Dynamic Power Management with Scheduled Switching Modes in Wireless Sensor Networks

Sausen, Paulo Sérgio; Sousa, J. R. B.; Spohn, Marco Aurélio; Perkusich, Ângelo; Lima, A.M.N.

doi:10.1109/mascots.2007.20

Cited by 15 publications

(23 citation statements)

References 13 publications

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“…This means that it may be advantageous for a node to process data, in order to compress it or to decide whether to transmit it or not and thus save energy while sleeping [7]. In [8], spatial and temporal correlations between measured samples are used to decrease the amount of transmissions, saving sensor node energy.…”

Section: Energy Saving In Wsnsmentioning

confidence: 99%

“…After that, the sensing-processing-(eventual) transmission-sleeping procedure occurs. For ease of understanding, Algorithm 1 is presented in two parts: the first one describes the initialization of sensor nodes (this takes two sampling to have the measurement and transmission queues filled (lines [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]; the second on is the "main loop" (lines 20-35), in which nodes transmit Algorithm 1: DECA running at node S i . forward the data from the neighbors; …”

Section: Distributed Energy Conservation Algorithm For Wireless Sensomentioning

confidence: 99%

“…We assume that each node operates switching between two states: (i) inactive-an energy saving state (sleep mode) [7], in which the sensor radio interface is switched off or (ii) active. In active state, a node is in one of four operation modes: (a) measuring, (b) processing, (c) transmission, and (d) receiving.…”

Section: Energy Modelmentioning

confidence: 99%

See 2 more Smart Citations

DECA: distributed energy conservation algorithm for process reconstruction with bounded relative error in wireless sensor networks

Henriques

Lovisolo²,

Rubinstein³

2016

J Wireless Com Network

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When a wireless sensor network (WSN) is employed to monitor environmental data, efficient energy usage is demanded so that the WSN lifetime can be extended. In this context, we propose a distributed energy conservation algorithm (DECA). DECA considers the known past to make predictions on the measurement change over time in order to put sensor nodes into sleep mode, saving energy. Sensor nodes compute their inactivity periods taking into account the inactivity periods of their neighboring nodes. The signals are reconstructed at the sink node, from the sampled values received from sensor nodes. DECA is designed to keep the reconstruction error of the monitored process below a given fraction of its actual value. Since DECA works on the application layer, it can be combined with medium access control and routing protocols to further improve energy management. The proposed algorithm is evaluated with simulations using real data. Results show gains in the network lifetime of up to 1000 % as compared to a WSN without any energy management, while maintaining the error of the reconstructed process at the sink within the desired fraction imposed. Since a strategy for managing the sleeping period in routing nodes is proposed, a metric for evaluating the effects of sleeping periods on network connectivity is also proposed. We have verified that the network connectivity is not impaired by the proposed algorithm, as packet delivery ratios above 90 % were obtained.

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Section: Energy Saving In Wsnsmentioning

confidence: 99%

Section: Distributed Energy Conservation Algorithm For Wireless Sensomentioning

confidence: 99%

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DECA: distributed energy conservation algorithm for process reconstruction with bounded relative error in wireless sensor networks

Henriques

Lovisolo²,

Rubinstein³

2016

J Wireless Com Network

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show abstract

“…In many cases, nodes are randomly spread over remote areas, making it difficult to perform any maintenance to the nodes. Hence, a node remains live while it has enough battery capacity for its normal operation, and the network lifetime strongly depends on the remaining capacity of the nodes in the network.A sensor node has a few basic components (see Figure 1) as follows [1,7]:…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

Development and Implementation of Wireless Sensor Network for the Electricity Substation Monitoring

Sausen¹,

Sausen²,

Salvadori³

et al. 2012

Wireless Sensor Networks - Technology and Applications

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“…Although the switching energy is considered negligible most of the time, it is strongly dependent on the technology (e.g., the radio) being used in the sensor nodes. In particular, for lowduty cycle networks such as sensor networks, the switching energy may impact on the overall energy consumption [27,28].…”

Section: Introductionmentioning

confidence: 99%

Broadcast routing in wireless sensor networks with dynamic power management and multi-coverage backbones

Sausen

Spohn

Perkusich

2010

Information Sciences

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Dynamic Power Management with Scheduled Switching Modes in Wireless Sensor Networks

Cited by 15 publications

References 13 publications

DECA: distributed energy conservation algorithm for process reconstruction with bounded relative error in wireless sensor networks

DECA: distributed energy conservation algorithm for process reconstruction with bounded relative error in wireless sensor networks

Development and Implementation of Wireless Sensor Network for the Electricity Substation Monitoring

Broadcast routing in wireless sensor networks with dynamic power management and multi-coverage backbones

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