V
Vojislav B. Misic
Researcher at Ryerson University
Publications - 345
Citations - 4417
Vojislav B. Misic is an academic researcher from Ryerson University. The author has contributed to research in topics: Wireless sensor network & Computer science. The author has an hindex of 32, co-authored 312 publications receiving 3760 citations. Previous affiliations of Vojislav B. Misic include University of Belgrade & University of Winnipeg.
Papers
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Proceedings ArticleDOI
Prioritized Access in a Channel-Hopping Cognitive Network with Spectrum Sensing
TL;DR: The results indicate that giving higher bandwidth allocation to low priority traffic class improves the performance of the network without affecting the performance for high priority class traffic.
Proceedings ArticleDOI
Performance of sensing-after-transmission policy in cognitive personal area networks
Vojislav B. Misic,Jelena Misic +1 more
TL;DR: This policy to mandate a predefined sensing duty after each packet transmission is described and its performance under a wide range of network and traffic parameters is investigated.
Proceedings ArticleDOI
IEEE 802.15.6-Based LTE Overlay Network with Priority Support
TL;DR: It is shown that the overlay network allows fair coexistence of PM2M and H2H traffic, and that high priority M2M traffic is distinctly prioritized over low priority one, even though the latter still enjoys satisfactory performance.
Proceedings ArticleDOI
Lifetime of a linear IEEE 802.15.4 sensor field with randomized sleep and bridge rotation
TL;DR: This work investigates the performance of a wireless sensor network composed of a number of IEEE 802.15.4 clusters interconnected in a linear structure and indicates that the proposed mechanism is capable of achieving fair distribution of energy consumption among nodes and the resulting maximization of network lifetime.
Posted Content
Detecting Fake Points of Interest from Location Data
TL;DR: In this article, a multi-layer perceptron (MLP) method was used to detect fake POI data in a much simpler way, where ground truth labels are obtained through real-world data, and the fake data is generated using an API, so we get a dataset with both the real and fake labels on the location data.