Hierarchical Routing Research Articles

Optimizing Wireless Sensor Network longevity with hierarchical chain-based routing and vertical network partitioning techniques

Efficient utilization of energy is a crucial concern in Wireless Sensor Networks (WSNs) to increase the network's longevity. However, it is impossible to investigate routing without considering the effective formation of chains or clustering methods to optimize the problem in WSNs. The proposed routing technique aims to extend the lifespan of sensors using various network partitioning techniques. The approach utilized in the strategy is PEGASIS (Power EfficientGathering in Sensor Information Systems) protocol, it uses Prim's Algorithm to modify the chain structure and is based on hierarchical chain-based routing. In order to transmit information from the working nodes to the base station (BS), we employ and vertical network partitioning techniques named EEPEG-PA-V. According to this approach, the transition is carried out when the node's residual energy is about to run out. The suggested method has the potential to enhance the average network longevity substantially when compared to existing routing techniques. For instance, EEPEG-PA improves it by 21.7092 % and EEPEG-PA-V by 29.9056 % compared to PEGASIS. Similarly, EEPEG-PA-V by 6.1708 % compared to EEPEG-PAacross various network sizes.

Retraction Note: Soft computing and trust-based self-organized hierarchical energy balance routing protocol (TSHEB) in wireless sensor networks

Retraction Note: Soft computing and trust-based self-organized hierarchical energy balance routing protocol (TSHEB) in wireless sensor networks

Survey broach for clustering in wireless sensors protocols and types

Sensor network applications have become part of everyday life applications. Creating such networks needs to meet its own constraints, such as: the energy consumption, scalability, fault tolerance, estimated cost, and the possibility of changing the network topography and the environment. In this work, a survey on hierarchical routing protocols in Wireless Sensor Networks (WSN) was performed. WSNs hold tiny nodes with ability of sensing, communications, and computation. Due to the power limitation of the sensor unit, several hierarchical routing protocols have been proposed for minimizing power exhaustion. Much research labor has been implemented to overcome weakness and to upgrade the performance of hierarchical routing protocols. So, a universal review is needed which can review latest technologies, construe functional and performance sides, and focus on issues and challenges of hierarchical routing protocol in WSNs. lastly, the paper focus on issues and challenges in current routing protocols of WSNs, which can aide in outlook research for election of appropriate research field and supply guidance in selection of energy awareness techniques in the design of improving energy of routing protocols for WSNs.

Energy-efficient hierarchical cluster-based routing strategies for Internet of Nano-Things: Algorithms design and experimental evaluations

Nanodevices (NDs), which are only a few nanometers (nm) in size, need to communicate with each other to perform complex operations. In nanonetworks, this communication typically involves multiple hops, requiring efficient routing protocols. Existing protocols are not well suited for nanonetworks due to their high resource consumption and setup overhead. In this paper, we propose three novel routing protocols for nanodevices. Non-Back Flooding Routing (NBFR) and Layer-Based Flooding Routing (LBFR) aim to reduce unnecessary packet transmission by utilizing distance and layer information based on received signal power. On the other hand, Tree-Based Forwarding Routing (TBFR) is a unicast-based approach that aims to transmit the packet to the destination using the shortest and most reliable path possible through a tree structure. The performance of these proposed methods is compared to well-known methods in terms of packet transmission, energy consumption, end-to-end delay, and setup overhead. TBFR achieved a packet transmission success of 92.95% in topology with the highest density of nanorouters (NRs), while it reached up to 99.57% for fewer nanorouters. Moreover, its end-to-end delay values are much lower than those of multi-path routing protocols. It also consumed one-fifth of the energy compared to its most challenging multi-path competitor, NBFR, regarding packet transmission success. However, for dense nanosensor (NS) topologies, NBFR and LBFR achieved higher packet transmission rates of 87.04% and 86.66%, respectively. Furthermore, in addition to achieving low end-to-end delays, the energy consumption of NBFR is very close to that of TBFR. In summary, the tests show that TBFR is more suitable for communication among nanorouters due to the requirement of building the tree structure, which results in a slightly higher setup overhead. In contrast, NBFR and LBFR are more suitable for communication between nanosensors because of their simplicity and low setup overhead. But, it should be noted that NBFR requires a larger header than the other alternatives.

A novel capsule network based on Multi-Order Descartes Extension Transformation

In recent years, the capsule network has significantly impacted deep learning with its unique structure that robustly handles spatial relationships and image deformations like rotation and scaling. While previous research has primarily focused on enhancing the structural network of capsule networks to process complex images, little attention has been given to the rich semantic information contained within the capsules themselves. We recognize this gap and propose the Multi-Order Descartes Expansion Capsule Network (MODE-CapsNet). By introducing the Multi-Order Descartes Expansion Transformation (MODET), this innovative architecture enhances the expressiveness of a single capsule by enabling its projection into a higher-dimensional space. As far as we know, this is the first significant enhancement at the single-capsule granularity level, providing a new perspective for improving capsule networks. Additionally, we proposed a hierarchical routing algorithm designed explicitly for the MODE capsules, significantly optimizing computational efficiency and performance. Experimental results on datasets (MNIST, Fashion-MNIST, SVHN, CIFAR-10, tiny-ImageNet) showed that MODE capsules exhibited improved separability and expressiveness, contributing to overall network accuracy, robustness, and computational efficiency.

Energy efficient routing for improving lifetime in MWSN: A clustering approach

A Mobile wireless sensor network (MWSN) consists of mobile sensor nodes, which can be deployed in any specific environment, and due to its’ mobility it can perform with rapid topological transformations of a network. The sensor nodes having limited battery power are used to collect specific data and this raw data is sent to a static sink node of the network. Under such a scenario, to avoid frequent disconnections due to topological change in the network and can avail more reliable data transmissions in energy awareness perspective, an energy efficient routing protocol for MWSNs to improve its lifetime is proposed in this paper by utilizing a clustering approach. A MWSN with random number of sensor nodes are initially considered and then, clustering algorithm K-means is used to find a predefined number of clusters with their initial cluster heads (CHs) and the centroid location of these clusters is also determined. The role of these CHs is to elect our DDBLACH (distance to sink and cluster centroid with battery level aware cluster head) nodes from each cluster, by sending and receiving intra-cluster messages among other member sensor nodes. A DDBLACH node is determined by using three factors, such as minimum distance from cluster centroid location, minimum distance from sink and the maximum battery level of the node from each cluster. These DDBLACH nodes are used to collect data from intra-cluster sensors and thereafter, send those towards sink node for further processing using tree based hierarchical routes. Finally, an energy efficient routing technique for MWSNs is proposed for data transmission from these DDBLACH nodes of clusters to a sink of the network. Here simulation results indicate the supremacy of our proposed scheme over other existing methods in various aspects, such as improving the presence of alive nodes and average network lifetime.

Hierarchical Routing with Optimization Algorithm for SDN: Enhancing Network Lifetime and QoS

Hierarchical Routing with Optimization Algorithm for SDN: Enhancing Network Lifetime and QoS

Retraction Note: Hierarchical energy-saving routing algorithm using fuzzy logic in wireless sensor networks

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s13635-023-00144-1.

Kalman filtering and sine arithmetic optimization algorithm (KSAOA) for wireless sensor network clustering

Hierarchical routing is a crucial approach for enhancing the energy efficiency of Wireless Sensor Networks (WSN). The selection of sensor clusters poses a Non-deterministic Polynomial (NP) problem, typically addressed using meta-heuristic algorithms. However, existing research has not adequately considered the observation error of node energy and the limited convergence performance of their meta-heuristic algorithms. To address these challenges, we proposes a novel approach called KSAOA (Kalman filtering and Sine Arithmetic Optimization Algorithm) for WSN clustering, aimed at prolonging the service lifetime and enhancing the network balance of WSN. The proposed model combines Kalman filtering for estimating node energy and the Sine Arithmetic Optimization Algorithm (SAOA) for clustering nodes. SAOA builds upon the Arithmetic Optimization algorithm (AOA) by introducing a sine factor to extend the exploration boundary and a levy flight strategy to improve local exploration. Additionally, greedy algorithms are employed in the data transfer phase to enhance operational efficiency. The experimental results demonstrate the effectiveness of the proposed method in tabular and graphical form. The results are analyzed through comparative experiments to show that the proposed strategy is effective in improving the balance of network lifetime and network energy consumption.

An Energy-Efficient Cluster-based data aggregation for agriculture irrigation management system using wireless sensor networks

Wireless Sensor Network (WSN) are becoming one of the demanding platforms, where sensor nodes are sensing and monitoring the physical or environmental conditions and transmit the data to the base station via multi-hop routing. Agriculture sector also adopted these networks to promote innovations for environmentally friendly farming methods, lower the management cost, and achieve scientific cultivation. This combination can improve crop yield, quality, and efficiency while saving energy and water. It is important to note that irrigation is one of the essential factors for agricultural success. In an agriculture irrigation application, a WSN is deployed in a specific area to control the irrigation in the field. But using WSN will generate massive redundancy in the data processing. So, there is lacking an efficient system for extensive sensor data processing. This research work presents an Energy-Efficient Cluster-Based Data Aggregation (E2CDA) Technique for agriculture irrigation systems in WSN. An improved probabilistic clustering method is used for preserving the optimal number of Cluster Head (CH) in each round of iteration. The proposed work is implemented in MATLAB, according to simulation results; the developed E2CDA method provides better result when compared with the existing approaches such as Low Energy Adaptive Clustering Hierarchical (LEACH) routing and centralized LEACH (LEACH-C) in terms of energy savings, network lifetime, packet delivery ratio, end-to-end delay, and packet drop ratio.

A Hybrid Clustered Ant Colony Optimization Approach for the Hierarchical Multi-Switch Multi-Echelon Vehicle Routing Problem with Service Times

In this paper, the Hierarchical Multi Switch Multi Echelon Vehicle Routing Problem with Service Times (HMSME-VRP-ST) is presented. This novel problem considers distribution applications in which goods are delivered either directly from a central depot or through intermediate facilities called switch points. Commodities are loaded into interchangeable containers called swap bodies at the central depot, and can be transferred from one vehicle to another at the switch points. The goal is to minimize fixed and variable costs of the vehicle fleet and swap-bodies while serving a predetermined set of customers. The routes are constrained by time and swap bodies by loading capacity. A mathematical model of the HMSME-VRP-ST is presented, and a Hybrid Clustered Ant Colony Optimization (HCACO) algorithm is proposed to address the complexity of the problem. The approach utilizes the ant-based clustering algorithm, combining the density and connectivity properties of clustering for creating promising neighborhoods with the solution construction methodology and pheromone-based memory of the Ant Colony Optimization framework. Additionally, two local search schemes based on Variable Neighborhood Descent are incorporated to further improve the generated solutions. The behavior of each HCACO variant is analyzed, and their results are compared to a Greedy Randomized Adaptive Search Procedure metaheuristic in 36 newly generated benchmarks comprising of clustered, uniformly random, and mixed clustered-random instances.

A variable neighborhood search approach for solving a real-world hierarchical multi-echelon vehicle routing problem involving HCT vehicles

This paper studies the Hierarchical Multi-Switch Multi-Echelon VRP (HMSME-VRP), a newly introduced VRP variant based on a real-world case involving High Capacity Vehicles (HCV). The problem originates from the policies of a distribution company in the Nordic countries where HCVs of up to 34.5 m and up to 76 tons are allowed. The HMSME-VRP offer a new way to model distribution problems to cover large geographical areas without substantial costs in infrastructure. Furthermore, it adds complexity to the standard VRP and, as such, remains NP-hard and difficult to solve to optimality. Indeed, it has been demonstrated that only very small instances can be solved to optimality by a commercial solver. Thus, in order to handle instances of real-world size, we propose two General Variable Neighborhood Search (GVNS) procedures, the second of which is adaptive, utilizing an intelligent reordering mechanism. In order to evaluate the proposed procedures, 48 benchmark instances of various sizes and characteristics are generated and made publicly available, comprising of clustered, random, and semi-clustered customers. The computational results show that both GVNS procedures outperform the exact solver. Additionally, the adaptive version outperforms the conventional version based on both average and best solutions. Furthermore, we present a statistical analysis to verify the superiority of the adaptive version.

An Analysis of Hierarchical Routing Strategy with Advanced Additional Sensors in WSNs

Two kinds of sensors will be discussed in some sort of wireless sensor networks (WSNs). One is named a normal sensor (called A_nodes) with fixed initial energy, which can get perception data from the surrounding environment and have functions of storage and forwarding. The other is named relay sensor (called B_node) with sufficient energy, which only can store data and forward data. Cluster heads (called A_heads) are chosen among A_nodes by probability mode to create clusters first. Local adjustments would be done inter-cluster. Then the sink is selected as the root and a backbone shortest path tree with hops limited is built dynamically from A_heads, B_Nodes and sink. Inside the backbone shortest path aggregation tree, two steps are done. The first step, select some B_node as cluster head (denoted as B_node) and make local adjustment intra-clusters if it is possible. The second step, adjust the shortest path aggregation tree. By using both particle swarm optimization (PSO) and ant colony optimization (ACO), a traveling salesman problem (TSP) cycle with shorter path length is obtained (this method is noted as TSP_PSO_ACO). This TSP cycle consists of all nodes in the aggregation backbone tree. Along this cycle, energy is offered to nodes except sink. The above strategy would be discussed for its feasibility and time complexity. The experimental results imply that the energy consumption is reduced by using local adjustment. The relative failure rate of nodes can be reduced in the later stage of WSN survival. Offering energy to a small number of nodes several times has no obvious impact on the overall energy consumption and survival time of WSN. The case with some small-sized obstacles in WSN area is also discussed. There are little affections on TSP cycles both from results on theoretical analysis and simulation if there are some obstacles with small size in WSN area.

FMRI, LFP, and anatomical evidence for hierarchical nociceptive routing pathway between somatosensory and insular cortices

The directional organization of multiple nociceptive regions, particularly within obscure operculoinsular areas, underlying multidimensional pain processing remains elusive. This study aims to establish the fundamental organization between somatosensory and insular cortices in routing nociceptive information. By employing an integrated multimodal approach of high-field fMRI, intracranial electrophysiology, and transsynaptic viral tracing in rats, we observed a hierarchically organized connection of S1/S2 → posterior insula → anterior insula in routing nociceptive information. The directional nociceptive pathway determined by early fMRI responses was consistent with that examined by early evoked LFP, intrinsic effective connectivity, and anatomical projection, suggesting fMRI could provide a valuable facility to discern directional neural circuits in animals and humans non-invasively. Moreover, our knowledge of the nociceptive hierarchical organization of somatosensory and insular cortices and the interface role of the posterior insula may have implications for the development of targeted pain therapies.

Optimization of Hierarchical Routing and Resource Allocation for Power Communication Networks With QKD

Optimization of Hierarchical Routing and Resource Allocation for Power Communication Networks With QKD

Knowledge Graph-Based Hierarchical Text Semantic Representation

Document representation is the basis of language modeling. Its goal is to turn natural language text that flows into a structured form that can be stored and processed by a computer. The bag-of-words model is used by most of the text-representation methods that are currently available. And yet, they do not consider how phrases are used in the text, which hurts the performance of tasks that use natural language processing later on. Representing the meaning of text by phrases is a promising area of future research, but it is hard to do well because phrases are organized in a hierarchy and mining efficiency is low. In this paper, we put forward a method called hierarchical text semantic representation using the knowledge graph (HTSRKG), which uses syntactic structure features to find hierarchical phrases and knowledge graphs to improve how phrases are evaluated. First, we use CKY and PCFG to build the syntax tree sentence by sentence. Second, we walk through the parse tree using the hierarchical routing process to obtain the mixed phrase semantics in passages. Finally, the introduction of the knowledge graph improves the efficiency of text semantic extraction and the accuracy of text representation. This gives us a solid foundation for tasks involving natural language processing that come after. Extensive testing on actual datasets shows that HTSRKG surpasses baseline approaches with respect to text semantic representation, and the results of a recent benchmarking study support this.

Efficient routing method for IoT networks using bee colony and hierarchical chain clustering algorithm

Hierarchical chain communication is an efficient technique for saving sensor energy while maintaining load balance and scalability. However, the long delay caused by the formation of long chains is a significant research gap in this paradigm. In this article, a two-level hierarchical chain routing scheme based on the bee colony algorithm called PEG_ABC is proposed to reduce transmission delay and take advantage of the benefits of chain communication. Initially, the remaining sensor energy, Euclidean distance between sensors, and Euclidean distance between sensors and base station are simultaneously adjusted to the fitness function of the bee colony algorithm to select the most suitable chain leader at the first level. Also, in our proposed method at the second level, the collected data in the leader nodes of each level is transferred to the BS through end-to-end path formation between the leaders based on the bee colony algorithm. At this level, the data transmission delay and remaining energy in the leader nodes adjust the fitness function of the bee colony algorithm. Simulation results show the efficiency of the proposed PEG_ABC scheme compared to literature research in terms of energy consumption, network lifetime, number of packets sent to BS, and transmission delay. Also, this article presents a relationship based on extensive simulation results to determine the optimal number of clusters in hierarchical chain communication.

Hybrid Gromov-Wasserstein Embedding for Capsule Learning.

Capsule networks (CapsNets) aim to parse images into a hierarchy of objects, parts, and their relationships using a two-step process involving part-whole transformation and hierarchical component routing. However, this hierarchical relationship modeling is computationally expensive, which has limited the wider use of CapsNet despite its potential advantages. The current state of CapsNet models primarily focuses on comparing their performance with capsule baselines, falling short of achieving the same level of proficiency as deep convolutional neural network (CNN) variants in intricate tasks. To address this limitation, we present an efficient approach for learning capsules that surpasses canonical baseline models and even demonstrates superior performance compared with high-performing convolution models. Our contribution can be outlined in two aspects: first, we introduce a group of subcapsules onto which an input vector is projected. Subsequently, we present the hybrid Gromov-Wasserstein (HGW) framework, which initially quantifies the dissimilarity between the input and the components modeled by the subcapsules, followed by determining their alignment degree through optimal transport (OT). This innovative mechanism capitalizes on new insights into defining alignment between the input and subcapsules, based on the similarity of their respective component distributions. This approach enhances CapsNets' capacity to learn from intricate, high-dimensional data while retaining their interpretability and hierarchical structure. Our proposed model offers two distinct advantages: 1) its lightweight nature facilitates the application of capsules to more intricate vision tasks, including object detection; and 2) it outperforms baseline approaches in these demanding tasks. Our empirical findings illustrate that HGW capsules (HGWCapsules) exhibit enhanced robustness against affine transformations, scale effectively to larger datasets, and surpass CNN and CapsNet models across various vision tasks.

Impact of Renewable Energy Resources on the Performance of DTN Networks in the Context of Hierarchical Routing Tree Topology (HRTT)

Delay-Tolerant Networks (DTN) are mobile networks specifically designed to operate in challenging wireless environments where connectivity is not guaranteed, and various issues such as frequent disconnections, long delivery delays, low delivery rates, and high error rates are common. One of the major problems of DTN networks is limited energy resources which impose strict constraints on the operational lifespan of DTN nodes. To solve these problems, we propose the adoption of a long-term strategy for energy management, based on the exploitation of renewable energies and the improvement of the HRTT topological architecture through the direct integration of renewable energy sources within DTN nodes. Indeed, DTN nodes powered by renewable energy resources, such as solar energy, have the potential to recharge themselves, extending their operational lifespan, increasing their storage and transmission capabilities, and reducing dependence on non-renewable energy resources. In this article, we assess the impact of limited energy resources and renewable energy resources like solar panels on the energy performance of DTN networks in the context of the Hierarchical Routing Tree Topology (HRTT), taking into account the average remaining energy as a performance metric. The evaluation of energy performance and the implementation of the HRTT topology are carried out using the ONE (Opportunistic Network Environment) simulator. According to the simulation results, the average remaining energy of nodes using renewable resources is significantly higher compared to the initial energy levels of nodes for all DTN protocols adopted by the nodes in the context of the HRTT topology. However, the average remaining energy of nodes using limited resources is lower compared to their initial energy levels for all DTN protocols adopted by the nodes in the context of the HRTT topology.

Performance Analysis of LEACH, SEP and Z-SEP Protocols in Heterogeneous Wireless Sensor Network

Wireless sensor networks use thousands of sensors to monitor the physical environment. This sensor node consumes a limited amount of energy since it is battery-powered. Researchers regularly develop energy-efficient routing protocols to extend the lifetime of WSNs. The threshold-based cluster head (CH) selection techniques used by hierarchical routing protocols have been shown to extend network lifetime in many studies. Through the implementation of several routing protocols, sensor networks can be made to last longer and consume less energy. Z-SEP introduces a new heterogeneous routing protocol using a hybrid communication mechanism. Others use clustering mechanisms, while others use the base station directly. Energy efficiency and stability are improved by 11% and 19%, respectively, over Z-SEP