Telos: enabling ultra-low power wireless research

Polastre, Joseph; Szewczyk, Robert; Culler, David

doi:10.1109/ipsn.2005.1440950

Cited by 1,179 publications

(956 citation statements)

References 10 publications

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“…On IEEE 802.15.4 radios we rely on a metric similar to that in MultihopLQI [49]. This is based on the Link Quality Indicator (LQI) provided by radio chips such as the ChipCon CC2420, which equips the widely-used TMote Sky [42] nodes. In absence of LQI, we use the inverse of the hop-count to a sink, a metric potentially inaccurate, yet used successfully in real deployments [6].…”

Section: Methodsmentioning

confidence: 99%

MUSTER: Adaptive Energy-Aware Multisink Routing in Wireless Sensor Networks

Mottola

Picco

2011

IEEE Trans. on Mobile Comput.

102

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Abstract-Wireless sensor networks (WSNs) are increasingly proposed for applications characterized by many-to-many communication, where multiple sources report their data to multiple sinks. Unfortunately, mainstream WSN collection protocols are generally designed to account for a single sink and, dually, WSN multicast protocols optimize communication from a single source.In this paper we present MUSTER, a routing protocol expressly designed for many-to-many communication. First, we devise an analytical model to compute, in a centralized manner, the optimal solution to the problem of simultaneously routing from multiple sources to multiple sinks. Next, we illustrate heuristics approximating the optimal solution in a distributed setting, and their implementation in MUSTER. To increase network lifetime, MUSTER minimizes the number of nodes involved in many-tomany routing and balances their forwarding load. We evaluate MUSTER in emulation and in a real WSN testbed. Results indicate that our protocol builds near-optimal routing paths, doubles the WSN lifetime, and overall delivers to the user 2.5 times the amount of raw data w.r.t. mainstream protocols. Moreover, MUSTER is intrinsically amenable to in-network aggregation, pushing the improvements up to a 180% increase in lifetime and a 4-time increase in data yield.

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Section: Methodsmentioning

confidence: 99%

MUSTER: Adaptive Energy-Aware Multisink Routing in Wireless Sensor Networks

Mottola

Picco

2011

IEEE Trans. on Mobile Comput.

102

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“…However, a frequency discrepancy of up to 200 kHz is possible between two radios operating in the same channel of the 2.4 GHz band. We examine the carrier frequency dispersion for TelosB sensor nodes [14] employing a Software Defined Radio (SDR) from Ettus Research [7] for spectrum analysis, and an Agilent 8648C Signal Generator as a reference (10 Hz accuracy and time-base stability below ±0.1 ppm typical). We verify such a frequency offset among nodes and observe no perceptible time variation in the frequencies, provided a constant room temperature is maintained.…”

Section: Hardwarementioning

confidence: 99%

On the Scalability of Constructive Interference in Low-Power Wireless Networks

Noda

Pérez-Penichet

Seeber³

et al. 2015

Lecture Notes in Computer Science

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Constructive baseband interference has been recently introduced in low-power wireless networks as a promising technique enabling low-latency network flooding and sub-microsecond time synchronisation among network nodes. The scalability of this technique has been questioned in regards to the maximum temporalmisalignment among baseband signals, due to the variety of path delays in thenetwork. By contrast, we find that the scalability is compromised, in the firstplace, by emerging fast fading in the composite channel, which originates in thecarrier frequency disparity of the participating repeaters nodes. We investigatethe multisource wave problem and show the resulting signal becomes vulnerablein the presence of noise, leading to significant deterioration of the link whenever the carriers have similar amplitudes. Abstract. Constructive baseband interference has been recently introduced in low-power wireless networks as a promising technique enabling low-latency network flooding and sub-µs time synchronisation among network nodes. The scalability of this technique has been questioned in regards to the maximum temporal misalignment among baseband signals, due to the variety of path delays in the network. By contrast, we find that the scalability is compromised, in the first place, by emerging fast fading in the composite channel, which originates in the carrier frequency disparity of the participating repeaters nodes. We investigate the multisource wave problem and show the resulting signal becomes vulnerable in the presence of noise, leading to significant deterioration of the link whenever the carriers have similar amplitudes.

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“…Tmote [10] from Moteiv Corporation is presented as an example of a popular WSN platform ( Figure 5). Many comparable platforms with similar hardware setups exist today.…”

Section: Tmote Sky From Moteiv Corporationmentioning

confidence: 99%

Wireless Sensor Network Platforms

Bischoff¹,

Meyer²,

Feltrin³

2008

Encyclopedia of Structural Health Monitoring

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A wireless sensor network is a network made up of many tiny intercommunicating computers equipped with one or several sensors. Each tiny computer represents a node of the network. The nodes are self‐contained units typically consisting of a power supply with limited capacity, a radio transceiver, a microcontroller, and one or more sensors. This article gives an introduction to wireless sensor networks for structural health monitoring, shows the general architecture of sensor nodes, and overviews current hardware and software platforms. Furthermore, it shows the characteristics and limits of such monitoring systems and gives advice to choose a suitable platform to an application. Because nodes are powered by an autonomous source, energy‐related aspects, energy storage, and scavenging are presented as well.

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Telos: enabling ultra-low power wireless research

Cited by 1,179 publications

References 10 publications

MUSTER: Adaptive Energy-Aware Multisink Routing in Wireless Sensor Networks

MUSTER: Adaptive Energy-Aware Multisink Routing in Wireless Sensor Networks

On the Scalability of Constructive Interference in Low-Power Wireless Networks

Wireless Sensor Network Platforms

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