T-MQM: Testbed based Multi-metric Quality Measurement of Sensor Deployment for Precision Agriculture-A Case Study

Kaiwartya, Omprakash; Abdullah, Abdul Hanan; Cao, Yue; Raw, Ram Shringar; Kumar, Sushil; Lobiyal, D. K.; Isnin, Ismail Fauzi; Liu, Xiulei; Shah, Rajiv Ratn

doi:10.1109/jsen.2016.2614748

Cited by 35 publications

(28 citation statements)

References 35 publications

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“…The simulation is performed in a machine having Intel Core i7-2500S 2.70 GHz processor, 16GB RAM, 64bit Linux OS. The other basic setting of parameters in simulation is approximately similar the parameter table and setting considered in [28,29]. Three different sets of network with 100, 500 and 1000, 1500, 2000 nodes are generated following Poisson process.…”

Section: ) Simulation Setting and Metricsmentioning

confidence: 99%

Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things

Kaiwartya

Abdullah

Cao

et al. 2018

IEEE Internet Things J.

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109

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Section: ) Simulation Setting and Metricsmentioning

confidence: 99%

Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things

Kaiwartya

Abdullah

Cao

et al. 2018

IEEE Internet Things J.

Self Cite

109

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“…The assignment of different parameters for the simulation is shown in Table . The parameters are quite similar to related experiments performed …”

Section: Performance Evaluationmentioning

confidence: 99%

Towards green computing in wireless sensor networks: Controlled mobility–aided balanced tree approach

Khatri

Kumar

Kaiwartya

et al. 2017

Int J Communication

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Network lifetime maximization has received continuous attention as green computing in wireless sensor networks. Recently, controlled mobility-based green computing has witnessed significant attention from academia and industrial research labs. It is due to the growing number of sensor-based services in mobility friendly nonhostile environments in our daily life. The intelligent mobility-aided repositioning of sensors is significantly challenging considering the critical constraints including irregular power depletion, static normal sensors, the correlation between sensor position, and coverage and connectivity. In this context, this paper proposes a network lifetime maximization framework based on balanced tree node switching. Specifically, a balanced tree-based network model for wireless sensor networks is designed focusing on energy consumption of sensor nodes in tree-based networks. The problem of lifetime maximization in tree-based network is identified considering energy loss rate, path load, and balancing factor. Two node-shifting algorithms are developed, namely, energy-based shifting and load-based shifting for balancing tree-based network in terms of energy. Analytical and simulation experimentbased comparative performance evaluation attests the benefit of the proposed framework as compared to the state-of-the-art techniques considering a number of energy-oriented metrics for wireless networks.Focusing on efficient and balanced energy consumption for uniform energy depletion among sensors, 2,4 various green computing techniques have been suggested. It includes duty cycle management, data aggregation, and controlled mobility-based lifetime maximization for green computing in WSNs. 5-9 One of the major constraints in duty cycle based techniques is the overhead involved in constructing broadcasting sets for assigning duty to the complete network. 10-14 It creates major network overhead and reduces actual data transmission capability of the network. Most of the data aggregation techniques are based on clustering approach, where networks are divided into manageable clusters to balance energy consumption in different areas of the networks. One of the major problem of the clusteringbased green computing is the frequent require of cluster head selection and updating. 11,[15][16][17][18][19] Recently, controlled mobility-based green computing in WSNs has witnessed considerable attention because of the growing significance of sensor technology in mobility-enabled nonhostile environment. In the mobility-based green computing, some designated mobile sensors intelligently change the network topology by repositioning their locations for balancing energy consumption among sensors. The intelligent repositioning is significantly challenging considering the 3 critical constraints of the network and sensor specifications. [20][21][22][23] The constraints include uneven power depletion, static nature of majority of normal sensors, and strong correlation between sensor position, and coverage and connectivity. In literature o...

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“…• Battery Switch Phase: Upon arrival at the selected CS with parking place navigated [14], the EV's battery (electricity consumed) is switched with the one (fully charged) maintained at that CS. This happens if the selected CS already maintains a number of fully charged batteries for switch.…”

Section: B System Cycle Of On-the-move Ev Charging Managementmentioning

confidence: 99%

A Cost-Efficient Communication Framework for Battery-Switch-Based Electric Vehicle Charging

et al. 2017

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Abstract-Charging management for Electric Vehicles (EVs) on-the-move has become an increasingly important research problem in smart cities. Major technical challenges include the selection of Charging Stations (CSs) to guide charging plans, and the design of cost-efficient communication infrastructure between the power grid and EVs. In this article, we first present a brief review on state-of-the-art EV charging management schemes. Next, by incorporating battery switch technology to enable fast charging service, a Publish/Subscribe (P/S) communication framework is provisioned to support the EV charging service. Upon that, we develop a fully distributed charging management scheme with the consideration of urban travel uncertainties, e.g., traffic congestions and drivers' preferences. This would benefit from low privacy sensitivity, as EVs' status information will not be released through management. Results demonstrate a guidance for the provisioning of P/S communication framework to improve EV drivers' experience, e.g., charging waiting time and total trip duration. Also, the benefit of P/S communication framework is reflected in terms of the communication efficiency. Open research issues of this emerging area are also presented.

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T-MQM: Testbed based Multi-metric Quality Measurement of Sensor Deployment for Precision Agriculture-A Case Study

Abstract: Abstract-

Cited by 35 publications

References 35 publications

Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things

Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things

Towards green computing in wireless sensor networks: Controlled mobility–aided balanced tree approach

A Cost-Efficient Communication Framework for Battery-Switch-Based Electric Vehicle Charging

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