Network-based distributed mobility management for network mobility

Ernest, Petro P.; Chan, H. Anthony; Falowo, Olabisi E.; Magagula, Linoh A.; Céspedes, Sandra

doi:10.1109/ccnc.2014.6866604

Cited by 4 publications

(4 citation statements)

References 15 publications

(23 reference statements)

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“…This solution improved the functionalities of mobility entities and centralized network entities. The mathematical analysis revealed that the packed tunnelling cost and the signalling overhead cost of the FDMA are lower than those of PNEMO (Ryu et al, 2013) and NBSP (Ernest et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

Privacy‐preserving federated learning cyber‐threat detection for intelligent transport systems with blockchain‐based security

et al. 2022

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Artificial intelligence (AI) techniques implemented at a large scale in intelligent transport systems (ITS), have considerably enhanced the vehicles' autonomous behaviour in making independent decisions about cyber threats, attacks, and faults. While, AI techniques are based on data sharing among the vehicles, it is important to note that sensitive data cannot be shared. Thus, federated learning (FL) has been implemented to protect privacy in vehicles. On the other hand, the integrity of data and the safety of aggregation are ensured by using blockchain technology. This paper applied classification approaches to VANET and ITS cyber‐threats detection at the vehicle. Subsequently, by using blockchain and by applying an aggregation strategy to different models, models from the previous step were uploaded in a smart contract. Lastly, we returned the updated models to the vehicles. Furthermore, we conducted an experimental study to measure the effectiveness of the proposed prototype. In this paper, the VeReMi data set was distributed in a balanced manner into five parts in the experimental study. Thus, classification techniques were executed by each vehicle separately, and models were generated. Upon the aggregation of the models in blockchain, they were returned to the vehicles. Lastly, the vehicles updated their decision functions and accessed the precision and accuracy of cyber‐threat detection. The results indicated that the precision and accuracy decreased by 7.1% on average with comparable F1‐score and recall. Our solution ensures the privacy preservation of vehicles whereas blockchain guarantees the safety of aggregation technique and low gas consumption.

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Section: Related Workmentioning

confidence: 99%

Privacy‐preserving federated learning cyber‐threat detection for intelligent transport systems with blockchain‐based security

et al. 2022

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“…In the dynamic TA planning, there are mainly tracking area planning methods based on time [16], based on movement [17,18], based on distance [19][20][21]. These methods dynamically and flexibly define the size of the TA, and can effectively prevent the occurrence of 'ping-pong' effect, reducing the signalling overhead.…”

Section: Related Workmentioning

confidence: 99%

Tracking areas planning based on spectral clustering in small cell networks

Lin

Waqas

et al. 2019

IET Communications

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“…Besides, based on the PMIPv6, Do and Kim [17] combined the LMA and MAG functions into each access router and introduced a proxy router to manage MNs' mobility and set up a central session database to track MNs and the proxy router. Furthermore, Ernest et al [18] improved these schemes by decomposing the logical functions of LMA to location management and resource management. It not only extends LMA to support the home network prefix allocation functions, but also extends MR to support the Delegating Router (DR) and Request Router (RR) functions.…”

Section: Dmm-based Schemes Dmm-related Schemes Applymentioning

confidence: 99%

“…It is worth to note from such application scenarios that the group is one of important characters in IoT [5][6][7]. Therefore, several works have focused on this problem and proposed lots of solutions [8][9][10][11][12][13][14][15][16][17][18][19][20] most of which tried to apply the NEtwork MObility (NEMO) [21] into the IoT.…”

Section: Introductionmentioning

confidence: 99%

The PMIPv6-Based Group Binding Update for IoT Devices

Guan

You

et al. 2016

Mobile Information Systems

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Internet of Things (IoT) has been booming with rapid increase of the various wearable devices, vehicle embedded devices, and so on, and providing the effective mobility management for these IoT devices becomes a challenge due to the different application scenarios as well as the limited energy and bandwidth. Recently, lots of researchers have focused on this topic and proposed several solutions based on the combination of IoT features and traditional mobility management protocols, in which most of these schemes take the IoT devices as mobile networks and adopt the NEtwork MObility (NEMO) and its variants to provide the mobility support. However, these solutions are in face of the heavy signaling cost problem. Since IoT devices are generally combined to realize the complex functions, these devices may have similar movement behaviors. Clearly analyzing these characters and using them in the mobility management will reduce the signaling cost and improve the scalability. Motivated by this, we propose a PMIPv6-based group binding update method. In particular, we describe its group creation procedure, analyze its impact on the mobility management, and derive its reduction ratio in terms of signaling cost. The final results show that the introduction of group binding update can remarkably reduce the signaling cost.

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Network-based distributed mobility management for network mobility

Cited by 4 publications

References 15 publications

Privacy‐preserving federated learning cyber‐threat detection for intelligent transport systems with blockchain‐based security

Privacy‐preserving federated learning cyber‐threat detection for intelligent transport systems with blockchain‐based security

Tracking areas planning based on spectral clustering in small cell networks

The PMIPv6-Based Group Binding Update for IoT Devices

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