Abstract
The ability to sense the environment is the cornerstone
of the Internet of Things (IoT), which is a rapidly expanding
paradigm that is altering the way we interact with machines.
IoT enables a range of new services to enhance the lives of endusers.
One of these services concerns activity recognition within
Ambient Assisted Living which can be used to help people live
independently at home for longer. Many of these applications can,
however, be prone to failure and vulnerable to attack. Extensive
research is therefore required to build towards a secure and
sustainable IoT. This work examines activity recognition in a
smart home environment using three different classifiers on a
well-known activity recognition dataset. Fail-dirty and device
shut-down data is introduced in the dataset to examine the
impact that this erroneous data has on the application. This
study found that it was possible to rank the importance of sensors
with regards to their influence on classification by observing how
these failures impacted the classifiers when compared to the fmeasure
produced from the classification of the clean data. This
work also found that while representing data in a binary format
obtains higher accuracy, it makes the classifier considerably more
vulnerable to dirty data. Lastly, this study found that decision
tree classifiers have an inherent vulnerability when it comes to
handling dirty data, resulting in a 24% reduction in performance
versus the clean data, due to the structuring and placement of
leaf nodes in the tree.
of the Internet of Things (IoT), which is a rapidly expanding
paradigm that is altering the way we interact with machines.
IoT enables a range of new services to enhance the lives of endusers.
One of these services concerns activity recognition within
Ambient Assisted Living which can be used to help people live
independently at home for longer. Many of these applications can,
however, be prone to failure and vulnerable to attack. Extensive
research is therefore required to build towards a secure and
sustainable IoT. This work examines activity recognition in a
smart home environment using three different classifiers on a
well-known activity recognition dataset. Fail-dirty and device
shut-down data is introduced in the dataset to examine the
impact that this erroneous data has on the application. This
study found that it was possible to rank the importance of sensors
with regards to their influence on classification by observing how
these failures impacted the classifiers when compared to the fmeasure
produced from the classification of the clean data. This
work also found that while representing data in a binary format
obtains higher accuracy, it makes the classifier considerably more
vulnerable to dirty data. Lastly, this study found that decision
tree classifiers have an inherent vulnerability when it comes to
handling dirty data, resulting in a 24% reduction in performance
versus the clean data, due to the structuring and placement of
leaf nodes in the tree.
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) |
| Place of Publication | Leicester, UK |
| Publisher | IEEE Xplore |
| Pages | 1908-1915 |
| Number of pages | 7 |
| ISBN (Electronic) | 978-1-7281-4034-6 |
| ISBN (Print) | 978-1-7281-4035-3 |
| DOIs | |
| Publication status | Published (in print/issue) - 9 Apr 2019 |
