, routing packets in a Wireless Sensor Network (WSN) is a challenging task, according to the limited resources available on the nodes of these networks, especially their energy sources. The use of Machine Learning (ML) techniques in a Software-Defined Network (SDN) topology has shown good potential for solving such a complex task. However, existing techniques emphasize finding the shortest paths to deliver the packets, which can overload certain nodes in the network, depending on their positioning. In this study, a new method is proposed to extend the lifetime of the WSN by balancing the loading on the nodes, using a Deep Reinforcement Learning (DRL) approach. By emphasizing the lifetime of the network, the proposed method has been able to discover and use alternative routes to deliver the packets, avoiding the use of nodes with low energy. Hence, the average number of hops the packets travel through has been increased, but the time required for the first node to exhaust its energy has been significantly increased.

DHT-Based Blockchain Dual-Sharding Storage Extension Mechanism

The expansion of blockchain storage has become a major problem limiting the application of blockchain. From the perspective of improving the scalability of blockchain storage, a DHT (distributed hash table)-based blockchain dual-sharding storage extension mechanism (DBDSM) is proposed. The nodes in the network are divided into m DHT clusters. Each cluster includes n nodes, and stores 1/m of the transaction data, and the nodes within each cluster store part of data allocated to that cluster. In this way, node storage pressure is alleviated. Furthermore, a hybrid query mechanism has been designed to achieve efficient querying of transaction data, without changing the original state data query. Simulation results showed without changing the original state data query, that the storage space consumed by the nodes is only s/(m × n) of that used in the traditional method; when the number of faulty nodes in the cluster does not exceed s − 1, the integrity of blockchain data can still be ensured. For transaction data queries, the average number of hops was 1.99, greatly improving query efficiency in the sharded state.

Optimal Placement of UDAP in Advanced Metering Infrastructure for Smart Metering of Electrical Energy Based on Graph Theory

This paper presents an algorithm to optimize the deployment of hubs for smart energy metering based on the Internet of Things. A georeferenced scenario is proposed in which each user must connect to a concentrator, either directly or through another user, minimizing the resources required to achieve connectivity. Consequently, to carry out the optimization, the minimum spanning tree between devices is found, in which the maximum connection distance and the capacity of the hubs are limited. Additionally, this work seeks to achieve a scalable algorithm applicable to any georeferenced scenario to be simulated. The main contribution of this work is an IoT-based smart metering architecture that optimizes resources and adapts to a scenario that changes or integrates more users to the energy metering network without losing the connectivity of the initial users. As a result of the application of the algorithm, a scenario route map is generated. The scenario’s parameters include the number of hops in the network, the optimal number of concentrators and their geographical location, the average number of hops, and the total distance of the path, among others. In this project, a georeferenced urban scenario was considered in which residential areas coexist with intelligent buildings. The scenario has growth stages in which the algorithm is applied, and in each one, the optimal route map is generated.

A Probabilistic Spray-and-Wait Routing Algorithm Based on Node Interest Preference in Delay Tolerant Networks

How to select proper relay nodes to ensure the successful delivery of messages is still a hot topic in delay tolerant networks (DTN). In this paper, we propose a probabilistic Spray-and-Wait routing algorithm based on node interest preference (called NIP-PSW). Firstly, considering the influence of the social attributes of nodes, we define a metric called node interest preference (NIP) to measure the probability of nodes becoming friends. Secondly, in view of the influence of node quality and connection time between nodes on message forwarding, we define the delivery probability (DP). Finally, according to the historical information of nodes, the node interest similarity (NIS) is proposed. In spray phase, NIP and DP are used to select the relay node and allocate the number of message copies adaptively. In wait phase, it is judged whether to forward the message to the encountering nodes again according to the NIS and the DP. In addition, the concept of message storage value (MSV) and the acknowledgment (ACK) mechanism are introduced to manage the buffer of nodes. The simulation results show that the NIP-PSW not only can significantly improve the delivery rate and reduce the average delay but also shows good performance in network overhead and average number of hops.

Spectrum-Sharing-Maximized Approaches With Shared-Path Protection in Elastic Optical Data Center Networks

The spectrum efficiency is a greatly important issue when we establish connection requests in elastic optical data center networks (EODCNs). In this article, we address the spectrum efficiency problems of the shared-path protection with spectrum-sharing-maximized approaches for the optical network survivability. Two integer linear program (ILP) models, i.e., flow-based and path-based ILP models, named FB-ILP and PB-ILP, are developed to minimize the frequency slots (FSs) occupied with the spectrum-sharing-maximized protection, and two heuristic approaches with the spectrum-sharing-maximized protection (HA-SSMP) and with the general spectrum-sharing protection (HA-GSSP) are also proposed to improve spectrum efficiency in EODCNs. For comparison, we introduce a heuristic approach with the existing shared-path protection (HA-ESPP) in EODCNs. On the one hand, simulation results show that FB-ILP can minimize the number of FSs occupied, but leads to the higher average number of hops and longer running time compared to PB-ILP, HA-SSMP, HA-GSSP, and HA-ESPP in static traffic scenario in EODCNs. On the other hand, the simulation results of our proposed HA-SSMP are very close to the solutions of FB-ILP. In dynamic traffic scenario, simulation results show that HA-SSMP significantly improves the spectrum efficiency and effectively suppresses the blocking probability, but leads to the higher average number of hops compared to HA-GSSP and HA-ESPP in EODCNs.

Destination-Aware Focused Beam Routing (D-FBR): A routing protocol for underwater wireless sensor networks

Delay Tolerant Networks enable data transmission in different applications, mainly in environments where communication infrastructure is missing. The existence of links is casual and even when links are created, they are short in time and unstable/unreliable in terms of connectivity. Thus, underwater network applications tend to be delay tolerant. In this paper, we propose a mobility-aware routing protocol for underwater wireless sensor networks. The protocol is based on Focused Beam Routing (FBR) protocol and considers nodes’ next destination location when making forwarding decisions. The routing protocol is called Destination-aware Focused Beam Routing (D-FBR) protocol and we compare its performance with FBR protocol for different FBR angles and different applications. We use Delivery Probability (DP), Average Number of Hops (ANH), Overhead Ratio (OR) and simulate our scenarios by The ONE simulator. Simulation results show that, for D-FBR protocol, when FBR angle is 30 ∘ , OR and ANH is improved by 34.5 % and 23.1 %, respectively, while DP is 6.5 % lower, compared to FBR protocol. Both D-FBR and FBR outperform Epidemic Routing Protocol.

Effect of Positional Disorders on Charge Transport in Nanocrystal Quantum Dot Thin Films

Understanding the impact of positional and energetic disorders in nanocrystal (NC) quantum dot thin films on charge transport is crucial to determine what to prioritize in terms of the synthesis and fabrication of these materials and to accelerate their development for electronics. Here, we computationally construct realistic NC thin films with different types of disorders and apply a density functional theory (DFT)-parameterized, kinetic Monte Carlo simulation to systematically study the effects of disorders on transport. We obtain statistics on the carrier transit pathways through the NC films and carrier residence times on individual NCs. This provides insights into the distribution of transit times across the thin films and the effective mobility. We conclude that the impact of positional disorders on charge transport depends on the type of disorder and how it affects the spacing between neighboring NCs. The formation of transport paths with short inter-NC distances can enhance mobility. Meanwhile, random packing (RP) of NCs and energetic disorders due to a distribution of NC sizes decreases mobility 2- to 4-fold. Because of the large reorganization energy of small NCs, increasing the electric field has little influence on the median residence time of a charge carrier on an NC; however, an electric field straightens the transport path of the charge carrier and reduces the average number of hops a carrier makes, which can slightly enhance mobility. Deep electronic trap states are especially detrimental to carrier mobility, particularly at low fields and when the films are otherwise highly ordered.

Performance evaluation of Focused Beam Routing for IoT applications in underwater environment

Underwater applications are becoming more and more interesting to industry and academy. They include data gathering for human safety and environment monitoring, control of underwater robots for various tasks and so on. Because of the accessibility limitations in underwater environment, applications tend to be automated and delay tolerant. In this paper, we consider IoT applications in underwater environment, while using Delay Tolerant Networking (DTN) carry–store–forwarding paradigm. DTN routing protocols are used to forward data from the monitoring mobile sensors to collecting devices at the water surface and vice-versa. One characteristic of routing protocols for DTN is flooding of messages to increase the delivery probability. For instance, Epidemic Routing (ER) protocol creates a copy of each message for each new node that does not already have the message in its memory. This increases the probability of delivery, but on the other hand, creates overhead in each node’s buffer, and uses a lot of valuable energy from the forwarding and receiving nodes. This work aims to analyze by simulations the performance of Focused Beam Routing (FBR) protocol for different FBR angles and different applications. We use Delivery Probability, Average Number of Hops, Overhead Ratio and Buffer Occupancy to simulate our scenarios by The ONE simulator. Simulation results show that for narrow angles of FBR the performance is better. In case of FBR-45, average hop count and overhead ratio are decreased by 10.9% and 16.6% respectively, compared to FBR-180. However, delivery probability decreases by only 3.9%.

Enhancing fault tolerance of wireless sensor networks by changing topology

Wireless sensor networks work by multi hop mode. The increase of intermediate forwarding nodes will bring the packet loss rate increase. In addition, a node which has fault will affect the entire link work. The average number of forwarding node influences the fault tolerance of wireless sensor networks. In order to ensure that wireless sensor networks can work smoothly and prolong the lifetime, the reducing of average number of forwarding node is necessary, namely reducing the average number of hops. The reducing of average hop number of whole network also reduces the packet loss rate and the energy consumption of nodes. The proposed algorithm in this paper cuts down the hops and improves the fault tolerance of wireless sensor networks.

Extending Wireless Sensor Networks’ Lifetimes Using Deep Reinforcement Learning in a Software-Defined Network Architecture

Routing packets in a Wireless Sensor Network (WSN) is a challenging task, according to the limited resources available on the nodes of these networks, especially their energy sources. The use of Machine Learning (ML) techniques in a Software-Defined Network (SDN) topology has shown a good potential toward solving such a complex task. However, existing techniques emphasize finding the shortest paths to deliver the packets, which can overload certain nodes in the network, depending on their positioning. In this study, a new method is proposed to extend the lifetime of the WSN by balancing the loading on the nodes, using a Deep Reinforcement Learning (DRL) approach. By emphasizing on the lifetime of the network, the proposed method has been able to discover and use alternative routes to deliver the packets, avoiding the use of nodes with low energy. Hence, the average number of hops the packets travel through has been increased but the time required for the first node to exhaust its energy has been significantly increased.

A Fast Heuristic for Gateway Location in Wireless Backhaul of 5G Ultra-Dense Networks

In 5G ultra-dense networks, a distributed wireless backhaul is an attractive solution for forwarding traffic to the core. The macro-cell coverage area is divided into many small cells. A few of these cells are designated as gateways and are linked to the core by high-capacity fiber optic links. Each small cell is associated with one gateway and all small cells forward their traffic to their respective gateway through multi-hop mesh networks. We investigate the gateway location problem and show that finding near-optimal gateway locations improves the backhaul network capacity. An exact p-median integer linear program is formulated for comparison with our novel K-GA heuristic that combines a Genetic Algorithm (GA) with K-means clustering to find near-optimal gateway locations. We compare the performance of K-GA with six other approaches in terms of average number of hops and backhaul network capacity at different node densities through extensive Monte Carlo simulations. All approaches are tested in various user distribution scenarios, including uniform distribution, bivariate Gaussian distribution, and cluster distribution. In all cases, K-GA provides near-optimal results, achieving average number of hops and backhaul network capacity within 2% of optimal while saving an average of 95% of the execution time.