A review on stochastic approach for dynamic power management in wireless sensor networks

Pughat, Anuradha; Sharma, Vidushi

doi:10.1186/s13673-015-0021-6

Cited by 39 publications

(21 citation statements)

References 54 publications

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“…In general, sensor nodes have insufficient computing capability in the wireless sensor network. In addition, they have resource constraints with respect to energy, memory, communication, and latency in communication [11,[14][15][16][17][18][19]]. …”

Section: Introductionmentioning

confidence: 99%

Data-Filtering System to Avoid Total Data Distortion in IoT Networking

2017

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Abstract:In the Internet of Things (IoT) networking, numerous objects are connected to a network. They sense events and deliver the sensed information to the cloud. A lot of data is generated in the IoT network, and servers in the cloud gather the sensed data from the objects. Then, the servers analyze the collected data and provide proper intelligent services to users through the results of the analysis. When the server analyzes the collected data, if there exists malfunctioning data, distortional results of the analysis will be generated. The distortional results lead to misdirection of the intelligent services, leading to poor user experience. In the analysis for intelligent services in IoT, malfunctioning data should be avoided because integrity of the collected data is crucial. Therefore, this paper proposes a data-filtering system for the server in the cloud. The proposed data-filtering system is placed in front of the server and firstly receives the sensed data from the objects. It employs the naïve Bayesian classifier and, by learning, classifies the malfunctioning data from among the collected data. Data with integrity is delivered to the server for analysis. Because the proposed system filters the malfunctioning data, the server can obtain accurate analysis results and reduce computing load. The performance of the proposed data-filtering system is evaluated through computer simulation. Through the simulation results, the efficiency of the proposed data-filtering system is shown.

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Section: Introductionmentioning

confidence: 99%

Data-Filtering System to Avoid Total Data Distortion in IoT Networking

2017

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“…Spectrum sensing and data transmission in cognitive relay network with optimal power allocation strategy is studied in [21]. Performance evaluation of data aggregation in cluster based wireless sensor network, effective implementation of security based algorithmic approach in mobile adhoc network, and stochastic approach for dynamic power management in wireless sensor network are studied in [22][23][24]. However in cooperative communication an efficient relay selection scheme through self-learning is studied in [25] where the authors define state, action, and reward to achieve a near optimum SER performance for relay selection.…”

Section: Related Workmentioning

confidence: 99%

An efficient transmission mode selection based on reinforcement learning for cooperative cognitive radio networks

Rahman

Lee

Koo

2016

Hum. Cent. Comput. Inf. Sci.

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Cooperative communication systems use cooperative relays for transmitting their data packets to the destination. Cooperative communication schemes having all relays participating in transmission may cause unnecessary wastes of most valuable spectrum resources. So it is mandatory to effectively select a transmission mode for cooperative cognitive radio networks (CCRNs). In this paper, an efficient transmission mode scheme based on Q-learning algorithm is proposed. State, action, and reward are defined to achieve a good performance on time delay and energy efficiency in data transmission as well as the interference to primary users during secondary users transmission. The proposed scheme selects an optimal action on the networks environment to maximize the total revenue of the multilateral metric. The simulation result shows that the proposed scheme can efficiently support the determination for the transmission mode and outperforms conventional schemes for a single metric in CCRNs.

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“…Sensor deployment problems have been studied in a variety of fields, including machine learning, robotics, computer vision, and computational geometry [10][11][12][13][14][15][16][17][18]. Especially in the field of machine learning and computational geometry, researches on sensor coverage issues are actively under way.…”

Section: Related Workmentioning

confidence: 99%

SensDeploy: efficient sensor deployment strategy for real-time localization

Lee

Shin

2017

Hum. Cent. Comput. Inf. Sci.

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In order to estimate the location of the user, the previous studies introduced many sensor deployment methods applying the sensor network. The important issues to consider when placing the sensors are a configuration cost and detection area of a sensor network. In other words, the sensors consisting the network should be optimally deployed by taking into account the coverage and connectivity of them. In this paper, a sensor signal is modeled as the Gaussian distribution based signal points group, and signal points in overlapping region between two different sensors are classified by e-SVM (support vector machine) method as each sensor group. In addition, a trilateration technique is used for estimating the position of a moving object. At this time, we efficiently deploy the sensors with f-Apriori method to maintain the connectivity between the sensors as well as to apply the trilateration. The proposed method can be utilized for optimal placement of sensors if we know a detection range of one sensor. In this paper, we introduce more effective and adaptive deployment method to consist the sensor network as taking into account the cost and the situation.

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A review on stochastic approach for dynamic power management in wireless sensor networks

Cited by 39 publications

References 54 publications

Data-Filtering System to Avoid Total Data Distortion in IoT Networking

Data-Filtering System to Avoid Total Data Distortion in IoT Networking

An efficient transmission mode selection based on reinforcement learning for cooperative cognitive radio networks

SensDeploy: efficient sensor deployment strategy for real-time localization

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