Low Network Load Research Articles

Application of Proportional Integral Derivative (PID) Control in Overvoltage Protection Systems for Low Voltage Network Loads

Electricity is a very essential need for every element of society in this modern era, with electricity, various kinds of work can be easily done. However, in electricity distribution, there are often disturbances that can be detrimental to consumers, One of the power lines that usually experiences interference is the Low Voltage Network (JTR) line. JTR is a transmission network with a low voltage classification between 220 volts and 280 volts. In this transmission, various kinds of disturbances often occur, such as overvoltage and voltage drops. In this research, the author discusses the overvoltage protection system using Proportional Integral Derivative (PID) control. PID control is a simple control method that several researchers have developed to overcome various electrical problems in this modern era. For this reason, the researcher will develop PID as a protection system to overcome overvoltage disturbances in JTR transmissions. The appropriate PID parameter value to overcome overvoltage in this study is the value Kp = 5; Ki = 0,3 Kd = 0.01, where the system is able to protect the overvoltage according to the setpoint value 220 volts.

Dynamic capacity sharing with multi-wavelength integrated transmitters in hybrid datacenter networks

In datacenters, bursty and unevenly distributed traffic may lead to serious network performance degradation. Various methods, including reconfigurable optical circuit switching (OCS), traffic control techniques, valiant load balancing (VLB), and so on, have been proposed to solve this problem. Based on these solutions, our method makes a trade-off between cost and performance. In this paper, we propose to use multi-wavelength tunable transmitters in our previously proposed modular arrayed waveguide grating (AWG)-based interconnection network. We discuss how the multiple wavelengths can be shared in the network and then propose a computational model to study its blocking probability. Closed-form equations for low network load cases are also derived to provide the analytical expression for the blocking probability. We verify the accuracy of our computational model through simulations. Comparing the blocking probability of networks with and without multi-wavelength integrated transmitters, we show that network performance can be considerably improved after replacement. When traffic burstiness is 1.25 and traffic skewness is 0.08, the blocking probability is reduced from 0.14 to 3.60×10−3 after replacing in each sending module one fixed laser with multi-wavelength tunable transmitters with four wavelengths. Furthermore, we also discuss how different factors influence the blocking probability and the maximum load with the given network performance requirement.

An adaptive detection model for IPv6 extension header threats based on deterministic decision automaton

The IPv6 extension header mechanism, a new feature of the IPv6 protocol, enhances flexibility and scalability but introduces numerous security threats like firewall evasion and covert channels. Existing threat detection methods face limitations in detection types, universality, and speed. Hence, an adaptive detection model for IPv6 extension header threats (ADM-DDA6) is proposed. Firstly, standard rule sets are designed for common IPv6 extension headers, successfully detecting 70 types of threats from THC-IPv6 and ExtHdr tools using only 20 rules. Secondly, by parsing IPv6 extension headers, matching rules, establishing transition relationships, and deciding packet threat status based on final states (Normal or Abnormal), complex threats like header disorder and header repetition can be detected. Finally, an adaptive rule matching method is introduced, which dynamically selects rule sets based on IPv6 extension header types, effectively reducing rule matching time. Experimental results show that under different threat magnitudes, ADM-DDA6 is 32% faster than Suricata v6.0.12 and 21.2% faster than Snort v3.1.61.0 in detection speed. Additionally, as the number of threats increases, on commodity hardware, ADM-DDA6 incurs only a 0.7% increase in CPU overhead with no significant memory consumption increase, maintains maximum throughput, and exhibits minor performance changes under low and moderate network load conditions.

Component analysis for energy-efficient multimedia networks utilizing 5G radio access technologies

The object of the research is the energy-efficient multimedia networks based on 5G wireless access technologies. The research is aimed at developing energy-efficient multimedia networks based on 5G wireless access technologies, considering the increasing importance of information and communication technologies (ICT) in modern society. With the proliferation of wireless access networks and the advancement of fifth-generation mobile networks (5G), there is a need to assess and reduce the environmental impact of ICT. The article specifically focuses on the challenges related to energy consumption and CO2 emissions in radio access networks, highlighting the responsibility to balance technological advancements with environmental concerns. The paper examines various components and technologies necessary for enhancing energy efficiency in multimedia networks. It discusses the concept of multimedia, including digital storage, data processing, and interactive elements. Statistical data is provided to underscore the significant energy consumption and carbon footprint of the ICT industry, with an emphasis on radio access networks. Heterogeneous networks, non-orthogonal multiple access (NOMA) technologies, and multiple-input multiple-output (MIMO) technologies are identified as key components for achieving energy efficiency. The importance of reducing the distance between transmitters and receivers in heterogeneous networks is emphasized, as well as the use of energy-saving strategies such as putting small base stations into sleep mode during low network loads. Special attention is given to the role of green data centers in reducing CO2 emissions and optimizing the use of green energy in high-performance networks. Proposed methods include leveraging renewable energy sources, improving hardware energy efficiency, and implementing energy-efficient routing. The findings offer valuable insights for the development and implementation of energy-efficient multimedia networks, particularly in the context of 5G networks. The interdisciplinary approach advocated in the conclusion emphasizes the collective efforts needed to address environmental challenges in the field of information and communication technologies. The combination of these technologies ensures efficient resource utilization and reduced environmental impact compared to similar known approaches.

Joint service placement and user assignment model in multi‐access edge computing networks against base‐station failure

AbstractMulti‐access edge computing (MEC) enables users to exploit the resources of cloud computing at a base station (BS) in proximity to the users where an MEC server is hosted. While we have advantage of being able to communicate with low latency and small network load in MEC networks, the resources in BSs are limited. One challenge is where to provide users with services from to make efficient use of resources. Furthermore, to enhance the reliability of MEC system, the case that a BS fails needs to be considered. This paper proposes a service placement and user assignment model with preventive start‐time optimization against a single BS failure in MEC networks. The proposed model preventively determines the service placement and user assignment in each BS failure pattern to minimize the worst‐case penalty which is the largest penalty among all failure patterns. We formulate the proposed model as an integer linear programming problem. We prove that the considered problem is NP‐hard. When the problem size becomes large, it may not be solved in a practical computation time. To solve larger size problems, we introduce two algorithms: one is the greedy algorithm with allocation upgrade and the other is with allocation upgrade and preemption. The results show that the introduced algorithms obtain solutions with smaller worst‐case penalty than the benchmark in a practical time.

Network structure and low-carbon governance performance: a qualitative comparative analysis

Network effectiveness is an important topic in network research. While many studies have been conducted in the Western contexts, few scholars have examined this topic in the area of environmental governance in the context of China. Taking the low-carbon governance networks of 16 cities in China as the research context, this article adopts the method of fuzzy set Qualitative Comparative Analysis to examine the relationship between network structure, social capital and governance performance. The analyses reveal two causal paths leading to network effectiveness and two causal paths leading to network ineffectiveness. The results indicate that networks combining high bridging social capital with high bonding social capital or combining high bridging social capital with low network load are more likely to demonstrate effectiveness. Networks with the combination of low bonding social capital and high network load, or the combination of high bonding social capital, low bridging social capital and network load are more likely to be ineffective.

E2M: An Efficient Emergency Management System

In an emergency situation, the required information is collected effectively by mobile devices. But, the low battery size of a mobile device with minimal computational sources is the bottlenecks. Hence the more power utilizing activities can be transferred to the cloud to minimize the load of the mobile devices. In fact, sometimes the lack of Internet access is an extremely difficult task to send mobile data from source to the target cloud. To overcome these problems, an Efficient Emergency Management System using NSGA-II optimization named as E2M has been proposed which works efficiently to manage the emergency situations. E2M is used to find the best mobile device which has higher rank and low Relative Network Load value by optimizing the accessible mobile devices in an emergency situation. The parameters such as precision, recall and F1-score values are used to measure the efficiency of E2M. The experimental results reveal that the E2M outperforms baseline algorithms.

DBSR: A Depth-Based Secure Routing Protocol for Underwater Sensor Networks

Depth-Based protocol has gained considerable attention as an efficient routing scheme for Underwater Wireless Sensor Networks UWSNs. It requires only depth information to perform the routing process. Despite this feature, UWSNs which operate with the employment of DBR protocol are vulnerable to depth spoofing attack. In this paper, Depth Based Secure Routing protocol is proposed to overcome this vulnerability. DBSR modifies traditional DBR routing algorithm by securing the depth information which is embedded in the header part of DBR packet. In addition to that, each node verifies the sender’s identity based on a digital signature scheme. We extensively evaluate the overhead and performance gain of DBSR for two signature schemes based on Elliptic Curve Cryptography method considering various network conditions. The simulation study is performed using NS3-based simulator. Our results show that DBSR can avoid depth-spoofing attack by achieving 95% and 85% delivery ratios under low and high network loads respectively. Contrary to popular belief, results show that careful utilization of cryptographic techniques is justifiable without significant overhead on the communication cost.

Method of intelligent content management in mobile networks

With the advent of 5G, the market has been expecting the immersive user experience with rich multimedia content. Meeting such requirements within the physical constraints of limited spectrum and infrastructure availability is a challenging task, which prevents operators to scale their services properly. Currently, mobile operators are forced to invest large amount of money in their infrastructure, in order to maximize the capacity by network densification and higher frequency reuse factors. The dark side of such trend is that infrastructure becomes more expensive, spectrum price is getting higher and total cost of ownership for operator increases drastically. Nowadays, with the rise of artificial intelligence, cloud and edge computing the network becomes more flexible that opens many opportunities to enhance the performance and user experience. In this paper, we propose a new approach for content management in mobile network by using predictive caching of rich multimedia content in edge servers. Proposed approach is based on the content popularity prediction by using recurrent neural networks, that allows to deliver corresponding content in the close proximity to the target end users by the time it will be needed. Simulation results show that the proposed model is more than 90% accurate for both daily and weekly timeframes. Furthermore, we develop a method of personalized content caching in user devices based on their subscriptions and preferences, to make sure that user will have the best experience. Proposed approach for content management allows to improve the overall network performance by proactive content caching during the time of low network load. Moreover, the proactive caching allows to download the content in the best quality, regardless of the network congestions and bottlenecks.

A density-aware probabilistic interest forwarding method for content-centric vehicular networks

Vehicular networks, compared to other wireless networks, face particular challenges due to the rapidly changing topology and intermittent connections. By eliminating the need to establish and maintain an end-to-end connection, Content-Centric Network (CCN) model has recently become an appropriate solution to meet the challenging demands of vehicular network communications. In this kind of network, the basic method of forwarding interest packets is flooding. This approach will result in excessive redundancy, serious contention, and collision to which it is referred as the broadcast storm problem. In this article, a probabilistic strategy is proposed to alleviate the impact of the broadcast storm on interest forwarding in content-centric vehicular networks. In this density-aware approach, each vehicle dynamically computes the probabilities based on the number of existing neighbors. A local density approximation method is presented, which uses the information provided by the newly modified Pending Interest Table (PIT) entries. Moreover, some time-based techniques are employed to give priority to potential forwarders. The simulation results indicate that the proposed work outperforms the basic CCN. With on average 40% lower network load and 65% fewer interests compared to the basic CCN, it shows only an overall 6% decrease in reachability.

Decentralized distribution-sampled classification models with application to brain imaging

BackgroundIn this age of big data, certain models require very large data stores in order to be informative and accurate. In many cases however, the data are stored in separate locations requiring data transfer between local sites which can cause various practical hurdles, such as privacy concerns or heavy network load. This is especially true for medical imaging data, which can be constrained due to the health insurance portability and accountability act (HIPAA) which provides security protocols for medical data. Medical imaging datasets can also contain many thousands or millions of features, requiring heavy network load. New methodOur research expands upon current decentralized classification research by implementing a new singleshot method for both neural networks and support vector machines. Our approach is to estimate the statistical distribution of the data at each local site and pass this information to the other local sites where each site resamples from the individual distributions and trains a model on both locally available data and the resampled data. The model for each local site produces its own accuracy value which are then averaged together to produce the global average accuracy. ResultsWe show applications of our approach to handwritten digit classification as well as to multi-subject classification of brain imaging data collected from patients with schizophrenia and healthy controls. Overall, the results showed comparable classification accuracy to the centralized model with lower network load than multishot methods. Comparison with existing methodsMany decentralized classifiers are multishot, requiring heavy network traffic. Our model attempts to alleviate this load while preserving prediction accuracy. ConclusionsWe show that our proposed approach performs comparably to a centralized approach while minimizing network traffic compared to multishot methods.

360-Degree Video Traffic Reduction Using Cloud Streaming in Mobile

Recently, commercialization of 360° video streaming service and various studies are being conducted in a mobile environment. It also makes 360° video streaming service through a mobile cloud available. The mobile cloud can easily provide characteristics of the terminal and existing features of the cloud service, such as contents and service of the application, in the mobile environment. Using this function, it is applied to 360° video streaming services in mobile environment. Unlike the conventional filming method which only shows the angle that camera operator intended, 360° videos can display the direction desired by the viewer in real time by recording a view in every direction at the same time. By displaying the real-time 3D image information to the user, the viewer can have more realistic contents and interactive experience. 360° video typically has a resolution of 4 k or more, which causes network load in mobile streaming. The adaptive HTTP streaming service currently provides 360 resolution video streams in proportion to their bandwidth. However, this method is not responsible for the quality of the video. Therefore, we propose a high-quality video streaming method with low network load in mobile environment. 360 video is divided into FoV (field of view) and non-FoV, and the image is transmitted with high quality for FoV and low quality for non-FoV. In this paper, we propose a method of FoV on the background frame (FBF) differentiated from the existing Http adaptive streaming method. It is possible to view high resolution video in a mobile environment while maintaining a regular level of video quality even in a non-viewing area.

A New Probabilistic Target Channel Selection Approach for Load Balancing in Cognitive Radio Networks

Cognitive radio is a technology targeting better utilization for the available frequency bands by allowing the unlicensed secondary users (SUs) to access the licensed frequency bands of the primary users (PUs). The SU vacates the channel at the presence of the PU and selects a target channel reactively or proactively to complete his interrupted transmission. Target channel sequence order can greatly affect the performance of the network in terms of delay, capacity, and utilization. The probabilistic sequence target channel selection approach was shown to distribute the load smoothly among the available channels resulting in good load balancing characteristics and increasing network capacity and stability for an appropriate distribution function. In this paper, a new proactive probabilistic sequence approach to determine the target channel for the interrupted SUs is proposed to achieve load balancing. The pre-emptive resume priority M/M/1 queueing network model is used to analyse the proposed approach to evaluate the latency and load balancing performance. Moreover, a new quantitative performance measure for balancing is introduced. The proposed approach is compared to other alternative spectrum handoff strategies and numerical results show the advantage of the proposed probabilistic sequence approach by improving balancing and channel capacity. Moreover, the extended data delivery time is improved at low network loads.

Low Polling Time TDM ONoC With Direction-Based Wavelength Assignment

Optical network-on-chip (ONoC) is a promising technology for future on-chip many-core communications. ONoC can achieve high bandwidth density and low communication latency under low network cost, which leads to an energy-efficient network. Time-division multiplexing (TDM)-based ONoC aims to solve the congestion problem faced by many optical circuit switching (OCS)-based mesh ONoCs. But TDM-based ONoC still suffers fromhigh network polling time, which causes high average network latency, especially under low network load. In this paper, we propose a novel TDMONoC using torus topology and direction-based wavelength assignment, named TTWA. TTWA ONoC combines TDM communication strategy and torus network topology to solve the congestion problem under the heavy network load faced by OCS-based ONoCs.With the utilization of direction-based wavelength assignment and torus topology, we further propose a high-efficiency communication strategy to improve the adjacent inter-cluster parallel communications and decrease the total TDM slot number, which contributes to a smaller network polling time and lower average network latency. A novel optical router is simultaneously designed to meet the communication strategy in TTWA. The analyses and simulation results indicate that TTWA ONoCimproves the polling time compared with other TDM-based ONoCs and achieves better network performance compared with the equivalent OCS-based ONoCs under various synthetic traffic patterns and realistic scientific applications. After the calculation,TTWA ONoC reduces the network insertion loss of OCS-based mesh ONoC 46.92% and that of OCS-based torus ONoC 31.75% and reduces the required laser power of OCS-based mesh ONoC 62.90% and that of OCSbased torus ONoC 40.66% under the worst-case situation.

Drop-Burst Length Evaluation of Urban VANETs

Networks performance is traditionally evaluated using packet delivery ratio (PDR) and latency (delay). We propose an addition mechanism the drop-burst length (DBL). Many traffic classes display varying application-level performance according to the pattern of drops, even if the PDR is similar. In this paper we study a number of VANET scenarios and evaluate them with these three metrics.Vehicular Ad-hoc Networks (VANETs) are an emerging class of Mobile Ad-hoc Network (MANETs) where nodes include both moving vehicles and fixed infrastructure. VANETs aim to make transportation systems more intelligent by sharing information to improve safety and comfort. Efficient and adaptive routing protocols are essential for achieving reliable and scalable network performance. However, routing in VANETs is challenging due to the frequent, high-speed movement of vehicles, which results in frequent network topology changes.Our simulations are carried out using NS2 (for network traffic) and SUMO (for vehicular movement) simulators, with scenarios configured to reflect real-world conditions. The results show that OLSR is able to achieve a best DBL performance and demonstrates higher PDR performance comparing to AODV and GPSR under low network load. However, with GPSR, the network shows more stable PDR under medium and high network load. In term of delay OLSR is outperformed by GPSR.

Research on Time Synchronization Algorithm of High Precision and Low Power Consumption Based on IRBRS WSNs

Based on the study of RBS algorithm in wireless sensor network, an improved algorithm named RBRS is presented, to solve the time synchronization problem of multi hop networks. On the basis of RBS, the broadcast group and least square linear regression methods were used to realize entire network time synchronization in the algorithm. Synchronization error and overhead with the existing improved BRS are compared in the article. The result shows that the algorithm has certain superiority in many algorithms, which is suitable for light and low power consumption network load. Through an emulation with the matlab software, the result indicates that error accumulation is lower in the optimized algorithm, the synchronization overhead is significantly reduced and can realize the time synchronization of the whole network. A time synchronization algorithm based on RBRS algorithm is proposed for multi-hop and the low power consumption. A variable period synchronization method is introduces in the algorithm: in line with Bias maximum a posterior estimation principle, the maximum phase offset is estimated to determine the synchronization period, which can reduce the number of node synchronization, and with the least square linear regression method, the periodic fitting clock offset. The Simulation in matlab indicate that RBRS algorithm can improve the synchronization accuracy and reduce energy consumption significantly, which is conducive to extend the life of the network.

A load balancing bufferless deflection router for network-on-chip**Project supported by the National Natural Science Foundation of China (Nos. 61474087, 61322405, 61376039).

The bufferless router emerges as an interesting option for cost-efficient in network-on-chip (NoC) design. However, the bufferless router only works well under low network load because deflection more easily occurs as the injection rate increases. In this paper, we propose a load balancing bufferless deflection router (LBBDR) for NoC that relieves the effect of deflection in bufferless NoC. The proposed LBBDR employs a balance toggle identifier in the source router to control the initial routing direction of X or Y for a flit in the network. Based on this mechanism, the flit is routed according to XY or YX routing in the network afterward. When two or more flits contend the same one desired output port a priority policy called nearer-first is used to address output ports allocation contention. Simulation results show that the proposed LBBDR yields an improvement of routing performance over the reported bufferless routing in the flit deflection rate, average packet latency and throughput by up to 13%, 10% and 6% respectively. The layout area and power consumption compared with the reported schemes are 12% and 7% less respectively.

Finding the biased-shortest path with minimal congestion in networks via linear-prediction of queue length

In this paper, we propose a biased-shortest path method with minimal congestion. In the method, we use linear-prediction to estimate the queue length of nodes, and propose a dynamic accepting probability function for nodes to decide whether accept or reject the incoming packets. The dynamic accepting probability function is based on the idea of homogeneous network flow and is developed to enable nodes to coordinate their queue length to avoid congestion. A path strategy incorporated with the linear-prediction of the queue length and the dynamic accepting probability function of nodes is designed to allow packets to be automatically delivered on un-congested paths with short traveling time. Our method has the advantage of low computation cost because the optimal paths are dynamically self-organized by nodes in the delivering process of packets with local traffic information. We compare our method with the existing methods such as the efficient path method (EPS) and the optimal path method (OPS) on the BA scale-free networks and a real example. The numerical computations show that our method performs best for low network load and has minimum run time due to its low computational cost and local routing scheme.

QoS LowPan for Internet of Things

6LoWPAN was introduced by the IETF as a standard protocol to interconnect tiny and constrained devices across IPv6 clouds. 6LoWPAN supports a QoS feature based on two priority bits. So far, little interest has been granted and this QoS feature and there are no implementations of such feature in real networks. In this paper, we evaluate the effectiveness of these priority bits in various scenarios. We show that under very heavy or very low network load, these bits have a limited effect on the delay.

Energy-Aware and Bandwidth-Efficient Hybrid Video Streaming Over Mobile Networks

Current cellular networks support video streaming over unicast or multicast. However, there exists a tradeoff between utilizing the two: i) unicast leads to higher network load, but lower energy consumption of mobile devices, and ii) multicast results in lower network load, but higher energy consumption. To make the best out of both, we propose to concurrently utilize unicast and multicast for minimizing the energy consumption of mobile devices and minimizing the load on cellular networks. Cellular networks support two multicast schemes: i) independent cell networks and ii) multi-cell single frequency networks, where multiple adjacent base stations operate on the same frequency. We first consider the less-complicated independent cell networks, and then extend our solution to single frequency networks for better performance. We formulate the resource allocation in hybrid multicast -unicast streaming systems as a binary integer programming problem. We describe optimal algorithms for the two multicast schemes. We then propose two efficient, heuristic, algorithms that run faster and provide close to optimal results. While our solution is general, for concreteness, we conduct detailed LTE packet-level simulations using OPNET. Our simulation results show the proposed algorithms i) scale to many more mobile devices than the state-of-the-art unicast-only approaches and ii) result in lower energy consumption than the latest multicast-only approaches. In addition, the algorithms designed for multi-cell single frequency networks outperform the algorithms designed for independent cell networks in all aspects, such as service ratio, spectral efficiency, energy saving, video quality, frame loss rate, initial buffering time, and number of re-buffering events.