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Ioanis Nikolaidis

Researcher at University of Alberta

Publications -  144
Citations -  1866

Ioanis Nikolaidis is an academic researcher from University of Alberta. The author has contributed to research in topics: Wireless sensor network & Wireless network. The author has an hindex of 19, co-authored 138 publications receiving 1617 citations. Previous affiliations of Ioanis Nikolaidis include Georgia Institute of Technology.

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Journal ArticleDOI

Smart homes and home health monitoring technologies for older adults: a systematic review

TL;DR: The level of technology readiness for smart homes and home health monitoring technologies is still low and the highest level of evidence found was in a study that supported home health technologies for use in monitoring activities of daily living, cognitive decline, mental health, and heart conditions in older adults with complex needs.
Proceedings ArticleDOI

A New Aggregate Local Mobility (ALM) Clustering Algorithm for VANETs

TL;DR: A beacon-based clustering algorithm aimed at prolonging the cluster lifetime in VANETs and incorporating a contention method to avoid triggering frequent re-organisations when two clusterheads encounter each other for a short period of time is presented.
Journal ArticleDOI

Aggregation convergecast scheduling in wireless sensor networks

TL;DR: This work proposes two new improvements to the problem of scheduling in wireless sensor networks for the purposes of aggregation convergecast, starting with a new lower bound on the schedule length in the tree construction phase.
Journal ArticleDOI

Monitoring building energy consumption, thermal performance, and indoor air quality in a cold climate region

TL;DR: In this article, the authors describe a framework for sensor-based monitoring of energy performance of buildings under occupancy, where different types of sensors are installed at different locations in 12 apartment units in a building in Fort McMurray, Alberta, Canada.
Journal ArticleDOI

The Smart-Condo: Optimizing Sensor Placement for Indoor Localization

TL;DR: This work proposes a methodology for planning the deployment of an array of privacy-respecting binary motion sensors, and argues this methodology is a good surrogate for maximizing localization accuracy, and proves that it bears exploitable properties that make it receptive to a simple optimization routine.