Transmitting and Gathering Streaming Data in Wireless Multimedia Sensor Networks Within Expected Network Lifetime

Shu, Lei; Zhou, Yan; Zhou, Zhangbing; Hauswirth, Manfred; Yu, Zhiwen; Hynes, Gearoid

doi:10.1007/s11036-008-0099-4

Cited by 29 publications

(20 citation statements)

References 30 publications

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“…The higher throughput is attributed to shorter hop for the travel of packet from node to sink. The proposed solution has lower delay than [1] and [19]. The reduction in delay is faster propagation within and across clusters.…”

Section: Resultsmentioning

confidence: 92%

“…Simulation was conducted with following parameters The proposed solution is compared with two other cross layer architectures proposed in [1] and [19] and detailed in survey. We measured following parameters The proposed solution has higher throughput than [1] and [19]. The higher throughput is attributed to shorter hop for the travel of packet from node to sink.…”

Section: Resultsmentioning

confidence: 99%

“…This approach does not consider application nature but to optimize the network usability it uses routing tree construction and bandwidth flow assignment. An efficient cross layer mechanism for multimedia data gathering in proposed in [19]. In this adaptive scheme the transmission Radius is changed dynamically and data generation Rate Adjustment (RRA) is based on a cross-layer design.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Energy Efficient QoS guaranteed Cross Layer Solution (EEQCL) for Mesh Backbone IOT Networks

2019

IJRTE

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IOT enables devices to be managed and monitored anywhere from internet. With IOT gaining popularity, billions of devices are connected to internet and they are being used for many applications like home automation, public safety, smart city, traffic monitoring etc. IOT interconnectivity to internet based on mesh network is the most used topology due to effectiveness in scalability and reliability. The mesh network must be optimized in terms of QOS, energy efficiency for the case of nature of traffic from IOT devices. Towards this end, this work proposes an energy efficient QOS guaranteed cross layer solution for mesh backbone based IOT network. The proposed cross layer solution applies changes at Application, session, network layer to achiness a better QOS and energy efficiency than existing solutions discussed in literature.

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Section: Resultsmentioning

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Energy Efficient QoS guaranteed Cross Layer Solution (EEQCL) for Mesh Backbone IOT Networks

2019

IJRTE

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“…It has been proved that a database oriented approach is helpful to manage the dynamic nature of WSN data for those applications [10], [11] and hence it motivated the researchers to treat WSN as a distributed database. Accordingly, WSN can be modelled as a distributed database where sensor devices act as data sources and stores the data with the sensor in the form of database across the network in distributed manner [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Data Mining Approach for IoT based WSN in Smart City

Khedr¹,

Osamy²,

Salim³

et al. 2019

IJACSA

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Wireless Sensor Network (WSN) is one of the most fundamental technologies of Internet of Things (IoT). Various IoT devices are connected to the internet by making use of WSN composed of different sensor nodes and actuators, where these sensor nodes collaborate and accomplish their tasks dynamically. The main objective of deploying WSN-based applications is to make high precision real-time observations, and it is extremely challenging because of the limited computing power of the sensors operating under constrained environments, resource constraints like energy, computation speed, bandwidth and memory, huge volume of high speed, heterogeneous and fast-changing WSN data. These challenges encouraged the researchers to concentrate deeper on exploring data mining techniques to extract the required information from the fast-changing sensor data in WSN and thereby efficiently handle the massive data generated by the WSNs. The increasing need of data mining techniques for WSN has inspired us to propose a distributed data mining technique that effectively handles the data generated by the nodes in the WSN and prolongs the lifespan of the network. Our work provides a novel cluster based scheme to mine the sensors data without moving it to cluster head (CH) or base station (BS) to achieve maximum performance in a WSN environment. The basic idea of the proposed work is that local computations are performed by utilizing the computing power at each sensor node and then the minimum higher level statistical summaries are exchanged, which decreases the energy dissipation in communication as the amount of the sensor data transferred is considerably reduced, and thereby the sensor network lifetime is maximized and also preserve the privacy of the sensor data.

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“…Since sensors are usually battery powered, their operation time (lifetime) are strictly constrained by the battery capacity. In recent years, many energy conserving strategies for WSNs have been proposed to prolong the network lifetime 3, including energy efficient packet forwarding schemes 4, 5 and multimedia streaming transmission schemes 6, etc. In this paper, we concentrate on sleeping management schemes for topology control in WSNs 7, 8, 9, 10.…”

Section: Introductionmentioning

confidence: 99%

Sleeping management for scalable topology control in wireless sensor networks

Yu¹,

Zhang²,

Gao³

et al. 2011

Wireless Communications

Self Cite

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Wireless sensor network (WSN) is an important instrument to realize wireless monitoring and control in various application fields. Energy conservation are crucial for WSNs to prolong the network lifetime. In this paper, we study sleeping management schemes which can efficiently control the network topology and significantly reduce the energy consumption by selectively turning off the radios of abundant sensor nodes. Based on the observation that a Matérn Hard‐core Process (MHP) could thin out evenly distributed nodes from a dense graph, we propose Backbone Energy Efficient Sleeping (BEES) management scheme, whose central idea is to generate and maintain the backbone by simulating MHPs. There are three attractive features of BEES: (i) the backbone size could be conveniently scaled according to the practical requirements; (ii) the backbone is energy efficient in the point of view of packet forwarding (or routing); (iii) the construction of backbone is robust to possible ranging errors, which ensures the feasibility and reliability in practice. Theoretical analysis and simulation experiments are carried out to demonstrate the correctness and effectiveness of BEES. Numerical results indicate that, compared with two existing sleeping management schemes, BEES provides about 30% wider range of scalability, consumes about 13% less routing energy, and achieves 11% ∼ 20% longer network lifetime under different traffic loads. Copyright © 2009 John Wiley & Sons, Ltd.

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Transmitting and Gathering Streaming Data in Wireless Multimedia Sensor Networks Within Expected Network Lifetime

Cited by 29 publications

References 30 publications

Energy Efficient QoS guaranteed Cross Layer Solution (EEQCL) for Mesh Backbone IOT Networks

Energy Efficient QoS guaranteed Cross Layer Solution (EEQCL) for Mesh Backbone IOT Networks

Privacy Preserving Data Mining Approach for IoT based WSN in Smart City

Sleeping management for scalable topology control in wireless sensor networks

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