Distributed Multi-Representative Re-Fusion Approach for Heterogeneous Sensing Data Collection

Liu, Anfeng; Liu, Xiao; Wei, Tianyi; Yang, Laurence T.; Rho, Seungmin; Paul, Anand

doi:10.1145/2974021

Cited by 25 publications

(20 citation statements)

References 31 publications

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“…The development of cloud computing benefits from the great enhancement of the ability and scope to collect data [9][10][11]. For example, the Internet of Things (IoT) [12][13][14] leverages the ubiquity of smart sensor-equipped devices such as smartphones, iPads and vehicle sensor devices [15][16][17][18][19], unmanned aerial vehicles [9,10] and so on to collect information at low costs and provides a new paradigm for solving complex data sensing based on applications of the significant demand for critical infrastructures such as industrial systems and massive critical infrastructures [20][21][22][23], remote patient care systems in healthcare [24,25], intelligent traffic management [13], and automated vehicles in transportation, environmental [26][27][28], correlation were sent to the same route path at first, it is believed that the probability and the ratio of aggregation would be enhanced greatly by such an active approach, which can significantly reduce network traffic.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Aggregation Based on Content Routing Scheme for Cloud Computing

Liu

et al. 2017

Symmetry

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Cloud computing has emerged as today's most exciting computing paradigm for providing services using a shared framework, which opens a new door for solving the problems of the explosive growth of digital resource demands and their corresponding convenience. With the exponential growth of the number of data types and data size in so-called big data work, the backbone network is under great pressure due to its transmission capacity, which is lower than the growth of the data size and would seriously hinder the development of the network without an effective approach to solve this problem. In this paper, an Intelligent Aggregation based on a Content Routing (IACR) scheme for cloud computing, which could reduce the amount of data in the network effectively and play a basic supporting role in the development of cloud computing, is first put forward. All in all, the main innovations in this paper are: (1) A framework for intelligent aggregation based on content routing is proposed, which can support aggregation based content routing; (2) The proposed IACR scheme could effectively route the high aggregation ratio data to the data center through the same routing path so as to effectively reduce the amount of data that the network transmits. The theoretical analyses experiments and results show that, compared with the previous original routing scheme, the IACR scheme can balance the load of the whole network, reduce the amount of data transmitted in the network by 41.8%, and reduce the transmission time by 31.6% in the same network with a more balanced network load.

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Section: Introductionmentioning

confidence: 99%

Intelligent Aggregation Based on Content Routing Scheme for Cloud Computing

Liu

et al. 2017

Symmetry

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“…The energy consumption model of this paper is similar to [14,15], and the energy consumption of nodes is mainly composed of event sensing, data transmission, data receiving and low power listening. Therefore, the energy consumption model can be expressed as:…”

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

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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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“…Based on IoT technologies, the Industrial Internet of Things (IIoTs) [13] corporates machine learning and big data technology, sensor data, and machine-to-machine (M2M) communications that have existed in industrial areas for years [14][15][16]. In IIoTs, huge amounts of wireless sensor nodes [17][18][19][20][21][22][23][24] can be deployed more conveniently than the previous wired network to monitor the whole process of industrial production from many angles, in real time or semi-real time. In such way, IIoTs is creating a new world for industrial manufacturing, where the workers or managers can manage their industrial manufacturing in more informed ways and can make more opportune and better informed decisions.…”

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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Distributed Multi-Representative Re-Fusion Approach for Heterogeneous Sensing Data Collection

Cited by 25 publications

References 31 publications

Intelligent Aggregation Based on Content Routing Scheme for Cloud Computing

Intelligent Aggregation Based on Content Routing Scheme for Cloud Computing

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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