ADMin: Adaptive monitoring dissemination for the Internet of Things

Trihinas, Demetris; Pallis, George; Dikaiakos, Marios D.

doi:10.1109/infocom.2017.8057144

Cited by 49 publications

(29 citation statements)

References 20 publications

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“…In Internet of Things data dissemination is first addressed in ADMin [11], it is a low-cost IoT framework that reduces the device energy consumption and the volume of data disseminated across the network. Based on IoT device disseminate data by monitoring streams based on run time knowledge.…”

Section: Related Workmentioning

confidence: 99%

“…computed for contact in equation 3and for time out equation 4δn (i) = (1-µ) *δn (i) + µ τk(i) (3) For Time out = (1-µ) *δn (i) (4) Where τn (i)) and δn (i) are the direct and indirect tightness of Node n in Interest i. We adopted the exponentially weighted moving average (EWMA) [11], which is one of the most effective schemes for online estimation and has been employed in many prior works, to maintain and update the nodal tightness.…”

Section: Indirect Tightness 1 Let δN (I) -Indirect Tightness Of Nodementioning

confidence: 99%

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Improved Data Dissemination using Intelligent Stimulus Mechanisms in IoT Network

Radhika¹,

Kulothungan²

2018

Proceedings of the International Conference for Phoenixes on Emerging Current Trends in Engineering and Management (PECTEAM 201

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Internet of Things (IoT) is virtually connecting object in the physical world to the internet, where a huge number of embedded devices relays on communication service offered by the internet protocols and these devices are termed as smart objects. Since the smart devices have low configuration it has constrained communication. During the communication, the data has to be disseminated with reliability. The enormous amount of data generated by heterogeneous devices do not achieve higher delivery ratio. Hence, reliable sensor data collection and appropriately disseminating data to targeted IoT device is critical in IoT environment. In the existing work, selfish behavior of each devices and the packet delivery delay are not considered. By observing this, the core challenge to disseminate the data efficiently under a unique network setting with multiple interest's data types. Two data dissemination schemes, namely the rule based data pull scheme and rule based data push scheme are proposed in this paper. The data pull scheme in IoT devices pull, the data from the data provider device, and in data push scheme the data providers generate data and push them to the intended users. In pull scheme, an effective mechanism known as cooperative game using intelligent agents is implemented. In addition, data sharing plays a crucial role between the devices where cooperativeness has been ensured in this work by cooperative game using intelligent agents and Pareto optimality. In data pushing model, a checking process is done using rules to monitor the receiver with best credits that gets the disseminated data form server which is processed by rule based online auction algorithm when comparing the Cooperative Scheme for Data Dissemination (CSDD) with existing model, this proposed model shows better performance in terms of data delivery ratio and delay in delivering the data to the destination.

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Section: Related Workmentioning

confidence: 99%

Section: Indirect Tightness 1 Let δN (I) -Indirect Tightness Of Nodementioning

confidence: 99%

Improved Data Dissemination using Intelligent Stimulus Mechanisms in IoT Network

Radhika¹,

Kulothungan²

2018

Proceedings of the International Conference for Phoenixes on Emerging Current Trends in Engineering and Management (PECTEAM 201

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“…It adapts the amount of data disseminated through the network over time [ 11 ]. Another framework transmits updates when the sensor readings are detected to be unusual, and have triggered dissemination [ 12 ], adapting the monitoring sensing intensity and dynamically adjusting the data volume payload.…”

Section: Introductionmentioning

confidence: 99%

Laplacian Scores-Based Feature Reduction in IoT Systems for Agricultural Monitoring and Decision-Making Support

Tsapparellas

Jin

Dai

et al. 2020

Sensors

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Internet of things (IoT) systems generate a large volume of data all the time. How to choose and transfer which data are essential for decision-making is a challenge. This is especially important for low-cost and low-power designs, for example Long-Range Wide-Area Network (LoRaWan)-based IoT systems, where data volume and frequency are constrained by the protocols. This paper presents an unsupervised learning approach using Laplacian scores to discover which types of sensors can be reduced, without compromising the decision-making. Here, a type of sensor is a feature. An IoT system is designed and implemented for a plant-monitoring scenario. We have collected data and carried out the Laplacian scores. The analytical results help choose the most important feature. A comparative study has shown that using fewer types of sensors, the accuracy of decision-making remains at a satisfactory level.

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“…The remedy to the above challenges is to suppress largescale temporal graphs with approximation techniques [14]. Ideally, an approximation technique dynamically adjusts the rate at which data are processed based on the current data stream evolution, such that when stable phases are detected, the data processing rate is reduced.…”

Section: Introductionmentioning

confidence: 99%

“…1: Graph Metric and Topology Structure Volatility for a Mobile Network with Fixed Temporal Granularity degree) are relatively stable in certain phases, it is actually a different set of nodes interacting in the network [17]. In light of this, ignoring that the particular connections actually span across different locations of the network, can severely affect capacity planning and service provisioning [14]. Thus, while reducing the metric processing rate preserves resources by computationally offloading graph processing engines, it hinders the challenge of missing structural changes in the graph topology which might capture and reveal significant insights.…”

Section: Introductionmentioning

confidence: 99%

ATMoN: Adapting the "Temporality" in Large-Scale Dynamic Networks

Trihinas

Chiroque

Pallis

et al. 2018

2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)

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With the widespread adoption of temporal graphs to study fast evolving interactions in dynamic networks, attention is needed to provide graph metrics in time and at scale. In this paper, we introduce ATMoN, an open-source library developed to computationally offload graph processing engines and ease the communication overhead in dynamic networks over an unprecedented wealth of data. This is achieved, by efficiently adapting, in place and inexpensively, the temporal granularity at which graph metrics are computed based on runtime knowledge captured by a low-cost probabilistic learning model capable of approximating both the metric stream evolution and the volatility of the graph topology. After a thorough evaluation with real-world data from mobile, face-to-face and vehicular networks, results show that ATMoN is able to reduce the compute overhead by at least 76%, data volume by 60% and overall cloud costs by at least 54%, while always maintaining accuracy above 88%.

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ADMin: Adaptive monitoring dissemination for the Internet of Things

Cited by 49 publications

References 20 publications

Improved Data Dissemination using Intelligent Stimulus Mechanisms in IoT Network

Improved Data Dissemination using Intelligent Stimulus Mechanisms in IoT Network

Laplacian Scores-Based Feature Reduction in IoT Systems for Agricultural Monitoring and Decision-Making Support

ATMoN: Adapting the "Temporality" in Large-Scale Dynamic Networks

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