Timer-Based Bloom Filter Aggregation for Reducing Signaling Overhead in Distributed Mobility Management

Ko, Haneul; Lee, Giwon; Pack, Sangheon; Kweon, Kisuk

doi:10.1109/tmc.2015.2411603

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…The main challenge ahead of DMM is to locate the UE from the distributed location databases at MAs. Existing solutions includes using distributed hash tables and Bloom filters [16].…”

Section: Related Workmentioning

confidence: 99%

The Power of Mobility Prediction in Reducing Idle-State Signaling in Cellular Systems: A Revisit to 4G Mobility Management

Hoseinitabatabei

Mohamed

Hassanpour

et al. 2020

IEEE Trans. Wireless Commun.

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Conventional mobility management schemes tend to hit the core network with increased signaling load when the cell size is shrinking and the user mobility speed increases. To mitigate this problem research community has proposed various intelligent mobility management schemes that take advantage of the predictability of the users mobility pattern. However, most of the proposed solutions are only focused on signaling of the active-state (i.e., handover signaling) and proposals on improvement of the idle-state signaling has been limited and were not well received from the industrial practitioners. This paper first surveys the major shortcomings of the existing proposals for the idle mode mobility management and then proposes a new architecture, namely predictive mobility management (PrMM) to mitigate the identified challenges. An analytical framework is developed and a closed form solution for the expected signaling overhead of the PrMM is presented. The results of numerical evaluations confirm that, depending on user mobility and network configuration, the PrMM efficiency can surpass the long term evolution (LTE) 4G signaling scheme by over 90%. Analysis of the results shows that the best performance is achieved at highly dense paging areas and lower cell crossing rates.

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“…The main challenge ahead of DMM is to locate the UE from the distributed location databases at MAs. Existing solutions includes using distributed hash tables and Bloom filters [16].…”

Section: Related Workmentioning

confidence: 99%

The Power of Mobility Prediction in Reducing Idle-State Signaling in Cellular Systems: A Revisit to 4G Mobility Management

Hoseinitabatabei

Mohamed

Hassanpour

et al. 2020

IEEE Trans. Wireless Commun.

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“…Therefore, the characteristic part of the bar code can be extracted for mathematical transformation to indicate the difference between similar elements magnified. Thus, the difference between the keys in the hash function of Bloom filter can be enlarged and the false positive rate can be reduced [21].…”

Section: Bar Code Processing Schemementioning

confidence: 99%

An improved algorithm based on Bloom filter and its application in bar code recognition and processing

Jiang

Zhao

Zai-feng

et al. 2018

J Image Video Proc.

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In many cases, databases are incompetent to meet the requirement of the quick query identification and processing of bar codes, such as the automatic sorting system of giant logistics warehouse. Bloom filter can be faster than databases, but its high false positive rate may seriously affect the efficiency of work. Although increasing the width of bit vector and the number of hash functions can reduce the false positive rate, the effect will be not significant after a certain threshold value, and this approach will increase the cost of processing time. So, it could not be increased indefinitely. This paper presents an improved algorithm based on Bloom filter and its application in bar code recognition and processing. The bit vector of Bloom filter is divided into two parts. Every element a i could be mapped to a part of the bit vector by some hash functions. For each element to amplify the difference by g (), which makes g (a i ) = a * i , the a * i is mapped to another part of the bit vector by some hash functions too. This algorithm can significantly reduce the false positive rate of the Bloom filter, but does not increase much time and space costs.

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“…The most well known data structures that have been used for this purpose are bloom filter (BF) [4] and quotient filter (QF) [5], which have been studied widely because of their efficient use for memory. The use of these structures can result in false positives (when it responds with positive query while the element is not in the set) but it cannot result in false negatives (informing that an element is not in the set white it is inserted) [6].…”

Section: Introductionmentioning

confidence: 99%

QCF for deep packet inspection

Alhisnawi

Ahmadi

2018

IET Networks

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Deep packet inspection (DPI) acts as a tool to control and classify incoming network traffic depending on users,content, applications and becomes a very important aspect of every network today. Instead of utilising only incoming packet header information, Internet service providers utilise DPI for security purposes, flow management, and routing. The highperformance challenges that face DPI techniques to manage line-speed packet processing with restricted available memory are the increasing traffic volumes and link rates. Bloom (BF) and quotient (QF) filters are effective randomised data structures for performing approximate membership queries. They have been employed in many networking applications including DPI. Nevertheless, they consume more calculations which degrade the system performance. Consequently, in this paper, the authors propose a new variant of membership query data structure, called quotient-based Cuckoo filter (QCF), that reflects a merging process between QF and CF to minimise the calculation overhead and employing it in a DPI system. QCF employs only two hash functions and consumes less calculation overhead than BF, QF, and CF. The implementation results show that using QCF as an identification tool in a DPI system result in a time improvement up to 77% over CF and up to 98% over BF and QF.

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Timer-Based Bloom Filter Aggregation for Reducing Signaling Overhead in Distributed Mobility Management

Cited by 11 publications

References 27 publications

The Power of Mobility Prediction in Reducing Idle-State Signaling in Cellular Systems: A Revisit to 4G Mobility Management

The Power of Mobility Prediction in Reducing Idle-State Signaling in Cellular Systems: A Revisit to 4G Mobility Management

An improved algorithm based on Bloom filter and its application in bar code recognition and processing

QCF for deep packet inspection

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