Dimitrios Lymberopoulos

Diversity in smartphone usage

Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.

A first look at traffic on smartphones

Using data from 43 users across two platforms, we present a detailed look at smartphone traffic. We find that browsing contributes over half of the traffic, while each of email, media, and maps contribute roughly 10%. We also find that the overhead of lower layer protocols is high because of small transfer sizes. For half of the transfers that use transport-level security, header bytes correspond to 40% of the total. We show that while packet loss is the main factor that limits the throughput of smartphone traffic, larger send buffers at Internet servers can improve the throughput of a quarter of the transfers. Finally, by studying the interaction between smartphone traffic and the radio power management policy, we find that the power consumption of the radio can be reduced by 35% with minimal impact on the performance of packet exchanges.

Energy-accuracy trade-off for continuous mobile device location

Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smartphones do include a GPS receiver, its frequent use is restricted due to high battery drain. We design and prototype an adaptive location service for mobile devices, a-Loc, that helps reduce this battery drain. Our design is based on the observation that the required location accuracy varies with location, and hence lower energy and lower accuracy localization methods, such as those based on WiFi and cell-tower triangulation, can sometimes be used. Our method automatically determines the dynamic accuracy requirement for mobile search-based applications. As the user moves, both the accuracy requirements and the location sensor errors change. A-Loc continually tunes the energy expenditure to meet the changing accuracy requirements using the available sensors. A Bayesian estimation framework is used to model user location and sensor errors. Experiments are performed with Android G1 and AT&T Tilt phones, on paths that include outdoor and indoor locations, using war-driving data from Google and Microsoft. The experiments show that a-Loc not only provides significant energy savings, but also improves the accuracy achieved, because it uses multiple sensors.

FM-based indoor localization

The major challenge for accurate fingerprint-based indoor localization is the design of robust and discriminative wireless signatures. Even though WiFi RSSI signatures are widely available indoors, they vary significantly over time and are susceptible to human presence, multipath, and fading due to the high operating frequency. To overcome these limitations, we propose to use FM broadcast radio signals for robust indoor fingerprinting. Because of the lower frequency, FM signals are less susceptible to human presence, multipath and fading, they exhibit exceptional indoor penetration, and according to our experimental study they vary less over time when compared to WiFi signals. In this work, we demonstrate through a detailed experimental study in 3 different buildings across the US, that FM radio signal RSSI values can be used to achieve room-level indoor localization with similar or better accuracy to the one achieved by WiFi signals. Furthermore, we propose to use additional signal quality indicators at the physical layer (i.e., SNR, multipath etc.) to augment the wireless signature, and show that localization accuracy can be further improved by more than 5%. More importantly, we experimentally demonstrate that the localization errors of FM andWiFi signals are independent. When FM and WiFi signals are combined to generate wireless fingerprints, the localization accuracy increases as much as 83% (when accounting for wireless signal temporal variations) compared to when WiFi RSSI only is used as a signature.

An empirical characterization of radio signal strength variability in 3-d IEEE 802.15.4 networks using monopole antennas

The wide availability of radio signal strength attenuation information on wireless radios has received considerable attention as a convenient means of deriving positioning information. Although some schemes have been shown to work in some scenarios, many agree that the robustness of such schemes can be easily compromised when low power IEEE 802.15.4 radios are used. Leveraging a recently installed sensor network testbed, we provide a detailed characterization of signal strength properties and link asymmetries for the CC2420 IEEE 802.15.4 compliant radio using a monopole antenna. To quantify the several factors of signal unpredictability due to the hardware, we have collected several thousands of measurements to study the antenna orientation and calibration effects. Our results show that the often overlooked antenna orientation effects are the dominant factor of the signal strength sensitivity, especially in the case of 3-D network deployments. This suggests that the antenna effects need to be carefully considered in signal strength schemes.

XYZ: a motion-enabled, power aware sensor node platform for distributed sensor network applications

This paper describes the XYZ, a new open-source sensing platform specifically designed to support our experimental research in mobile sensor networks. The XYZ node is designed around the OKI ML67Q500x ARM THUMB Microprocessor and the IEEE 802.15.4 compliant CC2420 radio from Chipcon. Its new features include support for two different CPU sleep modes and a long-term ultra low power sleep mode for the entire node. This allows the XYZ and its peripheral boards to transition into deep sleep for extended time intervals. To support mobility hardware control and computations, XYZ supports a wide dynamic range and power options. In low power configuration the node resembles existing small low power nodes. When needed, the node can scale up its resources to perform more powerful computations. Mobility is enabled with an additional accessory board that allows the node to move along a horizontal string. In this paper we provide an overview of the XYZ architecture and provide an insightful power characterization of the different operational modes to allow the users to optimize their platforms for power.

A realistic evaluation and comparison of indoor location technologies: experiences and lessons learned

We present the results, experiences and lessons learned from comparing a diverse set of technical approaches to indoor localization during the 2014 Microsoft Indoor Localization Competition. 22 different solutions to indoor localization from different teams around the world were put to test in the same unfamiliar space over the course of 2 days, allowing us to directly compare the accuracy and overhead of various technologies. In this paper, we provide a detailed analysis of the evaluation studys results, discuss the current state-of-the-art in indoor localization, and highlight the areas that, based on our experience from organizing this event, need to be improved to enable the adoption of indoor location services.

LittleRock: Enabling Energy-Efficient Continuous Sensing on Mobile Phones

Todays mobile phones come with a rich set of built-in sensors such as accelerometers, ambient light sensors, compasses, and pressure sensors, which can measure various phenomena on and around the phone. Gathering user context such as user activity, geographic location, and location type requires continuous sampling of sensor data. However, such sampling shortens a phones battery life because of the associated energy overhead. This article examines the root causes of this energy overhead and shows that energy-efficient continuous sensing can be achieved through proper system design.

Sensor Localization and Camera Calibration in Distributed Camera Sensor Networks

Camera sensors constitute an information rich sensing modality with many potential applications in sensor networks. Their effectiveness in a sensor network setting however greatly relies on their ability to calibrate with respect to each other, and other sensors in the field. This paper examines node localization and camera calibration using the shared field of view of camera pairs. Using a new distributed camera sensor network we compare two approaches from computer vision and propose an algorithm that combines a sparse set of distance measurements with image information to accurately localize nodes in 3D. Our algorithms are evaluated using a network of iMote2 nodes equipped with COTS camera modules. The sensor nodes identify themselves to cameras using modulated LED emissions. Our indoor experiments yielded a 2-7cm error in a 6x6m room. Our outdoor experiments in a 30x30m field resulted in errors 20-80cm, depending on the method used.

mPlatform: a reconfigurable architecture and efficient data sharing mechanism for modular sensor nodes

We present mPlatform, a new reconfigurable modular sensornet platform that enables real-time processing on multiple heterogeneous processors. At the heart of the mPlatform is a scalable high- performance communication bus connecting the different modules of a node, allowing time-critical data to be shared without delay and supporting reconfigurability at the hardware level. Furthermore, the bus allows components of an application to span across different processors/modules without incurring much overhead, thus easing the program development and supporting software reconfigurability. We describe the communication architecture, protocol, and hardware configuration, and the implementation in a low power, high speed complex programmable logic device (CPLD). An asynchronous interface decouples the local processor of each module from the bus, allowing the bus to operate at the maximum desired speed while letting the processors focus on their real time tasks such as data collection and processing. Extensive experiments on the mPlatform prototype have validated the scalability of the communication architecture, and the high speed, reconfigurable intermodule communication that is achieved at the expense of a small increase in the power consumption. Finally, we demonstrate a realtime sound source localization application on the mPlatform, with four channels of acoustic data acquisition, FFT, and sound classification, that otherwise would be infeasible using traditional buses such as I2C.