In heterogeneousWireless Rechargeable Sensor Networks (WRSNs) comprising both fixed and mobile nodes, existing energy replenishment strategies face severe challenges in cost, efficiency, and scalability. Fixed sensor clusters require efficient area-based charging to maintain network density, while dynamic sensors with unpredictable trajectories demand highly responsive on-demand service. Conventional single-modal or simplistic hybrid schemes cannot simultaneously satisfy these distinct and often conflicting operational requirements, leading to resource mismatch and critically limiting the overall network lifetime and quality of service. This paper introduces an Adaptive Co-operative Dual-Mode Charging Scheduling (AC-DCS) scheme, which integrates two novel algorithms. For fixed sensor clusters, the Portable-Charger Deployment Planning (PCDP) algorithm optimizes “one-to-many” charging using portable directional modules; for dynamic sensors, the Deep Adaptive Rescue Selector (DARS), a lightweight reinforcement learning model, provides ”one-to-one” on-demand service. Extensive simulations validate that in a 500-node network, AC-DCS improves the sensor survival rate by over 23%, improves mobile energy efficiency by approximately 65%, and reduces charging latency by at least 33% compared to state-of-the-art benchmarks, offering a robust solution for long-term large-scale IoT deployments.

The JPEG 2000 image compression standard is designed for a broad range of data compression applications. The discrete wavelet transformation (DWT), central to the signal analysis and important in the JPEG 2000, is quite susceptible to computer-induced errors. The errors can be spread to many output transform coefficients if the DWT is implemented by using lifting scheme. This paper proposes an efficient error tolerance scheme (ETS) to detect errors occurring in DWT. A pipeline-based DWT structure is also developed in this paper to speed up the error detection process. The proposed ETS design uses weighting sums of the DWT coefficients at the output compared with an equivalent check value derived from the input-data. With the proposed ETS design, the errors introduced at DWT can be effectively detected. Additionally, the results of error detection can be further analyzed and evaluated to show the capability of error tolerance. Some standard images are used as test samples to verify the feasibility of the proposed ETS design. Experimental results and comparisons show that the proposed ETS has good performance in error detection time and error tolerance capability.