Optimum ConvergeCast Scheduling in Wireless Sensor Networks

Bakshi, Mahesh; Jaumard, Brigitte; Narayanan, Lata

doi:10.1109/tcomm.2018.2848271

Cited by 8 publications

(17 citation statements)

References 32 publications

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“…2) Data collection research that considers data fusion and considers the time required for data collection. For example, sometimes the network needs to query the maximum, minimum, average temperature, humidity, and other data in the network [37], [39]. In this application, an infinite number of data packets can be merged into one data packet.…”

Section: Related Workmentioning

confidence: 99%

“…In this application, an infinite number of data packets can be merged into one data packet. In such applications, it is expected that the required data can be obtained in the shortest possible time after the query of the application is sent, that is, the time required for data collection is the shortest [37], [39]. Therefore, in this kind of data collection, on the one hand, it is necessary to maximize the data integration as much as possible, and at the same time make the sink collect the data of the entire network in the shortest time.…”

Section: Related Workmentioning

confidence: 99%

“…Another type of research that considers data fusion and reduces delay [37], when multiple data packets meet, they can be fused into one data packet, so that after optimization, each node only needs to send one data packet in a round of data collection. This type of data fusion generally uses TDMA, which allocates active slots to each node in advance, and only in the awake state can the node has data operations, and when there is no data operation, it sleeps to save energy [39]. This data fusion solution generally uses data routing from the bottom of the network to the sink layer by layer.…”

Section: Introductionmentioning

confidence: 99%

“…Each node collects data from its child nodes and merges them into a data packet and then routes forward. The effective allocation of data operation slots to nodes can effectively reduce the time required for data collection, that is, reduce the convergecast time [39].…”

Section: Introductionmentioning

confidence: 99%

“…However, convergecast requires data to be generated periodically, however, in real-life, there are not many WSNs that meet this condition [39]. Most WSNs are networks that generate non-periodic data.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Data Aggregation Scheme Based on Differentiated Threshold Configuring Joint Optimal Relay Selection in WSNs

Liu

Zeng

et al. 2021

IEEE Access

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In Wireless Sensor Networks (WSNs), when multiple data packets meet during routing to the sink, redundant data can be removed through data fusion, thereby reducing the amount of data transmitted, and increasing the life of the network. However, how to increase the data fusion rate as much as possible and ensure that the delay is lower than the deadline is a challenge issue. To solve this problem, a Differentiated Threshold Configuring joint Optimal Relay Selection based Data Aggregation (DTC-ORS-DA) scheme is proposed, which can significantly reduce redundant data routing and guarantee the delay for WSN. The main innovation is as follow: (1) In DTC-ORS-DA scheme, there are two thresholds: data volume threshold and time threshold. Data routing can only be performed when or of the node meet the threshold requirements, so that the data can be fully integrated to minimize the amount of data to be transmitted. More importantly, DTC-ORS-DA adopts differentiated threshold settings based on the characteristics of unbalanced energy consumption in WSNs, and sets a smaller threshold in the far sink area with sufficient energy, so that data packets can be routed quickly. And the near sink area where the energy is tight uses a larger threshold to maximize data fusion, so that the combination can make the data fusion high, the energy is effectively used, and the delay is small. (2) We propose a priority-based relay selection algorithm, which enables child nodes to dynamically select the parent node with the highest priority based on the number of data packets, waiting time, and remaining energy. In the process of routing, the probability of nodes with many data packets or long waiting time being selected as transmission relay is high, which can either increase the data fusion rate or reduce the delay. Finally, the performance comparison with Common data collection Scheme (CS) proves that, the DTC-ORS-DA scheme reduces the average delay by 10.74%-19.91%, increases the life cycle by 9.81% at most, and the energy utilization rate is increased by 6.67%-9.48%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Efficient Data Aggregation Scheme Based on Differentiated Threshold Configuring Joint Optimal Relay Selection in WSNs

Liu

Zeng

et al. 2021

IEEE Access

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Compressive Sensing-Based Clustering Joint Annular Routing Data Gathering Scheme for Wireless Sensor Networks

et al. 2019

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Compressed sensing technology is one of the effective techniques to effectively reduce the amount of data transmission in wireless sensor networks. Compressed sensing technology can reduce the amount of data that a node undertakes from n to m, where m n, but we still hope to further reduce the amount of data that the node bears to improve network lifetime. In this paper, a Compressive Sensing based Clustering Joint Annular Routing Data Gathering (CS-CARDG) scheme is proposed to improve the network life. The key technology adopted by CS-CARDG scheme is: data is collected by cluster. The network first forms a cluster, and each node in the cluster sends the data packet to the cluster head. Each cluster forms mdimensional data according to the requirements of the compressed sensing technology to ensure that the data can be recovered. When the cluster head node routes the m-dimensional data to the sink, the CS-CARDG scheme adopts a two-stage routing scheme with the same ring routing and shortest path that is completely different from the previous scheme. The same ring routing means that the cluster heads with the same number of sink hops are routed around the ring for one week to route the compressed sensing data of the same ring to a node in the ring. In this way, each sub-dimension data in the same ring is routed to the corresponding node of each ring through the same-loop route, and then the shortest-circuit strategy of the second phase is started. That is, from the outermost ring, the same fractal data is sequentially compressed from the outside to the inside, and is routed to the sink by the shortest path. In this round of data collection, the number of data packets that the nodes in the near-sink one-hop range bears is only m, and the nodes in the near-sink region directly send data to the sink node, thereby reducing the amount of data that the node bears to m k + 1, where k is the number of nodes within the node's broadcast radius.. In this paper, the compressed sensing strategies proposed in the past are compared by detailed theoretical analysis. The theoretical analysis results show that the CS-CARDG strategy proposed in this paper can effectively reduce the amount of data carried by nodes. This scheme reduces the amount of data in the network from 1Reduced the amount of data by at least 20%. In the network with R = 480 m, the energy utilization rate can reach more than 90%.INDEX TERMS Wireless sensor networks, annular routing, compressed sensing, clustering, energy utilization.The associate editor coordinating the review of this article and approving it for publication was Yuyu Yin. according Ref. [5]. The current sensing-based devices have far exceeded the number of humans, and are growing at a rapid rate. With the development of microprocessor technology [6], the computing and storage capabilities of these sensing-based devices have been greatly

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Adding Duty Cycle Only in Connected Dominating Sets for Energy Efficient and Fast Data Collection

et al. 2019

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In wireless sensor networks (WSNs), energy efficiency and low delay are two pivotal issues for data collection. Wireless sensor networks are composed of energy-constrained sensor nodes. While sensing the environment, the rapid transmission of data to the sink is critical for many applications. In order to save energy, the duty cycle based mode is adopted in most studies, but this method increases the data transmission delay. Thus, an Adding Duty Cycle Only in Connected Dominating Sets (ADCOCDS) approach is proposed in this paper, which can effectively reduce network energy consumption and delay. The main innovations of the ADCOCDS scheme are as follows: (1) Unlike the mode of the duty cycle for all nodes in the previous WSNs, in ADCOCDS scheme, we choose a part of nodes to construct as a connected dominating set, and these nodes adopt the mode of duty cycle. For other nodes in the network, the radio transmitter is turned off to save energy when there is no data to transmit, and when these nodes have data to send, they turn on the radio transmitter and send the data to nodes in the dominating set, then nodes in the dominating set transfer the data to sink. In ADCOCDS scheme, since only a small number of nodes adopt the duty cycle mode, the energy efficiency of the network is high; (2) In WSNs, we observed that the energy consumption of nodes in the near-sink area is higher than that in the far-sink area. Therefore, in the ADCOCDS scheme, we increase the duty cycle of nodes according to their energy consumption to reduce the delay. Through theoretical analysis and experimental results, the ADCOCDS scheme can effectively reduce energy consumption and delay by 12.09%-43.17% and 21.85%-57.96%, respectively, while improving network lifetime by 10.71%-24.13% compared with previous strategies.

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Optimum ConvergeCast Scheduling in Wireless Sensor Networks

Cited by 8 publications

References 32 publications

An Efficient Data Aggregation Scheme Based on Differentiated Threshold Configuring Joint Optimal Relay Selection in WSNs

An Efficient Data Aggregation Scheme Based on Differentiated Threshold Configuring Joint Optimal Relay Selection in WSNs

Compressive Sensing-Based Clustering Joint Annular Routing Data Gathering Scheme for Wireless Sensor Networks

Adding Duty Cycle Only in Connected Dominating Sets for Energy Efficient and Fast Data Collection

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