Mobile-Fixed Integration for Next-Generation Mobile Network

2016 ◽  
pp. 466-484
Author(s):  
David Cortés-Polo ◽  
Jose-Luis González-Sánchez ◽  
Francisco J. Rodríguez-Pérez ◽  
Javier Carmona-Murillo
Keyword(s):  
Mobile Communications ◽  
Packet Loss ◽  
Loss Rate ◽  
Access Network ◽  
Mobile Network ◽  
Packet Loss Rate ◽  
Analytical Models ◽  
Interconnected Systems ◽  
The Internet ◽  
Mobility Protocols

In recent years, the growth the in number of heterogeneous interconnected systems, as well as the emergence of new requirements in applications and services are progressively changing the original simplicity and transparency of the Internet architecture. When this architecture was designed, the main goal was to interconnect stationary host. Therefore, the appearance of mobile communications has made necessary to adapt traditional protocols in order to accommodate mobile users. This implies a new interaction between the mobile network and the fixed access network. This chapter describes the main IP mobility protocols and presents a novel classification, which relates the integration of the mobility protocol with the access network. The chapter also presents analytical models to evaluate the registration updates cost and the packet loss rate of the classified protocols.

Future Trends in Mobile-Fixed Integration for Next Generation Networks

2017 ◽  
Vol 1 (1) ◽  
pp. 33-53 ◽  
Author(s):  
David Cortés-Polo ◽  
Jesús Calle-Cancho ◽  
Javier Carmona-Murillo ◽  
José-Luis González-Sánchez
Keyword(s):  
Mobile Communications ◽  
Packet Loss ◽  
Loss Rate ◽  
Access Network ◽  
Mobile Network ◽  
Packet Loss Rate ◽  
Analytical Models ◽  
The Internet ◽  
Future Trends ◽  
Mobility Protocols

In recent years, the growth the in the number of heterogeneous interconnected systems, as well as the emergence of new requirements in applications and services are progressively changing the original simplicity and transparency of the Internet architecture. When this architecture was designed, the main goal was to interconnect stationary host. Therefore, the appearance of mobile communications has made necessary to adapt traditional protocols in order to accommodate mobile users. This implies a new interaction between the mobile network and the fixed access network. This paper describes the main IP mobility protocols both centralized and distributed paradigms, and emergent approaches based on software defined networking. Moreover, a novel classification is presented, which relates the integration of the mobility protocol with the access network. Analytical models evaluate the registration updates cost and the packet loss rate of the classified protocols.

Packet loss rate monitoring model of IOT based on differential evolution algorithm

2021 ◽  
pp. 1-12
Author(s):  
Yinghua Feng ◽  
Wei Yang
Keyword(s):  
Internet Of Things ◽  
Differential Evolution ◽  
Packet Loss ◽  
Loss Rate ◽  
Differential Evolution Algorithm ◽  
Packet Loss Rate ◽  
Data Space ◽  
Monitoring Model ◽  
Evolution Algorithm ◽  
The Internet Of Things

In order to overcome the problems of high energy consumption and low execution efficiency of traditional Internet of things (IOT) packet loss rate monitoring model, a new packet loss rate monitoring model based on differential evolution algorithm is proposed. The similarity between each data point in the data space of the Internet of things is set as the data gravity. On the basis of the data gravity, combined with the law of gravity in the data space, the gravity of different data is calculated. At the same time, the size of the data gravity is compared, and the data are classified. Through the classification results, the packet loss rate monitoring model of the Internet of things is established. Differential evolution algorithm is used to solve the model to obtain the best monitoring scheme to ensure the security of network data transmission. The experimental results show that the proposed model can effectively reduce the data acquisition overhead and energy consumption, and improve the execution efficiency of the model. The maximum monitoring efficiency is 99.74%.

scholarly journals Sensing Coverage Algorithm of Sparse Mobile Sensor Node with Trade-Off between Packet Loss Rate and Transmission Delay

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Kehua Zhao ◽  
Yourong Chen ◽  
Siyi Lu ◽  
Banteng Liu ◽  
Tiaojuan Ren ◽  
...  
Keyword(s):  
Packet Loss ◽  
Data Transmission ◽  
Loss Rate ◽  
Packet Loss Rate ◽  
Transmission Delay ◽  
Sensor Nodes ◽  
Mobile Sensor ◽  
Sensing Coverage ◽  
Mobile Sensor Node ◽  
Monitoring Area

To solve the problem of sensing coverage of sparse wireless sensor networks, the movement of sensor nodes is considered and a sensing coverage algorithm of sparse mobile sensor node with trade-off between packet loss rate and transmission delay (SCA_SM) is proposed. Firstly, SCA_SM divides the monitoring area into several grids of same size and establishes a path planning model of multisensor nodes’ movement. Secondly, the social foraging behavior of Escherichia coli in bacterial foraging is used. A fitness function formula of sensor nodes’ moving paths is proposed. The optimal moving paths of all mobile sensor nodes which can cover the entire monitoring area are obtained through the operations of chemotaxis, replication, and migration. The simulation results show that SCA_SM can fully cover the monitoring area and reduce the packet loss rate and data transmission delay in the process of data transmission. Under certain conditions, SCA_SM is better than RAND_D, HILBERT, and TCM.

scholarly journals Wireless Body Area Network (WBAN)-Based Telemedicine for Emergency Care

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2153 ◽  
Author(s):  
Latha R ◽  
Vetrivelan P
Keyword(s):  
Packet Loss ◽  
Loss Rate ◽  
Bayes Theorem ◽  
Data Rate ◽  
Packet Loss Rate ◽  
Medical Decision ◽  
Area Network ◽  
Body Area ◽  
Delay Sensitive ◽  
Emergency Monitoring

This paper is a collection of telemedicine techniques used by wireless body area networks (WBANs) for emergency conditions. Furthermore, Bayes’ theorem is proposed for predicting emergency conditions. With prior knowledge, the posterior probability can be found along with the observed evidence. The probability of sending emergency messages can be determined using Bayes’ theorem with the likelihood evidence. It can be viewed as medical decision-making, since diagnosis conditions such as emergency monitoring, delay-sensitive monitoring, and general monitoring are analyzed with its network characteristics, including data rate, cost, packet loss rate, latency, and jitter. This paper explains the network model with 16 variables, with one describing immediate consultation, as well as another three describing emergency monitoring, delay-sensitive monitoring, and general monitoring. The remaining 12 variables are observations related to latency, cost, packet loss rate, data rate, and jitter.

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