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.

Many applications are enabled by the ability to capture videos on a smartphone and to have these videos uploaded to an Internetconnected 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 realworld 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.

Most sensor network research and software design has been guided by an architectural principle that permits multi-node data fusion on small-form-factor, resource-poor nodes, or motes. We argue that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future largescale sensor network deployments will be tiered, consisting of motes in the lower tier and masters, relatively unconstrained 32-bit platform nodes, in the upper tier. Masters provide increased network capacity. Tenet constrains multinode fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact.

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 building's 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.

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The vasculature is an essential component of the circulatory system that plays a vital role in the development, homeostasis, and disease of various organs in the human body. The ability to emulate the architecture and transport function of blood vessels in the integrated context of their associated organs represents an important requirement for studying a wide range of physiological processes. Traditional in vitro models of the vasculature, however, largely fail to offer such capabilities. Here we combine microfluidic three-dimensional (3D) cell culture with the principle of vasculogenic self-assembly to engineer perfusable 3D microvascular beds in vitro. Our system is created in a micropatterned hydrogel construct housed in an elastomeric microdevice that enables coculture of primary human vascular endothelial cells and fibroblasts to achieve de novo formation, anastomosis, and controlled perfusion of 3D vascular networks. An open-top chamber design adopted in this hybrid platform also makes it possible to integrate the microengineered 3D vasculature with other cell types to recapitulate organ-specific cellular heterogeneity and structural organization of vascularized human tissues. Using these capabilities, we developed stem cell-derived microphysiological models of vascularized human adipose tissue and the blood−retinal barrier. Our approach was also leveraged to construct a 3D organotypic model of vascularized human lung adenocarcinoma as a high-content drug screening platform to simulate intravascular delivery, tumor-killing effects, and vascular toxicity of a clinical chemotherapeutic agent. Furthermore, we demonstrated the potential of our platform for applications in nanomedicine by creating microengineered models of vascular inflammation to evaluate a nanoengineered drug delivery system based on active targeting liposomal nanocarriers. These results represent a significant improvement in our ability to model the complexity of native human tissues and may provide a basis for developing predictive preclinical models for biopharmaceutical applications.

Microscale soft-robots hold great promise as safe handlers of delicate micro-objects but their wider adoption requires micro-actuators with greater efficiency and ease-of-fabrication. Here we present an elastomeric microtube-based pneumatic actuator that can be extended into a microrobotic tentacle. We establish a new, direct peeling-based technique for building long and thin, highly deformable microtubes and a semi-analytical model for their shape-engineering. Using them in combination, we amplify the microtube’s pneumatically-driven bending into multi-turn inward spiraling. The resulting micro-tentacle exhibit spiraling with the final radius as small as ~185 μm and grabbing force of ~0.78 mN, rendering itself ideal for non-damaging manipulation of soft, fragile micro-objects. This spiraling tentacle-based grabbing modality, the direct peeling-enabled elastomeric microtube fabrication technique, and the concept of microtube shape-engineering are all unprecedented and will enrich the field of soft-robotics.