skip to main content

Title: Extending Battery Life for Wi-Fi-Based IoT Devices: Modeling, Strategies, and Algorithm
Wi-Fi is one of the key wireless technologies for the Internet of things (IoT) owing to its ubiquity. Low-power operation of commercial Wi-Fi enabled IoT modules (typically powered by replaceable batteries) is critical in order to achieve a long battery life, while maintaining connectivity, and thereby reduce the cost and frequency of maintenance. In this work, we focus on commonly used sparse periodic uplink traffic scenario in IoT. Through extensive experiments with a state-of-the-art Wi-Fi enabled IoT module (Texas Instruments SimpleLink CC3235SF), we study the performance of the power save mechanism (PSM) in the IEEE 802.11 standard and show that the battery life of the module is limited, while running thin uplink traffic, to ~30% of its battery life on an idle connection, even when utilizing IEEE 802.11 PSM. Focusing on sparse uplink traffic, a prominent traffic scenario for IoT (e.g., periodic measurements, keep-alive mechanisms, etc.), we design a simulation framework for single-user sparse uplink traffic on ns-3, and develop a detailed and platform-agnostic accurate power consumption model within the framework and calibrate it to CC3235SF. Subsequently, we present five potential power optimization strategies (including standard IEEE 802.11 PSM) and analyze, with simulation results, the sensitivity of power consumption to specific network characteristics (e.g., round-trip time (RTT) and relative timing between TCP segment transmissions and beacon receptions) to present key insights. Finally, we propose a standard-compliant client-side cross-layer power saving optimization algorithm that can be implemented on client IoT modules. We show that the proposed optimization algorithm extends battery life by 24%, 26%, and 31% on average for sparse TCP uplink traffic with 5 TCP segments per second for networks with constant RTT values of 25 ms, 10 ms, and 5 ms, respectively.  more » « less
Award ID(s):
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
ACM International Symposium on Mobility Management and Wireless Access
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. In this paper, we introduce a neural network (NN)-based symbol detection scheme for Wi-Fi systems and its associated hardware implementation in software radios. To be specific, reservoir computing (RC), a special type of recurrent neural network (RNN), is adopted to conduct the task of symbol detection for Wi-Fi receivers. Instead of introducing extra training overhead/set to facilitate the RC-based symbol detection, a new training framework is introduced to take advantage of the signal structure in existing Wi-Fi protocols (e.g., IEEE 802.11 standards), that is, the introduced RC-based symbol detector will utilize the inherent long/short training sequences and structured pilots sent by the Wi-Fi transmitter to conduct online learning of the transmit symbols. In other words, our introduced NN-based symbol detector does not require any additional training sets compared to existing Wi-Fi systems. The introduced RC-based Wi-Fi symbol detector is implemented on the software-defined radio (SDR) platform to further provide realistic and meaningful performance comparisons against the traditional Wi-Fi receiver. Over the air, experiment results show that the introduced RC based Wi-Fi symbol detector outperforms conventional Wi-Fi symbol detection methods in various environments indicating the significance and the relevance of our work. 
    more » « less
  2. Accessing the Internet through Wi-Fi networks offers an inexpensive alternative for offloading data from mobile broadband connections. Businesses such as fast food restaurants, coffee shops, hotels, and airports, provide complimentary Internet access to their customers through Wi-Fi networks. Clients can connect to the Wi-Fi hotspot using different wireless devices. However, network administrators may apply traffic shaping to control the wireless client's upload and download data rates. Such limitation is used to avoid overloading the hotspot, thus providing fair bandwidth allocation. Also, it allows for the collection of money from the client in order to have access to a faster Internet service. In this paper, we present a new technique to avoid bandwidth limitation imposed by Wi-Fi hotspots. The proposed method creates multiple virtual wireless clients using only one physical wireless interface card. Each virtual wireless client emulates a standalone wireless device. The combination of the individual bandwidth of each virtual wireless client results in an increase of the total bandwidth gained by the attacker. Our proposed technique was implemented and evaluated in a real-life environment with an increase in data rate up to 16 folds. 
    more » « less
  3. Apple Wireless Direct Link (AWDL) is a key protocol in Apple’s ecosystem used by over one billion iOS and macOS devices for device-to-device communications. AWDL is a proprietary extension of the IEEE 802.11 (Wi-Fi) standard and integrates with Bluetooth Low Energy (BLE) for providing services such as Apple AirDrop. We conduct the first security and privacy analysis of AWDL and its integration with BLE. We uncover several security and privacy vulnerabilities ranging from design flaws to implementation bugs leading to a man-in-the-middle (MitM) attack enabling stealthy modification of files transmitted via AirDrop, denial-of-service (DoS) attacks preventing communication, privacy leaks that enable user identification and long-term tracking undermining MAC address randomization, and DoS attacks enabling targeted or simultaneous crashing of all neighboring devices. The flaws span across AirDrop’s BLE discovery mechanism, AWDL synchronization, UI design, and Wi-Fi driver implementation. Our analysis is based on a combination of reverse engineering of protocols and code supported by analyzing patents. We provide proof-of-concept implementations and demonstrate that the attacks can be mounted using a low-cost ($20) micro:bit device and an off-the-shelf Wi-Fi card. We propose practical and effective countermeasures. While Apple was able to issue a fix for a DoS attack vulnerability after our responsible disclosure, the other security and privacy vulnerabilities require the redesign of some of their services. 
    more » « less
  4. Packet-level network simulators such as ns-3 require accurate physical (PHY) layer models for packet error rate (PER) for wideband transmission over fading wireless channels. To manage complexity and achieve practical runtimes, suitable link-to-system mappings can convert high fidelity PHY layer models for use by packet-level simulators. This work reports on two new contributions to the ns-3 Wi-Fi module, which presently only contains error models for Single Input Single Output (SISO), additive white Gaussian noise (AWGN) channels. To improve this, a complete implementation of a link-to-system mapping technique for IEEE 802.11 TGn fading channels is presented that involves a method for efficient generation of channel realizations within ns-3. The runtimes for the prior method suffers from scalability issues with increasing dimensionality of Multiple Input Multiple Output (MIMO) systems. We next propose a novel method to directly characterize the probability distribution of the"effective SNR" in link-to-system mapping. This approach is shown to require modest storage and not only reduces ns-3 runtime, it is also insensitive to growth of MIMO dimensionality. We describe the principles of this new method and provide details about its implementation, performance, and validation in ns-3. 
    more » « less
  5. Internet-of-things (IoT) introduce new attack surfaces for power grids with the usage of Wi-Fi enabled high wattage appliances. Adversaries can use IoT networks as a foothold to significantly change load demands and cause physical disruptions in power systems. This new IoT-based attack makes current security mechanisms, focusing on either power systems or IoT clouds, ineffective. To defend the attack, we propose to use a data-centric edge computing infrastructure to host defense mechanisms in IoT clouds by integrating physical states in decentralized regions of a power grid. By enforcing security policies on IoT devices, we can significantly limit the range of malicious activities, reducing the impact of IoT-based attacks. To fully understand the impact of data-centric edge computing on IoT clouds and power systems, we developed a cyber-physical testbed simulating six different power grids. Our preliminary results show that performance overhead is negligible, with less than 5% on average. 
    more » « less