Adaptive low-power Wireless Sensor Network architecture for smart street furniture-based crowd and environmental measurements

Nassar, M.A., Luxford, L., Cole, P., Oatley, G. and Koutsakis, P.ORCID: 0000-0002-4168-0888 (2019) Adaptive low-power Wireless Sensor Network architecture for smart street furniture-based crowd and environmental measurements. In: 2019 IEEE 20th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), 10 - 12 June 2019, Washington DC, USA

Abstract

Street furniture such as bins, seats and bus shelters can become “smart” with the inclusion of wireless sensor nodes, which consist of environmental sensors, wireless modules, processors and microcontrollers. One of the most crucial challenges for smart street furniture is how to manage power consumption efficiently without affecting data freshness. In this work, we propose a novel Wireless Sensor Network (WSN)architecture for smart street furniture. Unlike existing WSNs which are based on a one-way communication model between wireless sensor nodes and the server, the proposed architecture employs a two-way communication model and a dynamic adaptation of the time interval of measurements to balance between power consumption and data updates. Our approach also provides a real-time low-power design for wireless sensor nodes which efficiently communicate the updated data instead of sending the same data on a regular basis. To the best of our knowledge, this is the first work in the relevant literature which extends the functionality of the wireless module in wireless sensor nodes to act not only as a station sending environmental data but also as soft Access Point (AP), sensing MAC addresses and WiFi signal strengths from surrounding WiFi-enabled devices. We have conducted experiments on the Murdoch University campus and our results show that our proposal improves lifetime of wireless sensor nodes up to 293% compared to static architectures similar to the ones that have been proposed in the literature. Moreover, network bandwidth is improved up to 38% without affecting data freshness. Finally, storage space for the database at the server is reduced up to 99%.

Item Type:	Conference Paper
Murdoch Affiliation(s):	Information Technology, Mathematics and Statistics
URI:	http://researchrepository.murdoch.edu.au/id/eprint/51232

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