As mobile devices continuously generate streams of images and videos, a new class of mobile deep vision applications are rapidly emerging, which usually involve running deep neural networks on these multimedia data in real-time. To support such applications, having mobile devices offload the computation, especially the neural network inference, to edge clouds has proved effective. Existing solutions often assume there exists a dedicated and powerful server, to which the entire inference can be offloaded. In reality, however, we may not be able to find such a server but need to make do with less powerful ones. To address these more practical situations, we propose to partition the video frame and offload the partial inference tasks to multiple servers for parallel processing. This paper presents the design of Elf, a framework to accelerate the mobile deep vision applications with any server provisioning through the parallel offloading. Elf employs a recurrent region proposal prediction algorithm, a region proposal centric frame partitioning, and a resource-aware multi-offloading scheme. We implement and evaluate Elf upon Linux and Android platforms using four commercial mobile devices and three deep vision applications with ten state-of-the-art models. The comprehensive experiments show that Elf can speed up the applications by 4.85× with saving bandwidth usage by 52.6%, while with <1% application accuracy sacrifice.

In-bed motion detection and classification are important techniques that can enable an array of applications, among which are sleep monitoring and abnormal movement detection. In this article, we present a low-cost, low-overhead, and highly robust system for in-bed movement detection and classification that uses low-end load cells. To detect movements, we have designed a feature that we refer to as Log-Peak, which can be extracted from load cell data that is collected through wireless links in an energy-efficient manner. After detection, we set out to achieve a precise body motion classification. Toward this goal, we define nine classes of movements, and design a machine learning algorithm using Support Vector Machine, Random Forest, and XGBoost techniques to classify a movement into one of nine classes. For every movement, we have extracted 24 features and used them in our model. This movement detection/classification system was evaluated on data collected from 40 subjects who performed 35 predefined movements in each experiment. We have applied multiple tree topologies for each technique to reach their best results. After examining various combinations, we have achieved a final classification accuracy of 91.5%. This system can be used conveniently for long-term home monitoring.

Running computer vision algorithms on images or videos collected by mobile devices represent a new class of latency-sensitive applications that expect to benefit from edge cloud computing. These applications often demand real-time responses (e.g., <;100 ms), which can not be satisfied by traditional cloud computing. However, the edge cloud architecture is inherently distributed and heterogeneous, requiring new approaches to resource allocation and orchestration. This paper presents the design and evaluation of a latency-aware edge computing platform, aiming to minimize the end-to-end latency for edge applications. The proposed platform is built on Apache Storm, and consists of multiple edge servers with heterogeneous computation (including both GPUs and CPUs) and networking resources. Central to our platform is an orchestration framework that breaks down an edge application into Storm tasks as defined by a directed acyclic graph (DAG) and then maps these tasks onto heterogeneous edge servers for efficient execution. An experimental proof-of-concept testbed is used to demonstrate that the proposed platform can indeed achieve low end-to-end latency: considering a real-time 3D scene reconstruction application, it is shown that the testbed can support up to 30 concurrent streams with an average perframe latency of 32ms, and can achieve 40% latency reduction relative to the baseline Storm scheduling approach.

As rapid expansion of the low-cost next billion devices, wireless sensor networks (WSN) undertake much denser low-end internet of things (IoT) nodes nowadays. In the meantime, the future next 5 generation (5G) radio base stations (BS) are granted more capabilities. Distributed cloud radio access network (C-RAN) is becoming available for the future massive WSN. However, real-world distributed C-RAN is less explored for low-end IoT based WSN due to its difficulties in implementation. In this paper, we built a distributed C-RAN which has tens of distributed radio frontends using USRP N210s in a 20 × 20 × 3 m3 area. By exploiting the inherent hardware properties of low-end IoT devices and the spatial diversity of distributed C-RAN system, we show the distributed C-RAN can potentially decode collided signals from low-end IoT devices with all signal processing been done on the cloud.

Accurate and continuous ammonia monitoring is important for laboratory animal studies and many other applications. Existing solutions are often expensive, inaccurate, or unsuitable for long-term monitoring. In this work, we propose a new ammonia monitoring approach that is low-power, automatic, accurate, and wireless.

Sensing bed vibrations caused by heartbeats has shown great potentials in detecting and monitoring a person's heartbeats during sleep, without requiring special mattress or sheets, or assuming certain sleeping position/posture. Earlier work has studied how to use this method to detect heartbeats when a single subject is on the bed, and in this study, we aim to separate the heartbeats when multiple subjects share the same bed and the vibration signals are mixed together. Our heartbeat separation algorithm is based upon signal unmixing via time-frequency masking [4], which was originally designed to extract individual voices from two audio mixtures. Though these two problems have similarity, separating heartbeat signals is much harder and poses new challenges, mainly because heartbeat signals have a much smaller frequency range than audio signals, fluctuate considerably from beat to beat, and propagate through a mattress that has much more complex propagation properties than the air.

Monitoring heartbeats takes an important role to ensure a person's health and well-being. Few of the existing systems are accurate, unobtrusive, robust and easy to install at the same time. Thus, we propose a completely unobtrusive system which can detect heartbeats during sleep by sensing the weak ballistic vibrations caused by heartbeats on any bed. The system, HB-Phone, is centered around the off-the-shelf geophone sensor and can be easily installed on an existing bed. In this demo, we demonstrate that our system can detect and extract heartbeats accurately and in real time, even with the presence of noise from the environment and gross body movements during sleep.

Using geophones to sense bed vibrations caused by ballistic force has shown great potential in monitoring a person's heart rate during sleep. It does not require a special mattress or sheets, and the user is free to move around and change position during sleep. Earlier work has studied how to process the geophone signal to detect heartbeats when a single subject occupies the entire bed. In this study, we develop a system called VitalMon, aiming to monitor a person's respiratory rate as well as heart rate, even when she is sharing a bed with another person. In such situations, the vibrations from both persons are mixed together. VitalMon first separates the two heartbeat signals, and then distinguishes the respiration signal from the heartbeat signal for each person. Our heartbeat separation algorithm relies on the spatial difference between two signal sources with respect to each vibration sensor, and our respiration extraction algorithm deciphers the breathing rate embedded in amplitude fluctuation of the heartbeat signal.

Industrial control systems (ICSs) are widely used in critical infrastructures, making them popular targets for attacks to cause catastrophic physical damage. As one of the most critical components in ICSs, the programmable logic controller (PLC) controls the actuators directly. A PLC executing a malicious program can cause significant property loss or even casualties. The number of attacks targeted at PLCs has increased noticeably over the last few years, exposing the vulnerability of the PLC and the importance of PLC protection. Unfortunately, PLCs cannot be protected by traditional intrusion detection systems or antivirus software. Thus, an effective method for PLC protection is yet to be designed. Motivated by these concerns, we propose a non-invasive powerbased anomaly detection scheme for PLCs. The basic idea is to detect malicious software execution in a PLC through analyzing its power consumption, which is measured by inserting a shunt resistor in series with the CPU in a PLC while it is executing instructions. To analyze the power measurements, we extract a discriminative feature set from the power trace, and then train a long short-term memory (LSTM) neural network with the features of normal samples to predict the next time step of a normal sample. Finally, an abnormal sample is identified through comparing the predicted sample and the actual sample. The advantages of our method are that it requires no software modification on the original system and is able to detect unknown attacks effectively. The method is evaluated on a lab testbed, and for a trojan attack whose difference from the normal program is around 0.63%, the detection accuracy reaches 99.83%.

Heartbeat monitoring during sleep is critically important to ensuring the well-being of many people, ranging from patients to elderly. Technologies that support heartbeat monitoring should be unobtrusive, and thus solutions that are accurate and can be easily applied to existing beds is an important need that has been unfulfilled. We tackle the challenge of accurate, low-cost and easy to deploy heartbeat monitoring by investigating whether off-the- shelf analog geophone sensors can be used to detect heartbeats when installed under a bed. Geophones have the desirable property of being insensitive to lower-frequency movements, which lends itself to heartbeat monitoring as the heartbeat signal has harmonic frequencies that are easily captured by the geophone. At the same time, lower-frequency movements such as respiration, can be naturally filtered out by the geophone. With carefully-designed signal processing algorithms, we show it is possible to detect and extract heartbeats in the presence of environmental noise and other body movements a person may have during sleep. We have built a prototype sensor and conducted detailed experiments that involve 43 subjects (with IRB approval), which demonstrate that the geophone sensor is a compelling solution to long-term at-home heartbeat monitoring. We compared the average heartbeat rate estimated by our prototype and that reported by a pulse oximeter. The results revealed that the average error rate is around 1.30% over 500 data samples when the subjects were still on the bed, and 3.87% over 300 data samples when the subjects had different types of body movements while lying on the bed. We also deployed the prototype in the homes of 9 subjects for a total of 25 nights, and found that the average estimation error rate was 8.25% over more than 181 hours' data.