The problem of distributed power allocation in wireless sensor network (WSN) localization systems is investigated in this paper, using the game theoretic approach. Existing research focuses on the minimization of the localization errors of individual agent nodes over all anchor nodes subject to power budgets. When the service area and the distribution of target nodes are considered, finding the optimal trade-off between localization accuracy and power consumption is a new critical task. To cope with this issue, we propose a power allocation game where each anchor node minimizes the square position error bound (SPEB) of the service area penalized by its individual power. Meanwhile, it is proven that the power allocation game is an exact potential game which has one pure Nash equilibrium (NE) at least. In addition, we also prove the existence of an ϵ -equilibrium point, which is a refinement of NE and the better response dynamic approach can reach the end solution. Analytical and simulation results demonstrate that: (i) when prior distribution information is available, the proposed strategies have better localization accuracy than the uniform strategies; (ii) when prior distribution information is unknown, the performance of the proposed strategies outperforms power management strategies based on the second-order cone program (SOCP) for particular agent nodes after obtaining the estimated distribution of agent nodes. In addition, proposed strategies also provide an instructional trade-off between power consumption and localization accuracy.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5982413PMC
http://dx.doi.org/10.3390/s18051480DOI Listing

Publication Analysis

Top Keywords

power allocation
16
agent nodes
12
localization accuracy
12
proposed strategies
12
distributed power
8
allocation wireless
8
wireless sensor
8
sensor network
8
potential game
8
power
8

Similar Publications

This study aims to establish and validate an ultrasound radiomics nomogram for preoperative prediction of central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) before operation. A retrospective analysis conducted on ultrasonic images and clinical features derived from 288 PTMC patients, who were divided into training cohorts ( = 201) and validating cohorts ( = 87) in a ratio of 7:3 base on the principle of random allocation. Radiomics features were extracted from the PTMC patients after ultrasonic examination, followed by dimension reduction and characteristic selection to construct the radiomics score (Radscore) using LASSO regression analysis.

View Article and Find Full Text PDF

Introduction: In response to blood shortages, providers face pressure to conserve blood. No metrics exist to calculate transfusion utility. We describe characteristics of survivors after high-volume resuscitation and evaluate transfusion utility in low-volume and high-volume resuscitation.

View Article and Find Full Text PDF

Introduction: The sequential parallel comparison design has emerged as a valuable tool in clinical trials with high placebo response rates. To further enhance its efficiency and effectiveness, adaptive strategies, such as sample size adjustment and allocation ratio modification can be employed.

Methods: We compared the performance of Jennison and Turnbull's method and the Promising Zone approach for sample size adjustment in a two-phase sequential parallel comparison design study.

View Article and Find Full Text PDF

Minimizing Delay and Power Consumption at the Edge.

Sensors (Basel)

January 2025

Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.

Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations.

View Article and Find Full Text PDF

Co-Optimization Operation of Distribution Network-Containing Shared Energy Storage Multi-Microgrids Based on Multi-Body Game.

Sensors (Basel)

January 2025

Xi'an Power Supply Company, State Grid Shaanxi Electric Power Co., Ltd., Xi'an 710032, China.

Under the carbon peaking and carbon neutrality target background, efficient collaborative scheduling between distribution networks and multi-microgrids is of great significance for enhancing renewable energy accommodation and ensuring stable system operation. Therefore, this paper proposes a collaborative optimization method for the operation of distribution networks and multi-microgrids with shared energy storage based on a multi-body game. The method is modeled and solved in two stages.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!