CAC: Context Adaptive Clustering for Efficient Data Aggregation in Wireless Sensor Networks

Jin, Guang-yao; Park, Myong-Soon

doi:10.1007/11753810_98

Cited by 13 publications

(6 citation statements)

References 6 publications

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“…Thus, we can apply the spatial correlation to compress transmission data. Spatial correlation compression schemes include minimum fusion Steiner tree (MFST) [25], adaptive fusion Steiner tree (AFST) [26], data fusion (DFuse) [27], context adaptive clustering (CAC) [28], CODA [29], clustered aggregation (CAG) [30], prediction-based monitoring (PREMON) [31], and data fusion using temporal and spatial correlations (DF-TS) [32]. Different from spatial correlation, data correlation uses information theory to encode high probability data with short length output.…”

Section: Related Workmentioning

confidence: 99%

Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence

et al. 2020

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Emerging IoT (Internet of Things) technologies have enjoyed tremendous success in a variety of applications. Since sensors in IoT consume a lot of energy to transmit their data, data compression used to prolong system lifetime has become a hot research topic. In many real-world applications, such as IEI (IoT of environmental intelligence), the required sensing data usually have limited error tolerance according to the QoS2 (quality of sensor service) or QoD (quality of decision-making) required. However, the bounded-error-pruned sensor data can achieve higher data correlation for better compression without jeopardizing QoS2/QoD. Moreover, the sensing data in widely spread sensors usually have strong temporal and spatial correlations. We thus propose a BESDC (bounded-error-pruned sensor data compression) scheme to achieve better leverage between the bounded error and compression ratio for sensor data. In this paper, our experiments on a sensor network of two-tier tree architecture consider four different environmental datasets including PM 2.5, CO, temperature and seismic wave with different scales of bounded errors. With the bounded errors required by the given IEI applications, our BESDC can reduce the total size of data transmission to minimize both energy consumption in sensor-tier devices and storage of fog-tier servers. The experimental results demonstrate that our BESDC can reduce transmission data by over 55% and save 50% energy consumption when assigning 1% of error tolerance within QoS2/QoD requirement. To the best of our knowledge, the proposed BESDC scheme can help other energy-efficient IoT schemes applying network topologies and routing protocols to further enhance energy-efficient IoT services.

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Section: Related Workmentioning

confidence: 99%

Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence

et al. 2020

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“…However, its aspects have not been utilized in the WSN area until recently. In [16], authors have proposed a "Context Adaptive Clustering" protocol which forms clusters of sensors with similar output data within the bound of given tolerance parameter. Moreover, a simple data aggregation technique has been employed without generating large errors.…”

Section: Key Issues Of Related Workmentioning

confidence: 99%

“…Thus, this attribute can be vigilantly utilized in the cluster formation and optimize normal operations. Some of the recent publications [16] [17] reflect the applicability of such technique. In this paper, we propose a routing protocol called the Context Aware Multilayer Hierarchical Protocol (CAMHP) which is based on the context of the environment.…”

Section: Introductionmentioning

confidence: 97%

Context-Aware Multilayer Hierarchical Protocol for Wireless Sensor Network

Haque

Matsumoto

Yoshida

2009

2009 Third International Conference on Sensor Technologies and Applications

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Evolution in Wireless Sensor Network has broadened its pervasive and ubiquitous applications in numerous fields. These applications often require accurate information collecting as well as uninterrupted, prolonged active service. Among the constituting elements of WSN, an energy efficient routing protocol plays a significant role in fulfilling such requirements. In this paper, an energy efficient routing protocol, Context Aware Multilayer Hierarchical Protocol (CAMHP) is proposed where the cluster formation is based on the context of the environment. This three layer hierarchical protocol implements cluster head and super cluster head role rotation techniques for uniform energy consumption across the network. Moreover, an efficient procedure is utilized to avoid similar data traffic from nodes to base station. Simulation results show that the proposed hierarchical protocol achieves significant performance improvement through progressive energy efficiency and longer active service.

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“…The approach defines a weight for each sensor's data which depends on the distance from the sample position to the target position. Another method proposed in [24] and [25] addresses the spatial correlation measurement by calculating the offset between different sensor readings. This approach simply calculates the error between two readings.…”

Section: B Motivation and Contributionsmentioning

confidence: 99%

Distributed Clustering-Based Aggregation Algorithm for Spatial Correlated Sensor Networks

Guo

Xiao

et al. 2011

IEEE Sensors J.

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Abstract-In wireless sensor networks, it is already noted that nearby sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires the research of in-network data aggregation. In this paper, an -local spatial clustering algorithm for sensor networks is proposed. By measuring the spatial correlation between data sampled by different sensors, the algorithm constructs a dominating set as the sensor network backbone used to realize the data aggregation based on the information description/summarization performance of the dominators. In order to evaluate the performance of the algorithm a pattern recognition scenario over environmental data is presented. The evaluation shows that the resulting network achieved by our algorithm can provide environmental information at higher accuracy compared to other algorithms.

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CAC: Context Adaptive Clustering for Efficient Data Aggregation in Wireless Sensor Networks

Cited by 13 publications

References 6 publications

Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence

Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence

Context-Aware Multilayer Hierarchical Protocol for Wireless Sensor Network

Distributed Clustering-Based Aggregation Algorithm for Spatial Correlated Sensor Networks

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