A broad range of embedded networked sensing (ENS) applications have appeared for large-scale systems, introducing new requirements leading to new embedded architectures, associated algorithms, and supporting software systems. These new requirements include the need for diverse and complex sensor systems that present demands for energy and computational resources, as well as for broadband communication. To satisfy application demands while maintaining critical support for low-energy operation, a new multiprocessor node hardware and software architecture, Low Power Energy Aware Processing (LEAP), has been developed. In this article, we described the LEAP design approach, in which the system is able to adaptively select the most energy-efficient hardware components matching an application’s needs. The LEAP platform supports highly dynamic requirements in sensing fidelity, computational load, storage media, and network bandwidth. It focuses on episodic operation of each component and considers the energy dissipation for each platform task by integrating fine-grained energy-dissipation monitoring and sophisticated power-control scheduling for all subsystems, including sensors. In addition to the LEAP platform’s unique hardware capabilities, its software architecture has been designed to provide an easy way to use power management interface and a robust, fault-tolerant operating environment and to enable remote upgrade of all software components. LEAP platform capabilities are demonstrated by example implementations, such as a network protocol design and a light source event detection algorithm. Through the use of a distributed node testbed, we demonstrate that by exploiting high energy-efficiency components and enabling proper on-demand scheduling, the LEAP architecture may meet both sensing performance and energy dissipation objectives for a broad class of applications.

The theoretical and practical frame of this thesis pertains to the intersection of the title's concepts. In this project we will explore how kinetic architecture could be approached through the design of an organism, focusing on its epidermic nature, and how a swarm of these organisms could configure a network into the urban web. In other words, how a city network of organisms could be structured, with the main characteristic of each one being its kinetic transmutation through interactive processes. Additionally we will illustrate how the epidermic character of the organism could be maintained through its complex morphology and the integration of essential characteristics of the human skin.

We show that existing methods for primary-backup replication may disrupt the timing behavior of an underlying service to the extent of making it unusable. We prove that the problem is inherent to the primary-backup model.

The usage of conventional assistive cane devices is critical in reducing the risk of falls, which are particularly detrimental for the elderly and disabled. Many of the individuals that experience the greatest risk of falling rely on cane devices for support of ambulation. However, the results of many studies have shown that incorrect cane usage is prevalent among cane users. The original SmartCane assistive system has been developed to provide a method for acquiring detailed motion data from cane usage. The cane itself, however, lacks any type of programmability as well as real-time data processing algorithms to provide feedback to the cane user. In this demonstration, we have incorporated an embedded computing platform into SmartCane and developed a real-time sensor information processing algorithm that provides direct detection of cane usage characteristics. The new system provides local data processing capability by classifying whether an individual is executing a stride with proper cane motion and applied forces. It also provides direct feedback information to the individual, thereby guiding the subject towards proper cane usage and reducing the risk of falls.

This paper describes a new embedded networked sensor platform architecture that combines hardware and software tools providing detailed, fine-grained real-time energy usage information. We introduce the LEAP2 platform, a qualitative step forward over the previously developed LEAP and other similar platforms. LEAP2 is based on anew low power ASIC system and generally applicable supporting architecture that provides unprecedented capabilities for directly observing energy usage of multiple subsystems in real-time. Real-time observation with microsecond-scale time resolution enables direct accounting of energy dissipation for each computing task as well as for each hardware subsystem. The new hardware architecture is exploited with our new software tools, etop and endoscope. A series of experimental investigations provide high-resolution power information in networking, storage, memory and processing for primary embedded networked sensing applications. Using results obtained in real-time we show that for a large class of wireless sensor network nodes, there exist several interdependencies in energy consumption between different subsystems. Through the use of our measurement tools we demonstrate that by carefully selecting the system operating points, energy savings of over 60% can be achieved while retaining system performance.

Dual-radio, dual-processor nodes are an emerging class of wireless sensor network devices that provide both low-energy operation as well as substantially increased computational performance and communication bandwidth for applications. In such systems, the secondary radio and processor operates with sufficiently low power that it may remain always vigilant, while the main processor and primary, high-bandwidth radio remain off until triggered by the application. By exploiting the high energy efficiency of the main processor and primary radio along with proper usage, net operating energy benefits are enabled for applications. The secondary radio provides a constantly available multi-hop network, while paths in the primary network exist only when required. This paper describes a topology control mechanism for establishing an end-to-end path in a network of dual-radio nodes using the secondary radios as a control channel toselectivelywake up nodes along the required end-to-end path. Using numerical models as well as testbed experimentation, we show that our proposed mechanism provides significant energy savings of more than 60% compared to alternative approaches, and that it incurs only moderately greater application latency.

A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipating sensor devices, as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, Low Power Energy Aware Processing (LEAP), has been developed. The LEAP architecture integrates fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems including sensor subsystems. The LEAP2 platform is a second generation LEAP system with even higher resolution energy monitoring as well as the unique ability to do per process and per application energy profiling via a dedicated high performance ASIC. Our demonstration will highlight this profiling capability through a custom monitoring application named etop.

Recent work in wireless embedded networked systems has followed heterogeneous designs, incorporating a mixture of elements from extremely constrained 8- or 16-bit “Motes” to less resource-constrained 32-bit embedded “Microservers.” Emstar is a software environment for developing and deploying complex applications on such heterogeneous networks. Emstar is designed to leverage the additional resources of Microservers by trading off some performance for system robustness in sensor network applications. It enables fault isolation, fault tolerance, system visiblity, in-field debugging, and resource sharing across multiple applications. In order to accomplish these objectives, Emstar is designed to run as a multiprocess system and consists of libraries that implement message-passing IPC primitives, services that support networking, sensing, and time synchronization, and tools that support simulation, emulation, and visualization of live systems, both real and simulated. We evaluate this work by discussing the Acoustic ENSBox, a platform for distributed acoustic sensing that we built using Emstar. We show that by leveraging existing Emstar services, we are able to significantly reduce development time while achieving a high degree of robustness. We also show that a sample application was developed much more quickly on this platform than it would have been otherwise.

Wireless sensor networks are characterized by collections of small, low-power nodes that collect information about the physical world. Concurrent transmissions caused by the well-known hidden terminal problem result in collisions and packet corruption. Since corrupted packets must be retransmitted, collisions add an additional burden to the already energy constrained system. We present an application-based approach to collision avoidance. We propose two specific algorithms; the first one follows TCP's congestion avoidance algorithm and adjusts the transmission rate when a collision occurs, while the second one shifts packet transmission times to minimize collisions. We evaluated both algorithms through simulations and our results show that our approach can reduce the number of collision-induced retransmissions by a factor of 8 and the energy consumption by up to 50%.

We propose Voronoi scoping, a distributed algorithm to constrain the dissemination of messages from different sinks. It has the property that a query originated by a given sink is forwarded only to the nodes for which that sink is the closest (under the chosen metric). Thus each query is forwarded to the smallest possible number of nodes, and per-node dissemination overhead does not grow with network size or with number of sinks. The algorithm has a simple distributed implementation and requires only a few bytes of state at each node. Experiments over a network of 54 motes confirm the algorithm's effectiveness.

Recently deployed Wireless Sensor Network systems (WSNs) are increasingly following heterogeneous designs, incorporating a mixture of elements with widely varying capabilities. The development and deployment of WSNs rides heavily on the availability of simulation, emulation, visualization and analysis support. In this work, we develop tools specifically to support heterogeneous systems, as well as to support the measurement and visualization of operational systems that is critical to addressing the inevitable problems that crop up in deployment. Our system differs from related systems in three key ways: in its ability to simulate and emulate heterogeneous systems in their entirety, in its extensive support for integration and interoperability between motes and microservers, and in its unified set of tools that capture, view, and analyze real time debugging information from simulations, emulations, and deployments.

No abstract available.