Jeongyeup Paek

Energy-efficient rate-adaptive GPS-based positioning for smartphones

Many emerging smartphone applications require position information to provide location-based or context-aware services. In these applications, GPS is often preferred over its alternatives such as GSM/WiFi based positioning systems because it is known to be more accurate. However, GPS is extremely power hungry. Hence a common approach is to periodically duty-cycle GPS. However, GPS duty-cycling trades-off positioning accuracy for lower energy. A key requirement for such applications, then, is a positioning system that provides accurate position information while spending minimal energy. In this paper, we present RAPS, rate-adaptive positioning system for smartphone applications. It is based on the observation that GPS is generally less accurate in urban areas, so it suffices to turn on GPS only as often as necessary to achieve this accuracy. RAPS uses a collection of techniques to cleverly determine when to turn on GPS. It uses the location-time history of the user to estimate user velocity and adaptively turn on GPS only if the estimated uncertainty in position exceeds the accuracy threshold. It also efficiently estimates user movement using a duty-cycled accelerometer, and utilizes Bluetooth communication to reduce position uncertainty among neighboring devices. Finally, it employs celltower-RSS blacklisting to detect GPS unavailability (e.g., indoors) and avoid turning on GPS in these cases. We evaluate RAPS through real-world experiments using a prototype implementation on a modern smartphone and show that it can increase phone lifetimes by more than a factor of 3.8 over an approach where GPS is always on.

Energy-delay tradeoffs in smartphone applications

Many applications are enabled by the ability to capture videos on a smartphone and to have these videos uploaded to an Internet-connected server. This capability requires the transfer of large volumes of data from the phone to the infrastructure. Smartphones have multiple wireless interfaces -- 3G/EDGE and WiFi -- for data transfer, but there is considerable variability in the availability and achievable data transfer rate for these networks. Moreover, the energy costs for transmitting a given amount of data on these wireless interfaces can differ by an order of magnitude. On the other hand, many of these applications are often naturally delay-tolerant, so that it is possible to delay data transfers until a lower-energy WiFi connection becomes available. In this paper, we present a principled approach for designing an optimal online algorithm for this energy-delay tradeoff using the Lyapunov optimization framework. Our algorithm, called SALSA, can automatically adapt to channel conditions and requires only local information to decide whether and when to defer a transmission. We evaluate SALSA using real-world traces as well as experiments using a prototype implementation on a modern smartphone. Our results show that SALSA can be tuned to achieve a broad spectrum of energy-delay tradeoffs, is closer to an empirically-determined optimal than any of the alternatives we compare it to, and, can save 10-40% of battery capacity for some workloads.

Monitoring civil structures with a wireless sensor network

Structural health monitoring (SHM) is an active area of research devoted to systems that can autonomously and proactively assess the structural integrity of bridges, buildings, and aerospace vehicles. Recent technological advances promise the eventual ability to cover a large civil structure with low-cost wireless sensors that can continuously monitor a buildings structural health, but researchers face several obstacles to reaching this goal, including high data-rate, data-fidelity, and time-synchronization requirements. This article describes two systems the authors recently deployed in real-world structures.

Energy-efficient positioning for smartphones using Cell-ID sequence matching

Many emerging location-aware applications require position information. However, these applications rarely use celltower-based localization because of its inaccuracy, preferring instead to use the more energy-hungry GPS. In this paper, we present CAPS, a Cell-ID Aided Positioning System. CAPS leverages near-continuous mobility and the position history of a user to achieve significantly better accuracy than the celltower-based approach, while keeping energy overhead low. CAPS is designed based on the insight that users exhibit consistency in routes traveled, and that cell-ID transition points that the user experiences can, on a frequently traveled route, uniquely identify position. To this end, CAPS uses a cell-ID sequence matching technique to estimate current position based on the history of cell-ID and GPS position sequences that match the current cell-ID sequence. We have implemented CAPS on Android-based smartphones and have extensively evaluated it at different locations, and for different platforms and carriers. Our evaluation results show that CAPS can save more than 90% of the energy spent by the positioning system compared to the case where GPS is always used, while providing reasonably accurate position information with errors less than 20% of the celltower-based scheme.

TOSThreads: thread-safe and non-invasive preemption in TinyOS

Many threads packages have been proposed for programming wireless sensor platforms. However, many sensor network operating systems still choose to provide an event-driven model, due to efficiency concerns. We present TOS-Threads, a threads package for TinyOS that combines the ease of a threaded programming model with the efficiency of an event-based kernel. TOSThreads is backwards compatible with existing TinyOS code, supports an evolvable, thread-safe kernel API, and enables flexible application development through dynamic linking and loading. In TOS-Threads, TinyOS code runs at a higher priority than application threads and all kernel operations are invoked only via message passing, never directly, ensuring thread-safety while enabling maximal concurrency. The TOSThreads package is non-invasive; it does not require any large-scale changes to existing TinyOS code. We demonstrate that TOSThreads context switches and system calls introduce an overhead of less than 0.92% and that dynamic linking and loading takes as little as 90 ms for a representative sensing application. We compare different programming models built using TOSThreads, including standard C with blocking system calls and a reimplementation of Tenet. Additionally, we demonstrate that TOSThreads is able to run computationally intensive tasks without adversely affecting the timing of critical OS services.

Structural damage detection and localization using NETSHM

Structural health monitoring (SHM) is an important application area for wireless sensor networks. SHM techniques attempt to autonomously detect and localize damage in large civil structures. Structural engineers often implement and test SHM algorithms in a higher level language such as C/Matlab. In this paper, we describe the design and evaluation of NETSHM, a sensor network system that allows structural engineers to program SHM applications in Mat-lab or C at a high level of abstraction. In particular, structural engineers do not have to understand the intricacies of wireless networking, or the details of sensor data acquisition. We have implemented a damage detection technique and a damage localization technique on a complete NETSHM prototype. Our experiments on small and medium-scale structures show that NETSHM is able to detect and localized damage perfectly with very few false-positives and no false negatives, and that it is robust even in realistic wireless environments

RCRT: Rate-controlled reliable transport protocol for wireless sensor networks

Emerging high-rate applications (imaging, structural monitoring, acoustic localization) will need to transport large volumes of data concurrently from several sensors. These applications are also loss-intolerant. A key requirement for such applications, then, is a protocol that reliably transports sensor data from many sources to one or more sinks without incurring congestion collapse. In this article, we discuss RCRT, a rate-controlled reliable transport protocol suitable for constrained sensor nodes. RCRT uses end-to-end explicit loss recovery, but places all the congestion detection and rate adaptation functionality in the sinks. This has two important advantages: efficiency and flexibility. Because sinks make rate allocation decisions, they are able to achieve greater efficiency since they have a more comprehensive view of network behavior. For the same reason, it is possible to alter the rate allocation decisions (for example, from one that ensures that all nodes get the same rate, to one that ensures that nodes get rates in proportion to their demands), without modifying sensor code at all. We evaluate RCRT extensively on a 40-node wireless sensor network testbed and show that RCRT achieves 1.7 times the rate achieved by IFRC and 1.4 times that of WRCP, two recently proposed interference-aware distributed rate-control protocols. We also present results from a 3-month-long 19-node real world deployment of RCRT in an imaging application and show that RCRT works well in real long-term deployments.

Image-based environmental monitoring sensor application using an embedded wireless sensor network.

This article discusses the experiences from the development and deployment of two image-based environmental monitoring sensor applications using an embedded wireless sensor network. Our system uses low-power image sensors and the Tenet general purpose sensing system for tiered embedded wireless sensor networks. It leverages Tenets built-in support for reliable delivery of high rate sensing data, scalability and its flexible scripting language, which enables mote-side image compression and the ease of deployment. Our first deployment of a pitfall trap monitoring application at the James San Jacinto Mountain Reserve provided us with insights and lessons learned into the deployment of and compression schemes for these embedded wireless imaging systems. Our three month-long deployment of a bird nest monitoring application resulted in over 100,000 images collected from a 19-camera node network deployed over an area of 0.05 square miles, despite highly variable environmental conditions. Our biologists found the on-line, near-real-time access to images to be useful for obtaining data on answering their biological questions.

Load Balancing Under Heavy Traffic in RPL Routing Protocol for Low Power and Lossy Networks

RPL is an IPv6 routing protocol for low-power and lossy networks (LLNs) designed to meet the requirements of a wide range of LLN applications including smart grid AMIs, industrial and environmental monitoring, and wireless sensor networks. RPL allows bi-directional end-to-end IPv6 communication on resource constrained LLN devices, leading to the concept of the Internet of Things (IoT) with thousands and millions of devices interconnected through multihop mesh networks. In this article, we investigate the load balancing and congestion problem of RPL. Specifically, we show that most of the packet losses under heavy traffic are due to congestion, and a serious load balancing problem appears in RPL in terms of routing parent selection. To overcome this problem, this article proposes a simple yet effective queue utilization based RPL (QU-RPL) that achieves load balancing and significantly improves the end-to-end packet delivery performance compared to the standard RPL. QU-RPL is designed for each node to select its parent node considering the queue utilization of its neighbor nodes as well as their hop distances to an LLN border router (LBR). Owing to its load balancing capability, QU-RPL is very effective in lowering queue losses and increasing the packet delivery ratio. We implement QU-RPL on a low-power embedded platform, and verify all of our findings through experimental measurements on a real testbed of a multihop LLN over IEEE 802.15.4. We present the impact of each design element of QU-RPL on performance in detail, and also show that QU-RPL reduces the queue loss by up to 84 percent and improves the packet delivery ratio by up to 147 percent compared to the standard RPL.

QU-RPL: Queue utilization based RPL for load balancing in large scale industrial applications

RPL is an IPv6 routing protocol for low-power and lossy networks (LLNs) designed to meet the requirements of a wide range of LLN applications including smart grid AMIs, industrial and environmental monitoring, and wireless sensor networks. RPL allows bi-directional end-to-end IPv6 communication on resource constrained LLN devices, leading to the concept of the Internet of Things (IoT) with thousands and millions of devices interconnected through multihop mesh networks. In this paper, we investigate the load balancing and congestion problem of RPL. Specifically, we show that most of packet losses under heavy traffic are due to congestion, and a serious load balancing problem exists in RPL in terms of routing parent selection. To overcome this problem, this paper proposes a simple yet effective queue utilization based RPL (QU-RPL) that significantly improves end-to-end packet delivery performance compared to the standard RPL. QU-RPL is designed for each node to select its parent node considering the queue utilization of its neighbor nodes as well as their hop distances to an LLN border router (LBR). Owing to its load balancing capability, QU-RPL is very effective in lowering the queue losses and increasing the packet delivery ratio. We verify all our findings through experimental measurements on a real testbed of a multihop LLN over IEEE 802.15.4.