Geoffrey Challen

PhoneLab: A Large Programmable Smartphone Testbed

As smartphones have emerged as the most widely deployed mobile computing platform, the scale of smartphone experimentation has lagged behind. New facilities enabling large-scale experiments are needed to ensure that research discoveries translate to the billions of smartphones in use today. To meet this challenge, we introduce PhoneLab, a 288-device smartphone testbed deployed at the University at Buffalo. PhoneLab provides access to smartphone users incentivized to participate in experiments while simplifying experiment data collection. The testbed will open for public experimentation in October, 2013, and continue to expand in 2014. To demonstrate the power of PhoneLab, we present three selected results from a usage characterization experiment run on 115 phones for 21 days. We use each result to motivate a future PhoneLab experiment, demonstrating how PhoneLab will enable mobile systems research.

PocketParker: pocketsourcing parking lot availability

Searching for parking spots generates frustration and pollution. To address these parking problems, we present PocketParker, a crowdsourcing system using smartphones to predict parking lot availability. PocketParker is an example of a subset of crowdsourcing we call pocketsourcing. Pocketsourcing applications require no explicit user input or additional infrastructure, running effectively without the phone leaving the users pocket. PocketParker detects arrivals and departures by leveraging existing activity recognition algorithms. Detected events are used to maintain per-lot availability models and respond to queries. By estimating the number of drivers not using PocketParker, a small fraction of drivers can generate accurate predictions. Our evaluation shows that PocketParker quickly and correctly detects parking events and is robust to the presence of hidden drivers. Camera monitoring of several parking lots as 105 PocketParker users generated 10;827 events over 45 days shows that PocketParker was able to correctly predict lot availability 94% of the time.

Model-free HVAC control using occupant feedback

Optimal control of Heating, Ventilation, and Air Conditioning (HVAC) is an important step towards reducing the carbon footprint of buildings and requires balancing energy reductions and occupant comfort. Conventional thermostats for temperature set points provide a centralised single point of user input, often leading to significant thermal discomfort for occupants. We propose to instead include users in the HVAC control loop through distributed smart-phone based votes about their thermal comfort for aggregated control of HVAC. Unlike existing approaches that require in-situ sensors or build complex comfort models of individual users, we propose a model- and sensor-free HVAC control algorithm that uses simple user input (hot/cold) and adapts to changing office occupancy or ambient temperature in real time. We develop an iterative data fusion algorithm that finds optimal temperature in offices with multiple users and propose techniques that can aggressively save energy by drifting indoor temperatures towards the outdoor temperature. Our evaluation is based on empirical data collected in 12 offices over a 3-week period and shows that adaptive HVAC control can save up to 60% of energy at a relatively small increase of 0.3°C in average occupant discomfort.

Crowdsourcing Access Network Spectrum Allocation Using Smartphones

The hundreds of millions of deployed smartphones provide an unprecedented opportunity to collect data to monitor, debug, and continuously adapt wireless networks to improve performance. In contrast with previous mobile devices, such as laptops, smartphones are always on but mostly idle, making them available to perform measurements that help other nearby active devices make better use of available network resources. We present the design of PocketSniffer, a system delivering wireless measurements from smartphones both to network administrators for monitoring and debugging purposes and to algorithms performing realtime network adaptation. By collecting data from smartphones, PocketSniffer supports novel adaptation algorithms designed around common deployment scenarios involving both cooperative and self-interested clients and networks. We present preliminary results from a prototype and discuss challenges to realizing this vision.

A walk on the client side: Monitoring enterprise Wifi networks using smartphone channel scans

During the one minute it takes to read this abstract, two billion smartphones worldwide will perform billions of Wifi channel scans recording the signal strength of nearby Wifi Access Points (APs). Yet despite this ongoing planetary-scale wireless network measurement, few systematic efforts are made today to recover this potentially valuable data. In this paper we ask the question: “Are the smartphone channel scans useful in monitoring enterprise Wifi networks?” More specifically, can these client-side measurements provide new insights compared to the AP-side measurements that enterprise Wifi networks already perform? Beginning with two Wifi scan datasets collected on two large scale smartphone testbeds, we conduct case studies that show how smartphone channel scans can be used to (1) improve AP spectrum management, and (2) predict the impact of AP failure or overload. In each case, a walk on the client side yields valuable insights for network operators that are otherwise impossible to gain from AP-side measurements, and together our results demonstrate the value of smartphone channel scans.

Energy-Performance Trade-offs on Energy-Constrained Devices with Multi-component DVFS

Battery lifetime continues to be a top complaint about smart phones. Dynamic voltage and frequency scaling (DVFS) has existed for mobile device CPUs for some time, and provides a trade off between energy and performance. Dynamic frequency scaling is beginning to be applied to memory as well to make more energy-performance tradeoffs possible. We present the first characterization of the behavior of the optimal frequency settings of workloads running both, under energy constraints and on systems capable of CPU DVFS and memory DFS, an environment representative of next-generation mobile devices. Our results show that continuously using the optimal frequency settings results in a large number of frequency transitions which end up hurting performance. However, by permitting a small loss in performance, transition overhead can be reduced and end-to-end performance and energy consumption improved. We introduce the idea of inefficiency as a way of constraining task energy consumption relative to the most energy-efficient settings, and characterize the performance of multiple workloads running under different inefficiency settings. Overall our results have multiple implications for next-generation mobile devices exposing multiple energy-performance tradeoffs.

Robust, Cost-Effective and Scalable Localization in Large Indoor Areas

Indoor location information plays a fundamental role in supporting various interesting location- aware indoor applications. Widely deployed WiFi networks make it feasible to perform indoor localization by first establishing a received signal strength (RSS) map covering the whole area based on a signal propagation model, then determining a location from an online RSS measurement given the RSS map. However, challenges remain in practical deployments, due to inaccurately estimated RSS values in the RSS map and insufficient number of access points (APs) in large indoor areas. To address these challenges, we develop a robust, cost-effective and scalable localization system (REAL). Our approach takes the error from the indoor radio signal propagation model into consideration. It also exploits information of unobserved APs at a given location and an optimal clustering method in the location searching phase. Our real-world experimental results demonstrate that REAL achieves considerable localization accuracy at a very low training cost even for a large indoor area. In addition, the results show that our scheme can also be effectively applied to Bluetooth networks with sparse signal coverage.

Wireless Protocol Validation Under Uncertainty

Runtime validation of wireless protocol implementations cannot always employ direct instrumentation of the device under test (DUT). The DUT may not implement the required instrumentation, or the instrumentation may alter the DUT’s behavior when enabled. Wireless sniffers can monitor the DUT’s behavior without instrumentation, but they introduce new validation challenges. Losses caused by wireless propagation prevent sniffers from perfectly reconstructing the actual DUT packet trace. As a result, accurate validation requires distinguishing between specification deviations that represent implementation errors and those caused by sniffer uncertainty.

Algorithms for CPU and DRAM DVFS under inefficiency constraints

Dynamic voltage and frequency scaling (DVFS) of both the core and DRAM provides opportunities to trade-off performance in order to save energy. Previous approaches to core and DRAM power management using DVFS used performance, specifically acceptable performance loss, as a constraint. We present energy management algorithms that coordinate core and DRAM frequency scaling under a specified energy budget. Approaches that work under performance constraints, as we will show, are not directly applicable to systems operating under energy constraints, as it is difficult to calculate the correct performance bounds in real-time to stay under an energy budget. Setting arbitrary energy budgets for a diverse set of applications can be harmful to application performance. We use the previously introduced concept of Inefficiency - the additional amount of energy above the minimum required energy that can be used to improve performance - to provide a dynamic energy constraint to our system. We introduce new power management algorithms that search the power and performance space to find the best performing point under this constraint. We demonstrate the efficacy of our algorithms using CPU DVFS and DRAM frequency scaling. We show that our algorithms have 24% lower tuning cost and save up to 5% energy with a little performance loss compared to a state-of-the-art performance constrained system.

The Missing Numerator: Toward a Value Measure for Smartphone Apps

While great strides have been made in measuring energy consumption, these measures alone are not sufficient to enable effective energy management on battery-constrained mobile devices. What is urgently needed is a way to put energy consumption into context by measuring the value delivered by mobile apps. While difficult to compute, an accurate value measure would enable cross-app comparison, app improvement, energy inefficient app detection, and effective runtime energy allocation and prioritization. Our paper motivates the problem, describes requirements for a value measure, discusses and evaluates several possible inputs to such a measure, and presents results from a preliminary (unsuccessful) attempt to formulate one.