MM3C: Multi-Source Mobile Streaming in Cache-Enabled Content-Centric Networks

Mau, Dung Ong; Taleb, Tarik; Chen, Min

doi:10.1109/glocom.2014.7417045

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Mathematically, it can be represented by Equation 30 in case of MTLBO f(x 5 ) = C e 1 · x 3 5 + C e 2 · x 2 5 + C e 3 · x 5 + C, (30) where x 5 is the moderation rate. It is normalized by mean 0.05253 and standard deviation 0.03086 coefficients (with 95% confidence bounds), ie, C e 1 = 16.58, C e 2 = 28.34, C e 3 = −123.6, and C = 159.2.…”

Section: Case V: Moderation Rate Versus Time Elapsedmentioning

confidence: 99%

“…Mau et al integrated content centric network with caching scheme that works for mobile multimedia streaming and radio access networks. The congestion and trace load were controlled by avoiding unnecessary pathways/routes and request.…”

Section: Reviewmentioning

confidence: 99%

“…The recent progression in the domain of Internet of Things (IoT) and edge/fog computing has forced many researchers to rethink about maintaining content retrieval time of the devices. [27][28][29] Mau et al 30 integrated content centric network with caching scheme that works for mobile multimedia streaming and radio access networks.…”

Section: Reviewmentioning

confidence: 99%

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Efficient content retrieval in fog zone using Nano‐Caches

Singh

Kumar

Sharma

2019

Concurrency and Computation

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Summary It is always desired to improve the response time from cloud servers, which deliver contents without buffering. As the penetration of mobile/fog devices is increasing, the limits of cellular ranges come under question. This question arises in spite of the fact that the current Internet Service Providers and data operators are adding cellular towers frequently to reduce delay and enhance performance. This performance can be improved by increasing Nano‐Cache(s) at the edges of the network for forwarding interrelated contents to remote corner of the earth. In this research work, Nano‐Caches are integrated for delivering contents efficiently, using search‐based optimization techniques, which are energy and response aware in nature. An algorithm, namely, Modified Teaching Learning‐Based Optimization(MTLBO), is devised and implemented in fog zone to find efficient route for forwarding contents using Nano‐Caches and subsequently to improve content retrieval time. Mathematical distribution model of traffic is used for simulation process. MTLBO is compared with existing algorithms, namely, Teaching Learning‐Based Optimization (TLBO) Algorithm and Simulated Annealing (SA) Algorithm. The design of experiments (DOE) was carried out to observe number of iterations, learning rate, and by changing the network size. Java library was used for observing values of memory and execution time. The results show that Modified Teaching Learning‐Based Optimization (MTLBO) approach is better than Teaching Learning‐Based Optimization (TLBO) approach as it has less overheads in terms of memory (considering number of fog caches) and network size for delivering contents at remote areas. In comparison to the Simulated Annealing (SA) algorithm, MTLBO performs better in terms of execution time, overhead in terms of memory, and scalability as function of network size.

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Section: Case V: Moderation Rate Versus Time Elapsedmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%