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.

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.

In this paper, we present SAGA, a system for building energy management that provides robust multi-hop wireless sensing, actuation, device management, plotting of historical data and a server backend API to support remote access. Information collected from sensor nodes is stored locally for quick retrieval even if outside network connectivity is lost or unavailable. When network connectivity is available, data is pushed using the extensible Message Passing Protocol (XMPP). This enables external server-side archiving of historical events and additional processing as well as secure bi-directional communication from gateways behind firewalls or with dynamic IP address like those found in broadband connected homes. SAGA provides a web interface that allows devices to be easily configured with aliases and grouped together based on sensor type. Individual and groups of sensors can be plotted to show relative comparisons of different sensor values. For example, selecting to plot a group of energy metering devices shows the overall distribution of energy consumption per-device. A local multi-resolution storage system provides optimized access to recent high-resolution data along with quick retrieval of pre-computed long-term averages. SAGA has been deployed in three homes around the Pittsburgh area, collecting data for more than two years.