Vibration sensor is becoming an essential part of Internet of Things (IoT), fueled by the quickly evolving technology improving the measurement accuracy and lowering the hardware cost. Vibration sensors physically attach to core equipments in control and manufacturing systems, e.g., motors and tubes, providing key insight into the running status of these devices. Massive readings from vibration sensors, however, pose new technical challenges to the analytical system, due to the non-continuous sampling strategy for sensor energy saving, as well as hardness of data interpretation. To maximize the utility and minimize the operational overhead of vibration sensors, we propose a new analytical framework, especially designed for vibration analysis based on its unique characteristics. In particular, our data engine targets to support Remaining Usefulness Lifetime (RUL) estimation, known as one of the most important problems in cyber-physical system maintenance, to optimize the replacement scheduling over the equipments under monitoring. Our empirical evaluations on real manufacturing sites show that scalable and accurate analysis over the vibration data enables to prolong the average lifetime of the tubes by 1.2x and reduce the replacement cost by 20%.

Fine-grained real-time metering is a fundamental service of wireless energy auditing networks, where metering data is transmitted from embedded wireless power meters to gateways for centralized processing, storage, and forwarding. Due to limited meter capability and wireless bandwidth, the increasing sampling rates and network scales needed to support new energy auditing applications pose significant challenges to metering data fidelity and secrecy . This article exploits the compression and encryption properties of compressive sensing (CS) to design a joint data compression and encryption (JICE) approach that addresses these two challenges simultaneously. Compared with a conventional signal processing pipeline that compresses and encrypts data sequentially, JICE reduces computation and space complexities due to its simple design. It thus leaves more processor time and available buffer space for handling lossy wireless transmissions. Moreover, JICE features an adaptive reconfiguration mechanism that selects the signal representation basis of CS at runtime among several candidate bases to achieve the best fidelity of the recovered data at the gateways. This mechanism enables JICE to adapt to changing power consumption patterns. On a smart plug platform, we implemented JICE and several baseline approaches including downsampling, lossless compression, and the pipeline approach. Extensive testbed experiments show that JICE achieves higher data delivery ratios and lower recovery distortions under a range of realistic settings. In particular, at a meter sampling rate of 8 Hz, JICE increases the number of meters supported by a gateway by 50%, compared with the commonly used pipeline approach, while keeping a signal distortion rate lower than 5%.

Distributed systems are now commonly used to manage massive data flooding from physical world, such as user-generated contents from online social media and communication records from mobile phones. The new generation of distributed data management systems, such as HBase, are designed to accept tuple insertions only, such that other database operations (e.g., deletion and update) are simply simulated by appending operation logs with keys associated to the target tuples. Such append-only store architecture maximizes the processing throughput on incoming data, but potentially incur higher costs on query processing, in which additional computation is needed to generate consistent snapshots of the database. Indexing is known as the key to enable efficient query processing by fast data retrieval and aggregation under such system architecture. This paper presents a new indexing scheme for distributed append-only stores. Our new scheme utilizes traditional index structures based on B-trees and its variant without the overhead of expensive node split, by using template-based tree construction. Optimized domain partitioning and multi-thread insertion techniques are further proposed to exploit the advantages of our template B-tree structure. Empirical evaluations show that our proposal outperforms existing solutions on insertion throughput by a large margin on a variety of real and synthetic workloads.

Fine-grained real-time metering is a fundamental service of wireless energy auditing networks, where metering data is transmitted from embedded power meters to gateways for centralized processing, storage, and forwarding. Due to limited meter capability and wireless bandwidth, the increasing sampling rates and network scales needed to support new energy auditing applications pose significant challenges to metering data fidelity and secrecy. This paper exploits the compression and encryption properties of compressive sensing (CS) to design a joint data compression and encryption (JICE) approach that addresses these two challenges simultaneously. Compared with a conventional signal processing pipeline that compresses and encrypts data sequentially, JICE reduces computation and storage complexities due to its simple design. It thus leaves more processor time and available buffer space for handling lossy wireless transmissions. Moreover, JICE features a machine-learning-based reconfiguration mechanism that adapts its signal representation basis to changing power patterns autonomously. On a smart plug platform, we implemented JICE and several baseline approaches including downsampling, lossless compression, and the pipeline approach. Extensive testbed experiments show that JICE achieves higher data delivery ratios and lower recovery distortions under a range of realistic settings. In particular, JICE increases the number of meters supported by a gateway by 50%, compared with the pipeline approach, while keeping a distortion rate lower than 5%.

This article considers a problem of periodically estimating energy consumption breakdowns for main appliances inside building using a single power meter and the knowledge of the ON/OFF states of individual appliances. In the first part of this article, we formulate the problem as a constrained convex optimization problem with tunable parameters. Then we propose an online algorithm that adaptively determines the optimization parameters to robustly estimate the breakdown information. The proposed solution is evaluated by experiment using a scaled-down proof-of-concept prototype with real measurements. In the second part, we provide detailed analysis to understand the performance of our proposed algorithm. We first develop a stochastic model to describe evolution of appliances’ ON/OFF states using continuous-time Markov chain. Then we derive analytical bounds of estimation error and the probability of a rank-deficient binary matrix. Those analytical bounds are verified by extensive simulations. Finally, we study the effect of collinearity of binary data matrix on estimation performance. Simulation results suggest that our algorithm is robust against the collinearity of binary dataset.

Given the ongoing widespread deployment of low frequency electricity sub-metering devices at residential and commercial buildings, fine-grained usage information of end-loads can bring a new powerful sensing modality in Cyber-Physical Systems (CPS). Motivated by the opportunity, this paper describes an algorithm of estimating the ON/OFF sequences for typical household end-loads in close-to-real-time using an off-the-shelf power meter. Unlike previous algorithms that lacks in scalability to support diverse applications in CPS our algorithm is designed to provide control knobs to support various trade-offs between accuracy and computation load or delay to satisfy the different application requirements. We experimentally verify the proposed algorithm using a collection of home appliances. Our experiment result shows that our algorithm is able to detect ON/OFF sequences of 7 appliances nearly without error and 3 appliances with moderate error rate less than 6% among 12 typical household appliances.

In this demonstration, we present a working prototype that uses an Anisotropic Magnetoresistive (AMR) sensor array to estimate the electricity usage on individual branches of an electricity panel. Our design enables the general public to retrofit an electricity panel: one simply needs to attach a compact AMR sensor array onto the panel. Our system can then exploit the inherent power patterns of electric loads to automatically infer the system parameters and estimate the per-branch currents accurately. Even for branches carrying small loads (e.g., a 30W fan), the estimation error of our system is below 10%.

We present a method to identify and localize people by leveraging existing CCTV camera infrastructure along with inertial sensors (accelerometer and magnetometer) within each person's mobile phones. Since a person's motion path, as observed by the camera, must match the local motion measurements from their phone, we are able to uniquely identify people with the phones' IDs by detecting the statistical dependence between the phone and camera measurements. For this, we express the problem as consisting of a two-measurement HMM for each person, with one camera measurement and one phone measurement. Then we use a maximum a posteriori formulation to find the most likely ID assignments. Through sensor fusion, our method largely bypasses the motion correspondence problem from computer vision and is able to track people across large spatial or temporal gaps in sensing. We evaluate the system through simulations and experiments in a real camera network testbed.

This work describes a new approach for localizing people by cooperative sensor fusion of lightweight camera and wearable accelerometer measurements. We present the algorithm to identify people moving around as they are detected by cameras deployed in the infrastructure. The algorithm uses a correlation metric to develop an ID matching algorithm that can associate people in the scene to their global ID emitted from a wireless accelerometer sensor node worn on their belts. First we conduct a set of preliminary experiments to verify that the quantities of interest easily measurable by off-the-shelf components. We validate our metric and the performance of the proposed ID matching algorithm using simulations on testbed data that also includes a crowded scenario.

This paper considers the problem of estimating the power breakdowns for the main appliances inside a building using a small number of power meters and the knowledge of the ON/OFF states of individual appliances. First we solve the breakdown estimation problem within a tree configuration using a single power meter and the knowledge of ON/OFF states and use the solution to derive an estimation quality metric. Using this metric, we then propose an algorithm for optimally placing additional power meters to increase the estimation certainty for individual appliances to the required level. The proposed solution is evaluated using real measurements, numerical simulations and by constructing a scaled down proof-of-concept prototype using binary sensors.

This article presents two lifetime models that describe two of the most common modes of operation of sensor nodes today, trigger-driven and duty-cycle driven. The models use a set of hardware parameters such as power consumption per task, state transition overheads, and communication cost to compute a node's average lifetime for a given event arrival rate. Through comparison of the two models and a case study from a real camera sensor node design we show how the models can be applied to drive architectural decisions, compute energy budgets and duty-cycles, and to preform side-by-side comparison of different platforms. Based on our models we present a MATLAB Wireless Sensor Node Platform Lifetime Prediction and Simulation Package (MATSNL). This demonstrates the use of the models using sample applications drawn from existing sensor node measurements.

This paper constructs a model for studying the energy efficiency of sensor node architectures featuring a pair of low-end, low-power processor and radio and a pair of high-end, energy efficient processor and radio. Such nodes can have a highly dynamic range of operation ranging from the collection of simple temperature measurements or motion detection all the way up to sophisticated signal processing of sensor data. Our model explores the energy efficiency tradeoffs related to the decision of which processor and which radio should be used for each task. For this we derive a general Semi-Markov Decision process model for maximizing the asymptotic lifetime of two alternate designs, one with dynamic and one with static interconnect. The resulting models are validated with simulation and are applied to the reported measurements from an existing platform with two radios and two processors. Our results show how to quantify the gains of such design with respect to the power consumption properties of each component. Furthermore, based on our power budget calculation, we conclude that the deisgn of reconfigurable interconnect between multiple processors and radios would result in efficiency gains despite the energy overhead such device may incur.