Dozer: Ultra-Low Power Data Gathering in Sensor Networks

Burri, Nicolas; Rickenbach, Pascal von; Wattenhofer, Roger

doi:10.1109/ipsn.2007.4379705

Cited by 122 publications

(146 citation statements)

References 19 publications

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“…Dozer [1], Koala [2], DISSense [3], Breath [4] are full vertical solutions that co-design different networking layers to reduce the duty cycle up to 0.1%. However, these staggering performance gain comes at some limitations on application profile.…”

Section: Related Workmentioning

confidence: 99%

Application-aware techniques for energy efficient data collection in Wireless Sensor Networks

Raza

2012

2012 IEEE International Conference on Pervasive Computing and Communications Workshops

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Abstract-Wireless Sensor Networks (WSNs) are distributed systems composed of battery-powered nodes that sense and collect information about the physical world. They enable applications in a wide variety of domains including but not limited to environmental monitoring, health care and disaster management. In such applications, nodes communicate the sensed information over multiple radio links until it reaches its destination referred to as the sink. As wireless communication is the most energy hungry operation, the data collection causes the biggest drain from battery. This motivates research on energy efficient mechanisms for data collection.Though there is a plethora of protocols proposed in research literature, they are not designed to collaborate with the applications. One opportunity from such collaboration is exploiting complete knowledge about application characteristics to make data collection more energy efficient. This enables underlying layers not to provision the resources more than the needs of the application and therefore save valuable battery power. The aim of this thesis is to explore a complex interplay between application characteristics and adaptive mechanisms across network stack using concrete real world deployments. It will propose a generic framework that integrates the adaptations to achieve near-optimal energy efficiency for heterogeneous applications.

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Section: Related Workmentioning

confidence: 99%

Application-aware techniques for energy efficient data collection in Wireless Sensor Networks

Raza

2012

2012 IEEE International Conference on Pervasive Computing and Communications Workshops

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“…For example, WirelessHART, an open standard for industrial process monitoring and control, uses TDMA to approach deterministic communication [24]. Dozer represents a cross-layer solution comprising MAC, topology control, and routing, where nodes employ local time synchronization to schedule wakeups among parents and children in the collection tree [6]. DRAND is a distributed, randomized TDMA slot assignment algorithm operating on a node's two-hop neighborhood [22].…”

Section: Related Workmentioning

confidence: 99%

The bus goes wireless: Routing-free data collection with QoS guarantees in sensor networks

Ferrari

Zimmerling

Thiele

et al. 2012

2012 IEEE International Conference on Pervasive Computing and Communications Workshops

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Abstract-We present the low-power wireless bus (LWB), a new communication paradigm for QoS-aware data collection in lowpower sensor networks. The LWB maps all communication onto network floods by using Glossy, an efficient flooding architecture for wireless sensor networks. Therefore, unlike current solutions, the LWB requires no information of the network topology, and inherently supports networks with mobile nodes and multiple data sinks. A LWB prototype implemented in Contiki guarantees bounded end-to-end communication delay and duplicate-free, inorder packet delivery-key QoS requirements in many control and mission-critical applications. Experiments on two testbeds demonstrate that the LWB prototype outperforms state-of-theart data collection and link layer protocols, in terms of reliability and energy efficiency. For instance, we measure an average radio duty cycle of 1.69 % and an overall data yield of 99.97 % in a typical data collection scenario with 85 sensor nodes on Twist. I. INTRODUCTIONProviding and analyzing Quality of Service (QoS) guarantees are key factors in making low-power wireless networks viable for a variety of applications, including safety-critical alarm systems, real-time control for factory automation, and mission-critical patient monitoring. These applications rely on a data collection service that provides bounded end-to-end delays, highly reliable packet delivery, and energy efficiency to achieve system lifetimes of several years. However, current solutions often fall short of these requirements, especially in multi-hop networks involving mobile nodes and multiple sinks.A major obstacle in current solutions is their strong dependency on the network topology. To cope with the multi-hop nature of low-power wireless networks, they let nodes continuously collect information about link qualities and neighboring nodes, serving, for example, as input to routing and TDMA scheduling algorithms [11], [15]. Keeping such network state up-to-date in a wireless environment, where channel conditions change frequently [27], consumes significant bandwidth and energy. Node mobility compounds the problem as network state becomes quickly outdated [9]. Moreover, many existing solutions use multiple protocols concurrently, which can lead to unintended interactions that impair system performance and cause failures difficult to find and fix [8], [25], or they employ cross-layer designs, which often increase complexity and thus hamper analysis of the provided QoS guarantees [26].To tackle the issues above, we adopt a clean-slate design and rethink the entire networking stack. In particular, we propose the low-power wireless bus (LWB), a novel communication paradigm for data collection with QoS guarantees.

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“…At the present state of the technology, small sensors, supporting communications in a range varying from 10 to 100 meters, like TinyNode 584 produced by Shockfish S.A. or T-node developed by SOWNet Technologies can be used for built such massive networks [5,9].…”

Section: Experimental Testsmentioning

confidence: 99%

Cooperative Training in Wireless Sensor and Actor Networks

Sorbelli

Ciotti

Navarra

et al. 2009

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Exploiting features of high density wireless sensor networks represents a challenging issue. In this work, the training of a sensor network which consists of anonymous and asynchronous sensors, randomly and massively distributed in a circular area around a more powerful device, called actor, is considered. The aim is to partition the network area in concentric coronas and sectors, centered at the actor, and to bring each sensor autonomously to learn to which corona and sector belongs. The new protocol, called Cooperative, is the fastest training algorithm for asynchronous sensors, and it matches the running time of the fastest known training algorithm for synchronous sensors. Moreover, to be trained, each sensor stays awake only a constant number of time slots, independent of the network size, consuming very limited energy. The performances of the new protocol, measured as the number of trained sensors, the accuracy of the achieved localization, and the consumed energy, are also experimentally tested under different network density scenarios.

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Dozer: Ultra-Low Power Data Gathering in Sensor Networks

Cited by 122 publications

References 19 publications

Application-aware techniques for energy efficient data collection in Wireless Sensor Networks

Application-aware techniques for energy efficient data collection in Wireless Sensor Networks

The bus goes wireless: Routing-free data collection with QoS guarantees in sensor networks

Cooperative Training in Wireless Sensor and Actor Networks

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