| 1. |
- Pham Van, Dung, et al.
(author)
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Machine-to-machine communications over FiWi enhanced LTE networks : A power-saving framework and end-to-end performance
- 2016
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In: Journal of Lightwave Technology. - : IEEE Press. - 0733-8724 .- 1558-2213. ; 34:4, s. 1062-1071
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Journal article (peer-reviewed)abstract
- To cope with the unprecedented acceleration of machine-to-machine (M2M) services over cellular networks, this paper envisions a highly converged network architecture based on the integration of high-capacity and reliable Ethernet fiber-wireless (FiWi) access networks with flexible and cost-effective 4G long term evolution (LTE) technology to support M2M connectivity in an end-to-end fashion, i.e., from air interface to transport (backhaul) network. In such emerging architecture, energy efficiency must be addressed in a comprehensive way, in which both wireless front-end and optical backhaul segments are considered at the same time to maximize the battery life of battery-constrained M2M devices as well as reduce operational expenditures for network operators, while maintaining acceptable network performance. Toward this end, an end-to-end power-saving framework is introduced in this paper that devises a timeout driven discontinuous reception (DRX) mechanism for LTE-enabled M2M devices and a polling-based power-saving mechanism for optical network units (ONUs) to improve the overall energy efficiency. End-to-end performance in terms of energy saving and packet delay is analytically modeled based on a semi-Markov process for the front-end and an M/G/1 queue for the backhaul. The obtained results indicate that the device battery life is significantly prolonged by extending the DRX cycle, whereas the backhaul energy consumption is minimized by incorporating the ONU power-saving modes into the dynamic bandwidth allocation process of the optical backhaul.
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| 2. |
- Rimal, B. P., et al.
(author)
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Cloudlet Enhanced Fiber-Wireless Access Networks for Mobile-Edge Computing
- 2017
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In: IEEE Transactions on Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1276 .- 1558-2248. ; 16:6, s. 3601-3618
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Journal article (peer-reviewed)abstract
- This paper proposes to enhance capacity-centric fiber-wireless (FiWi) broadband access networks based on data-centric Ethernet technologies with computation- and storage-centric cloudlets to provide reliable cloud services at the edge of FiWi networks and thereby realize the vision of mobile-edge computing (MEC). To reduce offload delay and prolong battery life of edge devices, a novel cloudlet-aware resource management scheme is proposed that incorporates offloading activities into the underlying FiWi dynamic bandwidth allocation process. The whole system is designed in two time division multiple access layers to enhance the network performance. To allow for the efficient coexistence of FiWi and MEC traffic, the offloaded traffic is scheduled outside the FiWi transmission slots. To thoroughly study the scheme's performance, a comprehensive analytical framework is developed that covers a rich set of performance metrics, including packet delay of both FiWi and MEC traffic, response time efficiency, offload gain-overhead ratio, energy efficiency, and battery life. Analytical results demonstrate the feasibility and effectiveness of the cloudlet-enhanced FiWi networks for MEC by employing the proposed solution. Further, we develop an experimental testbed to validate the accuracy of our analytical model via real-world measurements.
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| 3. |
- Rimal, B. P., et al.
(author)
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Mobile Edge Computing Empowered Fiber-Wireless Access Networks in the 5G Era
- 2017
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In: IEEE Communications Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 0163-6804 .- 1558-1896. ; 55:2, s. 192-200
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Journal article (peer-reviewed)abstract
- The expected stringent requirements of future 5G networks such as ultra-low latency, user experience continuity, and high reliability will drive the need for highly localized services within RANs in close proximity to mobile subscribers. In light of this, the mobile edge computing (MEC) concept has emerged, which aims to unite telco, IT, and cloud computing to deliver cloud services directly from the network edge. To facilitate better understanding of MEC, this article first discusses its potential service scenarios and identifies design challenges of MEC-enabled networks. Given the importance of scaling up research in the area of network integration and convergence in support of MEC toward 5G, the article explores the possibilities of empowering integrated fiber-wireless (FiWi) access networks to offer MEC capabilities. More specifically, envisioned design scenarios of MEC over FiWi networks for typical RAN technologies (i.e., WLAN, 4G LTE, LTE-A HetNets) are investigated, accounting for both network architecture and enhanced resource management. The performance of MEC over Ethernet-based FiWi networks in terms of delay, response time efficiency, and battery life of edge devices is then analyzed. The obtained results demonstrate the feasibility and effectiveness of the proposed MEC over FiWi concept.
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| 4. |
- Rimal, B. P., et al.
(author)
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Mobile-edge computing vs. centralized cloud computing in fiber-wireless access networks
- 2016
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In: Proceedings - IEEE INFOCOM. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467399555 ; , s. 991-996
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Conference paper (peer-reviewed)abstract
- The advent of Internet of Things and 5G applications renders the need for integration of both centralized cloud computing and emerging mobile-edge computing (MEC) with existing network infrastructures to enhance storage, processing, and caching capabilities in not only centralized but also distributed fashions for supporting both delay-tolerant and mission-critical applications. This paper investigates performance gains of centralized cloud and MEC enabled integrated fiber-wireless (FiWi) access networks. A novel resource management scheme incorporating both centralized cloud and MEC offloading activities into the underlying FiWi dynamic bandwidth allocation process is proposed. An analytical framework is developed to model packet delay, response time efficiency, and gain-offload overhead ratio for both cloud and conventional broadband access traffic. The obtained results demonstrate the feasibility of implementing conventional cloud and MEC in FiWi access networks, while not affecting network performance of broadband access traffic.
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| 5. |
- Van, Dung pham, et al.
(author)
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Fiber optic vs. wireless sensors in energy-efficient integrated FiWi smart grid networks : An energy-delay and TCO comparison
- 2016
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In: Proceedings - IEEE INFOCOM. - : IEEE. - 9781467399531
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Conference paper (peer-reviewed)abstract
- This paper aims at designing an ecoconscious future-proof sensor enhanced fiber-wireless (SFiWi) network based on EPON, WLAN, wireless sensor (WS), and fiber optic sensor (FOS) technologies as a shared communications infrastructure for broadband access and smart grids. A total cost of ownership (TCO) model is developed to help utilities decide whether to deploy WSs or FOSs in different scenarios and estimate sensor-related costs. To prolong battery life of wireless devices and maximize the overall energy efficiency, a novel energy conservation scheme for SFiWi networks (ECO-SFiWi) is proposed. ECO-SFiWi designs the whole network in three TDMA layers to enhance network performance, while scheduling network components to sleep outside their transmission slots. A comprehensive energy saving model accounting for both optical backhaul and wireless front-end components and a delay analysis based on M/G/1 queuing are presented. Results reveal that with their extremely long lifetime and ability to sustain in harsh environments, FOSs are superior to WSs when advanced interrogation techniques are deployed to reduce their total cost. ECO-SFiWi achieves more than 89% of energy savings, while maintaining low delay for both broadband and smart grid traffic in typical scenarios. FPGA hardware emulation and analytical results match well verifying the effectiveness of ECO-SFiWi.
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