An Energy Estimation Model for Mobile Sensor Networks

Tariq, Muhammad; Macuha, Martin; Park, Yong Jin; Sato, Toru

doi:10.1109/sensorcomm.2010.82

Cited by 9 publications

(9 citation statements)

References 26 publications

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“…In the sequel, we consider the impact of the number of nodes on the energy saving gain expression in (8), (9). The number of node is in the range of 10 to 100 in WSNs.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Tariq et al [9] proposes a comprehensive energy model to estimate the overall energy consumption through power dissipation, for both static and mobile sensors in the potential sensor fields. Wang et al [10] proposes an energy-efficient transmission scheme for poweradjustable radio to optimize transmit energy efficiency subject to given overflow and delay constraints.…”

Section: Related Workmentioning

confidence: 99%

“…In such networks, the nodes are often powered by battery, and energy efficient operations are critical to prolong the lifetime of the connections. Designing an energy efficient and Maximal lifetime for such networks is a challenging issue [1][2][3][4][5][6][7][8][9][10][11][12]. However, for real-time sensing, latency and reliability are of paramount importance, whereas in battery powered sensor networks, energy efficiency is an important metric.…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficient Based Maximal Lifetime Routing in Wireless Sensor Networks

Chen¹,

Luo²,

Sun³

2014

IJFGCN

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“…In the sequel, we consider the impact of the number of nodes on the energy saving gain expression in (8), (9). The number of node is in the range of 10 to 100 in WSNs.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Efficient Based Maximal Lifetime Routing in Wireless Sensor Networks

Chen¹,

Luo²,

Sun³

2014

IJFGCN

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“…Energy consumption during the self-deployment of mobile sensors depends on the energy consumed through power dissipation during a sensor idle listening and sleeping timings, during message exchanges (retransmission in case of lossy links), and during movement of a sensor [16] hj(x, y) location to be accessed in the presence of obstacles consumption due to these processes during total considered time T n is given by: where p idle represents the power dissipation in a sensor radio idle mode such that when a sensor is ready to transmit or receive data any time. p sleep represents the radio sleep mode or radio off state mode.…”

Section: ) Energy Consumption Analysismentioning

confidence: 99%

Diffusion Based Self-Deployment Algorithm for Mobile Sensor Networks

Tariq

Zhou

Park

et al. 2010

2010 IEEE 72nd Vehicular Technology Conference - Fall

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Mobile Sensor Networks (MSN) are used for network load balancing, prolonging network lifetime, and improving network coverage by monitoring critical areas where manual sensor deployment cannot be performed. Addressing the problem of how to achieve maximum network coverage and network uniformity, after deploying sensors in critical areas randomly, is of significant importance recently. In this paper, we design an energy efficient distributed self-deployment algorithm, which is based on the diffusion of mobile sensors in the Region of Interest (ROI). Mobile sensors are diffused from denser sensors area to lesser or uncovered area in ROI, on the basis of localized information. Our algorithm considers ROI with the absence as well as presence of obstacles. Resemblance in the numerical and simulation analysis confirms the concreteness of our algorithm.

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“…In such networks, the nodes are often powered by battery, and energy efficient operations are critical to prolong the lifetime of the connections. Designing an energy efficient and maximize lifetime for such networks is a challenging issue [1][2][3][4][5][6][7][8][9][10][11][12]. However, for real-time sensing, latency and reliability are of paramount importance, whereas in battery powered sensor networks, energy efficiency is an important metric.…”

Section: Introductionmentioning

confidence: 99%

Network Coding based Energy Efficient Routing Algorithm in Wireless Sensor Networks

Xiang¹

2014

Proceedings of the 2014 International Conference on Future Computer and Communication Engineering

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-Wireless sensor networks (WSNs) have received significant attention recently. Wireless sensors are deployed to achieve network load balancing, prolonging network lifetime, and improving network coverage. To achieve these goals, energy efficiency directly affects battery life and thus is a critical design parameter for WSNs. In this paper, we propose an Network Coding based Energy Efficient Routing Algorithm in WSNs to prolong lifetime (NC-ER), which is able to dramatically prolong network lifetime while effectively improve energy efficient. We also present an algorithm for deciding the network lifetime scheme for a node to further improve energy efficient by minimizing redundant packet transmissions. Simulation results show that, with the proposed NC-ER Algorithm in WSN throughput, network lifetime and energy efficiency can be improved in most of cases. It is an available approach to routing decision.

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An Energy Estimation Model for Mobile Sensor Networks

Cited by 9 publications

References 26 publications

Energy Efficient Based Maximal Lifetime Routing in Wireless Sensor Networks

Energy Efficient Based Maximal Lifetime Routing in Wireless Sensor Networks

Diffusion Based Self-Deployment Algorithm for Mobile Sensor Networks

Network Coding based Energy Efficient Routing Algorithm in Wireless Sensor Networks

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