Energy efficient real-time data aggregation in wireless sensor networks

Hu, Yi; Yu, Nuo; Jia, Xiaohua

doi:10.1145/1143549.1143710

Cited by 34 publications

(16 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two performance metrics are used to evaluate the data collection from WBANs using cloudlet (Akkaya et al, 2005;Hu et al, 2006), transmission power and packet delay. The transmission power and packet delay are a direct measure of the communication energy and delay expenditure from the PDA device in WBAN to the cloudlet or the enterprise cloud.…”

Section: Performance Metricsmentioning

confidence: 99%

Cloud-assisted data management in wireless body area networks

Quwaider

Jararweh

2017

IJCSE

View full text Add to dashboard Cite

This paper presents an efficient large-scale data collection in wireless body area network (WBAN) in the presence of cloudlet-based prototype system. The key contribution of this paper is to collect the observed data of WBANs in a large-scale and convey it in consistent manner to the other end of service providers. A model of WBANs is proposed in this work including virtualised machines and cloudlet in order to characterise the efficient WBANs data collection. A scalable storage and processing infrastructure is proposed to support large-scale WBANs system, which is efficiently capable to handle the big data generated by large number of WBANs users. The proposed model supports effective cost communication technologies through Wi-Fi technology. Performance results of the proposed prototype are evaluated using advanced CloudSim simulator. The performance results show that the consumed power and packet delay of the collected data are decreased by increasing the number of virtualised machines and cloudlets in the monitored area. The results show also that the performance depends on the method of the virtualised cloudlet distribution in the target area for a given number of users.

show abstract

Section: Performance Metricsmentioning

confidence: 99%

Cloud-assisted data management in wireless body area networks

Quwaider

Jararweh

2017

IJCSE

View full text Add to dashboard Cite

show abstract

“…Data aggregation subject to latency constraints is a more difficult problem, and only a few solutions have been proposed in the literature [15]. A centralized solution to the problem of data aggregation subject to latency constraints is proposed in [16].…”

Section: Related Workmentioning

confidence: 99%

“…Each sensor node that generates traffic for the sensor network maintains a CBR traffic of 5 packets/second throughout the simulation. The simulation Procedure recvDataPacket (srcNode, SENSIQOS packet, nextHopVector) Result: Schedule the packet for transmission (1) currentClass ← getTOS (Packet) ; (2) if isDestination then / * if the packet is for self * / / * Forward the packet to the application layer * /; (3) doSending (upPort, packet) ; (4) else (5) body ←getBody (packet) ; (6) timestamp ← getTimestamp (packet) ; (7) progressDistance ← distance (sender,destination) -distance (self, destination) ; (8) timeS ← progressDistance/networkSpeed; (9) deadline ← getDeadline (packet) ; (10) qid ←getQueryID (packet) ; / * Retrieve packets that belong to the query * /; (11) pkts ←lookupPacketsinCache (qid) ; (12) if pkts / = 0 then (13) cancel timeout for the packets; / * Aggregate packets using the aggregation function * /; (14) Aggpacket ←doAggregate (pkts, p) ; (15) waitTime ←GetWaitTime (timeS, deadline, progressDistance) ; (16) if waitTime == 0 then (17) ForwardToDestination(packet); (18) else (19) Set timer for the waitTime; / * Store the packet in the node's memory * /; (20) InsertPacketsinCache (Aggpacket) (21) end ( is run for 500 seconds, and the results are taken from the average of 100 runs of the simulation and are shown with 95% confidence intervals. Confidence intervals are calculated using the method of independent replications [32] for these simulations unless otherwise mentioned.…”

Section: Simulation Environmentmentioning

confidence: 99%

An Energy-Efficient Data Delivery Scheme for Delay-Sensitive Traffic in Wireless Sensor Networks

Sabbineni

Chakrabarty

2010

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

We propose a novel data-delivery method for delay-sensitive traffic that significantly reduces the energy consumption in wireless sensor networks without reducing the number of packets that meet end-to-end real-time deadlines. The proposed method, referred to as SensiQoS, leverages the spatial and temporal correlation between the data generated by events in a sensor network and realizes energy savings through application-specific in-network aggregation of the data. SensiQoS maximizes energy savings by adaptively waiting for packets from upstream nodes to perform in-network processing without missing the real-time deadline for the data packets. SensiQoS is a distributed packet scheduling scheme, where nodes make localized decisions on when to schedule a packet for transmission to meet its end-to-end real-time deadline and to which neighbor they should forward the packet to save energy. We also present a localized algorithm for nodes to adapt to network traffic to maximize energy savings in the network. Simulation results show that SensiQoS improves the energy savings in sensor networks where events are sensed by multiple nodes, and spatial and/or temporal correlation exists among the data packets. Energy savings due to SensiQoS increase with increase in the density of the sensor nodes and the size of the sensed events.

show abstract

“…Hu et al (Hu et al, 2006) investigate the energy efficiency of data aggregation tree in WSNs. An analytic model based on IEEE 802.15.4 CSMA/CA is developed to compute a node's worst case delay in aggregating data from all child nodes.…”

Section: Rt Data Processingmentioning

confidence: 99%

Real-Time Qos Support in Wireless Sensor Networks: A Survey

Chen

Song

et al. 2007

IFAC Proceedings Volumes

107

View full text Add to dashboard Cite

Real-time wireless sensor networks are becoming more and more important by the requirement of message delivery timeliness in emerging new applications. Supporting real-time QoS in sensor networks faces severe challenges due to the wireless nature, limited resource, low node reliability, distributed architecture and dynamic network topology. There are tradeoffs between different application requirements including energy efficiency and delay performance. This paper studies the state of the art of current real-time solutions including MAC protocols, routing protocols, data processing strategies and cross-layer designs. Some research challenges and future design favors are also identified and discussed.

show abstract

Energy efficient real-time data aggregation in wireless sensor networks

Cited by 34 publications

References 16 publications

Cloud-assisted data management in wireless body area networks

Cloud-assisted data management in wireless body area networks

An Energy-Efficient Data Delivery Scheme for Delay-Sensitive Traffic in Wireless Sensor Networks

Real-Time Qos Support in Wireless Sensor Networks: A Survey

Contact Info

Product

Resources

About