Devices implementing newer wireless standards continue to displace older wireless technology. As 802.11ac access points (APs) are rapidly adopted in enterprise environments, new challenges arise. This paper first presents an overview of trends in enterprise wireless networks based on a large-scale measurement study, in which we collect data from an anonymous subset of millions of radio access points in hundreds of thousands of real-world deployments. Based on the observed data and our experience deploying wireless networks at scale, we then propose two techniques that we have implemented in Meraki APs to improve both overall network capacity and performance perceived by end users: (i) a dynamic channel assignment algorithm, TurboCA, that adjusts to frequent RF condition changes, and (ii) a novel approach, FastACK, that improves the end-to-end performance of TCP traversing high-throughput wireless links. Finally, we evaluate TurboCA with metrics taken from a variety of real-world networks and evaluate TCP performance of FastACK with extensive testbed experiments.

Imagine a user typing on a laptop keyboard while wearing a smart watch. This paper asks whether motion sensors from the watch can leak information about what the user is typing. While its not surprising that some information will be leaked, the question is how much? We find that when motion signal processing is combined with patterns in English language, the leakage is substantial. Reported results show that when a user types a word $W$, it is possible to shortlist a median of 24 words, such that $W$ is in this shortlist. When the word is longer than $6$ characters, the median shortlist drops to $10$. Of course, such leaks happen without requiring any training from the user, and also under the (obvious) condition that the watch is only on the left hand. We believe this is surprising and merits awareness, especially in light of various continuous sensing apps that are emerging in the app market. Moreover, we discover additional "leaks" that can further reduce the shortlist -- we leave these exploitations to future work.

This study presents several extensions to our previous work on the PipeProbe system, a mobile sensor system for identifying the spatial topology of hidden water pipelines (i.e., non-moldable pipes such as copper and PVC) behind walls or under floors [Lai et al. 2010]. The PipeProbe system works by dropping a tiny wireless sensor capsule into the source of a water pipeline. As the PipeProbe capsule traverses the pipelines, it gathers and transmits pressure and angular velocity readings. Through spatiotemporal analysis of these sensor readings, the proposed algorithm locates all turning points in the pipelines and maps their 3D spatial topology. This study expands upon previous research by developing new sensing techniques that identify variable-diameter pipes and differentiate 90-degree pipe turns from 45-degree pipe bends.

This study presents TriopusNet, a mobile wireless sensor network system for autonomous sensor deployment in pipeline monitoring. TriopusNet works by automatically releasing sensor nodes from a centralized repository located at the source of the water pipeline. During automated deployment, TriopusNet runs a sensor deploy-ment algorithm to determine node placement. While a node is flowing inside the pipeline, it performs placement by extending its mechanical arms to latch itself onto the pipe's inner surface. By continuously releasing nodes into pipes, the TriopusNet system builds a wireless network of interconnected sensor nodes. When a node runs at a low battery level or experiences a fault, the TriopusNet system releases a fresh node from the repository and performs a node replacement algorithm to replace the failed node with the fresh one. We have evaluated the TriopusNet system by creating and collecting real data from an experimental pipeline testbed. Comparing with the non-automated static deployment, TriopusNet is able to use less sensor nodes to cover a sensing area in the pipes while maintaining network connectivity among nodes with high data collection rate. Experimental results also show that TriopusNet can recover from the network disconnection caused by a battery-depleted node and successfully replace the battery-depleted node with a fresh node.

Sensor convergence on the mobile phone is spawning a broad base of new and interesting mobile applications. As applications grow in sophistication, raw sensor readings often require classification into more useful application-specific high-level data. For example, GPS readings can be classified as running, walking or biking. Unfortunately, traditional classifiers are not built for the challenges of mobile systems: energy, latency, and the dynamics of mobile.

This paper presents PipeProbe, a mobile sensor system for determining the spatial topology of hidden water pipelines behind walls. PipeProbe works by dropping a tiny wireless sensor capsule into the source of the water pipelines. As the PipeProbe capsule traverses the pipelines, it gathers and transmits pressure and angular velocity readings. Through spatio-temporal analysis on the sensor readings, our algorithm locates all turning points in the pipelines and maps their 3D spatial topology. We evaluated the PipeProbe system by developing a prototype and using data collected in our experimental testbed. Results show that the PipeProbe system successfully located and estimated 90% of all pipe tube lengths within 8-cm accuracy on average tube lengths of 76 cm. PipeProbe also successfully located 90% of all turning points within 15-cm accuracy on average length paths of 335cm.

We propose PipeProbe, a mobile sensor system for mapping water pipelines hidden inside cement walls or under floor coverings. PipeProbe works by dropping a tiny sensor capsule into the source of the water pipelines. As the PipeProbe capsule traverses the pipelines, it gathers accelerometer and water pressure readings. Through a novel estimation method based on Bernoulli's principle, we reconstruct the 3D spatial layout of the water pipelines. The PipeProbe system is non-intrusive and requires no alteration to the water pipeline infrastructure.

We propose PipeProbe, a mobile sensor system for mapping hidden water pipelines inside cement walls or under floor coverings. PipeProbe works by dropping a sensor capsule into the source of the water pipelines. As the PipeProbe capsule traverses the pipelines, it gathers water pressure and accelerometer readings. Given the pressure sensor readings, we can identify the height of PipeProbe. Accelerometer is used to recognize different types of water pipeline shape when PipeProbe travels through. The PipeProbe system is non-intrusive and requires no alteration to the water pipeline infrastructure.

This work proposes the novel use of spinning beacons for precise indoor localization. The proposed "SpinLoc" (Spinning Indoor Localization) system uses "spinning" (i.e., rotating) beacons to create and detect predictable and highly distinguishable Doppler signals for sub-meter localization accuracy. The system analyzes Doppler frequency shifts of signals from spinning beacons, which are then used to calculate orientation angles to a target. By obtaining orientation angles from two or more beacons, SpinLoc can precisely locate stationary or slow-moving targets. After designing and implementing the system using MICA2 motes, its performance was tested in an indoor garage environment. The experimental results revealed a median error of 40~50 centimeters and a 90% error of 70~90 centimeters.

This work extends the declarative sensornet programming model with automated program optimizations that attempt to minimize energy expenditure at various points in the communication stack.