Hierarchical Protocol Research Articles

HAMIL: Hierarchical aggregation-based multi-instance learning for microscopy image classification

Multi-instance learning is common for computer vision tasks, especially in biomedical image processing. Traditional methods for multi-instance learning focus on designing feature aggregation methods and multi-instance classifiers, where the aggregation operation is performed either in the feature extraction or learning phase. As deep neural networks (DNNs) achieve great success in image processing via automatic feature learning, certain feature aggregation mechanisms need to be incorporated into common DNN architecture for multi-instance learning. Moreover, flexibility and reliability are crucial considerations to deal with varying quality and number of instances. In this study, we propose a hierarchical aggregation network for multi-instance learning, called HAMIL. The hierarchical aggregation protocol enables feature fusion in a defined order, and the simple convolutional aggregation units lead to an efficient and flexible architecture. We assess the model performance on two microscopy image classification tasks, namely protein subcellular localization using immunofluorescence images and gene annotation using spatial gene expression images. The experimental results show that HAMIL outperforms the state-of-the-art feature aggregation methods and the existing models for addressing these two tasks. The visualization analyses also demonstrate the ability of HAMIL to focus on high-quality instances.

Leader-Based Consensus in Directed Networks of Homogeneous Positive Agents With Multiple Inputs

Abstract In this paper, the non-zero finite positive state consensus problem of leader-following multi-agent systems (MASs) connected over a directed network is investigated. The multi-agent network consists of homogeneous linear time-invariant (LTI) positive agents whose minimal positive state-space realization is assumed to be known. In this paper, a state-feedback hierarchical control protocol is proposed where the local controller synthesizes a singular, Lyapunov stable, and Metzler system matrix with a simple dominant eigenvalue at origin. The obtained consensus vector is the positive eigenvector associated with the zero eigenvalue of the synthesized system matrix. The controller gain matrices are obtained by solving a set of necessary and sufficient conditions derived in linear programing framework. A numerical example is given to elucidate the usefulness of the proposed results.

EE-MSWSN: Energy-Efficient Mobile Sink Scheduling in Wireless Sensor Networks

Data gathering using mobile sink (MS) based on rendezvous points (RPs) is a need in several Internet of Things (IoT) applications. However, devising energy-efficient and reliable tour planning strategies for MS is a challenging issue, considering that sensors have finite buffer space and disparate sensing rates. This is even more challenging in delay-tolerant networks, where it is more desirable to select the shortest traveling path. There exist several algorithms on MS scheduling, which are based on hierarchical protocols for data forwarding and data collection. These algorithms are lacking efficient tradeoff between the Quality-of-Service (QoS) requirements in terms of energy efficiency, reliability, and computational cost. Besides, these algorithms have shown high packet losses while jointly performing MS tour planning and buffer overflow management. To address these limitations, we propose EE-MSWSN, an energy-efficient MS wireless sensor network that reliably collects data by implementing efficient buffer management. It forms novel clustered tree-based structures to cover all the network, and select each RP based on 1) hop count; 2) number of accumulated data in each clustered tree; and 3) distance to the stationary sink. The extensive simulation results verify that the EE-MSWSN minimizes tour length for various network configurations and incurs less energy consumption while reliably gathering data without packet losses as compared with existing protocols.

A Systematic Review on the Energy Efficiency of Dynamic Clustering in a Heterogeneous Environment of Wireless Sensor Networks (WSNs)

There are a variety of applications for wireless sensor networks (WSNs), such as military, health monitoring systems, natural disasters, smartphones, and other surveillance systems. While the primary purpose of sensor nodes is to collect unattended data in hostile environments, many are placed in large numbers and operate independently. Due to limited capabilities, power is often limited. Therefore, these nodes are grouped into clusters to increase communication efficiency. In WSNs, two different routing protocols are possible: apartment and hierarchical or clustering protocols. Due to their significant role in minimizing energy consumption, hierarchical methods have become very popular in clustering. In cluster-based methods, nodes are organized into clusters, and the sensor node with the most resources is appointed as the cluster head (CH). In this paper, we present a Systematic Literature Review (SLR) explaining the difficulties in developing cluster-based methods, critical factors for clustering, and hierarchical clustering protocols. The most important factor of a routing protocol for WSN is the energy consumption and lifetime of a network. Focusing on energy consumption, different cluster-based methods were analyzed to determine which technology should be deployed by analyzing specific criteria to support the selection process. Additionally, the pros and cons of different protocols are listed with their relevance in specific scenarios. To identify these protocols, a systematic literature review was conducted using research studies published from 2010 to 2021, with 30 papers analyzed in the final phase. Based on the results of this SLR, several issues need to be further investigated with respect to the interaction of the potential technology with the Internet of Things (IoT) and Vehicular Ad-Hoc Networks (VANETs).

A Hierarchical and Location-Aware Consensus Protocol for IoT-Blockchain Applications

Blockchain-based IoT systems can manage IoT devices and achieve a high level of data integrity, security, and provenance. However, incorporating existing consensus protocols in many IoT systems limits scalability and leads to high computational cost and consensus latency. In addition, location-centric characteristics of many IoT applications paired with limited storage and computing power of IoT devices bring about more limitations, primarily due to the location-agnostic designs in blockchains. We propose a hierarchical and location-aware consensus protocol (LH-Raft) for IoT-blockchain applications inspired by the original Raft protocol to address these limitations. The proposed LH-Raft protocol forms local consensus candidate groups based on nodes’ reputation and distance to elect the leaders in each sub-layer blockchain. It utilizes a threshold signature scheme to reach global consensus and the local and global log replication to maintain consistency for blockchain transactions. To evaluate the performance of LH-Raft, we first conduct an extensive numerical analysis based on the proposed reputation mechanism and the candidate group formation model. We then compare the performance of LH-Raft against the classical Raft protocol from both theoretical and experimental perspectives. We evaluate the proposed threshold signature scheme using Hyperledger Ursa cryptography library to measure various consensus nodes’ signing and verification time. Experimental results show that the proposed LH-Raft protocol is scalable for large IoT applications and significantly reduces the communication cost, consensus latency, and agreement time for consensus processing.

Hierarchical Traffic Flow Prediction Based on Spatial-Temporal Graph Convolutional Network

In recent years, traffic flow prediction has attracted more and more interest from both academia and industry since such information can provide effective guidance for traffic management or driving planning and enhance traffic safety and efficiency. But due to the complicated spatial-temporal dependence in actual roads and the limitation of intersection monitoring equipment, there are still many challenges in spatial-temporal traffic flow prediction. In this paper, we propose a novel hierarchical traffic flow prediction protocol based on spatial-temporal graph convolutional network (ST-GCN), which incorporates both spatial and temporal dependence of intersection traffic to achieve a more accurate traffic flow prediction. Different from existing works, our proposed protocol with the Adjacent-Similar algorithm can also effectively predict the traffic flow of the intersections without historical data. Experiments based on practical traffic data of the city of Qingdao, China demonstrate that our proposed ST-GCN-based traffic flow prediction protocol outperforms the state-of-the-art baseline models. Moreover, as for the intersections without historical data, we can also obtain a good prediction accuracy.

Hierarchical stable election protocol for WSN‐based IoT inhabitant and environmental monitoring applications

SummaryWireless sensor networks (WSNs) play an essential role in IoT applications, especially for long‐term data gathering and monitoring applications. The deployment field and the limited energy sources are two critical constraints directly influencing WSN lifespan, and both must be considered while designing a routing protocol for WSN‐based IoT applications. Routing the data consumes a significant amount of energy in such an extensive network. Several hierarchical clustering protocols have been developed in recent years to address this purpose. We propose a new hierarchical stable election protocol (HISEP), an improved version of the Stable Election Protocol (SEP) equipped with the Dual CH election algorithm using a new enhanced threshold formula. Accepting nodes' heterogeneity and incorporating Voronoi‐based cluster formation ensures that the 2D/3D WSN network stays alive for an extended period and the HISEP algorithm will be suitable for habitat tracking and environmental monitoring applications. The simulation results show that the proposed algorithm outperforms other protocols in terms of routing efficiency and network lifespan.

Energy Prediction and Energy Management in Kinetic Energy-Harvesting Wireless Sensors Network for Industry 4.0

Real-time control and monitoring are some of the main goals of Industry 4.0. To meet these requirements, sensors are needed at every step of the production process. Wireless sensors (WS) are better suited due to their flexibility but are limited in energy. In this work, kinetic energy harvesting using piezoelectric technologies are considered to ensure the energy autonomy of a Wireless Sensor Network (WSN). First, unlike most existing works, this paper focuses on WSN rather than a single WS since the control of the entirety of most industrial processes requires several WSs. The solution proposed here is based on deep learning of the harvestable power signals at each sensor deployed on the monitoring system. Specifically, vibration measurements were performed at 12 locations on an ore crushing mill in a mine. From there, a mechanical–electrical conversion model considering the system’s dynamics was set up to evaluate the power profile each of the WSs can harvest. Considering that the harvestable power has many peaks due to the different operating states of the engine, we first proposed a Predictor of the Harvestable Power from Vibrations (PHPV). Using a large database, compared to a state-of-the-art predictor, the Predictor of the Harvestable Energy from vibrations (PHEV) allows for significantly reducing the Root Mean Square Error (RMSE). More specifically, the lowest reduction achieved for RSME ranged from 9.4 μW (with PHEV) to 5.9 μW (with PHPV). A decrease in RMSE ranging from 18.45 to 4 μW was obtained for another measurement point. Since harvest rates differ from one location to another, a Hierarchical Energy-Balancing Protocol (HEBP) is proposed to maximize the number of WS capable of transmitting information about the system’s state, thus avoiding an interruption of the network coverage. In the HEBP, it is envisaged that some WSs, besides transmitting data, will supply other nodes with an energy deficit to allow them to communicate information about their location. For a minimum packet size of up to 1100 bits, the energy autonomy of all the WSs is ensured, unlike only 66% of the nodes with the previous protocols.

Abnormalities of the Width of the Four-Chamber View and the Area, Length, and Width of the Ventricles to Identify Fetuses at High-Risk for D-Transposition of the Great Arteries and Tetralogy of Fallot.

The prenatal detection of D-Transposition of the great arteries (D-TGA) and tetralogy of Fallot (TOF) has been reported to be less than 50% to as high as 77% when adding the outflow tracts to the four-chamber screening protocol. Because many examiners still struggle with the outflow tract examination, this study evaluated whether changes in the size and shape of the heart in the 4CV as well as the ventricles occurred in fetuses with D-TGA and TOF could be used to screen for these malformations. Forty-four fetuses with the pre-and post-natal diagnosis of D-TGA and 44 with TOF were evaluated between 19 and 36 weeks of gestation in which the 4CV was imaged. Measurements of the end-diastolic width, length, area, and global sphericity index were measured for the four-chamber view and the right and left ventricles. Using z-score computed values, logistic regression was performed between the 88 study and 200 control fetuses using the hierarchical forward selection protocol. Logistic regression identified 10 variables that correctly classified 83/88 of fetuses with TOF and TGA, for a sensitivity of 94%. Six of 200 normal controls were incorrectly classified for a false-positive rate of 3%. The area under the receiver operator classification curve was 98.1%. The true positive rate for D-TGA was 93.2%, with a false-negative rate to 6.8%. The true positive rate for TOF was 95.5%, with a false negative rate of 4.5%. Measurements of the 4CV and of the RV and LV may help identify fetuses at risk for D-TGA or TOF.

Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems

In recent years, various applications have emerged requiring linear topologies of wireless sensor networks (WSN). Such topologies are used in pipeline (water/oil/gas) monitoring systems. The linear structure has a significant impact on network performance in terms of delay, throughput, and power consumption. Regarding communication efficiency, routing protocols play a critical role, considering the special requirements of linear topology and energy resources. Therefore, the challenge is to design effective routing protocols that can address the diverse requirements of the monitoring system. In this paper, we present various wireless communication technologies and existing leak detection systems. We review different routing protocols focusing on multi-hop hierarchical protocols, highlighting the limitations and design issues related to packet routing in linear pipeline leak detection networks. Additionally, we present a LoRa multi-hop model for monitoring aboveground oil pipelines. A set of model parameters are identified such as the distance between sensors. In addition, the paper determines some calculations to estimate traffic congestion and energy consumption. Several alternative model designs are investigated. The model is evaluated using different multi-hop communication scenarios, and we compare the data rate and energy to provide an energy-efficient and low-cost leak detection system.

EEIT2-F: energy-efficient aware IT2-fuzzy based clustering protocol in wireless sensor networks

<p><span>Improving the network lifetime is still a vital challenge because most wireless sensor networks (WSNs) run in an unreached environment, and offer almost impossible human access and tracking. Clustering is one of the most effective methods for ensuring that the relevant device process takes place to improve network scalability, decrease energy consumption and maintain an extended network lifetime. Many research have been developed on the numerous effective clustering algorithms to address this problem. Such algorithms almost dominate on the cluster head (CH) selection and cluster formation; using the intelligent type1 fuzzy-logic (T1-FL) scheme. In this paper, we suggest an interval type2 FL (IT2-FL) methodology that assumes uncertain levels of a decision to be more efficient than the T1-FL model. It is the so-called energy-efficient interval type2 fuzzy (EEIT2-F) low energy adaptive clustering hierarchical (LEACH) protocol. The IT2-FL system depends on three inputs of the residual energy of each node, the node distance from the base station (sink node), and the centrality of each node. Accordingly, the simulation results show that the suggested clustering protocol outperforms the other existing proposals in terms of energy consumption and network lifetime.</span></p>

Application of Artificial Intelligence Combined with 5G Technology in the Reform of English Teaching in Universities.

The language assistance and learning sectors have currently undergone restructuring in the period of fifth-generation (5G) communication and artificial intelligence, influenced by technologies, cloud services, learning techniques, speaker identification, language processing, virtual environments, expanded actuality, and blended actuality. This study proposes a routing protocol of modernized energy-optimized low-energy adaptive clustering hierarchical protocol (M-LEACH) using artificial intelligence and 5G Internet technologies for online English teaching. A dataset of 6,600,000 items, containing 3,250,000 favorable and unfavorable texts in English, is employed. The dataset is preprocessed using normalization to eliminate the impulse noises. Discriminant features are extracted using a variational autoencoder (VAE), and a random forest (RF) classifier is used to classify the features with accurate performance. The performance of the protocol is measured in terms of transmission rate, alive nodes, energy consumption, and a number of transmitted packets. Results show that the proposed M-Leach protocol provides a high transmission rate, maximum transmitted packets, more alive nodes, and minimum energy consumption as compared to other protocols. The proposed protocol will transform online English teaching from closed to open, and passive to active learning, dramatically changing the time and space scenarios, as well as the supply levels of English education.

A new hierarchical architecture and protocol for key distribution in the context of IoT-based smart cities

The Internet of Things (IoT) is a system of objects such as traditional computers, cameras, sensors and other things that are interconnected via a network to gather, process, and exchange context relevant data. Security is considered as one of the most important challenges facing IoT. Key distribution techniques are at the core of security provision and are critical components of any system in order to establish a secure connection between any two objects. In this paper a new Hierarchical Key Distribution (HKD) architecture and a set of Hierarchical Hybrid Key Distribution (H2KD) protocols are presented for IoT environment to support the provision of smart cities applications. The applicability of this protocols has been studied over HKD architecture and simulated using AVISPA tool and using Burrows–Abadi–Needham (BAN) logic. The targeted features of designing the architecture and the protocols are supporting mobility, scalability, heterogeneity and supporting constrained nodes’ limited capabilities. The performance of H2KD set of protocols has been evaluated based on a quantitative measure of several performance metrics including the memory storage, the computation cost for the constraint nodes, the number of messages exchanged and needed to establish a new session key for a mobile node. Scalability and resilience of the proposed protocols have been also measured. Simulation results show that H2KD protocols are safe against various attacks. Moreover, the analysis results show that the H2KD reduces communication, computation, and the storage costs for constraint nodes by 51.5% compared with related protocols. Additionally, H2KD has been found to provide more scalability and resilience than rival protocols.

A Multi-Zone Based Hierarchical Protocol for Routing in Wireless Sensor Networks

Wireless sensor networks (WSNs) are used in many applications due to their small size and low cost. However, due to the limited battery constraint in WSNs, it faces several challenges of node failure, data drop, etc. There are many routing algorithms in the literature that ensures the energy efficiency routing by enhancing the network lifetime. But still the challenges of data aggregation, dynamic path selection needs much attention to further maximize the nodes lifetime. In this paper, a multi-hop routing is proposed with an additionally deployed master node to minimize the load on cluster head (CH) and base station (BS) node. The master node is deployed in the middle of the network, and it has been assumed that a master node is static node with power connection. The proposed network is divided into multiple zones according to nodes energy. The proposed network has shown better throughput and network lifetime. Also, the proposed scheme has shown lesser data drop and failure rate.

Congestion Management of GSTEB Routing Protocol Using K-Means Algorithm in Wireless Sensor Network

A sensor node comprises of enormous nodes which can a variety of tasks such as recognize, indulgence, communication, and influence resource. Data transmission is the main aspect from starting to target. Once it is transferred between the node and within-cluster, the congestion will arise either node or in a resource allotment. So that it will affect the unremitting delay of reaching data, flow, the defeat of information to the destination, and the unfavorable use of a significant quantity of energy in the node. In this work, the impact of machine learning algorithms is studied using the GSTEB hierarchical protocol. By altering the type of input parameters, K-Means clustering on node energy usage can be performed. If congestion occurs, our proposal outlines a strategy for dealing with it and improving the CHPSCCC (Clustered Highly Prioritized Application-Specific Congestion Control Clustering Protocol) approach for congestion management. Simulation results show that in the proposed system there is significant growth of rises in the lifetime, energy, throughput, and packet loss.

Positive consensus of multi-agent systems with hierarchical control protocol

In this paper, the positive state consensus problem of a network of homogeneous LTI positive agents is presented. A hierarchical control protocol is applied to achieve finite positive state consensus over directed networks. The minimal state-space realization considered for the interconnected positive agents is not restricted to be (internally) positive state-space realization, which is in contrast to the existing literature. A set of sufficient conditions is derived in linear programming framework which is solved to find controller gain matrices involved in the consensus protocol. Also, in this paper, an observer-based hierarchical control protocol has been derived to achieve finite positive state consensus. Numerical examples are given to demonstrate the usefulness of the proposed results.

A Review of Cluster Based Energy Efficient Routing Protocols for Wireless Sensor Networks

In recent times wireless sensor networks have grown extremely and become increasingly striking in big variety of applications due to their low cost, low power, small in size, self-organizing behavior in unsympathetic environments. Routing is a vital technology in WSN. There are many routing protocols like: Location-based Protocols, Data Centric Protocols, Hierarchical Protocols, Mobility-based Protocols, Multipath-based Protocols, Heterogeneity-based Protocols, quality of service (QoS)-based Protocols etc. Clustering is used to protract the lifetime of the wireless sensor networks. It involves grouping of sensor nodes into clusters and electing cluster heads (CHs) for all the clusters. CHs collect the info from respective cluster’s nodes and forward the aggregated data to base station. In this paper we present the study of various clustering based energy efficient routing protocols of wireless sensor networks and compared them on various parameters. like Delivery Delay, Algorithm Complexity, Energy Efficiency, Load Balancing.

A Low-Power Distributed Visual Sensor Network for Real-Time Barcode Localization and Identification.

A novel low-power distributed Visual Sensor Network (VSN) system is proposed, which performs real-time collaborative barcode localization, tracking, and robust identification. Due to a dynamic triggering mechanism and efficient transmission protocols, communication is organized amongst the nodes themselves rather than being orchestrated by a single sink node, achieving lower congestion and significantly reducing the vulnerability of the overall system. Specifically, early detection of the moving barcode is achieved through a dynamic triggering mechanism. A hierarchical transmission protocol is designed, within which different communication protocols are used, depending on the type of data exchanged among nodes. Real-Time Transport Protocol (RTP) is employed for video communication, while the Transmission Control Protocol (TCP) and Long Range (LoRa) protocol are used for passing messages amongst the nodes in the VSN. Through an extensive experimental evaluation, we demonstrate that the proposed distributed VSN brings substantial advantages in terms of accuracy, power savings, and time complexity compared to an equivalent system performing centralized processing.

Optimasi Protokol LEACH Menggunakan PSO Pada Mobile Ad-Hoc Network

Mobile ad-hoc network is a wireless network with variable topology and communication routes. In a MANET network, nodes have the role of finding and handling routes to each node in the network. There are several challenges such as bandwidth, power, and direction of movement of nodes. A routing protocol is needed to determine the route and ensure that the packets sent successfully arrive at their destination to solve this problem,. In large-scale networks, the routing structure requires redundant route information, resulting in frequent overhead. We investigated the use of the LEACH protocol as a hierarchical protocol to solve the network overhead. The proposed research also adds a PSO algorithm to optimize the performance of the LEACH protocol. The research method uses simulation methods and evaluation of routing models using Network Simulator. Evaluation is prioritized on the use of traffic TCP and UDP. The results show that TCP traffic has better delay, packet loss, and PDR values compared to UDP traffic when using the LEACH protocol. When the PSO algorithm is added, the delay value in TCP traffic decreases by 47%, the packet loss value decreases by 78%, and the PDR value also increases by 0.1%.

Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK.

The human immunodeficiency virus 1 (HIV-1) protease is an important target for treating HIV infection. Our goal was to benchmark a novel molecular docking protocol and determine its effectiveness as a therapeutic repurposing tool by predicting inhibitor potency to this target. To accomplish this, we predicted the relative binding scores of various inhibitors of the protease using CANDOCK, a hierarchical fragment-based docking protocol with a knowledge-based scoring function. We first used a set of 30 HIV-1 protease complexes as an initial benchmark to optimize the parameters for CANDOCK. We then compared the results from CANDOCK to two other popular molecular docking protocols Autodock Vina and Smina. Our results showed that CANDOCK is superior to both of these protocols in terms of correlating predicted binding scores to experimental binding affinities with a Pearson coefficient of 0.62 compared to 0.48 and 0.49 for Vina and Smina, respectively. We further leveraged the Database of Useful Decoys: Enhanced (DUD-E) HIV protease set to ascertain the effectiveness of each protocol in discriminating active versus decoy ligands for proteases. CANDOCK again displayed better efficacy over the other commonly used molecular docking protocols with area under the receiver operating characteristic curve (AUROC) of 0.94 compared to 0.71 and 0.74 for Vina and Smina. These findings support the utility of CANDOCK to help discover novel therapeutics that effectively inhibit HIV-1 and possibly other retroviral proteases.