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MILP-driven Network Planning Framework for Energy Efficiency and Coverage Maximization in IoT Mesh Networks
Authors:
Ishmal Sohail,
Attiq Zeeshan,
M. Umar Khan,
Syed Zubair,
Rana Fayyaz Ahmad,
Faizan Hamayat
Abstract:
In the era of digital transformation, the global deployment of internet of things (IoT) networks and wireless sensor networks (WSNs) is critical for applications ranging from environmental monitoring to smart cities. Large-scale monitoring using WSNs incurs high costs due to the deployment of sensor nodes in the target deployment area. In this paper, we address the challenge of prohibitive deploym…
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In the era of digital transformation, the global deployment of internet of things (IoT) networks and wireless sensor networks (WSNs) is critical for applications ranging from environmental monitoring to smart cities. Large-scale monitoring using WSNs incurs high costs due to the deployment of sensor nodes in the target deployment area. In this paper, we address the challenge of prohibitive deployment costs by proposing an integrated mixed-Integer linear programming (MILP) framework that strategically combines static and mobile Zigbee nodes. Our network planning approach introduces three novel formulations, including boundary-optimized static node placement (MILP-Static), mobile path planning for coverage maximization (MILP-Cov), and movement minimization (MILP-Mov) of the mobile nodes. We validated our framework with extensive simulations and experimental measurements of Zigbee power constraints. Our results show that boundary-optimized static placement (MILP-Static) achieves 53.06% coverage compared with 33.42% of the random approach. In addition, MILP-Cov for path planning reaches 97.95% coverage, while movement minimization (MILP-Mov) reduces traversal cost by 40%. Our proposed framework outperforms the benchmark approaches to provide a foundational solution for cost-effective global IoT deployment in resource constrained environments.
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Submitted 7 December, 2025;
originally announced December 2025.
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Secure Energy Transactions Using Blockchain Leveraging AI for Fraud Detection and Energy Market Stability
Authors:
Md Asif Ul Hoq Khan,
MD Zahedul Islam,
Istiaq Ahmed,
Md Masud Karim Rabbi,
Farhana Rahman Anonna,
MD Abdul Fahim Zeeshan,
Mehedi Hasan Ridoy,
Bivash Ranjan Chowdhury,
Md Nazmul Shakir Rabbi,
GM Alamin Sadnan
Abstract:
Peer-to-peer trading and the move to decentralized grids have reshaped the energy markets in the United States. Notwithstanding, such developments lead to new challenges, mainly regarding the safety and authenticity of energy trade. This study aimed to develop and build a secure, intelligent, and efficient energy transaction system for the decentralized US energy market. This research interlinks t…
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Peer-to-peer trading and the move to decentralized grids have reshaped the energy markets in the United States. Notwithstanding, such developments lead to new challenges, mainly regarding the safety and authenticity of energy trade. This study aimed to develop and build a secure, intelligent, and efficient energy transaction system for the decentralized US energy market. This research interlinks the technological prowess of blockchain and artificial intelligence (AI) in a novel way to solve long-standing challenges in the distributed energy market, specifically those of security, fraudulent behavior detection, and market reliability. The dataset for this research is comprised of more than 1.2 million anonymized energy transaction records from a simulated peer-to-peer (P2P) energy exchange network emulating real-life blockchain-based American microgrids, including those tested by LO3 Energy and Grid+ Labs. Each record contains detailed fields of transaction identifier, timestamp, energy volume (kWh), transaction type (buy/sell), unit price, prosumer/consumer identifier (hashed for privacy), smart meter readings, geolocation regions, and settlement confirmation status. The dataset also includes system-calculated behavior metrics of transaction rate, variability of energy production, and historical pricing patterns. The system architecture proposed involves the integration of two layers, namely a blockchain layer and artificial intelligence (AI) layer, each playing a unique but complementary function in energy transaction securing and market intelligence improvement. The machine learning models used in this research were specifically chosen for their established high performance in classification tasks, specifically in the identification of energy transaction fraud in decentralized markets.
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Submitted 21 June, 2025;
originally announced June 2025.
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POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization
Authors:
Usman Naseem,
Robert Geislinger,
Juan Ren,
Sarah Kohail,
Rudy Garrido Veliz,
P Sam Sahil,
Yiran Zhang,
Marco Antonio Stranisci,
Idris Abdulmumin,
Özge Alacam,
Cengiz Acartürk,
Aisha Jabr,
Saba Anwar,
Abinew Ali Ayele,
Simona Frenda,
Alessandra Teresa Cignarella,
Elena Tutubalina,
Oleg Rogov,
Aung Kyaw Htet,
Xintong Wang,
Surendrabikram Thapa,
Kritesh Rauniyar,
Tanmoy Chakraborty,
Arfeen Zeeshan,
Dheeraj Kodati
, et al. (18 additional authors not shown)
Abstract:
Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We introduce POLAR, a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. Polarization is annotated along three axes, nam…
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Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We introduce POLAR, a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. Polarization is annotated along three axes, namely detection, type, and manifestation, using a variety of annotation platforms adapted to each cultural context. We conduct two main experiments: (1) fine-tuning six pretrained small language models; and (2) evaluating a range of open and closed large language models in few-shot and zero-shot settings. The results show that, while most models perform well in binary polarization detection, they achieve substantially lower performance when predicting polarization types and manifestations. These findings highlight the complex, highly contextual nature of polarization and demonstrate the need for robust, adaptable approaches in NLP and computational social science. All resources will be released to support further research and effective mitigation of digital polarization globally.
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Submitted 5 February, 2026; v1 submitted 26 May, 2025;
originally announced May 2025.