QUOIN: Incentive Mechanisms for Crowd Sensing Networks

Ota, Kaoru; Dong, Mianxiong; Gui, Jinsong; Liu, Anfeng

doi:10.1109/mnet.2017.1500151

Cited by 101 publications

(53 citation statements)

References 12 publications

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“…(6) end if (7) if node ∈ 1 , calculate self-energy-consumption and add it to its information. (8) end for (9) end for (10) Sink node receive data from node V ∈ 1 (11) Sink node select max energy consumption value from data received, broadcast it to all node (12) for = 1 to ⌈ / ⌉: (13) for = 1 to ℎ : (14) node V in ( , ) receives the max energy consumption value (15) node V in ( , ) calculates and adjusts transmitting power according to max energy consumption value (16) node V in ( , ) calculates the new (17) end for (18) end for Algorithm 1: Adaptive transmitting power algorithm.…”

Section: Aprf Schemementioning

confidence: 99%

“…In addition to network lifetime, reliable data collection is another important performance metric [13][14][15]. Since the main goal of wireless sensor networks is to provide a wide variety of data for various applications in order to make decisions based on the resulting data, highly reliable data collection is of great importance to the network and applications [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of this method is less energy consumption, and the biggest drawback is that data delay is high due to multiple retransmissions. (b) Collaborative communication method [5,15,16,25,31]: collaborative communication method is also an effective way to improve the reliability of network transmission. In this method, when the sender sends data to the destination node, the intermediate node also monitors the data, and if the destination node does not receive the data successfully, the intermediate node will retransmit the data.…”

Section: Introductionmentioning

confidence: 99%

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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: Aprf Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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 WSNs, data codes are transmitted for device-to-device communication (D2D) by multihop transmission way. To save energy, the duty cycle work model is adopted by nodes [38,40,41]. In the duty cycle work model, the node has two status: sleep and active.…”

Section: Introductionmentioning

confidence: 99%

Construction Low Complexity and Low Delay CDS for Big Data Code Dissemination

et al. 2018

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The diffusion of codes is an important processing technology for big data networks. In previous scheme, data analysis was conducted for small samples of big data and complex problems that cannot be processed by big data technology. Due to the limited capacity of intelligence device, a better method is to select a set of nodes (intelligence device) to form a connected dominating set (CDS) to save energy, and constructing CDS is proved to be a complete NP problem. However, it is a challenge to reduce the communication delay and complexity for urgent data transmission in big data. In this paper, an appropriate duty cycle control (ADCC) scheme is proposed to reduce communication delay and complexity while improving energy efficient in CDS-based WSNs. In ADCC scheme, the method for constructing CDS is proposed at lower complexity. Nodes in CDS are selected according to the degree of nodes. Then, duty cycle of dominator nodes in CDS is higher than that of dominated nodes, so the communication delay in the proposed scheme is far less than that of previous scheme. The duty cycle of dominated nodes is small to save energy. This is because the number of dominator nodes in CDS is far less than the number of dominated nodes whose duty cycle is small; thus, the total energy consumption of the network is less than that of the previous scheme. As a result, the performance of energy consumption and communication delay and complex have been improved. Its complexity O ∑ v i=0 m − i + 2m − 2v − s is reduced a lot for big data. The theoretical analysis shows that compared to the previous scheme, the transmission delay can be reduced 25-92% and the energy efficiency is improved by about 80% while retaining network lifetime.

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“…The wireless sensor networks have been developed for a long time [21][22][23][24][25][26]; with the development of wireless portable devices and sensor technology, the application of wireless sensor network in the industrial field becomes the focus of attention [2,4]. Industrial wireless sensor networks (IWSNs) is emerging in this background; it does not require wiring to be deployed at any time and has simple requirements for complex industrial sites; the device is small and easy to deploy and has powerful functions that can be used to detect and monitor a variety of visible and invisible physical phenomena in close range and high precision; and the strong practicality makes it to have broad application prospects in various fields of industrial production [1,2,9,10], especially with the rise of cloud computing [27] and fog computing [28][29][30], to make its development face greater opportunities.…”

Section: Introductionmentioning

confidence: 99%

Cross-layer design for reducing delay and maximizing lifetime in industrial wireless sensor networks

Tan

Liu

Zhao

et al. 2018

J Wireless Com Network

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Low delay and long lifetime are a very important issue for industrial wireless sensor networks (IWSNs) in which it require long-time monitoring of industrial sites and respond quickly to events that is monitored; therefore, high delay communications can cause serious damage to property and personnel at industrial field. Due to delay, lifetime, and other performance involved to multiple layers, it is difficult to optimize from a single layer. Therefore, a cross-layer design optimal scheme for reducing delay and maximizing lifetime (RDML) scheme is proposed for IWSNs which is from several layers such as transmitted power, duty cycle, and node deployment positions to optimize the network performance of delay and lifetime etc. Firstly, due to the node which sends a packet within a cycle, different duty cycle leads to different selection of the modulation level, resulting in different power consumption efficiency of transmitting data. Through careful analysis, the optimal value of the duty cycle is given which has the lowest energy consumption per bit. In fact, the energy consumption of the node is not balanced. Therefore, an optimization method of changing the duty cycle is proposed. In this paper, larger duty cycle is chosen for nodes with residual energy to improve the reliability of data transmission, reducing the probability of data retransmission, so that the network delay can be reduced in IWSNs. Third, based on the previous analysis, a network optimization deployment algorithm is proposed, which not only maximizes the energy efficiency of a single node but also maximizes the network lifetime and the total network energy efficiency. Both our comprehensive theoretical analysis results indicate that the performance of RDML scheme is better than the previous studies. Relative to equal distance and optimal duty cycle scheme, the RDML scheme can reduce the delay by 19-30% and increase the lifetime by more than 43%.

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QUOIN: Incentive Mechanisms for Crowd Sensing Networks

Cited by 101 publications

References 12 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

Construction Low Complexity and Low Delay CDS for Big Data Code Dissemination

Cross-layer design for reducing delay and maximizing lifetime in industrial wireless sensor networks

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