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Ammar Qarariyah

dblp:232/5841 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2019
0000-0003-3869-1268ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Routing and switching · 61% Internet of things and sensor networks · 39%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Routing and switching
routing protocol
0.412019
Zone Probabilistic Routing for Wireless Sensor Networks · IEEE Trans. Mob. Comput. 2019
Routing and switching › routing algorithms
stochastic routing
0.412019
Zone Probabilistic Routing for Wireless Sensor Networks · IEEE Trans. Mob. Comput. 2019
Internet of things and sensor networks
wireless sensor network
0.412019
Zone Probabilistic Routing for Wireless Sensor Networks · IEEE Trans. Mob. Comput. 2019
Internet of things and sensor networks › energy management
energy balancing
0.112019
Zone Probabilistic Routing for Wireless Sensor Networks · IEEE Trans. Mob. Comput. 2019

Methods — techniques the papers use, named apart from their topics

simulation · 0.4
YearPublicationVenuePosition
2019 Solving higher order PDEs with isogeometric analysis on implicit domains using weighted extended THB-splines
Ammar Qarariyah, Tianhui Yang, Jiansong Deng
Comput. Aided Geom. Des.1
2019 Zone Probabilistic Routing for Wireless Sensor Networks
abstract
This article modeled the data routing problem in Wireless Sensor Networks as an in-zone random process. The data packets are randomly routed from the source to the sink within the defined RoutingZone via any-path. The proposed “Zone Probabilistic Routing (ZPR)” is a distributed probabilistic and randomized anycast routing protocol. In ZPR, the forwarding probability distribution is defined by multiplying the Four Probability Distributions (4PD) namely: direction, transmission distance, perpendicular distance, and residual energy. In order to meet different performance requirements for different applications, these probability distributions are completely controllable via a set of exponential control-parameters (direction control, transmission distance control, perpendicular distance control, and residual energy control). This set of parameters is user-oriented and can be modified prior to nodes deployment to achieve different performances. Through extensive simulations and experimental results, the optimal values for these exponential control-parameters have been obtained to meet different performance requirements in terms of energy consumption, energy balancing, network lifetime, and delay. Furthermore, through an extensive performance evaluation study and simulation of large-scale scenarios, the results showed that our proposed ZPR protocol achieved better performance compared to the state-of-the-art solutions in terms of network lifetime, energy consumption, and data routing efficiency.
Ammar Hawbani, Xingfu Wang, Adili Abudukelimu, Hassan Kuhlani, Yaser Sharabi, Ammar Qarariyah, Aiman Ghannami
IEEE Trans. Mob. Comput.6