Energy Minimization in Multi-Task Software-Defined Sensor Networks

Zeng, Deze; Li, Peng; Guo, Song; Miyazaki, Toshiaki; Hu, Jiankun; Xiang, Yong

doi:10.1109/tc.2015.2389802

Cited by 122 publications

(79 citation statements)

References 34 publications

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“…Data aggregation is a method of reducing data transmission that is closely related to the strategy of this paper [1,3,6,27] and is an important type of operation in wireless sensor networks [37][38][39][40][41]. The basic principle is that there is a correlation between the data perceived among dense sensor network nodes.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Transmission Power Control for Reliable Data Forwarding in Sensor Based Networks

Teng

Liu

et al. 2018

Wireless Communications and Mobile Computing

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In wireless sensor networks (WSNs), many applications require a high reliability for the sensing data forwarding to sink. Due to the lossy nature of wireless channels, achieving reliable communication through multihop forwarding can be very challenging. Broadcast technology is an effective way to improve the communication reliability so that the data can be received by multiple receiver nodes. As long as the data of any one of the receiver nodes is transmitted to the sink, the data can be transmitted successfully. In this paper, a cross-layer optimization protocol named Adaptive transmission Power control based Reliable data Forwarding (APRF) scheme by using broadcast technology is proposed to improve the reliability of network and reduce communication delay. The main contributions of this paper are as follows: (1) for general data aggregation sensor networks, through the theoretical analysis, the energy consumption characteristics of the network are obtained. (2) According to the case that the energy consumption of nearsink area is high and that in far-sink area is low, a cross-layer optimization method is adopted, which can effectively improve the data communication by increasing the transmission power of the remaining energy nodes. (3) Since the reliability of communication is improved by increasing the transmission power of the node, the number of retransmissions of the data packet is reduced, so that the delay of the packet reaching the sink node is reduced. The theoretical and experimental results show that, applying APRF scheme under initial transmission power of 0 dBm, although the lifetime dropped by 13.77%, delay could be reduced by 40.37%, network reliability could be reduced by 10.08%, and volume of data arriving at sink increased by 10.08% compared with retransmission-only mechanism.

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Section: Related Workmentioning

confidence: 99%

Adaptive Transmission Power Control for Reliable Data Forwarding in Sensor Based Networks

Teng

Liu

et al. 2018

Wireless Communications and Mobile Computing

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“…In deep crowd sensing (DCS), there are more detailed requests for the data collection [33][34][35][36][37][38]. First of all, in this aspect of data quality, the demand of sensing data in target area is , which means that the application is of high quality if the collected data is not less than .…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

Liu

et al. 2018

Wireless Communications and Mobile Computing

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To tackle the issue in deep crowd sensing, a Time and Location Correlation Incentive (TLCI) scheme is proposed for deep data gathering in crowdsourcing networks. In TLCI scheme, a metric named "Quality of Information Satisfaction Degree" (QoISD) is to quantify how much collected sensing data can satisfy the application's QoI requirements mainly in terms of data quantity and data coverage. Two incentive algorithms are proposed to satisfy QoISD with different view. The first algorithm is to ensure that the application gets the specified sensing data to maximize the QoISD. Thus, in the first incentive algorithm, the reward for data sensing is to maximize the QoISD. The second algorithm is to minimize the cost of the system while meeting the sensing data requirement and maximizing the QoISD. Thus, in the second incentive algorithm, the reward for data sensing is to maximize the QoISD per unit of reward. Finally, we compare our proposed scheme with existing schemes via extensive simulations. Extensive simulation results well justify the effectiveness of our scheme. The QoISD can be optimized by 81.92%, and the total cost can be reduced by 31.38%.

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“…As a result, the analytical results are more accurate and many multiple-fold coverage problems can be analyzed. Zeng et al [28] studied the coverage problem for the sensor network where the node deployment satisfies the twodimension normal distribution. Wu et al [29] proposed a probability-based calculation method for the node redundancy degree, according to which, the node could independently calculate its own redundancy degree.…”

Section: Related Workmentioning

confidence: 99%

ENCP: a new Energy-efficient Nonlinear Coverage Control Protocol in mobile sensor networks

Sun

Zhao

Xing

2018

J Wireless Com Network

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The node deployment in mobile sensor networks (MSNs) is mostly performed in a random method. However, a large number of redundant nodes may exist due to the randomness. As a result, severe data congestion may be caused and the quality of coverage (QoC) is undermined. In order to solve this QoC problem, we propose an Energy-efficient Nonlinear Coverage Control Protocol (ENCP). This protocol utilizes the normal distribution to calculate the minimal number of sensors which is required to guarantee coverage over the monitoring area. We also balance the node energy consumption and achieve the collaborative scheduling among all the sensor nodes. Meanwhile, when a certain QoC is guaranteed, we present the calculation model for the normal distribution of the sensing ranges and the proportional relationship between different parameters in the QoC function. Finally, simulation results show that the ENCP could not only improve the network QoC and network coverage rate but also effectively control the energy exhaustion at the nodes. Therefore, the network lifetime can be effectively prolonged.

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Energy Minimization in Multi-Task Software-Defined Sensor Networks

Cited by 122 publications

References 34 publications

Adaptive Transmission Power Control for Reliable Data Forwarding in Sensor Based Networks

Adaptive Transmission Power Control for Reliable Data Forwarding in Sensor Based Networks

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

ENCP: a new Energy-efficient Nonlinear Coverage Control Protocol in mobile sensor networks

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