Branko Kerkez

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

Feasibility analysis of controller design for adaptive channel hopping

Communication reliability in Wireless Sensor Networks (WSNs) is challenged by narrow-band interference and persistent multichannel fading. Frequency-agile communication protocols have been recently designed and standardized to increase reliability. These protocols, however, do not adapt the set of channels they hop on to the environment. In this paper, we evaluate the efficiency of a controller which continuously samples all available frequency channels in order to operate on a channel which performs reasonably well. We show that the overall average link Packet Delivery Ratio when using this controller reaches 99.4%, and is higher compared to a single channel solution, on any channel. We evaluate the efficiency of this approach by simulating its behavior on connectivity traces gathered during a real-world deployment. This data set is dense in time and sufficiently large in number of nodes and time to be statistically valid. We believe that the use of connectivity traces for performance evaluation will become commonplace as the number and variety of these traces increases.

TDMA-Based Dual-Mode Communication for Mobile Wireless Sensor Networks

Small highly mobile robots, and in particular micro air vehicles (MAVs), are well suited to the task of exploring unknown indoor environments such as buildings and caves. Such a task imposes a number of requirements on the underlying communication infrastructure, with differing goals during various stages of the mission. This work addresses those requirements with a hybrid communications infrastructure consisting of a stationary mesh network along with the mobile nodes. The combined network operates in two independent modes, coupling a highly efficient, low duty cycle, low throughput mode for routing and persistent sensing with a burst mode for high data rate communication. By strategically distributing available frequency channels between the mobile agents and the stationary nodes, the overall network provides reliable long-term communication paths while maximizing data throughput when needed.

Adaptive measurements of urban runoff quality

An approach to adaptively measure runoff water quality dynamics is introduced, focusing specifically on characterizing the timing and magnitude of urban pollutographs. Rather than relying on a static schedule or flow-weighted sampling, which can miss important water quality dynamics if parameterized inadequately, novel Internet-enabled sensor nodes are used to autonomously adapt their measurement frequency to real-time weather forecasts and hydrologic conditions. This dynamic approach has the potential to significantly improve the use of constrained experimental resources, such as automated grab samplers, which continue to provide a strong alternative to sampling water quality dynamics when in-situ sensors are not available. Compared to conventional flow- or time-weighted sampling schemes, which rely on preset thresholds, a major benefit of the approach is the ability to dynamically adapt to features of an underlying hydrologic signal. A 28 km2 urban watershed was studied to characterize concentrations of total suspended solids (TSS) and total phosphorus. Water quality samples were autonomously triggered in response to features in the underlying hydrograph and real-time weather forecasts. The study watershed did not exhibit a strong first flush and intra-event concentration variability was driven by flow acceleration, wherein the largest loadings of TSS and total phosphorus corresponded with the steepest rising limbs of the storm hydrograph. The scalability of the proposed method is discussed in the context of larger sensor network deployments, as well the potential to improving control of urban water quality. This article is protected by copyright. All rights reserved.

A hybrid system model of seasonal snowpack water balance

It is estimated that seasonal snow cover is the primary source of water supply for over 60 million people in the western United States. Informed decision making, which ensures reliable and equitable distribution of this limited water resource, thus needs to be motivated by an understanding of the physical snowmelt process. We present a direct application of hybrid systems for the modeling of the seasonal snowmelt cycle, and show that through the hybrid systems framework it is possible to significantly reduce the complexity offered by conventional PDE modeling methods. Our approach shows how currently existing heuristics can be embedded into a coherent mathematical framework to allow for powerful analytical techniques while preserving physical intuition about the problem. Snowmelt is modeled as a three state hybrid automaton, representing the sub-freezing, sub-saturated, and fully saturated physical states that an actual snowpack experiences. We show that the model accurately reproduces melt patterns, by simulating over actual data sets collected in the Sierra Nevada mountains. We further explore the possibility of merging this model with a currently existing wireless sensing infrastructure to create reliable prediction techniques that will feed into large scale control schemes of dams in mountain areas.

Open storm: a complete framework for sensing and control of urban watersheds

Leveraging recent advances in technologies surrounding the Internet of Things, “smart” water systems are poised to transform water resources management by enabling ubiquitous real-time sensing and control. Recent applications have demonstrated the potential to improve flood forecasting, enhance rainwater harvesting, and prevent combined sewer overflows. However, adoption of smart water systems has been hindered by a limited number of proven case studies, along with a lack of guidance on how smart water systems should be built. To this end, we review existing solutions, and introduce open storm—an open-source, end-to-end platform for real-time monitoring and control of watersheds. Open storm includes (i) a robust hardware stack for distributed sensing and control in harsh environments (ii) a cloud services platform that enables system-level supervision and coordination of water assets, and (iii) a comprehensive, web-based “how-to” guide, available on open-storm.org, that empowers newcomers to develop and deploy their own smart water networks. We illustrate the capabilities of the open storm platform through two ongoing deployments: (i) a high-resolution flash-flood monitoring network that detects and communicates flood hazards at the level of individual roadways and (ii) a real-time stormwater control network that actively modulates discharges from stormwater facilities to improve water quality and reduce stream erosion. Through these case studies, we demonstrate the real-world potential for smart water systems to enable sustainable management of water resources.

Leveraging real-time hydrologic data for the control of large-scale water distribution systems in the Sierra Nevada

Recent water shortages, particularly evident in the state of California, are calling for better predictive capabilities, and improved management techniques for existing water distribution infrastructure. One particular example involves large-scale water distribution systems (on the scale of reservoirs and dams) in the Sierra Nevada, where the majority of the states water is obtained from melting snow. Current control strategies at this scale rely on sparse data sets, and are often based on statistical predictions of snowmelt. Sudden, or unexpected, snowmelt can thus often lead to dam-overtopping, or downstream flooding. This paper assesses the feasibility of employing real-time hydrologic data, acquired by large-scale wireless sensor networks (WSNs), to improve current water management strategies. A sixty node WSN, spanning a square kilometer, was deployed in the Kings River Experimental Watershed, a research site in the Southern Sierra Nevada, at an elevation of 1,600-2,000 m. The network provides real time information on a number of hydrologic variables, with a particular emphasis on parameters pertaining to snowmelt processes. We lay out a system architecture that describes how this real-time data could be coupled with hydrologic models, estimation-, optimization-, and control-techniques to develop an automated water management infrastructure. We also investigate how data obtained by such networks could be used to improve predictions of water quantities at nearby reservoirs.

Sampling Strategies in Forest Hydrology and Biogeochemistry

Many aspects of forest hydrology have been based on accurate but not necessarily spatially representative measurements, reflecting the measurement capabilities that were traditionally available. Two developments are bringing about fundamental changes in sampling strategies in forest hydrology and biogeochemistry: (a) technical advances in measurement capability, as is evident in embedded sensor networks and remotely sensed measurements and (b) parallel advances in cyberinfrastructure and numerical modeling that can help turn these new data into knowledge. Although these developments will potentially impact much of hydrology, they bring up particular opportunities in forest hydrology (Bales et al. 2006). New sensor technology for most biogeochemical components has lagged that for water and energy, advances in measuring forest–atmosphere exchange of carbon by eddy correlation being an exception.

Shaping Streamflow Using a Real-Time Stormwater Control Network

“Smart” water systems are transforming the field of stormwater management by enabling real-time monitoring and control of previously static infrastructure. While the localized benefits of active control are well-established, the potential for system-scale control of watersheds is poorly understood. This study shows how a real-world smart stormwater system can be leveraged to shape streamflow within an urban watershed. Specifically, we coordinate releases from two internet-controlled stormwater basins to achieve desired control objectives downstream—such as maintaining the flow at a set-point, and generating interleaved waves. In the first part of the study, we describe the construction of the control network using a low-cost, open-source hardware stack and a cloud-based controller scheduling application. Next, we characterize the system’s control capabilities by determining the travel times, decay times, and magnitudes of various waves released from the upstream retention basins. With this characterization in hand, we use the system to generate two desired responses at a critical downstream junction. First, we generate a set-point hydrograph, in which flow is maintained at an approximately constant rate. Next, we generate a series of overlapping and interleaved waves using timed releases from both retention basins. We discuss how these control strategies can be used to stabilize flows, thereby mitigating streambed erosion and reducing contaminant loads into downstream waterbodies.