In-network context inference in IoT sensory environment for efficient network resource utilization

Verma, Rahul Kumar; Pattanaik, K. K.; Bharti, Sourabh; Saxena, Divya

doi:10.1016/j.jnca.2019.01.013

Cited by 16 publications

(6 citation statements)

References 33 publications

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“…When a task is executed by taking a specific time for the processing of data, energy is consumed and its measurement unit is joule (J). The sensor devices consumed the maximum energy during the transmission, sensing and execution of tasks [40]. The energy consumption of the proposed system has been calculated with the help of (1).…”

Section: Energy Consumptionmentioning

confidence: 99%

A Fault Tolerant Data Management Scheme for Healthcare Internet of Things in Fog Computing

2021

KSII TIIS

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Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through welldefined components and steps. A two way fault-tolerant mechanism i.e., task-based faulttolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.

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Section: Energy Consumptionmentioning

confidence: 99%

A Fault Tolerant Data Management Scheme for Healthcare Internet of Things in Fog Computing

2021

KSII TIIS

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“…Analysis and estimation of the Quality of Experience confirmed that the performance of the Edge Computing application is highly sensitive on task scheduling technique, as well as on network parameters or their brokering methods. On the basis of VM mitigation, server resources and task complexity evaluation, the online and offline edge computing network offloading algorithms were proposed [14], [15]. They include path selection, tier decomposition, Ad Hoc cloud-assisted and partitioning techniques.…”

Section: Previous Researchmentioning

confidence: 99%

Performance Analysis of Edge Computing in IoT

Skirelis¹,

Navakauskas²

2020

ELEKTRON ELEKTROTECH

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Upcoming 5G technology and the demand for near real-time response services raise the need for optimizing current IoT solutions. The aim of this paper is to model and execute the performance analysis of network structures suitable for Edge Computing in IoT. The prior research into different topology and parameter sets have not provided sufficient clarity, on which parameters had a considerable impact on overall system performance; therefore, repetitive simulations were performed to express dispersion of alternating values, as well as determining its confidence intervals. The paper presents Edge Computing service simulation setup on known and newly derived network topologies with different complexity varying network bandwidth and network delay parameters. The experimental investigation has revealed that the IoT configuration network is more sensitive to network topology, while the Internet configuration is more sensitive to network parameters. The discussion on the results received debates possible causes of performance differences in network parameters and device configurations, the comparison to similar state-of-the-art research results has also been presented. Finally, conclusions with recommendations based on the results acquired have been provided.

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“…Our proposed mechanism eventually generates common sensing tasks serving the requirements of other similar queries by sharing the data, thus reducing network traffic and energy consumption significantly. To reduce the energy consumption associated with communication, various in-network processing [24][27] [23] and data sharing [28][25] mechanisms have been proposed. However, the issues related to the task dissemination is not addressed yet and this paper focuses on reducing the number of sensing tasks to go inside the network by exploiting the functional requirement similarity among application queries.…”

Section: Related Workmentioning

confidence: 99%

A Query Processing Framework for Efficient Network Resource Utilization in Shared Sensor Networks

Verma

Pattanaik

Bharti

et al. 2020

ACM Trans. Sen. Netw.

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Shared Sensor Network (SSN) refers to a scenario where the same sensing and communication resources are shared and queried by multiple Internet applications. Due to the burgeoning growth in Internet applications, multiple application queries can exhibit overlapping in their functional requirements, such as the region of interest, sensing attributes, and sensing time duration. This overlapping results in redundant sensing tasks generation leading to the increased overall network traffic and energy consumption. Existing approaches operate on data sharing among various tasks to minimize the upstream traffic. However, no existing work attempts to prevent the redundant task generation to reduce the downstream traffic. Moreover, the allocation of suitable sensor nodes to meet the Quality of Service (QoS) requirements of the queries is still an open issue. This article proposes an end-to-end query processing framework (named, QueryPM) that first, calculates the functional requirements similarity among queries to prevent the redundant task generation. Then, it takes the QoS and functional requirements into account while allocating the tasks on the sensor nodes. Extensive simulations on the proposed approach show that downstream traffic, upstream traffic, and energy consumption reduced to 60%, 20--40%, and 40%, respectively, as compared to state-of-the-art mechanisms.

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In-network context inference in IoT sensory environment for efficient network resource utilization

Cited by 16 publications

References 33 publications

A Fault Tolerant Data Management Scheme for Healthcare Internet of Things in Fog Computing

A Fault Tolerant Data Management Scheme for Healthcare Internet of Things in Fog Computing

Performance Analysis of Edge Computing in IoT

A Query Processing Framework for Efficient Network Resource Utilization in Shared Sensor Networks

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