Network Life Research Articles

Analisis Performansi Algoritma LEACH-C pada Wireless Sensor Network

Wireless Sensor Network (WSN) is a wireless network infrastructure that requires sensor nodes to process information and communicate. Small sensor nodes generally use batteries as their energy source, causing sensor nodes to have limited power, therefore energy efficiency is needed to extend network life. The solution to overcome these problems is by using the clustering method. In this study the cluster-based routing algorithm used is LEACH-C which can function to allocate overall energy consumption between sensor nodes. In this study, LEACH-C divides the network into several clusters, each of which has a cluster head (CH). cluster head is performed at the base station based on the average energy size of all nodes. The research simulation uses a scenario of changing nodes 50, 100, and 150. The simulation results show that the number of nodes affects the value of energy consumption, the number of data packets received, and the number of packet losses.

Analytical Study for Optimization Techniques to Prolong WSNs Life

Wireless Sensor Networks (WSNs) have become the most cost-effective monitoring solution due to their low cost, despite their major drawback of limited power due to dependence on batteries. Each Sensor Node (SN) is clustered in a particular location and forms a network by self-organizing. They often operate in some of the world's most unusual or dangerous conditions. Networking errors, memory and processor limitations, and energy constraints all pose problems for WSN developers. Many problems in WSNs are expressed as multivariate optimization problems that are solved using biologically inspired techniques. Particle swarm optimization (PSO) is an easy, algorithmically sound, and robust optimization technique. It has been used to address problems like Clustering, data routing, Cluster Head (CH) collection, and data collecting in WSNs. This paper presents a brief analysis of WSN studies in which the PSO algorithm was used as the primary or secondary algorithm for enhancing lifespan of WSNs, focusing on results that show energy efficiency in the sensors, extending the network's life. Index Term— Cluster Head selection, Clustering, PSO, Routing, Wireless Sensor Networks.

Low energy split-cluster wireless acceleration sensors for motion and posture change recognition in sports

The rise of the national fitness trend has led more and more people to join the ranks of physical fitness, and the current physical education resources and human resources have not been able to keep pace with the development of sports. Due to the lack of guidance, many beginners of sports are not standardized, and their movements are not effective and even cause injuries. Sports action recognition technology can make up for this lack and realize the action recognition and guidance for sports players. In this paper, we build a sports movement and posture recognition model based on acceleration sensors, introduce Selectable Mode Vocoder (SMV) algorithm to optimize the performance, and ensure the effectiveness of sensor data transmission through a low energy multi-layer clustered wireless sensor network. The experiment mainly compares the generation results of the first energy depleted node in the entire network with the number of rounds with energy depleted nodes reaching 10 %. The experimental results show that the low-energy multi-layer clustered wireless sensor network effectively shortens the data transmission path, reduces the energy consumption of nodes, extends the network life time, ensures the data transmission effectiveness, and provides network guarantees for sports action recognition. In addition, in the comparison experiments, the action recognition model in this paper shows better recognition effect and efficiency with obvious performance advantages.

Link quality and energy efficient optimal simplified cluster based routing scheme to enhance lifetime for wireless body area networks

Monitoring of patient’s health in the medical industry can be enabled using wireless body area networks (WBANs), which are already used for various purposes, including assisting in human safety. It is imperative to use better power management strategies since the body sensors are small and the battery cannot hold a charge for a long time. Due to the vast amounts of information generated by medical sensors, resource-constrained networks face a significant challenge when guaranteeing the specified quality of service (QoS). Moreover, the WBAN regularly meets the primary hassle of QoS degradation because of congestion WBAN structure can easily compromise heterogeneous and complex networks. Either inappropriate data collection or using energy effectively to transmit medical data without the expense of travel and length has become an important one. To address this issue, the present research work ‘Link Quality and Energy Efficient Optimal Clustering-Multipath (LEOC-MP)’ scheme tries to explore an answer. The main goals of the LEOC-MP (Optimal Link Quality and Energy Efficient Optimal Clustering-Multipath) system are to guarantee node-to-node link quality, lengthen network life, and compute high-performing cluster heads to guarantee reliable multi path data transfer. This work was executed in three phases. First, an optimal simplified clustering technique for data collection from body sensors using an improved pelican optimization (ICO) algorithm is introduced. Next, multiple design constraints for node rank computation, energy efficiency, link quality, path loss, distance, and delay are used. Besides, an Auto-Regressive Probabilistic Neural Network (AR-PNN) is introduced to optimize those design constraints and compute the cluster head (CH) of each cluster. Multipath firing is then performed using a moderated puffer-fish optimization (MPO) algorithm that finds the closest optimal and shortest node to transmit optimal drug data. The work is simulated using an NS-3 environment, and the results are obtained. The outcome of this work is analyzed with existing methodologies, and the results prove that the present work consistently outperforms the existing methodologies.

Research on university laboratory network security based on Cloud Computing

Abstract As information technology increasingly develops, cloud computing has become an emerging computer network technology, which brings great convenience to network life. In order to ensure network security while fully enjoying this convenience, this paper established the laboratory cloud platform and network topology using the single node deployment mode of Openstack, obtained the software system architecture by applying B/S three-tier architecture model for laboratory development, and finally constructed cloud computing system of the laboratory by integrating encryption technology, digital signature technology, fingerprint recognition technology and dynamic password technology. After functionality, security and performance tests of the laboratory cloud platform, the application effect indicates that, some functional modules of the platform are in line with the desired results, the operation success rate is up to 100%, a 64G server can assume the operation of about 22 virtual machines, the number of server receiving and processing reaches 33 per second, and the throughput climbs up to 980,000 bytes per second. Therefore, it is concluded that with the support of cloud computing, the laboratory basically meets the desired use demand, which can effectively intercept unauthorized operation instructions, and get good carrying capacity and load capacity while strengthening network security.

An IoT-Enabled Intelligent and Secure Manufacturing Model Using Blockchain in Hybrid Cloud Communication System

The smart manufacturing system can become a linked network with the help of the Internet of Things (IoT). Devices connected to the IoT are susceptible to various attacks and assaults. An effective protection plan is needed to ensure that the billions of IoT nodes are protected from these hazards. The security mechanisms on IoT devices are ineffective due to resource limitations. As a result, the academic community has recently paid attention to the cloud-, fog-, and edge-based IoT systems. A robust cloud provider is in the cloud or fog to perform computationally demanding activities, including safety, data analysis, decision-making process, and monitoring. Hash identities and upgraded Rivest–Shamir–Adleman (RSA) have been used to secure the IoT device’s data. A four-prime integer of 512 bits makes up the proposed security algorithm. A hash signature is used to provide device authentication. An effective clustering method for sensing devices based on the node level, separation from the clusters, remaining energy, and fitness has been presented for long network life. The suggested swarm-based method determines the sensor nodes’ fitness. A deep neural network- (DNN-) based resource scheduling algorithm (DNN-RSM) is meant to reduce the delay and communications overhead for IoT components in the hybrid cloud system. For optimum resource allocation, all queries originating from the cluster head are categorised using DNN based on their storage, processing, and bandwidth needs. The suggested structure delivers better outcomes, particularly regarding energy use, delay, and safety level. The results of the simulation provide credence to the concept that the proposed strategy is superior to the current system. The suggested scheme includes stringent security, decreased energy usage, decreased latency, and efficient resource utilization.

Estimation of coverage and energy in bio inspired wireless sensors using Harris hawk algorithm

Wireless sensor networks have various sensors which are wide spread and also equipped with supplies. For the deployment sensor nodes are used for capturing the information, the region of interest is selected and the nodes are deployed. Lower sensing power degrades the DC supply reducing the life of wireless sensor networks, this can also be due to improper sensor deployment. Based on the above various wireless sensor network algorithms are available to compute and implement the required optimal figures. Harris hawk is among the one such algorithm used in wireless sensors. It works on the principle of the bird Harris hawk which catches the prey from a very high altitude, resemblance to this and many other features it is implemented in wireless sensors. Various wireless sensor characteristics can be found, figured and tabulated which are essential in this domain. The characteristics like coverage, connectivity, location, energy, and can be estimated. In the work the coverage and energy fitness is estimated using Harris hawk algorithm and its results are being illustrated.

Congestion-Free Cluster Formation and Energy Efficient Path Selection in Wireless Sensor Networks using ButPCNN

Today, network congestion is a common occurrence that needs to be focused on and effectively addressed, particularly in Wireless Sensor Networks (WSN) for packed type networks. The main causes of congestion in WSN are a lack of channel capacity and energy waste. This study's major goal is to develop Energy Efficient Congestion Free Path Selection Protocol (ECFPSP) protocol, which aims to reduce network congestion. By selecting the most appropriate main cluster head (PCH) and secondary cluster head (SCH), the ECFPSP protocol is proposed to decrease end-to-end delay time and extend the network lifetime. The suggested protocol implements a routing protocol that provides security by avoiding hostile nodes and reducing data loss. It also routes the nodes. Hence, a Congestion-Free Cluster Formation is provided to increase the lifetime of the network by proposed ButPCNN approach. To decrease packet loss and conserve energy, this research also uses brand-new cluster-based WSNs. In comparison to other standard protocols, the simulation results reveal that ButPCNN has a reduced packet drop rate, which increases the ratio of packet distribution, network life, and residual energy. As a result, the suggested method enhances congestion control performance while using less energy and a recently developed strategy is suggested to successfully enhance network performance. The proposed ButPCNN gives 25 percent improvement to optimize traffic on overloaded node than the other traditional approaches.

Mechanical equipment fault diagnosis based on wireless sensor network data fusion technology

Abstract To save network energy consumption and prolong network life cycle in complex mechanical fault diagnosis, a research method of data fusion routing protocol algorithm based on wireless sensor network (WSN) is proposed. The specific content of the method is as follows: First, the low-energy adaptive clustering hierarchy algorithm is analyzed and discussed. On this basis, the prim route fusion algorithm is proposed to realize the effective utilization of energy and prolong the life of the network. Then, the WSN is abstracted as an undirected graph. From the perspective of saving the energy of the whole network, several current algorithms for building fusion trees are compared. The experimental results show that the prim algorithm consumes energy only after 700 rounds of clustering, while the leach clustering algorithm consumes energy only after 500 rounds. This shows that applying the prim algorithm can reduce the energy consumption of the whole network and prolong the life cycle of the network. However, the algorithm is carried out on the premise of uniform distribution of nodes, and there is a certain gap with the specific application of WSN in mechanical fault diagnosis. In the comparison of node energy consumption, it is found that compared with using the shortest path tree, using the central point of graph algorithm can greatly save the energy consumption of the node and has better performance. Practice has proved that this method can effectively remove redundant data information and solve the problem of unreliable data collected by a single sensor node. It is more suitable for the specific application of WSN in mechanical fault diagnosis.

Positive public opinion guidance model based on dual learning in social network

Internet public opinion is closely related to our life in social network. The wanton growth of some negative public opinions has an extremely serious impact on the social stability and national security. After the guidance of government manually, some negative public opinion is well controlled and people’s life gain more positive energy. How to use Internet technology to automatically and promptly guide public opinion events and reduce the harm to society is currently challenging research. Therefore, in this paper, we propose a positive public opinion guidance model based on dual learning for negative Internet public opinion, hereinafter denoted to as the dual-PPOG model. Firstly, we use the Fast Unfolding algorithm to divide social networks into the public opinion guidance communities. In these communities, we detect the positive opinion guider and negative opinion receiver by our defined PageRank (PR) variant. Secondly, inspired by dual learning, we construct the public opinion guidance model and evaluate whether the guidance is successful through the feedback signal. Through the repeated guidance of the positive opinion guider to the negative opinion receiver, the public opinion guidance is successful. This is the main process for the dual positive public opinion mechanism. Finally, we guide the remaining nodes based on the opinion dynamics. The experiment demonstrates beneficial effects of our proposed model of dual-PPOG. Experimental results on three real-world datasets intercepted from Twitter, E-mail and Facebook show that the model of dual-PPOG can capture useful information in the network topology. Compared with the methods of HK, AE, Random and AIA on the three datasets from small to large in scale, the percentage of positive opinion increased by 4%, 6.9%, and 2.7% respectively, which shows our approach achieve significant improvements and effectiveness compared to all baselines.

Network life time augmentation of WSN through efficient energy using GAN algorithm

The minimal energy sensor nodes are critical to the long-term viability of any wireless sensor network WSN). Clustering is used for this purpose. Choosing an efficient Cluster Head (CH) is critical in such cluster-based networks, as they are accountable for aggregating and transmitting data from their associate nodes to the base station (BS). A Generative Adversarial Networks (GAN) is proposed in this work to improve the selection of CH. As part of the fitness function, nodes’ residual energy, average energy, and inter-cluster distance are all considered. In an effort to further reduce energy consumption, a GAN routing method is proposed for use at the base station level for Efficient Energy. Simulations are used to evaluate the proposed ideal.The WSNs which require long life time with minimum cost sensors demand the proposed work. The research about the human unattainable places can be fit to necessitate this work. This method supports the maintenance of mines and petroleum refineries. In terms of energy consumption and network life expectancy, the results demonstrate a substantial improvement. And also, the proposed technique is analyzed and compared along with the existing approaches as Low-Energy Adaptive Clustering Hierarchy (Security based (S-LEACH), Cluster based (C-LEACH, More Energy Efficient-LEACH) (ME-LEACH) schemes. The proposed method detects the best location of storage-nodes for the sensor network. There is no need of agitation on battery drain up of storage-nodes (because of wireless recharge) which is a highly energy spending unit. The proposed method improves the network lifetime by a significant level. The proposed method is best fit to mines, petroleum refineries, forest department and military. The proposed method behaves as not only better storage scheme but also best fit to retrieval schemes.

6LoWPAN MAC layer parameters optimization using evolutionary algorithm based ANN topology in wireless body area networks

Since the IPv6 Wireless Personal Area Network (6LoWPAN) can be utilized for information dissemination, this network gains significant attention in recent years. Proxy mobile IPv6 (PMIPv6) is standard for mobility control based on network at entire IP wireless applications. But, group-based body area networks cannot respond effectively. A new improved group flexibility system decrease the number of control messages contain router requests as well as advertising messages when compared to the group-based PMIPv6 protocol, in order to minimize delay and signaling costs. The IEEE 802.15.4 standard for low-power personal area networks (6LoWPAN) complies through IPv6-compliant MAC and physical layers. If the default parameters, excessive collisions, packet loss, and great latency occur arbitrarily in high traffic by default MAC parameters while using a great number of 6LoWPAN nodes. The implemented Whale optimization algorithm is based on artificial neural network optimization, genetic algorithm or particle swarm optimization to choose and authenticate MAC parameters. This manuscript proposes a novel intelligent method for choosing optimally configured MAC 6LoWPAN layer set parameters. Results of simulations based on the metrics such as Average delay time (ADT), Average signaling cost, Delivery ratio, Energy consumption, Latency, Network Life time (Nlt), Packet Overhead (PO), Packet loss. The performance of the proposed method provides 19.08%, 25.87%, 31.98%, 26.98%, 31.98%, 26.98% and 23.89% lower Latency, 12.67%, 25.98%, 31.98%, 26.98%, 27.98%, 31.97% and 27.85% lower Packet Overhead and 19.78%, 27.96%, 37.98%, 18.09%, 28.97%, 27.98% and 56.04% higher Delivery ratio compared with the existing methods such as 6LoWPAN-NUM-OHCA-FFA, 6LoWPAN-GTCCF-PSO, 6LoWPAN- DODAG-ACO, 6LoWPAN- MAC-GA-PSO, 6LoWPAN-NCG-DTC-NGIPSA and 6LoWPAN-TDMA-GTS-SHJA algorithms respectively.

Layering of edge node for jamming attack detection and elimination in wireless sensor network

SummaryWireless sensor network (WSN) is an environmental monitoring system exposing sensor nodes to various attacks. Jamming is an attack that disrupts the entire sensor network and affects the network's life span. The jammer broadcasts the signal at a frequency similar to the broadcast signal to jam the corresponding sensor node. There are many jamming detection schemes that only detect the jamming node, but they do not focus on eliminating the node that is not jamming. In this article, a novel strategy is used along with optimization to eliminate the disturbance in the sensor network and save the power consumed by nodes. The system uses the edge node, the sub‐edge node, and the cluster head for detection of jamming attack. Hyperbolic optimization is implemented in the cluster to eliminate data transmission failures. The proposed model considers the node's round trip time to detect jamming attacks. The optimization method considers the cluster head as the cluster member based on the probability of jamming in the network. The design is implemented and analyzed in MATLAB platform. As a result, the proposed method has improved the detection accuracy to 11.1% compared with extant techniques.

MMSCM: A multiple mobile sinks coverage maximization based hierarchical routing protocol for mobile wireless sensor networks

AbstractMobile wireless sensor network (MWSN) adapts to the requirements of mobility of civil and military network devices and has received a lot of attention. Designing an efficient, low‐energy, and fault‐tolerant routing protocol for the frequently changing topology of highly mobile MWSNs is challenging. Most routing protocols today only support the movement of sensor nodes or only sink. This paper proposes a Multiple Mobile Sinks Coverage Maximization based hierarchical routing protocol for mobile wireless sensor networks (MMSCM), which allows sensor nodes and sinks to move simultaneously. The MMSCM divides the monitoring area into virtual grids and deploys a movable sink in each grid cell. Sink selects the appropriate next hop for each cluster head in the area to build an optimal routing forest and collects data from all routing tree root nodes at a uniform speed along the specified path with maximum coverage. Simulation results show that, compared with current advanced routing algorithms, the MMSCM protocol can better solve the network delay problem and improve data throughput while extending the network life cycle, which is more suitable for time‐sensitive application scenarios.

A reliable position-based routing scheme for controlling excessive data dissemination in vehicular ad-hoc networks

In the past two decades, the automotive industry has undergone tremendous changes, as this field has become one of the fastest developing and growing fields, especially with the progress of global digitization and massive research related to networks. So, it comes as no surprise that self-driving vehicles are ubiquitous mainly relying on Vehicular ad-hoc networks (VANETs). This transformation ensures improved road navigation and traffic congestion avoidance by relying on Rapid Data Deployment (DD). On the other hand, although DD achieves high connection reliability, it may affect network bandwidth and performance. In addition, excessive DD causes frequent link outages resulting in reduced data delivery, massive packet loss, and premature end of network life. This paper presents an integrated architecture proposal for a vehicle dynamic assistance architecture, based on reliable methods to ensure steering accuracy while minimizing the energy expended for controlling the DD rate via VANETs. The proposed architecture integrates between Software-Defined Networks (SDN) and fog computing based on dealing with the mobility factors that exploit vehicle communication behaviors. Such integration will aid in improving network performance in terms of packet delivery and DD. The study also discusses how to take into consideration the Euclidean distance, geographical routing information, residual power ratio, and latency time to maximize network stability and avoid possible link disruption. The simulation results prove that there is a 62% to 70% enhancement of the whole power consumption and network throughput, depending on the implementation of the proposed position-based routing approach. Interestingly, the proposed routing protocol is a dual-phase routing protocol with a 90% of SDN data packet delivery ratio and an 82% of SDN data loss reduction. So, when the SDN fails to deliver packets, the proposed position-based routing handles them as a parallel mechanism of SDN.

The Scenarios of Artificial Intelligence and Wireframes Implementation in Engineering Education

Artificial intelligence (AI) and related technologies have become important components of the digital economy and affect core areas of our increasingly technologically networked lives. Specific AI technology can support diverse learners and how AI is used in particular in the context of Adult Education to provide a more inclusive and flexible learning environment. Artificial intelligence has been changing education for a long time now, and it is becoming more pervasive in our lives. The authors analyzed many advantages of using AI as an educational tool, such as increasing student engagement by improving grading accuracy by automatically scoring essays with machine learning algorithms. We present a case on how wireframes can be used in website design. Several existing tools have been examined, and their ability to generate interfaces from sketches has been explored. Creating wireframes, prototypes, design mock-ups, and layouts followed the web development process and is now a significant part of website development in engineering education. The paper aims to give the direct opportunity to observe examples of AI wireframes for engineering education.

Application of Wireless Sensor Network Computer Technology in Financial Management System

In the current enterprise competition, the core of the management information system is the financial management information system. Building a more comprehensive and efficient financial management information system can effectively establish a more efficient innovative enterprise and promote the transformation and development of the enterprise. This paper first designs a set of financial management system templates, including general ledger, accounts receivable, and accounts payable. Second, according to the characteristics of wireless sensor network technology, this paper proposes a financial management system based on the wireless sensor network and designs a load balancing topology control algorithm (LHTCA algorithm), which balances the energy consumption of network nodes and helps to improve the network life cycle. Finally, the management system is tested and validated. The test results show that the financial management system can realize the electronicization of financial data and paperless vouchers, improve the efficiency of financial work, and promote the sharing and integration of resources.

Energy Efficient Clustering and Routing Using Hybrid Fuzzy with Modified Rider Optimization Algorithm in IoT - Enabled Wireless Body Area Network

Wireless sensor networks are widely used in various Internet of Things applications, including healthcare, underwater sensor networks, body area networks, and multiple offices. Wireless Body Area Network (WBAN) simplifies medical department tasks and provides a solution that reduces the possibility of errors in the medical diagnostic process. The growing demand for real-time applications in such networks will stimulate significant research activity. Designing scenarios for such critical events while maintaining energy efficiency is difficult due to dynamic changes in network topology, strict power constraints, and limited computing power. The routing protocol design becomes crucial to WBAN and significantly impacts the communication stack and network performance. High node mobility in WBAN results in quick topology changes, affecting network scalability. Node clustering is one of many other mechanisms used in WBANs to address this issue. We consider optimization factors like distance, latency, and power consumption of IoT devices to achieve the desired CH selection. This paper proposes a high-level CH selection and routing approach using a hybrid fuzzy with a modified Rider Optimization Algorithm (MROA). This research work is implemented using MATLAB software. The simulations are carried out under a range of conditions. In terms of energy consumption and network life time, the proposed scheme outperforms current state-of-the-art techniques like Low Energy Adaptive Clustering Hierarchy (LEACH), Energy Control Routing Algorithm (ECCRA), Energy Efficient Routing Protocol (EERP), and Simplified Energy Balancing Alternative Aware Routing Algorithm (SEAR).

Research on Network Resource Collection and Distribution Strategies in Big Data-Driven Multimedia Teaching

The multimedia teaching information data generated in the era of big data are increasing at a huge scale, but the computing environment used before is difficult to meet users’ requirements for data integration. Multimedia information has the characteristics of diversity and structure diversity, which lead to the situation that the storage and processing methods are too complicated and time-consuming to varying degrees. This brings great challenges to big data analysis. Therefore, the fusion of this part of the data information requires a large-scale data parallel framework environment to ensure that multiple data calculation instructions can be executed each time, improve the range of problem solving, and essentially improve the complexity of multimedia teaching information. The main index of large-scale data fusion is computing speed. Aiming at the performance defects of the open source computing framework Hadoop, this paper intends to establish a new computing model. This model uses distributed data sets to replace the previous data structures and solve some problems in the process of data integration, thereby significantly improving the computation efficiency and reducing resource usage. Starting from the network’s perception of energy resources and considering that the randomness of energy available to nodes in the energy harvesting cognitive relay different systems will lead to different data volumes such as throughput and resource utilization, this paper takes the network’s throughput as the optimization network object for resource collection and allocation research. Experiment description: compared with the previous resource management, this model can reduce the total consumption of the system; compared with the single base station mode, the proposed method can extend the network life cycle to a greater extent; meanwhile, it ensures the use of users. Based on the stability analysis of the data, an optimal solution is obtained that can maximize the stable throughput of cognitive users and effectively improve resource collection and allocation.

Impact of Fading Channel to Energy Harvesting Relay on Cooperative Communication Systems

Channel conditions strongly influence the performance of the wireless communication system. Fading is one of the main problems caused by signal propagation via various paths. Recently, cooperative communication systems have been extensively studied to overcome this problem. However, the power consumption in the system relaying information to the destination increases, shortening the network's life. Therefore, this paper analyzes the impact of three Raleigh and Rician fading channel scenarios on cooperative communication performance in which a relay uses energy harvesting based on power splitting (EH-PS). The three scenarios are Rayleigh-Rayleigh fading (I), Rician- Rayleigh fading (II), and Rician-Rician fading (III). Subsequently, the throughput performance and energy efficiency are evaluated based on the maximum power splitting (PS) ratio. The simulation results indicate that the PS ratio in scenario III is higher than in scenarios I and II. Thus, the Rician fading channel can accumulate more energy in the relay due to the line-of-sight path between the source and the relay. Hence, scenario III's throughput and energy efficiency are higher than the other scenarios. The energy efficiency for scenario III is 33.08% and 12.14% higher than scenarios I and II, respectively. Therefore, the EH-PS cooperative communication system with the Rician fading channels has the highest throughput and is promising for future 5G technology.