Real-time quality of service with delay guarantee in sensor networks

Ouni, Sofiane; Gherairi, Salsabil; Kamoun, Farouk

doi:10.1504/ijsnet.2011.037304

Cited by 15 publications

(6 citation statements)

References 20 publications

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“…The Figure 2 indicates also that our complete analytical model (AM) offers better network lifetime prediction compared with that ignoring overhearing and interference energy waste (AMwithoutOverInterf) which is a similar approach to [1,6] works. In addition, our complete analytical model (AM) predicts network lifetime better than model not considering overhearing energy waste (AMwithoutOverhear) which is a similar approach to [2,4] works.…”

Section: Network Lifetime Predictionmentioning

confidence: 85%

“…The authors in [1,2,4,6] analyzed the network lifetime for wireless sensor networks. The authors in [1,6] considered that the energy cost of a node is the ratio of the total energy consumed over the initial battery energy.…”

Section: Related Work: Energy Consumption and Delay Analysismentioning

confidence: 99%

“…The authors in [1,6] considered that the energy cost of a node is the ratio of the total energy consumed over the initial battery energy. Thus, the total energy consumed by a node during the network lifetime should be less than its initial energy.…”

Section: Related Work: Energy Consumption and Delay Analysismentioning

confidence: 99%

“…The third is the analysis model not considering collision due to interference energy waste (noted AMwithoutInterf). The last is the analysis model not considering overhearing and collision due to interference energy waste (noted AMwithoutOverInterf) which is a similar approach to [1,6] works. The Figure 2 indicates that there is a slight difference between network lifetime predicted by our analytical model (AM) and that found by NS-2 simulator.…”

Section: Network Lifetime Predictionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting Communication Delay and Energy Consumption for IEEE 802.15.4/Zigbee Wireless Sensor Networks

Ouni¹,

Ayoub²

2013

IJCNC

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Section: Network Lifetime Predictionmentioning

confidence: 85%

Section: Related Work: Energy Consumption and Delay Analysismentioning

confidence: 99%

Section: Related Work: Energy Consumption and Delay Analysismentioning

confidence: 99%

Section: Network Lifetime Predictionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Communication Delay and Energy Consumption for IEEE 802.15.4/Zigbee Wireless Sensor Networks

Ouni¹,

Ayoub²

2013

IJCNC

Self Cite

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show abstract

“…In this context, the authors in [2] define an algorithm that transforms a wireless sensor network graph to a communication tree which is created periodically in order to balance load with regard to real time constraint. This global reorganization approach requires that all sensor nodes reorganize periodically depending on energy cost and delay constraint.…”

Section: Related Workmentioning

confidence: 99%

Global versus Local Re-Association Approach to Extend the Lifetime of Large-Scale IEEE 802.15.4/ZigBee Wireless Sensor Networks

Ayoub

Ouni

Kamoun

2012

2012 5th International Conference on New Technologies, Mobility and Security (NTMS)

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Thanks to recent technological improvements, new products using the Wireless Sensor Networks (WSNs) are used to control some real world physical phenomena. A wireless sensor network consists of a large number of nodes with limited energy source. Lifetime of such a network is strongly dependent on the lifetime of its nodes' batteries. However, to successfully achieve their mission, WSNs should meet a required lifetime. The ZigBee Alliance and the IEEE 802.15.4 standard afford a complete protocol stack providing key features that typically fit the requirements of WSNs. This protocol defines the association procedure that allows the sensors to self-organize into a resulting topology through which data can be routed toward their destination. It defines also a re-association procedure performed only by orphaned devices.In this paper, we propose new association approaches designed to optimize the initial topology formation of IEEE 802.15.4/ZigBee network. We propose global and local reassociation approaches in order to balance the energy consumption over time and extend the network lifetime accordingly. Then, we use simulation to highlight the efficiency of our proposals in terms of energy conservation and packet delivery for IEEE 802.15.4/ZigBee sensor networks. Compared to the global re-association approach, the local one can deal with the network scalability which is a major characteristic of WSNs.

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Random time source protocol in wireless sensor networks and synchronization in industrial environments

Wang¹,

Zeng

Huang

et al. 2011

Wireless Communications

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Reliability is a crucial aspect of time synchronization for industrial wireless applications in wireless sensor networks. Existing time synchronization algorithms often provide good synchronization in laboratory environments; however, outdoor environments with associated radio interference influence the performance of time synchronization. In this paper, we propose a random time source protocol for industrial wireless applications in wireless sensor network synchronization. Each synchronized node randomly selects its time source for each period in order to prevent reliance on a fixed time source because this may lead to resynchronization once the source fails. We have implemented the algorithm on the SIA2420 platform using TINYOS, and the results show the reliability of our protocol.

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Real-time quality of service with delay guarantee in sensor networks

Cited by 15 publications

References 20 publications

Predicting Communication Delay and Energy Consumption for IEEE 802.15.4/Zigbee Wireless Sensor Networks

Predicting Communication Delay and Energy Consumption for IEEE 802.15.4/Zigbee Wireless Sensor Networks

Global versus Local Re-Association Approach to Extend the Lifetime of Large-Scale IEEE 802.15.4/ZigBee Wireless Sensor Networks

Random time source protocol in wireless sensor networks and synchronization in industrial environments

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