A Fast, Adaptive, and Energy-Efficient Data Collection Protocol in Multi-Channel-Multi-Path Wireless Sensor Networks

Liew, Soung-Yue; Tan, Cheng-Kiat; Gan, Ming-Lee; Goh, Hock Guan

doi:10.1109/mci.2017.2773800

Cited by 20 publications

(15 citation statements)

References 30 publications

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“…On the other hand, similar to SF allocation in a mesh LoRa network, the adoption of parallel transmission by using multiple channels has already been implemented in conventional multi-hop single-root data collection networks. In data collection networks, the proposed protocols [17], [18] usually construct a static channel assignment approach to maintain the simplicity of channel coordination. Other researchers [17] proposed TMCP to assign different channels to disjoint trees and operate parallel transmissions among sub-trees for data collection.…”

Section: B Sf Allocation In a Multi-hop Networkmentioning

confidence: 99%

“…However, the paper does not discuss the balance between different sub-trees. A more recent proposal [18] involved a multi-channel multi-path data collection protocol based on Basketball Net Topology (BNT), which maintains not only a tree-based topology but also the connectivities between peer nodes located at the same height in the tree. This protocol enables child nodes to rejoin the network, even when their parent node disappeared from the original tree structure, by using peer links to communicate with other nodes.…”

Section: B Sf Allocation In a Multi-hop Networkmentioning

confidence: 99%

“…They presented a Tree-based Multichannel Protocol (TMCP) in which a Breadth-First Searching (BFS) algorithm was adopted to construct a fat tree rooted at the sink node and then assigned the channel from the top layer to the bottom layer according the topology. Similarly, Liew et al [18] employed the first available channel algorithm that adopts BFS to assign a channel to a node after the first available channel is determined. However, these approaches ignored the balance between different subnets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving the Capacity of a Mesh LoRa Network by Spreading-Factor-Based Network Clustering

Zhu¹,

Liao²,

Sakdejayont³

et al. 2019

IEEE Access

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LoRa is a low-power long-range IoT standard that uses the chirp spread spectrum technique, and we have strived to further extend its coverage by utilizing the direct device-to-device (D2D) links to construct a multi-hop relay network. In LoRa, the spreading factor (SF) is an important parameter, which not only provides great flexibility between the data rate and sensitivity but also presents a new dimension for multiple accesses. Our approach to improving the capacity of a multihop LoRa network is to attempt to off-load the data traffic into several subnets by utilizing this multiple-access dimension. Each subnet rooted at a sink node is allocated a specific SF on the basis of network clustering. This enables packet transmission in parallel with multiple SFs to become feasible. To allow such parallel transmissions, our considerations are: 1) ensuring the connectivity of all subnets; 2) off-loading the traffic according to the number of nodes, data rates, and network topologies of each subnet; and 3) shortening the airtime of each subnet by reducing the hop count. Toward these objectives, we present a tree-based SF clustering algorithm (TSCA) to conduct SF allocation in a multihop LoRa network. The TSCA focuses on balancing the airtime between the subnets while ensuring connectivity. Furthermore, we use simulations to show that our approach can significantly increase the network performance compared with other approaches. We additionally deploy a real-chip experiment to evaluate the feasibility of parallel transmission in practice. INDEX TERMS Low-power wide area network (LPWAN), LoRa, spreading factor (SF) allocation algorithm, tree-based spreading factor clustering algorithm (TSCA), multi-hop network.

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Section: B Sf Allocation In a Multi-hop Networkmentioning

confidence: 99%

Section: B Sf Allocation In a Multi-hop Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving the Capacity of a Mesh LoRa Network by Spreading-Factor-Based Network Clustering

Zhu¹,

Liao²,

Sakdejayont³

et al. 2019

IEEE Access

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“…So hardware architectures and the protocols are intended with WSNs. The efficiency of energy is the most significant objective for network design …”

Section: Introductionmentioning

confidence: 99%

“…The efficiency of energy is the most significant objective for network design. 5 This dense positioning leads to a few main difficulties, such as overhearing transmissions, the prospect of redundant transmissions, and collision in sent data. These problems further improve the necessity for providing enhanced usage of energy in WSNs.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

Pattnaik

Sahu

2020

Int J Communication

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Summary The problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system lifetime.

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RISC32‐E: Field programmable gate array based sensor node with queue system to support fast encryption in Industrial Internet of Things applications

See

Mok

Lee

et al. 2020

Circuit Theory & Apps

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SummaryIndustrial Internet of Things (IIoT) is an emerging technology that relies on the use of massively connected sensor nodes to gather industrial‐related data. The collected data are used for postanalysis to generate insights for reducing production down time, cost optimization, and predictive maintenance. One of the key requirements for sensor node in such application is the data confidentiality; as such sensor data may potentially leak the manufacturing and industrial secret to their competitors. In this paper, a field programmable gate array (FPGA)‐based sensor node with Advanced Encryption Standard (AES) crypto‐processor is proposed to safeguard the sensor data. A novel queue system is proposed to further reduce the data processing time and energy consumption. The proposed queue system is able to achieve 1.48× speed up and ∼16% energy reduction, which makes it a competitive candidate for Industrial IoT applications. The technique developed in this paper can also be extended to implement FPGA‐based gateway with encryption feature, which is very useful for edge computing in IoT applications.

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A Fast, Adaptive, and Energy-Efficient Data Collection Protocol in Multi-Channel-Multi-Path Wireless Sensor Networks

Cited by 20 publications

References 30 publications

Improving the Capacity of a Mesh LoRa Network by Spreading-Factor-Based Network Clustering

Improving the Capacity of a Mesh LoRa Network by Spreading-Factor-Based Network Clustering

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

RISC32‐E: Field programmable gate array based sensor node with queue system to support fast encryption in Industrial Internet of Things applications

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