Underwater Sensor Networks, Oceanography and Plankton Assemblages

Bondarenko, O. A.; Kininmonth, Stuart; Kingsford, Michael J.

doi:10.1109/issnip.2007.4496921

Cited by 15 publications

(11 citation statements)

References 7 publications

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“…Secondly, as there will be a very high density of sensor nodes, that is, they will be placed very close to each other, we can expect readings between neighboring nodes to be correlated during most parts of the day. This assumption can be verified by looking at data that has been collected from Nelly Bay in the GBR as shown in Figure 2 [Bondarenko et al 2007]. Figure 3(a) presents a matrix that shows three characteristics of the five deployed sensors: temperature readings (d), correlation between the readings of any two sensors (c), and how correlation varies over time (b).…”

Section: Assumptionsmentioning

confidence: 84%

A distributed and self-organizing scheduling algorithm for energy-efficient data aggregation in wireless sensor networks

Chatterjea

Nieberg

Meratnia

et al. 2008

ACM Trans. Sen. Netw.

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Wireless sensor networks (WSNs) are increasingly being used to monitor various parameters in a wide range of environmental monitoring applications. In many instances, environmental scientists are interested in collecting raw data using long-running queries injected into a WSN for analyzing at a later stage, rather than injecting snap-shot queries containing data-reducing operators (e.g., MIN, MAX, AVG) that aggregate data. Collection of raw data poses a challenge to WSNs as very large amounts of data need to be transported through the network. This not only leads to high levels of energy consumption and thus diminished network lifetime but also results in poor data quality as much of the data may be lost due to the limited bandwidth of present-day sensor nodes. We alleviate this problem by allowing certain nodes in the network to aggregate data by taking advantage of spatial and temporal correlations of various physical parameters and thus eliminating the transmission of redundant data. In this article we present a distributed scheduling algorithm that decides when a particular node should perform this novel type of aggregation. The scheduling algorithm autonomously reassigns schedules when changes in network topology, due to failing or newly added nodes, are detected. Such changes in topology are detected using cross-layer information from the underlying MAC layer. We first present the theoretical performance bounds of our algorithm. We then present simulation results, which indicate a reduction in message transmissions of up to 85% and an increase in network lifetime of up to 92% when compared to collecting raw data. Our algorithm is also capable of completely eliminating dropped messages caused by buffer overflow.

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Section: Assumptionsmentioning

confidence: 84%

A distributed and self-organizing scheduling algorithm for energy-efficient data aggregation in wireless sensor networks

Chatterjea

Nieberg

Meratnia

et al. 2008

ACM Trans. Sen. Netw.

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“…Such networks can be used for soil moisture reporting in agriculture [43], infrastructure supervision, intrusion detection [44] and transport systems [45]. Underwater sensor networks rely on immersed sensors and are used in a variety of applications such as ocean supervision [46], water quality monitoring [47], disaster prevention, surveillance [48] and pipeline monitoring.…”

Section: Underground and Underwater Sensor Networkmentioning

confidence: 99%

Energy efficiency in wireless sensor networks: A top-down survey

Rault¹,

Bouabdallah²,

Challal³

2014

Computer Networks

786

452

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The design of sustainable wireless sensor networks (WSNs) is a very challenging issue. On the one hand, energyconstrained sensors are expected to run autonomously for long periods. However, it may be cost-prohibitive to replace exhausted batteries or even impossible in hostile environments. On the other hand, unlike other networks, WSNs are designed for specific applications which range from small-size healthcare surveillance systems to large-scale environmental monitoring. Thus, any WSN deployment has to satisfy a set of requirements that differs from one application to another. In this context, a host of research work has been conducted in order to propose a wide range of solutions to the energysaving problem. This research covers several areas going from physical layer optimization to network layer solutions. Therefore, it is not easy for the WSN designer to select the efficient solutions that should be considered in the design of application-specific WSN architecture.We present a top-down survey of the trade-offs between application requirements and lifetime extension that arise when designing wireless sensor networks. We first identify the main categories of applications and their specific requirements. Then we present a new classification of energy-conservation schemes found in the recent literature, followed by a systematic discussion as to how these schemes conflict with the specific requirements. Finally, we survey the techniques applied in WSNs to achieve trade-off between multiple requirements, such as multi-objective optimisation.

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“…they will be placed very close to each other, we can expect readings between neighboring nodes to be correlated during most parts of the day. This assumption can be verified by looking at data that has been collected from Nelly Bay in the GBR shown in Figure 5.1 [37]. As the sensor nodes will be placed on the reef for possibly a number of years, we assume that the topology of the network is relatively static.…”

Section: Assumptionsmentioning

confidence: 96%

“…shows the average rate of change of the trend of a sensor reading per epoch per node. For the sake of comparison, we have also included data obtained from two real life data sets: (i) temperature data from Nelly Bay in the GBR [37] and (ii) temperature data from the Intel Berkeley Lab [22]. It can be seen that the real-life data set clearly falls within the low variability category.…”

Section: Data Set Generation and Classificationmentioning

confidence: 99%

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Distributed and self-organizing data management strategies for wireless sensor networks

Chatterjea¹

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Underwater Sensor Networks, Oceanography and Plankton Assemblages

Cited by 15 publications

References 7 publications

A distributed and self-organizing scheduling algorithm for energy-efficient data aggregation in wireless sensor networks

A distributed and self-organizing scheduling algorithm for energy-efficient data aggregation in wireless sensor networks

Energy efficiency in wireless sensor networks: A top-down survey

Distributed and self-organizing data management strategies for wireless sensor networks

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