Abstract-As sensor networks gain traction and begin to scale, we will be increasingly faced with challenges associated with managing large-scale time-series data. In this paper, we present a cloud-to-edge partitioned architecture called Respawn that is capable of serving large amounts of time-series data from a continuously updating datastore with access latencies low enough to support interactive real-time visualization. Respawn targets sensing systems where resource-constrained edge node devices may only have limited or intermittent network connections linking them to a cloud-backend. The cloud-backend provides aggregate storage and transparent dispatching of data queries to edge node devices. Data is downsampled as it enters the system creating a multi-resolution representation capable of lowlatency range-base queries. Lower-resolution aggregate data is automatically migrated from edge nodes to the cloud-backend both for improved consistency and caching. In order to further mask latency from users, edge nodes automatically identify and migrate blocks of data that contain statistically interesting features. We show through simulation and micro-benchmarking that Respawn is able to run on ARM-based edge node devices connected to a cloud-backend with the ability to serve thousands of clients and terabytes of data with sub-second latencies.
Abstract-Time synchronization in wireless sensor networks is important for event ordering and efficient communication scheduling. In this paper, we introduce an external hardwarebased clock tuning circuit that can be used to improve synchronization and significantly reduce clock drift over long periods of time without waking up the host MCU. This is accomplished through two main hardware sub-systems. First, we improve upon the circuit presented in [1] that synchronizes clocks using the ambient magnetic fields emitted from power lines. The new circuit uses an electric field front-end as opposed to the original magnetic-field sensor, which makes the design more compact, lower-power, lower-cost, exhibit less jitter and improves robustness to noise generated by nearby appliances. Second, we present a low-cost hardware tuning circuit that can be used to continuously trim a micro-controller's low-power clock at runtime. Most time synchronization approaches require a CPU to periodically adjust internal counters to accommodate for clock drift. Periodic discrete updates can introduce interpolation errors as compared to continuous update approaches and they require the CPU to expend energy during these wake up periods. Our hardware-based external clock tuning circuit allows the main CPU to remain in a deep-sleep mode for extended periods while an external circuit compensates for clock drift. We show that our new synchronization circuit consumes 60% less power than the original design and is able to correct clock drift rates to within 0.01 ppm without power hungry and expensive precision clocks.
Abstract-In this paper, we present the architecture, design and experiences from a wirelessly managed microgrid deployment in rural Les Anglais, Haiti. The system consists of a three-tiered architecture with a cloud-based monitoring and control service, a local embedded gateway infrastructure and a mesh network of wireless smart meters deployed at 52 buildings. Each smart meter device has an 802.15.4 radio that enables remote monitoring and control of electrical service. The meters communicate over a scalable multi-hop TDMA network back to a central gateway that manages load within the system. The gateway also provides an 802.11 interface for an on-site operator and a cellular modem connection to a cloud-backend that manages and stores billing and usage data. The cloud backend allows occupants in each home to pre-pay for electricity at a particular peak power limit using a text messaging service. The system activates each meter within seconds and locally enforces power limits with provisioning for theft detection. We believe that this fine-grained micro-payment model can enable sustainable power in otherwise unfeasible areas.This paper provides a chronology of our deployment and installation strategy that involved GPS-based site mapping along with various network conditioning actions required as the network evolved. Finally, we summarize key lessons learned and hypothesis about additional hardware that could be used to ease the tracing of faults like short circuits and downed lines within microgrids.
Non-Technical Loss (NTL) represents a major challenge when providing reliable electrical service in developing countries, where it often accounts for 11-15% of total generation capacity [1]. NTL is caused by a variety of factors such as theft, unmetered homes, and inability to pay, which at volume can lead to system instability, grid failure, and major financial losses for providers.In this paper, we investigate error sources and techniques for separating NTL from total losses in microgrids. We adopt and compare two classes of approaches for detecting NTL: (1) model-driven and (2) data-driven. The model-driven class considers the primary sources of state uncertainty including line losses, meter consumption, meter calibration error, packet loss, and sample synchronization error. In the data-driven class, we use two approaches that learn grid state based on training data. The first approach uses a regression technique on an NTL-free period of grid operation to capture the relationship between state error and total consumption. The second approach uses an SVM trained on synthetic NTL data. Both classes of approaches can provide a confidence interval based on the amount of detected NTL. We experimentally evaluate and compare the approaches on wireless meter data collected from a 525-home microgrid deployed in Les Anglais, Haiti. We see that both are quite effective, but that the data-driven class is significantly easier to implement. In both cases, we are able to experimentally evaluate to what degree we can reliably separate NTL from total losses.
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