Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks

Yang, Guisong; Peng, Zhiwei; He, Xingyu

doi:10.3390/s18113697

Cited by 10 publications

(18 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yang et al [22] introduced an emerging concept-the 'utilization of working-sleeping cycle' of sensor nodes to prolong the network lifetime. The working-sleeping cycle can be segmented into two categories-synchronous and asynchronous working-sleeping cycle.…”

Section: Related Research Workmentioning

confidence: 99%

“…In this paper, we considered a heterogeneous WSN in which all sensor nodes are deployed uniformly in a field bounded by L × L m 2 region. Each static sensor node with a unique ID can acquire its neighbors' ID by sharing a probe message as discussed in [22,26]. Also, we assumed that all sensor nodes follow the asynchronous working-sleeping cycle strategy with their working time as W V and sleeping time as S V .…”

Section: Network Modelmentioning

confidence: 99%

“…Non-hierarchical routing protocols are proposed on the basis of overhearing, flooding, and information related to the advertisement of the sink's position through agent node selection, whereas hierarchical routing protocols are proposed on the basis of (i) grid-based, tree-based, cluster-based and area-based routing. Different hierarchical routing protocols have their own merits and demerits, but as far as cluster-based hierarchical routing protocols are concerned, researchers have been challenged with a task of achieving an optimal balance between end-end delay and energy consumption [4][5][6]21].Yang et al [22] introduced an emerging concept-the 'utilization of working-sleeping cycle' of sensor nodes to prolong the network lifetime. The working-sleeping cycle can be segmented into two categories-synchronous and asynchronous working-sleeping cycle.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks

Anees

Zhang

Baig

et al. 2020

Sensors

View full text Add to dashboard Cite

Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper proposes a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS) in order to overcome the energy consumption and network lifetime bottlenecks. The asynchronous working-sleeping cycle of sensor nodes could be exploited to make an opportunistic connection between sensor nodes in heterogeneous clustering. HFECS incorporates two levels of hierarchy in the network and energy heterogeneity is characterized using three levels of energy in sensor nodes. HFECS gathers local sensory data from sensor nodes and utilizes multi-attribute decision modeling and the entropy weight coefficient method for cluster formation and the cluster head election procedure. After cluster formation, HFECS uses the same techniques for performing data fusion at the first hierarchical level to reduce the redundant information flow from the first-second hierarchical levels, which can lead to an improvement in energy consumption, better utilization of bandwidth and extension of network lifetime. Our simulation results reveal that HFECS outperforms the existing benchmark schemes of heterogeneous clustering for larger network sizes in terms of half-life period, stability period, average residual energy, network lifetime, and packet delivery ratio.Sensors 2020, 20, 913 2 of 28 overall decrease in energy consumption and also reduces the interference among sensor nodes due to specific allocation of timeslots for communication purposes [4,5]. The clustering topology in WSN can effectively optimize the data redundancy by significantly reducing the size of the collected data using data aggregation and data fusion techniques at CH level. The aggregated or fused data can then be forwarded to the Base Station (BS) for further processing and accurate decision making of interested events [4][5][6].The recent literature shows that researchers have proposed a working-sleeping cycle strategy in WSN to save battery power in case the sensor nodes are idle and not performing any of the designated tasks. Alfayez et al. [7] discussed that the sensor nodes go to sleep to save their battery power and wake up before performing their routine operations. These node scheduling techniques can be categorized as synchronous and asynchronous working-sleeping scheduling. These node scheduling techniques are designed in accordance with the scenario to prolong the network lifetime and improve energy utilization by creating an opportunistic node connection between sensor nodes. According to [8][9][10][11][12], Opportunistic Routing (OR) is a paradigm for wireless networks that benefits from broadcast characteristics of a wireless medium by selecting multiple sensor nodes as candidate forwarders. In [10-12], a set of nodes were selected as potential forwarders that transmitted the data packet according to some special criteria after receiving data packet from their neighb...

show abstract

Section: Related Research Workmentioning

confidence: 99%

Section: Network Modelmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks

Anees

Zhang

Baig

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The current standardized data collection tree for WSNs is built based on 6LoWPAN [1] and the corresponding routing protocol, RPL [2], which is quite complicated and experiences a high overhead [13][14][15]. RPL/6LoWPAN builds the tree topology as follows.…”

Section: Related Workmentioning

confidence: 99%

An Information-Centric Semantic Data Collection Tree for Wireless Sensor Networks

Dinh

Kim

2020

Sensors

View full text Add to dashboard Cite

Data collection is an important application of wireless sensor networks (WSNs) and Internet of Things (IoT). Current routing and addressing operations in WSNs are based on IP addresses, while data collection and data queries are normally information-centric. The current IP-based approach incurs significant management overheads and is inefficient for semantic data collection and queries. To address the above issue, this paper proposes a semantic data collection tree (sDCT) construction scheme to build up a semantic data collection tree for wireless sensor networks. The semantic tree is rooted at the edge/sink and supports data collection tasks, queries, and configurations efficiently. We implement the sDCT in Contiki and evaluate the performance of the sDCT in comparison with the state-of-the-art scheme, 6LoWPAN/RPL and L2RMR, using telosb sensors under various scenarios. The obtained results show that the sDCT achieves a significant improvement in terms of the energy efficiency and the packet transmissions required for data collection or a query task compared to 6LoWPAN/RPL and L2RMR.

show abstract

“…In this work, the data collection can be initiated by the sink whenever required. Inspired by the work of Yang et al (2018), the probe message transmitting method is adopted here in the routing process. Once the subsequent forwarder node is selected via fuzzy logic and the source information is analyzed from the probe message, the link connectivity between the adjacent nodes is calculated, which results in the identification of opportunistic nodes for connection; hence, a spanning tree can be constructed with the sink node as root and optimal paths can be identified for data transmission.…”

Section: List Of Abbreviationsmentioning

confidence: 99%

MDRP: An Energy-Efficient Multi-Disjoint Routing protocol in WSNs for Smart Grids

Deepa

Din

2020

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

Rapid increase in sensor electronics has expanded the call for sensor networks in IoT-based devices. Smart grid is a part of IoT framework, which can be used to screen and manage traffic congestion, electricity influxes, extreme weather, and so on. This is done through a network of transmission lines, smart sensors, and smart meters. It is required to distribute and accumulate information remotely on timely basis from different stages of the grid. The periodic data from the smart meters are transferred to MDMS through WSN's. In WSN's, depletion of energy due to unequal load on the sensors is a serious issue, which is to be addressed as it affects the operations of the entire network. To assist these traffic requirements and to boost the network lifetime, asynchronous work sleep cycle approach can be used to create node connections. In this article, an energy-efficient adaptive fuzzy-based multi-disjoint routing protocol in WSN's for smart grids abbreviated as (MDRP) is proposed, where the next hop node is decided through fuzzy logic. Once the subsequent node is decided, a spanning tree is constructed with the sink node as its root, which calculates the optimal path cost, to transmit the collected data. Furthermore, the simulation results show that the proposed MDRP performs better in terms of network lifetime, packet delivery ratio, total energy consumption, etc.

show abstract

Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks

Cited by 10 publications

References 32 publications

Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks

Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks

An Information-Centric Semantic Data Collection Tree for Wireless Sensor Networks

MDRP: An Energy-Efficient Multi-Disjoint Routing protocol in WSNs for Smart Grids

Contact Info

Product

Resources

About