58 research outputs found
Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions
Technology solutions must effectively balance economic growth, social equity,
and environmental integrity to achieve a sustainable society. Notably, although
the Internet of Things (IoT) paradigm constitutes a key sustainability enabler,
critical issues such as the increasing maintenance operations, energy
consumption, and manufacturing/disposal of IoT devices have long-term negative
economic, societal, and environmental impacts and must be efficiently
addressed. This calls for self-sustainable IoT ecosystems requiring minimal
external resources and intervention, effectively utilizing renewable energy
sources, and recycling materials whenever possible, thus encompassing energy
sustainability. In this work, we focus on energy-sustainable IoT during the
operation phase, although our discussions sometimes extend to other
sustainability aspects and IoT lifecycle phases. Specifically, we provide a
fresh look at energy-sustainable IoT and identify energy provision, transfer,
and energy efficiency as the three main energy-related processes whose
harmonious coexistence pushes toward realizing self-sustainable IoT systems.
Their main related technologies, recent advances, challenges, and research
directions are also discussed. Moreover, we overview relevant performance
metrics to assess the energy-sustainability potential of a certain technique,
technology, device, or network and list some target values for the next
generation of wireless systems. Overall, this paper offers insights that are
valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the
Communications Societ
Fall Detection Using Channel State Information from WiFi Devices
Falls among the independently living elderly population are a major public health worry, leading to injuries, loss of confidence to live independently and even to death. Each year, one in three people aged 65 and older falls and one in five of them suffers fatal or non fatal injuries. Therefore, detecting a fall early and alerting caregivers can potentially save lives and increase the standard of living. Existing solutions, e.g. push-button, wearables, cameras, radar, pressure and vibration sensors, have limited public adoption either due to the requirement for wearing the device at all times or installing specialized and expensive infrastructure. In this thesis, a device-free, low cost indoor fall detection system using commodity WiFi devices is presented. The system uses physical layer Channel State Information (CSI) to detect falls. Commercial WiFi hardware is cheap and ubiquitous and CSI provides a wealth of information which helps in maintaining good fall detection accuracy even in challenging environments. The goals of the research in this thesis are the design, implementation and experimentation of a device-free fall detection system using CSI extracted from commercial WiFi devices. To achieve these objectives, the following contributions are made herein. A novel time domain human presence detection scheme is developed as a precursor to detecting falls. As the next contribution, a novel fall detection system is designed and developed. Finally, two main enhancements to the fall detection system are proposed to improve the resilience to changes in operating environment. Experiments were performed to validate system performance in diverse environments. It can be argued that through collection of real world CSI traces, understanding the behavior of CSI during human motion, the development of a signal processing tool-set to facilitate the recognition of falls and validation of the system using real world experiments significantly advances the state of the art by providing a more robust fall detection scheme
SoK: Inference Attacks and Defenses in Human-Centered Wireless Sensing
Human-centered wireless sensing aims to understand the fine-grained
environment and activities of a human using the diverse wireless signals around
her. The wireless sensing community has demonstrated the superiority of such
techniques in many applications such as smart homes, human-computer
interactions, and smart cities. Like many other technologies, wireless sensing
is also a double-edged sword. While the sensed information about a human can be
used for many good purposes such as enhancing life quality, an adversary can
also abuse it to steal private information about the human (e.g., location,
living habits, and behavioral biometric characteristics). However, the
literature lacks a systematic understanding of the privacy vulnerabilities of
wireless sensing and the defenses against them.
In this work, we aim to bridge this gap. First, we propose a framework to
systematize wireless sensing-based inference attacks. Our framework consists of
three key steps: deploying a sniffing device, sniffing wireless signals, and
inferring private information. Our framework can be used to guide the design of
new inference attacks since different attacks can instantiate these three steps
differently. Second, we propose a defense-in-depth framework to systematize
defenses against such inference attacks. The prevention component of our
framework aims to prevent inference attacks via obfuscating the wireless
signals around a human, while the detection component aims to detect and
respond to attacks. Third, based on our attack and defense frameworks, we
identify gaps in the existing literature and discuss future research
directions
One Clock to Rule Them All: A Primitive for Distributed Wireless Protocols at the Physical Layer
Implementing distributed wireless protocols at the physical layer today is challenging because different nodes have different clocks, each of which has slightly different frequencies. This causes the nodes to have frequency offset relative to each other, as a result of which transmitted signals from these nodes do not combine in a predictable manner over time. Past work tackles this challenge and builds distributed PHY layer systems by attempting to address the effects of the frequency offset and compensating for it in the transmitted signals. In this paper, we address this challenge by addressing the root cause - the different clocks with different frequencies on the different nodes. We present AirClock, a new wireless coordination primitive that enables multiple nodes to act as if they are driven by a single clock that they receive wirelessly over the air. AirClock presents a synchronized abstraction to the physical layer, and hence enables direct implementation of diverse kinds of distributed PHY protocols. We illustrate AirClock's versatility by using it to build three different systems: distributed MIMO, distributed rate adaptation for wireless sensors, and pilotless OFDM, and show that they can provide significant performance benefits over today's systems
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