Energy-Aware Unified Data Collection and Energy Replenishment in Consumer Mobile WSNs

Rechargeable Battery-powered Sensor Nodes (SNs) in Wireless Rechargeable Sensor Networks (WRSNs) are essential components of Internet of Things (IoT) applications. Previous research on traditional WRSN frameworks mostly relied on independent mobile sinks (MSs) for data gathering and mobile chargers (MCs) for SN energy replenishment using wireless energy transfer. This dual-function approach leads to suboptimal network performance due to increased costs in formulating separate routing paths, scheduling complexity, increased energy usage, and inefficient coverage. Thereby, the concept of mobile-sink-cum-charger (MSCs) has become more prevalent in WRSNs by combining both MS and MC functionalities into a single mobile unit that optimizes resource usage, reduces network complexity and extends network lifetime. Moreover, popular research solutions struggle as the introduction of mobile sensor nodes (MSNs) in modern WRSNs further complicates charging synchronization, as dynamic mobility disrupts predictable energy replenishment strategies. The proposed work addresses these challenges and introduces a Hybrid Mode of Wireless Charging (P-HMWC). This novel cluster-based hierarchical approach integrates primary static sink-cum-chargers (PSSC) and auxiliary mobile sink-cum-chargers (AMSC) to optimize data collection and energy replenishment considering mobile SNs. The combined functionalities of PSSC and AMSC in P-HMWC reduce redundant energy consumption, optimize AMSC trajectories within clusters, and ensure continuous sensor operation with better service coverage under dynamic topological changes. The cluster-tree-based approach enhances scalability, network lifetime, and charging efficiency while minimizing synchronization overhead among MSNs and MSCs. Extensive simulation results demonstrate that P-HMWC operates more efficiently under hierarchical topology when compared with star single-hop charging and k-graph-based strategies. It also outperforms existing baseline approaches (e.g., J-METDC, TTC-ASSO, GSA-FCR, PSOCR, and GECR) in key performance metrics such as energy efficiency, packet reception ratio, end-to-end delay, charging efficiency and network throughput. Integrating PSSC and AMSCs charging components ensures better service coverage, lower energy wastage, and extended network longevity, making P-HMWC a promising solution for next-generation WRSNs.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Consumer Electronics
Published
2025-05-01
DOI
https://doi.org/10.1109/tce.2025.3574170
Citations
2
Primary Topic
IoT-based Smart Home Systems
Type
article
Field-Weighted Citation Impact
1.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Energy-Aware Unified Data Collection and Energy Replenishment in Consumer Mobile WSNs

Tarachand Amgoth, M. Mallikarjuna
2 citations
IEEE Transactions on Consumer Electronics
IoT-based Smart Home Systems
1.46
article

Energy-Aware Unified Data Collection and Energy Replenishment in Consumer Mobile WSNs

Tarachand Amgoth, M. Mallikarjuna
article en
2 citations

Abstract

Rechargeable Battery-powered Sensor Nodes (SNs) in Wireless Rechargeable Sensor Networks (WRSNs) are essential components of Internet of Things (IoT) applications. Previous research on traditional WRSN frameworks mostly relied on independent mobile sinks (MSs) for data gathering and mobile chargers (MCs) for SN energy replenishment using wireless energy transfer. This dual-function approach leads to suboptimal network performance due to increased costs in formulating separate routing paths, scheduling complexity, increased energy usage, and inefficient coverage. Thereby, the concept of mobile-sink-cum-charger (MSCs) has become more prevalent in WRSNs by combining both MS and MC functionalities into a single mobile unit that optimizes resource usage, reduces network complexity and extends network lifetime. Moreover, popular research solutions struggle as the introduction of mobile sensor nodes (MSNs) in modern WRSNs further complicates charging synchronization, as dynamic mobility disrupts predictable energy replenishment strategies. The proposed work addresses these challenges and introduces a Hybrid Mode of Wireless Charging (P-HMWC). This novel cluster-based hierarchical approach integrates primary static sink-cum-chargers (PSSC) and auxiliary mobile sink-cum-chargers (AMSC) to optimize data collection and energy replenishment considering mobile SNs. The combined functionalities of PSSC and AMSC in P-HMWC reduce redundant energy consumption, optimize AMSC trajectories within clusters, and ensure continuous sensor operation with better service coverage under dynamic topological changes. The cluster-tree-based approach enhances scalability, network lifetime, and charging efficiency while minimizing synchronization overhead among MSNs and MSCs. Extensive simulation results demonstrate that P-HMWC operates more efficiently under hierarchical topology when compared with star single-hop charging and k-graph-based strategies. It also outperforms existing baseline approaches (e.g., J-METDC, TTC-ASSO, GSA-FCR, PSOCR, and GECR) in key performance metrics such as energy efficiency, packet reception ratio, end-to-end delay, charging efficiency and network throughput. Integrating PSSC and AMSCs charging components ensures better service coverage, lower energy wastage, and extended network longevity, making P-HMWC a promising solution for next-generation WRSNs.

IEEE Transactions on Consumer ElectronicsVol. 71(2)
Indian Institute of Technology Dhanbad (IN)
Affordable and clean energy
Openalex Percentile: Top 17%
IoT-based Smart Home Systems
1.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.