A Graph-Based Fault-Tolerant Approach to Modeling QoS for IoT-Based Surveillance Applications

Thomas, Diya; Orgun, Mehmet A.; Hitchens, Michael; Shankaran, Rajan; Mukhopadhyay, Subhas Chandra; Ni, Wei

doi:10.1109/jiot.2020.3022941

Cited by 16 publications

(4 citation statements)

References 25 publications

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“…If there are too many clusters, each cluster is connected to a cluster head, which is one of the devices operating in the cluster. The failure of cluster nodes is traced, and the cluster is dynamically adjusted through weighted graphs as proposed by Thomas et al [17]. The dynamically adjusted graphs are then treated as a linear model, and then FTA is constructed.…”

Section: Proposed Methodsmentioning

confidence: 99%

Hybrid models for computing fault tolerance of IoT networks

Chokara

Jammalamadaka

2023

TELKOMNIKA

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Many Internet of Things (IoT) -based networks are being built to develop applications spanning multiple domains. Many small to large devices connected in various ways increases the risk of IoT networks failing. Small devices in the devices layer frequently fail due to their small size and high usage. Intermittent failures of the IoT networks lead to catastrophes at times. The IoT systems must be designed to be fault-tolerant. Fault tolerance of IoT networks must be computable so that the same can be considered while designing IoT networks. However, the computation of fault tolerance of IoT networks is complex, especially when heterogeneous structures are used for building a specific IoT network. Fault tree-based models are not suitable for computing fault-tolerance of complex models, which requires probability assessment. Hybrid fault tolerance computing models have been presented in this paper that consider both linear and probabilistic methods of computing the fault tolerance considering many complex networking topologies used in each layer of IoT networks. The fault-tolerance computing models are formal methods that can be used to compute the fault tolerance of any IoT network built with any internal processing. The accuracy of fault tolerance computing is 12.9% higher than other methods.

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Section: Proposed Methodsmentioning

confidence: 99%

Hybrid models for computing fault tolerance of IoT networks

Chokara

Jammalamadaka

2023

TELKOMNIKA

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“…IV 1 is zero because all vertices included in SV are activated in the first iteration. Therefore, CG can be expressed as Equation (10). Finally, CG is Equation (11) because n(SV)•(CG 1 + CA 1 + CS 1 ) is CSP by Equation (7).…”

Section: Algorithm 1 Gas Vertex-programmentioning

confidence: 99%

“…These dynamic graphs are used to analyze changes in real time to create business value [6][7][8]. For example, the correlations between objects in the Internet of things (IoT) or disaster management are illustrated using dynamic graphs and analyzed in real time to detect and forecast disasters [9][10][11]. In a social network, an interaction change between items or users is modeled as a dynamic graph, and an event is detected or a recommendation service is provided [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Cost Model Based Incremental Processing in Dynamic Graphs

et al. 2022

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Incremental graph processing has been developed to reduce unnecessary redundant calculations in dynamic graphs. In this paper, we propose an incremental dynamic graph-processing scheme using a cost model to selectively perform incremental processing or static processing. The cost model calculates the predicted values of the detection cost and processing cost of the recalculation region based on the past processing history. If there is a benefit of the cost model, incremental query processing is performed. Otherwise, static query processing is performed because the detection cost and processing cost increase due to the graph change. The proposed incremental scheme reduces the amount of computation by processing only the changed region through incremental processing. Further, it reduces the detection and disk I/O costs of the vertex, which are calculated by reusing the subgraphs from the previous results. The processing structure of the proposed scheme stores the data read from the cache and the adjacent vertices and then performs only memory mapping when processing these graph. It is demonstrated through various performance evaluations that the proposed scheme outperforms the existing schemes.

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“…An indispensable aspect of data provenance is the location at which the data is captured. In many situations, the IoT data is only meaningful if correctly associated with the three-dimensional (3D) locations of the origin of the data, especially in many public safety and security related IoT applications [6], [7]. One of these application scenarios is inside an airplane, where a large number of IoT sensors can be deployed to tag and track safety devices and equipment.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors

Wang¹,

Li²,

Ni³

et al. 2021

Preprint

Self Cite

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Many location-based services use Received Signal Strength (RSS) measurements due to their universal availability. In this paper, we study the association of a large number of low-cost Internet-of-Things (IoT) sensors and their possible installation locations, which can enable various sensing and automation-related applications. We propose an efficient approach to solve the corresponding permutation combinatorial optimization problem, which integrates continuous space cooperative localization and permutation space likelihood ascent search. A convex relaxation-based optimization is designed to estimate the coarse locations of blindfolded devices in continuous 3D spaces, which are then projected to the feasible permutation space. An efficient Cramér-Rao Lower Bound based likelihood ascent search algorithm is proposed to refine the solution. Extensive experiments were conducted to evaluate the performance of the proposed approach, which show that the proposed approach significantly outperforms state-ofthe-art combinatorial optimization algorithms and achieves close-to-100% accuracy with affordable execution time.

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A Graph-Based Fault-Tolerant Approach to Modeling QoS for IoT-Based Surveillance Applications

Cited by 16 publications

References 25 publications

Hybrid models for computing fault tolerance of IoT networks

Hybrid models for computing fault tolerance of IoT networks

Cost Model Based Incremental Processing in Dynamic Graphs

Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors

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