ew 15(5): e1

Research Article

Wi-Fi Hotspot Auto-Discovery: A Practical & Energy-Aware System for Smart Objects using Cellular Signals

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  • @ARTICLE{10.4108/eai.22-7-2015.2260071,
        author={Nithyananthan Poosamani and Injong Rhee},
        title={Wi-Fi Hotspot Auto-Discovery: A Practical \& Energy-Aware System for Smart Objects using Cellular Signals},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={2},
        number={5},
        publisher={EAI},
        journal_a={EW},
        year={2015},
        month={8},
        keywords={wi-fi sensing, cellular signals, smart objects, location fingerprinting, energy-efficiency},
        doi={10.4108/eai.22-7-2015.2260071}
    }
    
  • Nithyananthan Poosamani
    Injong Rhee
    Year: 2015
    Wi-Fi Hotspot Auto-Discovery: A Practical & Energy-Aware System for Smart Objects using Cellular Signals
    EW
    EAI
    DOI: 10.4108/eai.22-7-2015.2260071
Nithyananthan Poosamani1,*, Injong Rhee1
  • 1: North Carolina State University
*Contact email: npoosam@ncsu.edu

Abstract

The Internet of Things (IoT) paradigm aims to interconnect a variety of heterogeneous Smart Objects (SO) using energy-efficient methodologies and standard communication protocols. A majority of consumer devices sold today come equipped with wireless LAN and cellular technology to connect with the world-wide network. To discover Wi-Fi hot spots, there is a need for constant scanning of Wi-Fi radio in these devices and results in significant battery drain. We present PRiSM, a practical system to automatically locate Wi-Fi hotspots while Wi-Fi radio is turned off, by using the statistical characteristics of cellular signals. Cellular signals are received at zero extra cost in mobile devices and hence PRiSM is highly energy-efficient. It is a lightweight client-side only implementation and needs no prior knowledge on floor plans or wireless infrastructure. We implement PRiSM on Android-based devices and show up to 96% of energy savings in Wi-Fi sensing operations which is equivalent to saving up to 16% of total battery capacity, together with an average prediction accuracy of up to 98%.