Energy-Collision Aware Data Aggregation Scheduling for Energy Harvesting Sensor Networks

Chen, Quan; Gao, Hong; Cai, Zhipeng; Cheng, Lianglun; Li, Jianzhong

doi:10.1109/infocom.2018.8486366

Cited by 58 publications

(22 citation statements)

References 19 publications

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“…Due to the random nature of ambient sources (e.g., shadows over solar energy harvesting panels), we assume that each node harvests energy with different efficiency. This is the same as described existing studies [6], [8]. No mode can harvest additional energy if the current energy storage is already in the maximum energy storage state.…”

Section: Energy Harvesting and Energy Cooperationmentioning

confidence: 79%

“…We used the Qualnet 7.4 network simulator * 1 , and we set our experiments similarly to previous studies [6], [10], [26], [31].…”

Section: Settingmentioning

confidence: 99%

“…The maximum energy of each node was 100 mJ. We decided a time slot as 4 seconds, and a node harvested energy per time slot randomly from 0.01 mJ to 1 mJ [6]. For example, the Silicon Labs' ENERGY-HARVESTING-RD * 2 can harvest energy from 0.01 mJ to 1 mJ per second.…”

Section: Settingmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Energy Depriving Malicious Nodes by Unsupervised Learning in Energy Harvesting Cooperative Wireless Sensor Networks

Gao

Amagata

Maekawa

et al. 2020

Journal of Information Processing

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This paper presents an unsupervised learning-based method for detecting energy depriving malicious nodes in an energy harvesting cooperative wireless sensor network (EHC-WSN). In EHC-WSNs, nodes wirelessly transfer a portion of their energy to their neighboring nodes if their neighboring nodes lack energy. A malicious energy depriving mode may falsibly show that it has little energy and in this way obtains energy from neighboring nodes, thus depriving them of energy. To detect these malicious nodes, we utilize a clustering method. In our method, each node first observes the energy of its neighboring nodes, then it utilizes this information to obtain data points for the clustering. After clusters are formed, each node judges on a cluster of data points from malicious nodes and makes a malicious node determination. We investigate the performance of our method and confirm that our method outperforms the baseline method in terms of detection accuracy and false detection rate.

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Section: Energy Harvesting and Energy Cooperationmentioning

confidence: 79%

“…We used the Qualnet 7.4 network simulator * 1 , and we set our experiments similarly to previous studies [6], [10], [26], [31].…”

Section: Settingmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Energy Depriving Malicious Nodes by Unsupervised Learning in Energy Harvesting Cooperative Wireless Sensor Networks

Gao

Amagata

Maekawa

et al. 2020

Journal of Information Processing

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“…Besides, the energy consumption for transmitting and computing big sensory data is quite high. Therefore, the existing sensory data acquisition, routing [3][4][5][6], data collection [7][8][9], and data computation [10,11] techniques are no longer applicable for the big sensory data. The authors in [3] design cluster-based routing protocol for wireless sensor networks with nonuniform distribution of sensors.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [7] study the approximated holistic aggregation algorithms based on uniform sampling. And the authors in [8] propose aggregation scheduling algorithms for energy harvesting sensor networks. However, the mass sensory data will increase the aggregation latency and affect the performances of these algorithms.…”

Section: Introductionmentioning

confidence: 99%

Retrieving the relative kernel dataset from big sensory data for continuous queries in IoT systems

Zhu

Wang

Cheng

et al. 2019

J Wireless Com Network

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Internet of Things (IoT) is rapidly developed and widely deployed in recent years, which makes the sensory data generated by IoT systems explode. The huge amount of sensory data generated by some IoT systems has already exceeded the storage, transmission, and computation capacities of IoT systems. However, the valuable sensory data which is highly related to a query in an IoT system is relatively small. The sensory data which is highly related to a query Q forms the relative kernel dataset of Q. Therefore, retrieving sensory data in the relative kernel dataset of a query instead of the raw sensory data will reduce the heavy burdens of an IoT system in terms of transmission and computation and then reduce the energy consumption of the IoT system. In this paper, we investigate the problem of retrieving relative kernel dataset from big sensory data for continuous queries in IoT systems. Two algorithms, relative kernel dataset retrieving algorithm and piecewise linear fitting-based relative kernel dataset retrieving algorithm, are proposed to retrieve the relative kernel dataset for continuous queries. Beside, algorithms for estimating the answers of continuous queries based on their relative kernel datasets are also proposed. Extensive simulation results are provided to verify the effectiveness and energy efficiency of the proposed algorithms.

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A classy energy efficient spider monkey optimization based clustering and data aggregation models for wireless sensor network

Arunachalam

Vimal

Gomathi³

et al. 2022

Concurrency and Computation

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SUMMARY Establishment of energy efficient and reliable data routing in wireless sensor network (WSN) is one of the most critical and challenging task in the recent days. Also, the overall performance and lifetime of WSN is highly depends on the energy level of sensor nodes, hence it is most essential to save the energy of network. For this purpose, the different types of clustering and data aggregation mechanisms are developed in the conventional works, which are focusing on improving both the energy conservation and lifetime of network. Yet, it facing the challenges of increased computational complexity, inefficient routing of data, high controlling overhead, and reduced reliability. Thus, the proposed work objects to develop a novel energy efficient mechanism by integrating the functionalities of advanced clustering, path selection, and data aggregation methodologies. Here, the spider monkey optimization based energy efficient routing protocol is developed for optimally selecting the cluster head (CH) based on certain parameters of energy, distance, and weight value. In this framework, the data transmission is performed between the source to destination nodes through the relay nodes and CHs, which helps to minimize the energy consumption of network. Then, the Classy Bellman‐Ford algorithm is deployed for identifying the best paths having shortest distance with the sink nodes. Consequently, an anticipated data aggregation mechanism is utilized for ensuring the security and reliability of data transmission in WSN. For evaluation assessment, various performance metrics have been utilized to validate the results of proposed methodology, and also the obtained values are compared with some other recent state‐of‐the‐art models for proving the betterment of proposed mechanism.

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Energy-Collision Aware Data Aggregation Scheduling for Energy Harvesting Sensor Networks

Cited by 58 publications

References 19 publications

Detecting Energy Depriving Malicious Nodes by Unsupervised Learning in Energy Harvesting Cooperative Wireless Sensor Networks

Detecting Energy Depriving Malicious Nodes by Unsupervised Learning in Energy Harvesting Cooperative Wireless Sensor Networks

Retrieving the relative kernel dataset from big sensory data for continuous queries in IoT systems

A classy energy efficient spider monkey optimization based clustering and data aggregation models for wireless sensor network

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