A Near-Optimal Energy Management Mechanism Considering QoS and Fairness Requirements in Tree Structure Wireless Sensor Networks

Tai, Kuang-Yen; Liu, Bo-Chen; Hsiao, Chiu-Han; Tsai, Ming-Chi; Lin, Frank Yeong-Sung

doi:10.3390/s23020763

Cited by 7 publications

(3 citation statements)

References 56 publications

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“…However, the algorithm does not consider the hierarchical structure of the tree, which can result in excessive levels of the tree structure and higher energy consumption during data transmission. In another study, Tai et al (2023) consider the known topology of the sensor network and propose a power control strategy for each node based on fairness and end-to-end Quality of Service (QoS) constraints. The objective is to reduce node energy consumption and prolong the overall network lifespan.…”

Section: Related Workmentioning

confidence: 99%

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes

Liu,

Zhang,

Liu

et al. 2024

PeerJ Computer Science

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Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5–20% lower communication overhead and energy consumption, 10–20% higher security, and 10–30% lower node mortality than existing algorithms.

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Section: Related Workmentioning

confidence: 99%

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes

Liu,

Zhang,

Liu

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Additionally, some approaches do not minimize energy consumption but optimize throughput [26], delivery probability [27], end-to-end latency [28][29][30], or fairness [31].…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Decentralized Broadcasting in Wireless Multi-Hop Networks

Sterz,

Klose,

Sommer

et al. 2023

Sensors

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Several areas of wireless networking, such as wireless sensor networks or the Internet of Things, require application data to be distributed to multiple receivers in an area beyond the transmission range of a single node. This can be achieved by using the wireless medium’s broadcast property when retransmitting data. Due to the energy constraints of typical wireless devices, a broadcasting scheme that consumes as little energy as possible is highly desirable. In this article, we present a novel multi-hop data dissemination protocol called BTP. It uses a game-theoretical model to construct a spanning tree in a decentralized manner to minimize the total energy consumption of a network by minimizing the transmission power of each node. Although BTP is based on a game-theoretical model, it neither requires information exchange between distant nodes nor time synchronization during its operation, and it inhibits graph cycles effectively. The protocol is evaluated in Matlab and NS-3 simulations and through real-world implementation on a testbed of 75 Raspberry Pis. The evaluation conducted shows that our proposed protocol can achieve a total energy reduction of up to 90% compared to a simple broadcast protocol in real-world experiments.

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“…Macroscopically, UFS policies for multicore systems have an overall goal, such as power-aware and maximum power savings with a performance penalty. To maintain performance while respecting power overhead, designers of communication and computing systems have begun to focus on power efficiency [ 9 , 10 ]. Thus, the latter becomes the main goal of the current UFS policy research, and the degradation index of processor performance can be left to the user to decide.…”

Section: Introductionmentioning

confidence: 99%

Imitation Learning-Based Performance-Power Trade-Off Uncore Frequency Scaling Policy for Multicore System

Xiao

Yang

2023

Sensors

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As the importance of uncore components, such as shared cache slices and memory controllers, increases in processor architecture, the percentage of uncore power consumption in the overall power consumption of multicore processors rises significantly. To maximize the power efficiency of a multicore processor system, we investigate the uncore frequency scaling (UFS) policy and propose a novel imitation learning-based uncore frequency control policy. This policy performs online learning based on the DAgger algorithm and converts the annotation cost of online aggregation data into fine-tuning of the expert model. This design optimizes the online learning efficiency and improves the generality of the UFS policy on unseen loads. On the other hand, we shift our policy optimization target to Performance Per Watt (PPW), i.e., the power efficiency of the processor, to avoid saving a percentage of power while losing a larger percentage of performance. The experimental results show that our proposed policy outperforms the current advanced UFS policy in the benchmark test sequence of SPEC CPU2017. Our policy has a maximum improvement of about 10% relative to the performance-first policies. In the unseen processor load, the tuning decision made by our policy after collecting 50 aggregation data can maintain the processor stably near the optimal power efficiency state.

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A Near-Optimal Energy Management Mechanism Considering QoS and Fairness Requirements in Tree Structure Wireless Sensor Networks

Cited by 7 publications

References 56 publications

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes

Energy-Efficient Decentralized Broadcasting in Wireless Multi-Hop Networks

Imitation Learning-Based Performance-Power Trade-Off Uncore Frequency Scaling Policy for Multicore System

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