Many wireless sensor network applications must resolve the inherent conflict between energy efficient communication and the need to achieve desired quality of service such as end-to-end communication delay. To address this challenge, we propose the Real-time Power-Aware Routing (RPAR) protocol, which achieves application-specified communication delays at low energy cost by dynamically adapting transmission power and routing decisions. RPAR features a power-aware forwarding policy and an efficient neighborhood manager that are optimized for resource-constrained wireless sensors. Moreover, RPAR addresses important practical issues in wireless sensor networks, including lossy links, scalability, and severe memory and bandwidth constraints. Simulations based on a realistic radio model of MICA2 motes show that RPAR significantly reduces the number of deadlines missed and energy consumption compared to existing real-time and energy-efficient routing protocols.

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Real-time Power-Aware Routing in Sensor Networks

Wireless sensor networks (WSNs) are attracting great interest in a number of application domains concerned with monitoring and control of physical phenomena, as they enable dense and untethered deployments at low cost and with unprecedented flexibility. However, application development is still one of the main hurdles to a wide adoption of WSN technology. In current real-world WSN deployments, programming is typically carried out very close to the operating system, therefore requiring the programmer to focus on low-level system issues. This not only distracts the programmer from the application logic, but also requires a technical background rarely found among application domain experts. The need for appropriate high-level programming abstractions, capable of simplifying the programming chore without sacrificing efficiency, has long been recognized, and several solutions have hitherto been proposed, which differ along many dimensions. In this article, we survey the state of the art in programming approaches for WSNs. We begin by presenting a taxonomy of WSN applications, to identify the fundamental requirements programming platforms must deal with. Then, we introduce a taxonomy of WSN programming approaches that captures the fundamental differences among existing solutions, and constitutes the core contribution of this article. Our presentation style relies on concrete examples and code snippets taken from programming platforms representative of the taxonomy dimensions being discussed. We use the taxonomy to provide an exhaustive classification of existing approaches. Moreover, we also map existing approaches back to the application requirements, therefore providing not only a complete view of the state of the art, but also useful insights for selecting the programming abstraction most appropriate to the application at hand.

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Programming wireless sensor networks: Fundamental concepts and state of the art

The conventional border patrol systems suffer from intensive human involvement. Recently, unmanned border patrol systems employ high-tech devices, such as unmanned aerial vehicles, unattended ground sensors, and surveillance towers equipped with camera sensors. However, any single technique encounters inextricable problems, such as high false alarm rate and line-of-sight-constraints. There lacks a coherent system that coordinates various technologies to improve the system accuracy. In this paper, the concept of BorderSense, a hybrid wireless sensor network architecture for border patrol systems, is introduced. BorderSense utilizes the most advanced sensor network technologies, including the wireless multimedia sensor networks and the wireless underground sensor networks. The framework to deploy and operate BorderSense is developed. Based on the framework, research challenges and open research issues are discussed.

BorderSense: Border patrol through advanced wireless sensor networks

QoS-aware MAC protocols for wireless sensor networks: A survey

The development of high-level programming environments is essential if wireless sensor networks are to be accessible to nonexperts. In this paper, we present the Regiment system, which consists of a high-level language for spatiotemporal macroprogramming, along with a compiler that translates global programs into node-level code. In Regiment, the programmer views the network as a set of spatially-distributed data streams. The programmer can manipulate sets of these streams that may be defined by topological or geographic relationships between nodes. Regiment provides a rich set of primitives for processing data on individual streams, manipulating regions, performing aggregation over a region, and triggering new computation within the network. In this paper, we describe the design and implementation of the Regiment language and compiler. We describe the deglobalization process that compiles a network-wide representation of the program into a node-level, event-driven program. Deglobalization maps region operations onto associated spanning trees that establish region membership and permit efficient in-network aggregation. We evaluate Regiment in the context of a complex distributed application involving rapid detection of spatially-distributed events, such as wildfires or chemical plumes. Our results show that Regiment makes it possible to develop complex sensor network applications at a global level.

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The regiment macroprogramming system

Target tracking systems, consisting of thousands of low-cost sensor nodes, have been used in many application domains such as battlefield surveillance, wildlife monitoring and border security. These applications need to meet certain real-time constraints in response to transient events, such as fast-moving targets. While the real-time performance is a major concern in these applications, it should be compatible with other important system properties such as energy consumption and accuracy. Hence, it is desirable to have the ability to exploit the tradeoffs among them. This work presents the real-time design and analysis of VigilNet, a large-scale sensor network system which tracks, detects and classifies targets in a timely and energy efficient manner. Based on a deadline partition method and theoretical derivations of each sub-deadline, we are able to make guided engineering decisions to meet the end-to-end tracking deadline. To confirm our design and obtain an empirical understanding of these tradeoffs, we invest significant efforts to perform large-scale simulations with 10,000 nodes as well as a field test with 200 XSM motes, running VigilNet. The results from both analysis and evaluation can serve as general design guidelines to build similar real-time systems.

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Achieving Real-Time Target Tracking UsingWireless Sensor Networks

Effective debugging usually involves watching program state to diagnose bugs. When debugging sensor network applications, this approach is often time-consuming and errorprone, not only because of the lack of visibility into system state, but also because of the difficulty to watch the right variables at the right time. In this paper, we present declarative tracepoints, a debugging system that allows the user to insert a group of action-associated checkpoints, or tracepoints, to applications being debugged at runtime. Tracepoints do not require modifying application source code. Instead, they are written in a declarative, SQL-like language called TraceSQL independently. By triggering the associated actions when these checkpoints are reached, this system automates the debugging process by removing the human from the loop. We show that declarative tracepoints are able to express the core functionality of a range of previously isolated debugging techniques, such as EnviroLog, NodeMD, Sympathy, and StackGuard. We describe the design and implementation of the declarative tracepoints system, evaluate its overhead in terms of CPU slowdown, illustrate its expressiveness through the aforementioned debugging techniques, and finally demonstrate that it can be used to detect real bugs using case studies of three bugs based on the development of the LiteOS operating system.

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Declarative tracepoints: a programmable and application independent debugging system for wireless sensor networks

Sensor networks open a new frontier for embedded-distributed computing. Paradigms for sensor network programming-in-the-large have been identified as a significant challenge toward developing large-scale applications. Classical programming languages are too low-level. This paper presents the design, implementation, and evaluation of EnviroSuite, a programming framework that introduces a new paradigm, called environmentally immersive programming, to abstract distributed interactions with the environment. Environmentally immersive programming refers to an object-based programming model in which individual objects represent physical elements in the external environment. It allows the programmer to think directly in terms of environmental abstractions. EnviroSuite provides language primitives for environmentally immersive programming that map transparently into a support library of distributed algorithms for tracking and environmental monitoring. We show how nesC code of realistic applications is significantly simplified using EnviroSuite and demonstrate the resulting system performance on Mica2 and XSM platforms.

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EnviroSuite: An environmentally immersive programming framework for sensor networks

This paper presents EnviroMic, a novel distributed acoustic monitoring, storage, and trace retrieval system. Audio represents one of the least exploited modalities in sensor networks to date. The relatively high frequency and large size of audio traces motivate distributed algorithms for coordinating recording tasks, reducing redundancy of data stored by nearby sensors, filtering out silence, and balancing storage utilization in the network. Applications of acoustic monitoring with EnviroMic range from the study of mating rituals and social behavior of animals in the wild to audio surveillance of military targets. EnviroMic is designed for disconnected operation, where the luxury of having a basestation cannot be assumed. We implement the system on a Tiny OS-based platform and systematically evaluate its performance through both indoor testbed experiments and a preliminary outdoor deployment. Results demonstrate up to a 4-fold improvement in effective storage capacity of the network compared to uncoordinated recording.

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EnviroMic: Towards Cooperative Storage and Retrieval in Audio Sensor Networks

This paper presents a new cooperative storage system for sensor networks geared for disconnected operation (where sensor nodes do not have a connected path to a basestation). The goal of the system is to maximize its data storage capacity by appropriately distributing storage utilization and opportunistically offloading data to external devices when possible. The system is motivated by the observation that a large category of sensor network applications, such as environmental data logging, does not require real-time data access. Such networks generally operate in a disconnected mode. Rather than focusing on multihop routing to a basestation, an important concern becomes (i) to maximize the effective storage capacity of the disconnected sensor network such that it accommodates the most data, and (ii) to take the best advantage of data upload opportunities when they become available to relieve network storage. The storage system described in this paper achieves the above goals, leading to significant improvements in the amount of data collected compared to non-cooperative storage. It is implemented in nesC for TinyOS and evaluated in TOSSIM through various application scenarios.

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Liqian Luo

Papers

Achieving Real-Time Target Tracking UsingWireless Sensor Networks

Declarative tracepoints: a programmable and application independent debugging system for wireless sensor networks

EnviroSuite: An environmentally immersive programming framework for sensor networks

EnviroMic: Towards Cooperative Storage and Retrieval in Audio Sensor Networks

EnviroStore: A Cooperative Storage System for Disconnected Operation in Sensor Networks