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<p>This paper considers mobility management for sixth-generation (6G) Ambient Internet of Things (IoT) networks using backscatter communication. Ambient IoT devices operate with batteryless or small-buffer energy storage, harvesting energy from ambient radio frequency (RF) signals. Conventional network-controlled handovers incur high signaling overhead and cannot scale to ultra-dense deployments. We propose an energy-aware device-initiated mobility management (EA-DIMM) framework. In EA-DIMM, devices trigger reattachment based on local measurements of energy availability, link quality, and queue status. Analytical expressions are derived for reattachment frequency, missed query probability, query success rate, and device fairness. Closed-form results are provided under exponential energy harvesting and log-normal signal-to-noise ratio (SNR) models. MATLAB simulations validate the analysis. Results show that EA-DIMM reduces reattachments by up to 90<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>%</mo> </math></EquationSource> </InlineEquation> compared to network-controlled schemes and extends network lifetime by over 30<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>%</mo> </math></EquationSource> </InlineEquation>, while maintaining high query success and fairness. The framework is suitable for scalable mobility management in ultra-dense, energy-constrained 6G backscatter networks.</p>

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Energy-aware device-initiated mobility management for ambient IoT in 6G backscatter networks

  • 期刊论文

  • 发表日期：

    2026年2月5日

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  • Rupender Singh

摘要

This paper considers mobility management for sixth-generation (6G) Ambient Internet of Things (IoT) networks using backscatter communication. Ambient IoT devices operate with batteryless or small-buffer energy storage, harvesting energy from ambient radio frequency (RF) signals. Conventional network-controlled handovers incur high signaling overhead and cannot scale to ultra-dense deployments. We propose an energy-aware device-initiated mobility management (EA-DIMM) framework. In EA-DIMM, devices trigger reattachment based on local measurements of energy availability, link quality, and queue status. Analytical expressions are derived for reattachment frequency, missed query probability, query success rate, and device fairness. Closed-form results are provided under exponential energy harvesting and log-normal signal-to-noise ratio (SNR) models. MATLAB simulations validate the analysis. Results show that EA-DIMM reduces reattachments by up to 90 \(\%\) % compared to network-controlled schemes and extends network lifetime by over 30 \(\%\) % , while maintaining high query success and fairness. The framework is suitable for scalable mobility management in ultra-dense, energy-constrained 6G backscatter networks.