An Efficient Data Aggregation Approach for Large Scale Wireless Sensor Networks

Karim, Lutful; Abdulsalam, Hanady M.; Moukadem, Imad

doi:10.1109/glocom.2010.5684014

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…Hence, data aggregation approaches should be designed considering the priority of sensor applications or data types. Most existing approaches [1], [4]- [8], [18], [23] do not consider this criteria to design a data aggregation approach.…”

Section: A High Level Architecturementioning

confidence: 99%

Dynamic Data Aggregation Approach for Sensor-Based Big Data

Al-kahtani¹,

Karim²

2018

ijacsa

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Sensors are being used in thousands of applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control. As these applications collect zettabytes of data everyday sensors play an integral role into big data. However, most of these data are redundant, and useless. Thus, efficient data aggregation and processing are significantly important in reducing redundant and useless data in sensor-based big data frameworks. Current studies on big data analytics do not focus on aggregating and filtering data at multiple layers of big data frameworks especially at the lower level at data collecting nodes (sensors) that reduce the processing overhead at the upper layer, i.e., big data server. Thus, this paper introduces a multi-tier data aggregation technique for sensorbased big data frameworks. While this work focuses more on data aggregation at sensor networks. To achieve energy efficiency it also demonstrates that efficient data processing at lower layers (sensor) significantly reduces overall energy consumption of the network and data transmission latency.

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Section: A High Level Architecturementioning

confidence: 99%

Dynamic Data Aggregation Approach for Sensor-Based Big Data

Al-kahtani¹,

Karim²

2018

ijacsa

Self Cite

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“…For instance, the application of target tracking might require periodic data collection for more precision. In this paper, we exploit the data aggregation scheme in and propose an energy efficient MAC protocol for target tracking based on dynamic convey tree collaboration. Thus, we only consider data aggregation in the sensor plane of a dynamic convey tree triggered by any target in the sensing field.…”

Section: Distributed Time Division Multiple Access Protocol Designmentioning

confidence: 99%

An energy efficient medium access control protocol for target tracking based on dynamic convey tree collaboration in wireless sensor networks

Wang

Shi

et al. 2012

Int J Communication

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SUMMARY Collisions and interferences among nodes pose a challenge for data aggregation in many applications, such as target tracking by adopting dynamic convey tree‐based collaboration (DCTC). Because coordination with a time division multiple access (TDMA) medium access control (MAC) might provide an opportunity for better interference control, in this paper, we refine slot allocation to nodes in a dynamic convey tree and design an energy efficient MAC protocol called dynamic‐time division multiple access (D‐TDMA). The D‐TDMA protocol avoids collisions and interferences and allocates contiguous active slots to nodes as far as possible during data aggregation from leaf nodes to a root node. As a result, energy consumption in switching from sleep to active state is saved. In comparison with Always‐On scheme, theoretical analysis results show that the proposed protocol D‐TDMA improves energy efficiency by up to 28.3% during one data aggregation. Furthermore, simulation results show that D‐TDMA does not suffer from collisions and interferences among nodes in a dynamic convey tree and performs similar throughput to that of Always‐On scheme. Because of its advantage of parallel and continuous scheduling among node pairs in the convey tree, D‐TDMA outperforms efficient slot reservation in both energy efficiency and low delay because of to slot saving. Copyright © 2012 John Wiley & Sons, Ltd.

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“…Once data packets are scheduled, they are routed through the nodes of a path from the source node to the BS. We introduce a dynamic data aggregation scheme , where aggregation types and functions change automatically or by end user on the basis of the application types and requirements. For instance, data sensing time of sensors can be set to a specific time of a day or can be periodic or can be event‐triggered on the basis of the data requirement or precision.…”

Section: Proposed Frameworkmentioning

confidence: 99%

An integrated framework for wireless sensor network management

Karim

Mahmoud

Khan

2012

Wirel. Commun. Mob. Comput.

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Wireless Sensor Networks (WSNs) have significant potential in many application domains, and are poised for growth in many markets ranging from agriculture and animal welfare to home and office automation. Although sensor network deployments have only begun to appear, the industry still awaits the maturing of this technology to realize its full benefits. The main constraints to large scale commercial adoption of sensor networks are the lack of available network management and control tools for determining the degree of data aggregation prior to transforming it into useful information, localizing the sensors accurately so that timely emergency actions can be taken at exact location, and scheduling data packets so that data are sent based on their priority and fairness. Moreover, due to the limited communication range of sensors, a large geographical area cannot be covered, which limits sensors application domain. Thus, we investigate a scalable and flexible WSN architecture that relies on multi-modal nodes equipped with IEEE 802.15.4 and IEEE 802.11 in order to use a Wi-Fi overlay as a seamless gateway to the Internet through WiMAX networks. We focus on network management approaches such as sensors localization, data scheduling, routing, and data aggregation for the WSN plane of this large scale multimodal network architecture and find that most existing approaches are not scalable, energy efficient, and fault tolerant. Thus, we introduce an efficient approach for each of localization, data scheduling, routing, and data aggregation; and compare the performance of proposed approaches with existing ones in terms of network energy consumptions, localization error, end-to-end data transmission delay and packet delivery ratio. Simulation results, theoretical and statistical analysis show that each of these approaches outperforms the existing approaches. To the best of our knowledge, no integrated network management solution comprising efficient localization, data scheduling, routing, and data aggregation approaches exists in the literature for a large scale WSN. Hence, we efficiently integrate all network management components so that it can be used as a single network management solution for a large scale WSN, perform experimentations to evaluate the performance of the proposed framework, and validate the results through statistical analysis. Experimental results show that our proposed framework outperforms existing approaches in terms of localization energy consumptions, localization accuracy, network energy consumptions and end-to-end data transmission delay.

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An Efficient Data Aggregation Approach for Large Scale Wireless Sensor Networks

Cited by 6 publications

References 16 publications

Dynamic Data Aggregation Approach for Sensor-Based Big Data

Dynamic Data Aggregation Approach for Sensor-Based Big Data

An energy efficient medium access control protocol for target tracking based on dynamic convey tree collaboration in wireless sensor networks

An integrated framework for wireless sensor network management

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