Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
Open accessAbstract
Small-to-medium scale smart buildings are an important part of the Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the major enabler for smart control in such environments. Reliability is among the key performance requirements for many loss-sensitive IoT and WSN applications, while Energy Consumption (EC) remains a primary concern in WSN design. Error-prone links, traffic intense applications, and limited physical resources make it challenging to meet these service goals—not only that these performance metrics often conflict with one another, but also require solving optimization problems, which are intrinsically NP-hard. Correctly forecasting Packet Delivery Ratio (PDR) and EC can play a significant role in different loss-sensitive application environments. With the ever-increasing availability of performance data, data-driven techniques are becoming popular in such settings. It is observed that a number of communication parameters like transmission power, packet size, etc., influence metrics like PDR and EC in diverse ways. In this work, different regression models including linear, gradient boosting, random forest, and deep learning are used for the purpose of predicting both PDR and EC based on such communication parameters. To evaluate the performance, a public dataset of the IEEE 802.15.4 network, containing measurements against more than 48,000 combinations of parameter configurations, is used. Results are evaluated using root mean square error and it turns out that deep learning achieves up to 98% accuracy for both PDR and EC predictions. These prediction results can help configure communication parameters taking into account the performance goals.
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Deep neural networks can monitor and optimize smart building operations.
“Deep neural networks can be used to monitor and optimize such operations [39].”
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- Authors
- Ateeq, M., Ishmanov, F., Afzal, M. K., & Naeem, M.
- Affiliations
- COMSATS University Islamabad (PK)
- Kwangwoon University (KR)
- Corresponding author
- Ishmanov, Farruh
- Afzal, Muhammad Khalil
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-01-14
- Volume
- 19
- Issue
- 2
- Pages
- 309
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6359337
- OpenAlex
- W2909008700
- Cited by
- 40
- References
- 31
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 4 · 2022: 11 · 2021: 6 · 2020: 7 · 2019: 4
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Kwangwoon University (Not Applicable)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT Networks and Protocols
- Physical Sciences › Engineering › Control and Systems Engineering › Wireless Sensor Networks and IoT
- Keywords
- Internet of Things, wireless sensor networks, load forecasting, smart buildings, IEEE 802.15.4, transmission power, packet size, deep learning regression, gradient boosting, random forest, linear regression, network reliability
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