A Topology Control Algorithm in Wireless Sensor Networks for IoT-based Applications

Nguyen, Nhat-Tien; Ho, Cuu V.; Le, Thien T. T.

doi:10.1109/isee2.2019.8921357

Cited by 15 publications

(7 citation statements)

References 8 publications

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“…The performance of EE-DTC is evaluated and compared to TBC [14] and LEACH-VA [18] based on ns2 [23] in this section.…”

Section: Performance Evaluation and Discussionmentioning

confidence: 99%

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Energy-Efficient Distributed Cluster-Tree Based Routing Protocol for Applications IoT-Based WSN

Tân¹,

Hoang²

2022

Proceedings of the Seventh International Conference on Research in Intelligent and Computing in Engineering

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4With the growth of wireless sensor network (WSN) technologies, the applications of IoT-based WSNs allow the interconnection of smart objects or sensors through the Internet. However, energy constraint is a major obstacle in WSN, which directly affects the lifespan of the network. Hence, many researchers have focused on how to program routing protocols to maximize energy conservation in WSNs. The clustering mechanism is demonstrated that separating the network into clusters may significantly decrease energy consumption. In this paper, we propose distributed tree-based clustering routing protocol for IoT applications (EE-DTC). In order to enhance efficient energy, EE-DTC chooses cluster head nodes based on the remaining energy, the location, and the density of nodes. In addition, to lengthen the network lifespan, we create multi-hop routes with short communication links intra-clusters by building the minimum spanning tree (MST) using the Kruskal algorithm. Our experiment results exhibit that the performance of EE-DTC overcomes the TBC and LEACH-VA protocols in terms of increasing network lifespan, reducing energy consumption, and improving efficient energy. Index Terms4Wireless sensor networks, routing protocol, energy-efficient, IoT, tree-based clustering.

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“…The performance of EE-DTC is evaluated and compared to TBC [14] and LEACH-VA [18] based on ns2 [23] in this section.…”

Section: Performance Evaluation and Discussionmentioning

confidence: 99%

“…The radio energy consuming model employed in our proposal to transmit the b-bit data packet ETX [11,19,23]:…”

Section: B Energy Consuming Modelmentioning

confidence: 99%

Energy-Efficient Distributed Cluster-Tree Based Routing Protocol for Applications IoT-Based WSN

Tân¹,

Hoang²

2022

Proceedings of the Seventh International Conference on Research in Intelligent and Computing in Engineering

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“…The System of sensor Sensing points then communicate towards the gateway Sensing Point, which is then transmitted towards the internet server. The lifetime ratio of the network can be improved by making use of topology control methods which can maintain quality of communication links and also reduce the amount of packets dropped [11].…”

Section: Literaure Surveymentioning

confidence: 99%

“…The Single System network as shown in the Fig.1 has 100 Sensing points which are spread in an area of 100*100m. Sensing Point 9 has been placed at the coordinate (9,11). Sensing Point 37 has been placed at the coordinate (42,2) and Sensing Point 20 has been placed at the coordinate (79,1) and Sensing Point 52 has been placed at the coordinate (90,10).…”

Section: Introductionmentioning

confidence: 99%

An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

L¹,

M²

2019

IJEAT

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WSN consist of set of Sensing points which are responsible for collecting the detected information and then send the packets towards control centre which is responsible for processing of data. The applications of WSN include environmental data analysis, defence data collection and information. The survey of algorithms is done for the improvement of lifetime ratio. Four different algorithms namely Random, Random-CGT, EGT-Random and GTEB algorithms. The four algorithms are compared and then it is proved GTEB exhibits best behaviour with respect to energy consumed, number of non-holes, number of holes, Non-Hole to Hole ratio, residual energy, overhead and throughput.

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“…These systems are continuously exposed to different environmental situations, such as communication interference and dynamic traffic loads, which may have different effects on the satisfaction levels of the NFRs. Such effects might include high energy consumption or poor packet delivery performance [27,35,52]. Hence, a SAS needs to adapt dynamically at runtime.…”

Section: Introductionmentioning

confidence: 99%

Decision-making under uncertainty: be aware of your priorities

2022

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Self-adaptive systems (SASs) are increasingly leveraging autonomy in their decision-making to manage uncertainty in their operating environments. A key problem with SASs is ensuring their requirements remain satisfied as they adapt. The trade-off analysis of the non-functional requirements (NFRs) is key to establish balance among them. Further, when performing the trade-offs it is necessary to know the importance of each NFR to be able to resolve conflicts among them. Such trade-off analyses are often built upon optimisation methods, including decision analysis and utility theory. A problem with these techniques is that they use a single-scalar utility value to represent the overall combined priority for all the NFRs. However, this combined scalar priority value may hide information about the impacts of the environmental contexts on the individual NFRs’ priorities, which may change over time. Hence, there is a need for support for runtime, autonomous reasoning about the separate priority values for each NFR, while using the knowledge acquired based on evidence collected. In this paper, we propose Pri-AwaRE, a self-adaptive architecture that makes use of Multi-Reward Partially Observable Markov Decision Process (MR-POMDP) to perform decision-making for SASs while offering awareness of NFRs’ priorities. MR-POMDP is used as a priority-aware runtime specification model to support runtime reasoning and autonomous tuning of the distinct priority values of NFRs using a vector-valued reward function. We also evaluate the usefulness of our Pri-AwaRE approach by applying it to two substantial example applications from the networking and IoT domains.

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A Topology Control Algorithm in Wireless Sensor Networks for IoT-based Applications

Cited by 15 publications

References 8 publications

Energy-Efficient Distributed Cluster-Tree Based Routing Protocol for Applications IoT-Based WSN

Energy-Efficient Distributed Cluster-Tree Based Routing Protocol for Applications IoT-Based WSN

An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

Decision-making under uncertainty: be aware of your priorities

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