SWAN-Lake: Opportunistic Distributed Sensing for Android Smartphones

Bozdog, Nicolae Vladimir; Das, Roshan Bharath; Halteren, Aart van; Bal, Henri E.

doi:10.4108/eai.15-12-2016.2267797

Cited by 1 publication

(4 citation statements)

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“…We benchmarked SenseLE against our own implementation of the SWAN-Lake framework, which we previously developed as a solution for opportunistic distributed sensing [8] (see Section II). We made this choice as we were interested to analyze the effectiveness of the power usage improvements employed by SenseLE compared to our previous work.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, we use Bluetooth Low Energy for connecting devices due to its energy efficiency and low latency properties. In the past we studied the feasibility of using standard Bluetooth for the same purpose [8]. We show that the two protocols, despite being related, require different approaches in practice.…”

Section: A Architecturementioning

confidence: 99%

“…In this work, we improve upon the results of our earlier SWAN-Lake system [8] by reducing the response times and power consumption, which are both requirements of IoT. We achieve this through a complete system re-design, resulting in a new framework for opportunistic acquisition and processing of sensor data, called SenseLE (Sense Low Energy).…”

Section: Introductionmentioning

confidence: 99%

“…Previously, we studied the feasibility of using standard Bluetooth for opportunistic distributed sensing [8]. In this paper we build upon that previous work and implement a much more efficient mechanism for sharing sensor data based on Bluetooth Low Energy (BLE).…”

Section: Introductionmentioning

confidence: 99%

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SenseLE: Exploiting Spatial Locality in Decentralized Sensing Environments

Bozdog

Makkes

Uta

et al. 2017

2017 IEEE International Symposium on Parallel and Distributed Processing With Applications and 2017 IEEE International Conferen

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Generally, smart devices, such as smartphones, smartwatches, or fitness trackers, communicate with each other indirectly, via cloud data centers. Sharing sensor data with a cloud data center as intermediary invokes transmission methods with high battery costs, such as 4G LTE or WiFi. By sharing sensor information locally and without intermediaries, we can use other transmission methods with low energy cost, such as Bluetooth or BLE. In this paper, we introduce Sense Low Energy (SenseLE), a decentralized sensing framework which exploits the spatial locality of nearby sensors to save energy in Internet-of-Things (IoT) environments. We demonstrate the usability of SenseLE by building a real-life application for estimating waiting times at queues. Furthermore, we evaluate the performance and resource utilization of our SenseLE Android implementation for different sensing scenarios. Our empirical evaluation shows that by exploiting spatial locality, SenseLE is able to reduce application response times (latency) by up to 74% and energy consumption by up to 56%.

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Section: Discussionmentioning

confidence: 99%

Section: A Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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