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Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone’s internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results.
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On the Impact of Mode Selection on Effective Capacity of Device-to-Device Communication
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Today there is no effective support for device-wide question answer- ing on mobile devices. State-of-the-art QA models are deep learning behemoths designed for the cloud which run extremely slow and require more memory than available on phones. We present DeQA, a suite of latency- and memory- optimizations that adapts existing QA systems to run completely locally on mobile phones. Specifi- cally, we design two latency optimizations that (1) stops processing documents if further processing cannot improve answer quality, and (2) identifies computation that does not depend on the ques- tion and moves it offline. These optimizations do not depend on the QA model internals and can be applied to several existing QA models. DeQA also implements a set of memory optimizations by (i) loading partial indexes in memory, (ii) working with smaller units of data, and (iii) replacing in-memory lookups with a key-value database. We use DeQA to port three state-of-the-art QA systems to the mobile device and evaluate over three datasets. The first is a large scale SQuAD dataset defined over Wikipedia collection. We also create two on-device QA datasets, one over a publicly available email data collection and the other using a cross-app data collection we obtain from two users. Our evaluations show that DeQA can run QA models with only a few hundred MBs of memory and provides at least 13x speedup on average on the mobile phone across all three datasets.more » « less
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A large fraction of users in developing regions use relatively inexpensive, low-end smartphones. However, the impact of device capabilities on the performance of mobile Internet applications has not been explored. To bridge this gap, we study the QoE of three popular applications -- Web browsing, video streaming, and video telephony -- for different device parameters. Our results demonstrate that the performance of Web browsing is much more sensitive to low-end hardware than that of video applications, especially video streaming. This is because the video applications exploit specialized coprocessors/accelerators and thread-level parallelism on multi-core mobile devices. Even low-end devices are equipped with needed coprocessors and multiple cores. In contrast, Web browsing is largely influenced by clock frequency, but it uses no more than two cores. This makes the performance of Web browsing more vulnerable on low-end smartphones. Based on the lessons learned from studying video applications, we explore offloading Web computation to a coprocessor. Specifically, we explore the offloading of regular expression computation to a DSP coprocessor and show an improvement of 18% in page load time while saving energy by a factor of four.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/LWC.2019.2901460
