Mobility Metric based LEACH-Mobile Protocol

Kumar, G. Santhosh; Paul, M.V. Vinu; Jacob, K. Poulose

doi:10.1109/adcom.2008.4760456

Cited by 118 publications

(70 citation statements)

References 19 publications

(16 reference statements)

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“…We evaluate the cost and benefit of the data fusion to adaptively adjust whether fusion shall be performed to minimize total energy consumption when energy-efficient node scheduling migrates from one node to the next. One of the most important weaknesses of LEACH [29] is load unbalance, i.e., as the CHs are selected randomly, some nodes may be selected as CHs, which are in close proximity of each other. The range-based algorithm uses the maximum likelihood method or trilateral rule to measure the distance between nodes, and thus calculate the location of the unknown node.…”

Section: Using Wireless Sensors For Earthquake Early Warning: Challenmentioning

confidence: 99%

Analysis of dynamic path loss based on the RSSI model for rupture location analysis in underground wireless sensor networks and its implications for Earthquake Early Warning System (EEWS)

Dutta

Mishra

Naskar

2015

AUSMT

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Sensors deployed in underground tunnels found that radio frequency signals suffer significant signal strength attenuation which can result in considerable variation of link quality on the receiving end. This study analyzes the received signal strength index (RSSI) based on the development of a theoretical wireless sensor model for data collection by enabling sensors to determine the location from which each data packet is obtained. To improve positioning accuracy, the complex radio wave propagation environment requires the use of a voronoi cell to minimize signal attenuation. A relatively simple calculation is used to predict the intensity and perception range of the received wireless signals to measure the extent of signal reduction in the attenuating rock medium. Simulation results show that RSSI-based localization and wireless network lifetime and throughput measurements are more accurate when the node concept is applied to the self-locating rupture zones than the maximum likelihood estimation method. The proposed minimum energy relay routing technique based on beacon node chain deployment is found to help correct localization errors resulting from interference caused by the underground tunnel environment. The extent of localization and power of the sensor nodes are determined based on the beacon node chain deployment of tunnel wireless sensor networks. The algorithm accounts for the distance and the corresponding RSSI between adjacent beacon nodes to calculate the actual path loss parameter in the tunnel. The proposed model can serve as the theoretical basis for locating ruptures in underground wireless sensor network nodes, thus maximizing the monitoring range of large scale tectonic environments while minimizing equipment cost. We recommend that this model can be field tested through a series of experiments by researchers and engineers working in seismology, telecommunication, and information technology.

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Section: Using Wireless Sensors For Earthquake Early Warning: Challenmentioning

confidence: 99%

Analysis of dynamic path loss based on the RSSI model for rupture location analysis in underground wireless sensor networks and its implications for Earthquake Early Warning System (EEWS)

Dutta

Mishra

Naskar

2015

AUSMT

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“…In the LEACH TDMA schedule, the cluster head receives data of the member node according to the TDMA schedule in order to receive the data. On the other hand, the TDMA of the LEACH-Mobile [4] in FIG2 transmits a request message that the cluster head allocates slots to the member nodes to receive data and transmits data to the nodes at corresponding time intervals. The node receiving the send-request message sends the data to the cluster head and the node that has not received the send-request message of the cluster head waits until it receives the send-request message.…”

Section: Leach-mobilementioning

confidence: 99%

The Comparison of the 3D graph for the energy-equal of LEACH-Mobile

Jang¹,

Jung²,

Lee³

2017

International journal of advanced smart convergence

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“…The amount of energy required for both sending and receiving data from node j to another node, computed as in [19], are given by:…”

Section: Energy Modelmentioning

confidence: 99%

“…While E Receive is the amount of energy consumed by a sensor node to receive k-bits message. α is the path loss exponent due to wireless channel transmission [19][20]. In Mobi-Sim, these parameters are left to be set by users.…”

Section: Energy Modelmentioning

confidence: 99%