Kevin Weekly

OpenWSN: a standards‐based low‐power wireless development environment

The OpenWSN project is an open-source implementation of a fully standards-based protocol stack for capillary networks, rooted in the new IEEE802.15.4e Time Synchronized Channel Hopping standard. IEEE802.15.4e, coupled with Internet of Things standards, such as 6LoWPAN, RPL and CoAP, enables ultra-low-power and highly reliable mesh networks, which are fully integrated into the Internet. The resulting protocol stack will be cornerstone to the upcoming machine-to-machine revolution. This article gives an overview of the protocol stack, as well as key integration details and the platforms and tools developed around it. The pure-C OpenWSN stack was ported to four off-the-shelf platforms representative of hardware currently used, from older 16-bit microcontroller to state-of-the-art 32-bit Cortex-M architectures. The tools developed around the low-power mesh networks include visualisation and debugging software, a simulator to mimic OpenWSN networks on a PC, and the environment needed to connect those networks to the Internet. Experimental results presented in this article include a network where motes operate at an average radio duty cycle well below 0.1% and an average current draw of 68  μA on off-the-shelf hardware. These ultra-low-power requirements enable a range of applications, with motes perpetually powered by micro-scavenging devices. OpenWSN is, to the best of our knowledge, the first open-source implementation of the IEEE802.15.4e standard. Copyright

Evaluating sinkhole defense techniques in RPL networks

In this work, we present the results of a study on the detrimental effects of sinkhole attacks on Wireless Sensor Networks (WSNs) which employ the Routing Protocol for LLNs (Low-power and Lossy Networks). A sinkhole is a compromised node which attempts to capture traffic with the intent to drop messages, thus degrading the end-to-end delivery performance, that is, reducing the number of messages successfully delivered to their destination. The mechanism by which the sinkhole captures traffic is by advertising an attractive route to its neighbors. We evaluate two countermeasures addressing the sinkhole problem: a parent fail-over and a rank authentication technique. We show via simulation that while each technique, applied alone, does not work all that well, the combination of the two techniques significantly improves the performance of a network under attack. We also demonstrate that, with the defenses described, increasing the density of the network can combat a penetration of sinkholes nodes, without needing to identify the sinkholes.

Environmental sensing by wearable device for indoor activity and location estimation

We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant-carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort.

Occupancy Detection via Environmental Sensing

Sensing by proxy (SbP) is proposed in this paper as a sensing paradigm for occupancy detection, where the inference is based on “proxy” measurements such as temperature and CO2 concentrations. The effects of occupants on indoor environments are captured by constitutive models comprising a coupled partial differential equation–ordinary differential equation system that exploits the spatial and physical features. Sensor fusion of multiple environmental parameters is enabled in the proposed framework. We report on experiments conducted under simulated conditions and real-life circumstances, when the variation of occupancy follows a schedule as the ground truth. The inference of the number of occupants in the room based on CO2 concentration at the air return and air supply vents by our approach achieves an overall mean squared error of 0.6044 (fractional person), while the best alternative by Bayes net is 1.2061 (fractional person). Results from the projected ventilation analysis show that SbP can potentially save 55% of total ventilation compared with the traditional fixed schedule ventilation strategy, while at the same time maintain a reasonably comfort profile for the occupants.Note to Practitioners—Building indoor occupancy is essential to facilitate heating, ventilation, and air conditioning (HVAC) control, lighting adjustment, and geofencing to achieve occupancy comfort and energy efficiency. The significance of this paper is the proposal of a paradigm of sensing that results in a parsimonious and accurate occupancy inference model, which holds considerable potential for energy saving and improvement of HVAC operations. Parameters of the model are data-driven, which exhibit long-term stability and robustness across all the occupants’ experiments. The proposed framework can also be applied to other tasks, such as indoor pollutants source identification, while requiring minimal infrastructure expenses. The data set and algorithm code are available to assist the comparison study.

Low-cost coarse airborne particulate matter sensing for indoor occupancy detection

In the energy-efficient smart building, occupancy detection and localization is an area of increasing interest, as services, such as lighting and ventilation, could be targeted towards individual occupants instead of an entire room or floor. Also, an increasing quantity and diversity of environmental sensors are being added to smart buildings to ensure the quality of services provided by the building. The need for particulate matter (PM) sensors in consumer devices such as air purifiers, is an example where manufacturing advances have made the sensors much less expensive than laboratory equipment. Beyond their original intended use, air quality, they can also be used for occupancy monitoring. The work presented in this article proposes to use a low-cost (<; 8 USD) PM sensor to infer the local movement of occupants in a corridor by sensing the resuspension of coarse (≥ 2.5 μm) particles. To obtain meaningful values from the inexpensive sensors, we have calibrated them against a laboratory-grade instrument. After calibration, we conducted a 7.8 hour experiment measuring coarse PM within a pedestrian corridor of a heavily-used office area. Comparing against ground truth data obtained by a camera, we show that the PM sensor readings are correlated with human activity, thus enabling statistical methods to infer one from the other.

Modeling and Estimation of the Humans' Effect on the CO 2 Dynamics Inside a Conference Room

We develop a data-driven, Partial Differential Equation-Ordinary Differential Equation (PDE-ODE) model that describes the response of the Carbon Dioxide (CO2) dynamics inside a conference room, due to the presence of humans, or of a user-controlled exogenous source of CO2. We conduct two controlled experiments in order to develop and tune a model whose output matches the measured output concentration of CO2 inside the room, when known inputs are applied to the model. In the first experiment, a controlled amount of CO2 gas is released inside the room from a regulated supply, and in the second, a known number of humans produce a certain amount of CO2 inside the room. For the estimation of the exogenous inputs, we design an observer, based on our model, using measurements of CO2 concentrations at two locations inside the room. We perform several simulation studies for the illustration of our design.

Indoor Occupant Positioning System Using Active RFID Deployment and Particle Filters

This article describes a method for indoor positioning of human-carried active Radio Frequency Identification (RFID) tags based on the Sampling Importance Resampling (SIR) particle filtering algorithm. To use particle filtering methods, it is necessary to furnish statistical state transition and observation distributions. The state transition distribution is obstacle-aware and sampled from a precomputed accessibility map. The observation distribution is empirically determined by ground truth RSS measurements while moving the RFID tags along a known trajectory. From this data, we generate estimates of the sensor measurement distributions, grouped by distance, between the tag and sensor. A grid of 24 sensors is deployed in an office environment, measuring Received Signal Strength (RSS) from the tags, and a multithreaded program is written to implement the method. We discuss the accuracy of the method using a verification data set collected during a field-operational test.

Autonomous River Navigation Using the Hamilton–Jacobi Framework for Underactuated Vehicles

Motorized floating sensors have distinct advantages over their non-actuated counterparts. A motorized unit can prevent the sensor from washing ashore or heading into dangerous areas, expanding the mission regions in which they can be feasibly operated. In this article, we present a control framework and describe the physically realized system used to prove its effectiveness. The controller uses two minimum-time-to-reach (MTTR) functions—one giving the time to reach the center of the river and one giving the time to reach the shoreline. The MTTR functions are constructed from solutions to Hamilton-Jacobi-Bellman-Isaacs (HJBI) Equations. Contours along these functions are used to define the state transition thresholds for an on-off controller. The first MTTR function is also used to construct the optimal bearing to travel back to the center of the river. We investigate the effectiveness of the controller using a software-in-the-loop (SIL) simulator. Using prototypes built at UC Berkeley, results from a field operational test in the Sacramento-San Joaquin River Delta are then presented to validate the simulation results.

Building-in-Briefcase: A Rapidly-Deployable Environmental Sensor Suite for the Smart Building

A building’s environment has profound influence on occupant comfort and health. Continuous monitoring of building occupancy and environment is essential to fault detection, intelligent control, and building commissioning. Though many solutions for environmental measuring based on wireless sensor networks exist, they are not easily accessible to households and building owners who may lack time or technical expertise needed to set up a system and get quick and detailed overview of environmental conditions. Building-in-Briefcase (BiB) is a portable sensor network platform that is trivially easy to deploy in any building environment. Once the sensors are distributed, the environmental data is collected and communicated to the BiB router via the Transmission Control Protocol/Internet Protocol (TCP/IP) and WiFi technology, which then forwards the data to the central database securely over the internet through a 3G radio. The user, with minimal effort, can access the aggregated data and visualize the trends in real time on the BiB web portal. Paramount to the adoption and continued operation of an indoor sensing platform is battery lifetime. This design has achieved a multi-year lifespan by careful selection of components, an efficient binary communications protocol and data compression. Our BiB sensor is capable of collecting a rich set of environmental parameters, and is expandable to measure others, such as CO2. This paper describes the power characteristics of BiB sensors and their occupancy estimation and activity recognition functionality. We have demonstrated large-scale deployment of BiB throughout Singapore. Our vision is that, by monitoring thousands of buildings through BiB, it would provide ample research opportunities and opportunities to identify ways to improve the building environment and energy efficiency.

Mobile phone based drifting lagrangian flow sensors

Mobile phone based drifters offer distinct advantages over those using custom electronic circuit boards. They leverage the inexpensive and modern hardware provided by the mobile phone market to supply water resource scientists with a new solution to sensing water resources. Mobile phone based drifters strategically address in situ sensing applications in order to focus on the large scale use of mobile phones dealing with communications, software, hardware, and system reliability. We have demonstrated that a simple design of a drifter built around an Android phone robustly survives many hours of experimental usage. In addition to the positioning capabilities of the phone via GPS, we also use the accelerometer of the phone to filter out samples when the drifter is in storage. The success of these drifters as passive mobile phone sensors has also led us to develop motorized mobile phone drifters.