230 research outputs found
MakeSense: An IoT Testbed for Social Research of Indoor Activities
There has been increasing interest in deploying IoT devices to study human
behaviour in locations such as homes and offices. Such devices can be deployed
in a laboratory or `in the wild' in natural environments. The latter allows one
to collect behavioural data that is not contaminated by the artificiality of a
laboratory experiment. Using IoT devices in ordinary environments also brings
the benefits of reduced cost, as compared with lab experiments, and less
disturbance to the participants' daily routines which in turn helps with
recruiting them into the research. However, in this case, it is essential to
have an IoT infrastructure that can be easily and swiftly installed and from
which real-time data can be securely and straightforwardly collected. In this
paper, we present MakeSense, an IoT testbed that enables real-world
experimentation for large scale social research on indoor activities through
real-time monitoring and/or situation-aware applications. The testbed features
quick setup, flexibility in deployment, the integration of a range of IoT
devices, resilience, and scalability. We also present two case studies to
demonstrate the use of the testbed, one in homes and one in offices.Comment: 20 pages, 11 figure
Using RF Transmissions from IoT Devices for Occupancy Detection and Activity Recognition
IoT ecosystems consist of a range of smart devices that generated a plethora of Radio Frequency (RF) transmissions. This provides an attractive opportunity to exploit already-existing signals for various sensing applications such as e-Healthcare, security and smart home. In this paper, we present Passive IoT Radar (PIoTR), a system that passively uses RF transmissions from IoT devices for human monitoring. PIoTR is designed based on passive radar technology, with a generic architecture to utilize various signal sources including the WiFi signal and wireless energy at the Industrial, Scientific and Medical (ISM) band. PIoTR calculates the phase shifts caused by human motions and generates Doppler spectrogram as the representative. To verify the proposed concepts and test in a more realistic environment, we evaluate PIoTR with four commercial IoT devices for home use. Depending on the effective signal and power strength, PIoTR performs two modes: coarse sensing and fine-grained sensing. Experimental results show that PIoTR can achieve an average of 91% in occupancy detection (coarse sensing) and 91.3% in activity recognition (fine-grained sensing)
RobustSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition
Deep neural networks have empowered accurate device-free human activity
recognition, which has wide applications. Deep models can extract robust
features from various sensors and generalize well even in challenging
situations such as data-insufficient cases. However, these systems could be
vulnerable to input perturbations, i.e. adversarial attacks. We empirically
demonstrate that both black-box Gaussian attacks and modern adversarial
white-box attacks can render their accuracies to plummet. In this paper, we
firstly point out that such phenomenon can bring severe safety hazards to
device-free sensing systems, and then propose a novel learning framework,
RobustSense, to defend common attacks. RobustSense aims to achieve consistent
predictions regardless of whether there exists an attack on its input or not,
alleviating the negative effect of distribution perturbation caused by
adversarial attacks. Extensive experiments demonstrate that our proposed method
can significantly enhance the model robustness of existing deep models,
overcoming possible attacks. The results validate that our method works well on
wireless human activity recognition and person identification systems. To the
best of our knowledge, this is the first work to investigate adversarial
attacks and further develop a novel defense framework for wireless human
activity recognition in mobile computing research
A taxonomy of cyber-physical threats and impact in the smart home
In the past, home automation was a small market for technology enthusiasts. Interconnectivity between devices was down to the owner’s technical skills and creativity, while security was non-existent or primitive, because cyber threats were also largely non-existent or primitive. This is not the case any more. The adoption of Internet of Things technologies, cloud computing, artificial intelligence and an increasingly wide range of sensing and actuation capabilities has led to smart homes that are more practical, but also genuinely attractive targets for cyber attacks. Here, we classify applicable cyber threats according to a novel taxonomy, focusing not only on the attack vectors that can be used, but also the potential impact on the systems and ultimately on the occupants and their domestic life. Utilising the taxonomy, we classify twenty five different smart home attacks, providing further examples of legitimate, yet vulnerable smart home configurations which can lead to second-order attack vectors. We then review existing smart home defence mechanisms and discuss open research problems
Occupancy Estimation Using Low-Cost Wi-Fi Sniffers
Real-time measurements on the occupancy status of indoor and outdoor spaces
can be exploited in many scenarios (HVAC and lighting system control, building
energy optimization, allocation and reservation of spaces, etc.). Traditional
systems for occupancy estimation rely on environmental sensors (CO2,
temperature, humidity) or video cameras. In this paper, we depart from such
traditional approaches and propose a novel occupancy estimation system which is
based on the capture of Wi-Fi management packets from users' devices. The
system, implemented on a low-cost ESP8266 microcontroller, leverages a
supervised learning model to adapt to different spaces and transmits occupancy
information through the MQTT protocol to a web-based dashboard. Experimental
results demonstrate the validity of the proposed solution in four different
indoor university spaces.Comment: Submitted to Balkancom 201
Sensing within smart buildings: A survey
Increasingly, buildings are being fitted with sensors for the needs of different sectors, such as education,
industry and business. Using Internet of Things (IoT) devices combined with analysis of data being generated
by these devices, it is possible to infer a number of metrics, e.g. building occupancy and activities of occupants.
The information thus gathered can be used to develop software applications to support energy management,
occupant comfort, and space utilization. This survey explores the use of sensors in smart building environments,
identifying different approaches to employ sensors in buildings. The most commonly used data-driven
approaches for activity recognition in such buildings is also investigated, concluding by highlighting current
research challenges and future research directions in this area
Contactless WiFi Sensing and Monitoring for Future Healthcare:Emerging Trends, Challenges and Opportunities
WiFi sensing has recently received significant interest from academics, industry, healthcare professionals and other caregivers (including family members) as a potential mechanism to monitor our aging population at distance, without deploying devices on users bodies. In particular, these methods have gained significant interest to efficiently detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems stems from its practical deployments in indoor settings and compliance from monitored persons, unlike other sensors such as wearables, camera-based, and acoustic-based solutions. This paper reviews state-of-the-art research on collecting and analysing channel state information, extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, untapped areas, and related trends.This work aims to provide an overarching view in understanding the technology and discusses its uses-cases from a perspective that considers hardware, advanced signal processing, and data acquisition
- …