scholarly journals Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5491
Author(s):  
Divya Gupta ◽  
Shalli Rani ◽  
Syed Hassan Ahmed ◽  
Sahil Verma ◽  
Muhammad Fazal Ijaz ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Network Architecture ◽  
Traffic Management ◽  
Hop Count ◽  
Caching Strategy ◽  
Content Caching ◽  
Cloud Databases ◽  
Future Demands ◽  
The Internet Of Things

The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user’s quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain.

scholarly journals Energy-Efficient Nonuniform Content Edge Pre-Caching to Improve Quality of Service in Fog Radio Access Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1422 ◽  
Author(s):  
Yi Cen ◽  
Yigang Cen ◽  
Ke Wang ◽  
Jingcong Li
Keyword(s):  
Energy Efficiency ◽  
Quality Of Service ◽  
Matrix Completion ◽  
Access Network ◽  
Processing Unit ◽  
Caching Strategy ◽  
Content Caching ◽  
Radio Access ◽  
Edge Content

The fog radio access network (F-RAN) equipped with enhanced remote radio heads (eRRHs), which can pre-store some requested files in the edge cache and support mobile edge computing (MEC). To guarantee the quality-of-service (QoS) and energy efficiency of F-RAN, a proper content caching strategy is necessary to avoid coarse content storing locally in the cache or frequent fetching from a centralized baseband signal processing unit (BBU) pool via backhauls. In this paper we investigate the relationships among eRRH/terminal activities and content requesting in F-RANs, and propose an edge content caching strategy for eRRHs by mining out mobile network behavior information. Especially, to attain the inference for appropriate content caching, we establish a pre-mapping containing content preference information and geographical influence by an efficient non-uniformed accelerated matrix completion algorithm. The energy consumption analysis is given in order to discuss the energy saving properties of the proposed edge content caching strategy. Simulation results demonstrate our theoretical analysis on the inference validity of the pre-mapping construction method in static and dynamic cases, and show the energy efficiency achieved by the proposed edge content pre-caching strategy.

scholarly journals Analysis of service quality indicators in a local positioning system based on ZigBee technology

2020 ◽  
pp. 1-9
Author(s):  
Elena Viktorovna Kokoreva ◽  
Ksenia Igorevna Shurygina
Keyword(s):  
Quality Of Service ◽  
Network Architecture ◽  
Traffic Management ◽  
Ieee 802.15.4 ◽  
Load Factor ◽  
Positioning System ◽  
Local Positioning System ◽  
Computational Simplicity ◽  
Ieee 802.15.4 Standard

One of the topical areas of the digital economy is the development of services based on the use of systems for local positioning of objects. The article presents the results of analytical modeling of a positioning system built on the equipment of the IEEE 802.15.4 standard. The purpose of this study is to evaluate the efficiency of data transmission in a ZigBee network. As a modeling tool, the mathematical apparatus of queuing networks (CeMO) was selected, which has proven its effectiveness for calculating the characteristics of various infocommunication systems. The method of analysis of average values was chosen by the authors to assess the quality of service indicators due to its computational simplicity and the adequacy of the results obtained. The authors have developed a structural diagram of the investigated positioning system. On the basis of the network architecture, a conceptual, algorithmic and software model in the form of a closed homogeneous queuing network has been developed and its characteristics have been calculated. The parameters obtained as a result of modeling make it possible to evaluate and analyze the quality of service indicators in the telecommunications segment of the local positioning system under study, such as latency, performance, network load factor and the probability of losses, which can be used for efficient traffic management in the telecommunications segment of the geolocation system based on IEEE 802.15.4 standard.

Simulation and Emulation Tools for Fog Computing

2020 ◽  
Vol 13 ◽  
Author(s):  
Simar Preet Singh ◽  
Rajesh Kumar ◽  
Anju Sharma ◽  
S. Raji Reddy ◽  
Priyanka Vashisht
Keyword(s):  
Quality Of Service ◽  
Programming Languages ◽  
Traffic Management ◽  
Fog Computing ◽  
Research Work ◽  
Research Experience ◽  
Computing Paradigm ◽  
Quality Of Service Parameters ◽  
User Friendly

Background: Fog computing paradigm has recently emerged and gained higher attention in present era of Internet of Things. The growth of large number of devices all around, leads to the situation of flow of packets everywhere on the Internet. To overcome this situation and to provide computations at network edge, fog computing is the need of present time that enhances traffic management and avoids critical situations of jam, congestion etc. Methods: For research purposes, there are many methods to implement the scenarios of fog computing i.e. real-time implementation, implementation using emulators, implementation using simulators etc. The present study aims to describe the various simulation and emulation tools for implementing fog computing scenarios. Results: Review shows that iFogSim is the simulator that most of the researchers use in their research work. Among emulators, EmuFog is being used at higher pace than other available emulators. This might be due to ease of implementation and user-friendly nature of these tools and language these tools are based upon. The use of such tools enhance better research experience and leads to improved quality of service parameters (like bandwidth, network, security etc.). Conclusion: There are many fog computing simulators/emulators based on many different platforms that uses different programming languages. The paper concludes that the two main simulation and emulation tools in the area of fog computing are iFogSim and EmuFog. Accessibility of these simulation/emulation tools enhance better research experience and leads to improved quality of service parameters along with the ease of their usage.

scholarly journals Application of intelligent orienteering based on Internet of things

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Donghui Zhang ◽  
Ruijie Liu
Keyword(s):  
Internet Of Things ◽  
Network Architecture ◽  
Emergency Services ◽  
Simulation Experiment ◽  
Experimental Studies ◽  
Signal Stability ◽  
Anchor Nodes ◽  
Set Up ◽  
The Relationship ◽  
The Internet Of Things

Abstract Orienteering has gradually changed from a professional sport to a civilian sport. Especially in recent years, orienteering has been widely popularized. Many colleges and universities in China have also set up this course. With the improvement of people’s living conditions, orienteering has really become a leisure sport in modern people’s life. The reduced difficulty of sports enables more people to participate, but it also exposes a series of problems. As the existing positioning technology is relatively backward, the progress in personnel tracking, emergency services, and other aspects is slow. To solve these problems, a new intelligent orienteering application system is developed based on the Internet of things. ZigBee network architecture is adopted in the system. ZigBee is the mainstream scheme in the current wireless sensor network technology, which has many advantages such as convenient carrying, low power consumption, and signal stability. Due to the complex communication environment in mobile signal, the collected information is processed by signal amplification and signal anti-interference technology. By adding anti-interference devices, video isolators and other devices, the signal is guaranteed to the maximum extent. In order to verify the actual effect of this system, through a number of experimental studies including the relationship between error and traffic radius and the relationship between coverage and the number of anchor nodes, the data shows that the scheme studied in this paper has a greater improvement in comprehensive performance than the traditional scheme, significantly improving the accuracy and coverage. Especially the coverage is close to 100% in the simulation experiment. This research has achieved good results and can be widely used in orienteering training and competition.

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

scholarly journals On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1400
Author(s):  
Muhammad Adnan ◽  
Jawaid Iqbal ◽  
Abdul Waheed ◽  
Noor Ul Amin ◽  
Mahdi Zareei ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Traffic Management ◽  
Vehicular Networks ◽  
Scheduling Algorithm ◽  
Main Research ◽  
First Come First Serve ◽  
Computing Framework ◽  
Simulation Results

Modern vehicles are equipped with various sensors, onboard units, and devices such as Application Unit (AU) that support routing and communication. In VANETs, traffic management and Quality of Service (QoS) are the main research dimensions to be considered while designing VANETs architectures. To cope with the issues of QoS faced by the VANETs, we design an efficient SDN-based architecture where we focus on the QoS of VANETs. In this paper, QoS is achieved by a priority-based scheduling algorithm in which we prioritize traffic flow messages in the safety queue and non-safety queue. In the safety queue, the messages are prioritized based on deadline and size using the New Deadline and Size of data method (NDS) with constrained location and deadline. In contrast, the non-safety queue is prioritized based on First Come First Serve (FCFS) method. For the simulation of our proposed scheduling algorithm, we use a well-known cloud computing framework CloudSim toolkit. The simulation results of safety messages show better performance than non-safety messages in terms of execution time.

Robust Networking: Dynamic Topology Evolution Learning for Internet of Things

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Ning Chen ◽  
Tie Qiu ◽  
Mahmoud Daneshmand ◽  
Dapeng Oliver Wu
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Evolutionary Process ◽  
Small Error ◽  
Cyber Attacks ◽  
Communication Capacity ◽  
Error Ratio ◽  
The Internet Of Things ◽  
Evolution Learning

The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore, the robustness of network topology is an important metric to measure the network communication capacity and the ability to resist the cyber-attacks induced by some failed nodes. While some algorithms have been proposed to enhance the robustness of IoT topologies, they are characterized by large computation overhead, and lacking a lightweight topology optimization model. To address this problem, we first propose a novel robustness optimization using evolution learning (ROEL) with a neural network. ROEL dynamically optimizes the IoT topology and intelligently prospects the robust degree in the process of evolutionary optimization. The experimental results demonstrate that ROEL can represent the evolutionary process of IoT topologies, and the prediction accuracy of network robustness is satisfactory with a small error ratio. Our algorithm has a better tolerance capacity in terms of resistance to random attacks and malicious attacks compared with other algorithms.

Optimized Collaborative Filtering Recommendation Based on User’s Social Relationships

2014 ◽  
Vol 1044-1045 ◽  
pp. 1484-1488
Author(s):  
Yue Kun Fan ◽  
Xin Ye Li ◽  
Meng Meng Cao
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Digital Libraries ◽  
Social Relationship ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Personalized Recommendation ◽  
Recommendation Algorithm

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

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