Key parameters decision for cloud computing: Insights from a multiple game model

Wang, Jiaze; Liu, Anfeng; Zhang, Shigeng

doi:10.1002/cpe.4200

Cited by 14 publications

(15 citation statements)

References 20 publications

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“…Figure 13 presents the simulation results of the energy consumptions of different nodes. With more nodes in a cluster, much energy is consumed because it must handle more amount of data packets, which can be seen from Equation (22). Since the power for transmitting a packet is increased with RICC, nodes always have a larger energy consumption compared with NCCC, except for those in the area very close to the sink.…”

Section: Energy Consumptionmentioning

confidence: 99%

“…The Internet of Things (IoT) [1][2][3][4] uses smart equipment such as sensor-based devices [5][6][7][8][9][10][11], smartphones [12] and vehicle sensor devices [13] to gathering information and give a different method for carrying out complex data-sensing-based tasks to satisfy the significant demands of critical infrastructures [14][15][16][17][18], such as environmental and weather monitoring systems [19][20][21][22][23]. Wireless Sensor Networks (WSNs) has large numbers of sensor nodes, it has huge potential to solve military and civilian domains [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Reliability Improved Cooperative Communication over Wireless Sensor Networks

Chen

Liu

et al. 2017

Symmetry

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Abstract:With the development of smart devices and connection technologies, Wireless Sensor Networks (WSNs) are becoming increasingly intelligent. New or special functions can be obtained by receiving new versions of program codes to upgrade their software systems, forming the so-called smart Internet of Things (IoT). Due to the lossy property of wireless channels, data collection in WSNs still suffers from a long delay, high energy consumption, and many retransmissions. Thanks to wireless software-defined networks (WSDNs), software in sensors can now be updated to help them transmit data cooperatively, thereby achieving more reliable communication. In this paper, a Reliability Improved Cooperative Communication (RICC) data collection scheme is proposed to improve the reliability of random-network-coding-based cooperative communications in multi-hop relay WSNs without reducing the network lifetime. In WSNs, sensors in different positions can have different numbers of packets to handle, resulting in the unbalanced energy consumption of the network. In particular, nodes in non-hotspot areas have up to 90% of their original energy remaining when the network dies. To efficiently use the residual energy, in RICC, high data transmission power is adopted in non-hotspot areas to achieve a higher reliability at the cost of large energy consumption, and relatively low transmission power is adopted in hotspot areas to maintain the long network lifetime. Therefore, high reliability and a long network lifetime can be obtained simultaneously. The simulation results show that compared with other scheme, RICC can reduce the end-to-end Message Fail delivering Ratio (MFR) by 59.4%-62.8% under the same lifetime with a more balanced energy utilization.

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Section: Energy Consumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability Improved Cooperative Communication over Wireless Sensor Networks

Chen

Liu

et al. 2017

Symmetry

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“…The main parameters of the system model used in this paper are similar to [7], and the parameters values are derived from the internal data tables of the prototype sensor nodes. Tables 1 and 2 list the relevant parameters used in this paper, the remaining parameters not in tables will be described in the specific calculation.…”

Section: Network Parametersmentioning

confidence: 99%

“…Industrial Internet of Things (IIoT) [1][2][3][4][5] as well as cloud computing [6][7][8][9][10] leverage the ubiquity of sensor-equipped devices such as smart portable devices, and smart sensor nodes to collect information at a low cost, providing a new paradigm for solving the complex sensing applications from the significant demands of critical infrastructure such as surveillance systems [11][12][13][14][15], remote patient…”

Section: Introductionmentioning

confidence: 99%

Fast and Efficient Data Forwarding Scheme for Tracking Mobile Targets in Sensor Networks

Zhao

Liu

et al. 2017

Symmetry

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Abstract:Transferring emergent target tracking data to sinks is a major challenge in the Industrial Internet of Things (IIoT), because inefficient data transmission can cause significant personnel and property loss. For tracking a constantly moving mobile target, sensing data should be delivered to sinks continuously and quickly. Although there is some related research, the end to end tracking delay is still unsatisfying. In this paper, we propose a Fast and Efficient Data Forwarding (FEDF) scheme for tracking mobile targets in sensor networks to reduce tracking delay and maintain a long lifetime. Innovations of the FEDF scheme that differ from traditional scheme are as follows: firstly, we propose a scheme to transmit sensing data through a Quickly Reacted Routing (QRR) path which can reduce delay efficiently. Duty cycles of most nodes on a QRR path are set to 1, so that sleep delay of most nodes turn 0. In this way, end to end delay can be reduced significantly. Secondly, we propose a perfect method to build QRR path and optimize it, which can make QRR path work more efficiently. Target sensing data routing scheme in this paper belongs to a kind of trail-based routing scheme, so as the target moves, the routing path becomes increasingly long, reducing the working efficiency. We propose a QRR path optimization algorithm, in which the ratio of the routing path length to the optimal path is maintained at a smaller constant in the worst case. Thirdly, it has a long lifetime. In FEDF scheme duty cycles of nodes near sink in a QRR path are the same as that in traditional scheme, but duty cycles of nodes in an energy-rich area are 1. Therefore, not only is the rest energy of network fully made use of, but also the network lifetime stays relatively long. Finally, comprehensive performance analysis shows that the FEDF scheme can realize an optimal end to end delay and energy utilization at the same time, reduce end to end delay by 87.4%, improve network energy utilization by 2.65%, and ensure that network lifetime is not less than previous research.

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“…Therefore, it is important to quickly send the perceived information to the sink. So it is very important to study how to reduce the delay effectively for IIoTs, edge network, and society network as well as cloud computing [33][34][35], and there are also some related research work [4,6,24,29,32,[36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things

Xiao

et al. 2018

J Wireless Com Network

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The Industrial Internet of Things (IIoTs) is creating a new world which incorporates machine learning, sensor data, and machine-to-machine (M2M) communications. In IIoTs, the length of the transmission delay is one of the pivotal performance because dilatory communication will cause heavy losses to industrial applications. In this paper, a learning-based synchronous (LS) approach from forwarding nodes is proposed to reduce the delay for IIoTs. In an asynchronous Media Access Control protocol, when senders need to send data, they always require to wait for their corresponding receiver to wake up. Thus, the delay here is greater than in the synchronous network. However, the synchronization cost of the whole network is enormous, and it is difficult to maintain. Therefore, LS mechanism uses a partial synchronization approach to reduce synchronization costs while effectively reducing delay. In LS approach, instead of synchronizing the nodes in the entire network, only sender nodes and part of the nodes in their forwarding node set are synchronized by self-learning methods, and accurate synchronization is not required here. Thus, the delay can be effectively reduced under the low cost. Secondly, the nodes near sink maintain the original duty cycle, while the nodes in the regions away from the sink use their remaining energy and perform synchronization operations, so as not to damage the network lifetime. Finally, because the synchronization in this paper is based on different synchronization periods among different nodes, it can improve the network performance by reducing the conflict between simultaneous data transmission. The theoretical analysis results show that compared with the previous approach FFSC, LS approach can reduce the end-to-end delay by 5. 13-11.64% and increase the energy efficiency by 14.29-17.53% under the same lifetime with a more balanced energy utilization.

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Key parameters decision for cloud computing: Insights from a multiple game model

Cited by 14 publications

References 20 publications

Reliability Improved Cooperative Communication over Wireless Sensor Networks

Reliability Improved Cooperative Communication over Wireless Sensor Networks

Fast and Efficient Data Forwarding Scheme for Tracking Mobile Targets in Sensor Networks

Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things

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