Optimizing Energy Consumption for Big Data Collection in Large-Scale Wireless Sensor Networks With Mobile Collectors

Ang, Li-Minn; Seng, Jasmine Kah Phooi; Zungeru, Adamu Murtala

doi:10.1109/jsyst.2016.2630691

Cited by 102 publications

(77 citation statements)

References 34 publications

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“…It means the sensor applications are responsible for the generation of big volume of data which is heterogeneous in nature. [5]. Fig.…”

Section: Efficient Data Collection Using Mobile Sink Scheme In Iotmentioning

confidence: 99%

“…As illustrated in Fig. 2, numerous sensors are deployed in various fields to collect different kinds of data, such as environmental data, transportational data, healthcare data and so forth, which is quite huge in volume and has different formats since it is originated from different sources [5]. Therefore, it is highly anticipated that the huge quantity and diverse range of data will be generated via sources containing sensors, which will have densely distributed WSNs with hundreds to thousands of nodes.…”

Section: Efficient Data Collection Using Mobile Sink Scheme In Iotmentioning

confidence: 99%

“…The unsolved problem in the area of big data is inefficiency in data gathering from different resources. So, many researchers are working to find solutions to solve these issues [5,7,8]. As the distributed wireless sensor networks generate the big data, hence it is also a potential candidate for research.…”

Section: Efficient Data Collection Using Mobile Sink Scheme In Iotmentioning

confidence: 99%

“…The RF value spectrum is between 0 and 1 and displays the proximity of each node equivalent to each cluster head. Cluster heads, µ and covariance matrix are recalculated by using updated responsibility values, and the number of nodes corresponding to a particular cluster are further calculated using (5).…”

Section: Em Algorithm For Clusteringmentioning

confidence: 99%

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Efficient Data Collection using Mobile Sink Scheme in IoT

Wala¹,

Chand²,

Sharma³

2019

IJITEE

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Wireless sensor networks (WSNs) have become increasingly important in the informative development of communication technology. The growth of Internet of Things (IoT) has increased the use of WSNs in association with large scale industrial applications. The integration of WSNs with IoT is the pillar for the creation of an inescapable smart environment. A huge volume of data is being generated every day by the deployment of WSNs in smart infrastructure. The collaboration is applicable to environmental surveillance, health surveillance, transportation surveillance and many more other fields. A huge quantity of data which is obtained in various formats from varied applications is called big data. The Energy efficient big data collection requires new techniques to gather sensor-based data which is widely and densely distributed in WSNs and spread over wider geographical areas. In view of the limited range of communication and low powered sensor nodes, data gathering in WSN is a tedious task. The energy hole is another considerable issue that requires attention for efficient handling in WSN. The concept of mobile sink has been widely accepted and exploited, since it is able to effectively alleviate the energy hole problem. Scheduling a mobile sink with energy efficiency is still a challenge in WSNs time constraint implementation due to the slow speed of the mobile sink. The paper addresses the above issues and the proposal contains four-phase data collection model; the first phase is the identification of network subgroups, which are formed due to a restricted range of communication in sensor nodes in a wide network, second is clustering which is addressed on each identified subgroup for reducing energy consumption, third is efficient route planning and fourth is based on data collection. The two time-sensitive route planning schemes are presented to build a set of trajectories which satisfy the deadline constraint and minimize the overall delay. We have evaluated the performance of our schemes through simulation and compared them with the generic enhanced expectation-maximization (EEM) mobility based scenario of data collection. Simulation results reveal that our proposed schemes give much better results as compared to the generic EEM mobility approach in terms of selected performance metrics such as energy consumption, delay, network lifetime and packet delivery ratio.

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“…It means the sensor applications are responsible for the generation of big volume of data which is heterogeneous in nature. [5]. Fig.…”

Section: Efficient Data Collection Using Mobile Sink Scheme In Iotmentioning

confidence: 99%

Section: Efficient Data Collection Using Mobile Sink Scheme In Iotmentioning

confidence: 99%

Section: Efficient Data Collection Using Mobile Sink Scheme In Iotmentioning

confidence: 99%

Section: Em Algorithm For Clusteringmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Data Collection using Mobile Sink Scheme in IoT

Wala¹,

Chand²,

Sharma³

2019

IJITEE

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show abstract

“…However, T2FL cluster model are not designed heterogeneity real-time application requirement of WSN's [20], [21], [22], and [23]. For meeting real-time application requirement [24] presented a data collection method, [25] proposed an energy efficient clustering based routing model, [26] proposed data prediction model, and [27], [28] proposed a energy efficient clustering routing design using cross layer design. Recently, many approach has been presented to minimize energy dissipation of sensor device [24], [25], [26], [27], and [28].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Mobile Sink Based Routing Model For Maximizing Lifetime of Wireless Sensor Network

Ghorpade¹,

Vijayakarthik.²

2019

GJCST

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Recently, wide adoption of wireless sensor networks (WSNs) has been seen for provision real-time and non-real-time application services. Provisioning these application service requires energy efficient routing design for WSN. Clustering technique is an efficient mechanism that plays a major role in minimizing energy dissipation of WSN. However, the existing model are designed considering minimizing energy consumption of sensor device considering homogenous. However, it incurs energy overhead among cluster head. Further, maximizing coverage time is not considered by exiting clustering approach considering heterogeneous network affecting lifetime performance. For overcoming issues of routing data packets in WSN, mobile sink has been used. Here, the sensor device will transmit packet in multihop fashion to the rendezvous and the mobile sink will move towards rendezvous points (RPs) to collect data, as opposed to all nodes. However, the exiting model designed so far incurs packet delay (latency) and energy (storage) overhead among sensor device. For overcoming research challenges, this work present energy efficient mobile sink based routing model for maximizing lifetime of wireless sensor network. Experiment are conducted to evaluate the performance of proposed model shows significant performance in terms of communication, routing overhead and lifetime of sensor network.

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REERS: Reliable and energy‐efficient route selection algorithm for heterogeneous Internet of things applications

Nayagi

Sivasankari

Ravi

et al. 2021

Int J Communication

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Summary Machine‐type communication (MTC) such as device‐to‐device and machine‐to‐machine communication is a technology used to realize the concept of Internet of things (IoT). The fifth generation (5G) IoT is composed of large density of wireless sensor device. The massive roll out of the MTC devices is a serious challenge for wireless communication networks from operational and management perspective, including massive access, route selection, energy efficiency and network congestion. Many proposals have been put forward by the research community to cater to the issues and difficulties of massive MTC (mMTC) access in large density sensor network‐enabled IoT communication environment. Recently, data aggregation with efficient route selection has attracted a lot of research attention owing to its robust ability to resolve the above‐mentioned challenges. Existing data routing and aggregation methodologies are not efficient when adopted to heterogeneous IoT applications as they fail to bring trade‐off between energy efficiency and latency minimization. This research work proposes reliable and energy‐efficient route selection (REERS) scheme to reduce energy dissipation and latency for catering both real‐time and non‐real‐time applications. Experiment outcomes shows the REERS schemes achieves much superior performance than existing routing and aggregation methodologies over latency reduction and energy efficiency.

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Optimizing Energy Consumption for Big Data Collection in Large-Scale Wireless Sensor Networks With Mobile Collectors

Cited by 102 publications

References 34 publications

Efficient Data Collection using Mobile Sink Scheme in IoT

Efficient Data Collection using Mobile Sink Scheme in IoT

Energy Efficient Mobile Sink Based Routing Model For Maximizing Lifetime of Wireless Sensor Network

REERS: Reliable and energy‐efficient route selection algorithm for heterogeneous Internet of things applications

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