Multicast Routing With Quality of Service Constraints in the Ad Hoc Wireless Networks

Idrissi, Abdellah; Yakine, Fadoua

doi:10.3844/jcssp.2014.1839.1849

Cited by 20 publications

(4 citation statements)

References 24 publications

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“…Network lifetime and energy performance have become the key characteristics for evaluating energy efficiency in Wireless Communications. Several techniques have recently been proposed focusing on minimizing energy consumption and improving the network lifetime [9,10]. WSN nodes are placed in remote, hard to reach places, where it is not practical to recharge or replace depleted batteries.…”

Section: Energy Harvesting Typesmentioning

confidence: 99%

Energy Harvesting in wireless communication: A survey

Yakine

Kenzi

2022

E3S Web Conf.

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Wireless Sensor Network is an emerging technology that has the potential to be used in futuristic applications. Sensor nodes are energy-constrained. They rely on batteries with limited capacity which impact their lifetime or mobility. To address this problem, energy harvesting technology is a solution that aims to avoid the premature energy depletion of nodes. It recharges their batteries using an energy harvesting system from the environment. In this review work, we present the concept of energy harvesting technology (EH) and Energy-Harvesting for Wireless Sensor Network (EH-WSN). We then discuss many schemes in the literature to save energy consumption of the energy harvesting sensor networks. We study their protocol design strategies and working principals. We also summarize their merits, demerits along with some future research directions.

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Section: Energy Harvesting Typesmentioning

confidence: 99%

Energy Harvesting in wireless communication: A survey

Yakine

Kenzi

2022

E3S Web Conf.

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“…We aim also to parallelise this algorithm by adopting it to a distributed environment based on MapReduce framework of Hadoop using big data technologies. In another work, we are also trying to project this problem in the mobile ad hoc networks (MANET) based on the works presented in Idrissi and Yakine (2014), Yakine and Idrissi (2015), Kumar et al (2016), Yakine et al (2016) and Abourezq et al (2016).…”

Section: Ws Kmentioning

confidence: 99%

Comparative study of <i>Top<SUB align="right">k</i> based on Fagin's algorithm using correlation metrics in cloud computing QoS

Handri

Idrissi

2020

IJITST

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With the exponential growth of cloud computing services recently, several internet technologies began to require the processing of multi-criteria ranking. The collaborative filtering methods and Top k selection computations have been proven to be more effective in information retrieval. In addition, they are widely used to evaluate the QoS for cloud services recommendation. However, the biggest challenge is not only to reduce the size of skyline results, but also to have a good response quality that reflects the user requirement. To deal with these problems, we propose in this paper an approach based on Top k algorithm combined with the weighted sum method. This approach is introduced for refining the skyline result using the Top k query advantages. Then in order to evaluate the performance of our approach, we compared the proposed algorithm with Fagin's one. The experimental results show the efficiency of our algorithm particularly in comparing the runtime results and using specific metrics of correlation.

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“…Extensive research has been conducted to address many issues, including quality of service (QoS) considerations. Most of the existing work proposes techniques to route flows more efficiently, such as admission control [5], coding packets to reduce the number of transmissions [6] and QoS routing which has been studied recently [7], [8]. The last technique requires not only finding a route from a source to a destination, but also calls for meeting end-toend QoS requirements, often expressed in terms of bandwidth and delay [9].…”

Section: Introductionmentioning

confidence: 99%

“…The last technique requires not only finding a route from a source to a destination, but also calls for meeting end-toend QoS requirements, often expressed in terms of bandwidth and delay [9]. These two parameters are particularly dependent on the topology of the network [8]. Energy management is another issue which has recently emerged in network topology-related considerations.…”

Section: Introductionmentioning

confidence: 99%

Flow Aggregation for Energy-Aware Ad-Hoc Wireless Networks Respecting QoS Provisions

Boukrim¹,

Antari²

2020

JTIT

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Emerging communication technologies are now leading developers to design IT systems taking into count their energy-related considerations. Much research performed in the area of ad-hoc wireless networks tends to distribute the ﬂows over all nodes of the network, which increases the amount of energy consumed by each node and reduces longevity of the network. To overcome these problems, this paper seeks to aggregate a set of ﬂows within a number of nodes that is as low as possible in order to be capable of routing those ﬂows. This proposal allows to maximize the number of network nodes that may be turned oﬀ. The proposed solution was formulated as an integer linear programming (ILP) problem using a set of energy and quality of service (QoS) constraints. This formulation minimizes the total energy consumed by the nodes to construct a topology network that is capable of meeting QoS requirement for a set ﬂows inserted into the network. To evaluate the eﬃciency of the proposed model, a performance-based comparison was conducted with another routing model. The simulation results show that the proposed model oﬀers better performance in terms of global energy consumption and network load

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Multicast Routing With Quality of Service Constraints in the Ad Hoc Wireless Networks

Cited by 20 publications

References 24 publications

Energy Harvesting in wireless communication: A survey

Energy Harvesting in wireless communication: A survey

Comparative study of <i>Top<SUB align="right">k</i> based on Fagin's algorithm using correlation metrics in cloud computing QoS

Flow Aggregation for Energy-Aware Ad-Hoc Wireless Networks Respecting QoS Provisions

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