SDN (Software-Defined Networks) is a new network communication prototype. SDN can control the wide range of network activities and its responsibilities to select an optimum route for end-users. Recent studies are focu...
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SDN (Software-Defined Networks) is a new network communication prototype. SDN can control the wide range of network activities and its responsibilities to select an optimum route for end-users. Recent studies are focusing on issues regarding routing congestion and delay of packets within SDNs. In this research work, an efficient and smart-based algorithm is proposed to change the directions of packets in SDN networks. The proposed model estimates the cost of the given paths in networks depending on five criteria;adaptive network packet size, accurate packet numbers, the overall required time interval, QoS (Quality of Service) link capacity (bandwidth), and the number of hops (shortest path). In this way, the optimal paths from sender to receiver can be easily determined. This mechanism allows the SDN controller to minimize the decision time that is needed for selecting the flows. According to the aforementioned criteria, a dataset has been created which contains information about routing delay. From the proposed model, three criteria which are packet size, number, and time have been used to find the optimal packet delay to be used later in the model to find the cost of each path. A benchmark comparison between state-of-the-art and the suggested algorithm reveals that the time consumption of selecting an optimal recovery path has a significant delay reduction which is estimated to be a few milliseconds. Consequently, it can reduce bottleneck routes and resource utilization. Experimental results indicate that the proposed algorithm has increased the QoE (Quality of Experience) of both objective and subjective video *** model reduced the delay time of route selection up to 96.3% and this leads to end-user satisfaction.
This paper presents different design assist techniques and demonstrates their impact on enhancing the intrinsic RRAM performance. We show that the read-before-write, current-limitation and write-termination techniques...
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ISBN:
(纸本)9781665474597
This paper presents different design assist techniques and demonstrates their impact on enhancing the intrinsic RRAM performance. We show that the read-before-write, current-limitation and write-termination techniques reduce by -47%, -56% and -13% the power consumption during the writing process, respectively. Combined with write verification and error correction code, the overall improvements are 87% in energy saving and -55% on access time. Based on representative RRAM macro (130nm CMOS), statistic (128kb) and endurance (1M cycles) characterizations, this works contributes to accelerate RRAM industrial adoption by highlighting the design-technology co-optimization contribution.
Marine pollution is a worldwide problem that affects the water environment, the economy and even the human health. Depending on the type of pollutant, it is required different techniques to remove it. In order to avoi...
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Marine pollution is a worldwide problem that affects the water environment, the economy and even the human health. Depending on the type of pollutant, it is required different techniques to remove it. In order to avoid higher impact and damage in the affected area, the most important action is to perform a quick detection and determine where the pollution stain or spill is. Due to its fast dispersion, the case of oil spills is especially critic because they are almost impossible to clean if the decontamination tasks do not start immediately. For this reason, in this paper we propose a system based on a smart algorithm which is able to detect, track and locate pollution stains, such as oil spills. The system is composed by wireless nodes that are able to move towards the end of the stain seeking its edge. Nodes use IEEE 802.15.4 and the Global Position System (GPS), to communicate the final position of the stain. Finally, we tested the operation of our system, using the routing protocols Ad hoc On-Demand Distance Vector (AODV) and Destination-Sequenced Distance Vector (DSDV). Our results show that it presents better performance when using reactive protocols.
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