Unified Power Management in Wireless Sensor Networks, Doctoral Dissertation, August 2006

Radio power management is of paramount concern in wireless sensor networks (WSNs) that must achieve long lifetimes on scarce amount of energy. Previous work has treated communication and sensing separately, which is insufficient for a common class of sensor networks that must satisfy both sensing and communication requirements. Furthermore, previous approaches focused on reducing energy consumption in individual radio states resulting in suboptimal solutions. Finally, existing power management protocols often assume simplistic models that cannot accurately reflect the sensing and communication properties of real-world WSNs. We develop a unified power management approach to address these issues. We first analyze the relationship between sensing and communication performance of WSNs. We show that sensing coverage often leads to good network connectivity and geographic routing performance, which provides insights into unified power management under both sensing and communication performance requirements. We then develop a novel approach called Minimum Power Configuration that ingegrates the power consumption in different radio states into a unified optimization framework. Finally, we develop two power management protocols that account for realistic communication and sensing properties of WSNs. Configurable Topology Control can configure a network topology to achieve desired path quality in presence of asymmetric and lossy links. Co-Grid is a coverage maintenance protocol that adopts a probabilistic sensing model. Co-Grid can satisfy desirable sensing QoS requirements (i.e., detection probability and false alarm rate) based on a distributed data fusion model

Towards a Unified Radio Power Management Architecture for Wireless Sensor Networks

In many wireless sensor networks, energy is an extremely limited resource. While many different power management strategies have been proposed to help reduce the amount of energy wasted, application developers still face two fundamental challenges when developing systems with stringent power constraints. First, existing power management strategies are usually tightly coupled with network protocols and other system functionality. This monolithic approach has led to standalone solutions that cannot easily be reused or extended to other applications or platforms. Second, different power management strategies make different and sometimes even conflicting assumptions about the rest of the system with which they need to interact. Without knowledge of which strategies are interoperable with which set of network stack protocols it is dificult for application developers to make informed decisions as to which strategy is most appropriate for their particular application. To address these challenges, we propose a Unified Power Management Architecture (UPMA) that supports the flexible composition of different power management strategies based on application requirements. We envision this architecture to consist of both low level programming interfaces, as well as high level modeling abstractions. These abstractions characterize the key properties of different applications, network protocols, and power management strategies. Using these properties, configuration tools can be created that match each application with the most appropriate network protocol and power management strategy suited to its needs

Link Layer Support for Unified Radio Power Management In Wireless Sensor Networks

Radio power management is of paramount concern in wireless sensor networks that must achieve long lifetimes on scarce amounts of energy. While a multitude of power management protocols have been proposed in the past, the lack of system support for flexibly integrating them with a diverse set of applications and network platforms has made them difficult to use. Instead of proposing yet another power management protocol, this paper focuses on providing link layer support towards realizing a Unified Power Management Architecture (UPMA) for flexible radio power management in wireless sensor networks. In contrast to the monolithic approaches adopted by existing power management solutions, we provide (1) a set of standard interfaces that allow different power management protocols existing at the link layer to be easily implemented on top of common MAC level functionality, (2) an architectural framework for enabling these protocols to be easily swapped in and out depending on the needs of the applications that require them, and (3) a mechanism for coordinating the existence of multiple applications, each of which may have different requirements for the same underlying power management protocol. We have implemented these features on the Mica2 and Telosb radio stacks in TinyOS-2.0. Microbenchmark results demonstrate that the separation of power management from MAC level functionality incurs a negligible decrease in performance when compared to existing monolithic implementations. Two case studies show that the power management requirements of multiple applications can be easily coordinated, sometimes even resulting in better power savings than any one of them can achieve individually

A Unified Architecture for Flexible Radio Power Management in Wireless Sensor Networks

A challenge for many wireless sensor networks is to remain operational for long periods of time on a very limited power supply. While many power management protocols have been proposed, a solution does not yet exist that allows them to be seamlessly integrated into the existing systems. In this paper we study the architectural support required to resolve this issue. We propose a framework that separates sleep scheduling from the basic MAC layer functionality and provide a set of unified interfaces between them. This framework enables different sleep scheduling policies to be easily implemented on top of multiple MAC layers. Such a flexibility allows applications to choose the best sleep scheduling policy based on their own particular needs. We demonstrate the practicality of our approach by implementing this framework on top of both the mica2 and telosb radio stacks in TinyOS 2.0. Our micro-benchmark results show that at the cost of a slight increase in code size, our framework significantly eases the development of new radio power management protocols across multiple WSN platform

A Performance-driven Framework for Customizing CSP Middleware Support

A Distributed Constraint Satisfaction Problem (DCSP) aims to find consistent assignments of values to a set of variables distributed on multiple nodes. Despite its simple definition, DCSPs can model a broad variety of traditional artificial intelligence problems. Furthermore, many problems found in emerging sensor-actuator networks can be formalized to DCSPs. However, due to the platform limitations of networked embedded systems such as sensor-actuators networks, building real-world applications for solving DCSPs not only requires the improved DCSP algorithms but also novel system approaches. This thesis first develops a performance-driven middleware framework for solving DCSP problems. Then the prototype system built with the framework is used to evaluate the performance of special-purpose middleware called nORB that was designed for a Boeing experimental sensor-actuator platform. To validate the design of nORB, various experiments are performed to compare the performance of nORB with other existing DOC middleware platforms. In investigating the problems revealed by the empirical results, we explored various optimization techniques for nORB. The resulting performance of nORB has been improved significantly and is comparable with the high-performance middleware TAO with a decrease in overall footprint

Localized and Configurable Topology Control in Lossy Wireless Sensor Networks

Recent empirical studies revealed that multi-hop wireless networks like wireless sensor networks and 802.11 mesh networks are inherently lossy. This finding introduces important new challenges for topology control. Existing topology control schemes often aim at maintaining network connectivity that cannot guarantee satisfactory path quality and communication performance when underlying links are lossy. In this paper, we present a localized algorithm, called Configurable Topology Control (CTC), that can configure a network topology to different provable quality levels (quantified by worst-case dilation bounds in terms of expected total number of transmisssions) required by applications. Each node running CTC computes its transmission power solely based on the link quality information collected within its local neighborhood and does not assume that the neighbor locations or communication ranges are known. Our simulations based on a realistic radio model of Mica2 motes show that CTC yields configurable communication performance and outperforms existing topology control algorithms that do not account for lossy links

Unleashing Exposed Terminals in Enterprise WLANs: A Rate Adaptation Approach

The increasing availability of inexpensive off-the-shelf 802.11 hardware has made it possible to deploy access points (APs) densely to ensure the coverage of complex enterprise environments such as business and college campuses. However, dense AP deployment often leads to increased level of wireless contention, resulting in low system throughput. A promising approach to address this issue is to enable the transmission concurrency of exposed terminals in which two senders lie in the range of one another but do not interfere each other\u27s receiver. However, existing solutions ignore the rate diversity of 802.11 and hence cannot fully exploit concurrent transmission opportunities in a WLAN. In this paper, we present TRACK - Transmission Rate Adaptation for Colliding linKs, a novel protocol for harnessing exposed terminals with a rate adaptation approach in enterprise WLANs. Using measurement-based channel models, TRACK can optimize the bit rates of concurrent links to improve system throughput while maintaining link fairness. Our extensive experiments on a testbed of 17 nodes show that TRACK improves system throughput by up to 67% and 35% over 802.11 CSMA and conventional approaches of harnessing exposed terminals

Towards a Performance Model for Special Purpose ORB Middleware

General purpose middleware has been shown effective in meeting diverse functional requirements for a wide range of distributed systems. Advanced middleware projects have also supported single quality-of-service dimensions such as real-time, fault tolerance, or small memory foot-print. However, there is limited experience supporting multiple quality-of-service dimensions in middleware to meet the needs of special purpose applications. Even though general purpose middleware can cover an entire spectrum of functionality by supporting the union of all features required by each application, this approach breaks down for distributed real-time and embedded sys-tems. For example, the breadth of features supported may interfere with small memory footprint requirements. In this paper, we describe experiments comparing application-level and mechanism-level real-time perfor-\mance of a representative sensor-network application running on three middleware alternatives: (1) a real-time object request broker (ORB) for small-footprint networked embedded sensor nodes, that we have named nORB, (2) TAO, a robust and widely-used general-purpose Real-Time CORBA ORB, and (3) ACE, the low-level middleware framework upon which both nORB and TAO are based. This paper makes two main contributions to the state of the art in customized middleware for distributed real-time and embedded applications. First, we present mechanism-level timing measurements for each of the alternative middleware layers and compare them to the observed performance of the sensor-network application. Second, we provide a preliminary performance model for the observed application timing behavior based on the mechanism-level measurements in each case, and suggest further potential performance optimizations that we plan to study as future work

Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPU

Many applications such as autonomous driving and augmented reality, require the concurrent running of multiple deep neural networks (DNN) that poses different levels of real-time performance requirements. However, coordinating multiple DNN tasks with varying levels of criticality on edge GPUs remains an area of limited study. Unlike server-level GPUs, edge GPUs are resource-limited and lack hardware-level resource management mechanisms for avoiding resource contention. Therefore, we propose Miriam, a contention-aware task coordination framework for multi-DNN inference on edge GPU. Miriam consolidates two main components, an elastic-kernel generator, and a runtime dynamic kernel coordinator, to support mixed critical DNN inference. To evaluate Miriam, we build a new DNN inference benchmark based on CUDA with diverse representative DNN workloads. Experiments on two edge GPU platforms show that Miriam can increase system throughput by 92% while only incurring less than 10\% latency overhead for critical tasks, compared to state of art baselines