In Artificial Intelligence of Things (AIoT)-enabled edge sensing systems, unmanned aerial vehicles have emerged as the dominant low-altitude monitoring platform owing to their wide-area coverage. However, in large-scale surveillance scenarios, sensor limitations and the inherent difficulty in preserving discriminative features for tiny targets hinder robust small object detection. Therefore, we propose the MSFR-DEF-YOLO framework, which combines densely efficient flow YOLO (DEF-YOLO) with multi-scale feature reconstruction (MSFR) auxiliary branches. We first enhance fine-grained feature extraction by optimizing the network structure and integrating an efficient downsampling module (EDM). Next, a dynamic dense feature pyramid (DDFP) is designed to incorporate dense cross-layer connections and bidirectional feature flows, enhancing multi-scale context consistency. Additionally, the introduction of the MSFR auxiliary branch integrates shallow and deep features from the backbone network, enabling feature reconstruction and information compensation, thereby further improving small object detection capabilities. To verify the improvements, we tested a range of network configurations on different YOLO versions. Our model outperforms other versions in the YOLO series. Moreover, on four public datasets, MSFR-DEF-YOLO achieves mAP50 improvements of 8.8%, 6.9%, 1.8%, and 6% compared to YOLOv8n, all while using only 20% of the parameters of YOLOv8n. To further reduce the computational overhead, this paper proposes a lightweight MSFR-L-YOLO model that utilizes a unidirectional path neck design. Compared to MSFR-DEF-YOLO, this model reduces the number of parameters by 15% while maintaining high efficiency, thereby reducing computational requirements and achieving comparable accuracy. This provides an efficient solution for AIoT-enabled airborne sensing systems.

In this paper, we propose stacking codes and decoding algorithms to correct burst deletion/insertion and random substitution errors in a non-binary segment-Lmaxburst-Ddeletion and maxburst-Sinsertion (NB-SBDI(L,D,S)) channel without knowing the codeword boundaries. Specifically, in the NB-SBDI(L,D,S) channel, at most a single non-binary burst (a block of consecutive bits/symbols) of deletions or insertions of length up toDorS, respectively, exists in a block ofLconsecutive non-binary symbols. The proposed stacking-codes are constructed by stacking multiple Reed-Muller(RM)/modified BCH/modified binary representation of Reed-Solomon (BRRS) codes, respectively, and no marker codes are involved. We show that, in the NB-SBDI(L,D,S) channel, the proposed approach can not only correct both burst deletions/insertions and random substitution errors, but also achieve error-free decoding for stacking-RM/modified stacking-BCH/modified stacking-BRRS codes for burst deletions/insertions as long as no substitution errors exist. Also, the modified stacking-BRRS code is more rate efficient than the existing Song code [1], the stacking-RM/modified stacking-BCH codes are more rate efficient than the Song code if max(D,S) and code length are small.

Media access control (MAC) plays a pivotal role in ensuring proper operation in wireless body area networks (WBAN). However, current solutions still cannot satisfy high performance requirements of low latency and energy efficiency for emergency data reporting. In this article, we propose a Multi- Factor Emergency Scheduling Scheme (MESS) for meeting such a strict demand. First, we design a Data Classification method including periodic data and emergency data. Unlike consistent data characteristics in other schemes, data heterogeneity is considered in our solution, which is more practical for different nodes. Second, we carefully devise a Multi-Factor Priority Division Scheme according to the Disease-Related Factor, Critical Degree Factor, Health Severity Factor and Age of Information Factor. This is more comprehensive consideration of the key characteristics of the node. In addition, we design a Dynamic Slot Allocation and Sequencing Approach, in which time slots of nodes are allocated based on the Data Classification and Multi-Factor Priority-Based Ordering. This enhances low latency and guarantees energy efficiency of nodes, and short waiting time of emergency data. Extensive simulations exhibit the advantages of MESS in terms of delay and energy efficiency.

Body area networks (BANs) are systems of wearable computing devices for long‐term monitoring of personal health care. BAN is an emerging technology for the worldwide ageing population. In the BAN system, the transceiver is the most energy‐consuming part of a sensor node and radio transmission in the vicinity of the human body is highly lossy and inefficient. Therefore, the energy of the sensor node constraints the life cycle and quality of service of the network; consequently, low‐cost protocol shaves attracted wide interest. This study proposes a frame structure model of a self‐adaptive guard band protocol, which introduces a GB in each time slot according to the allowed maximum time drift of the crystal, adaptively adjusts the value of the GB based on the actual time drift, and then ensures that the node simultaneously maintains the sleeping state and synchronisation with the coordinator during beacon transmission, thus reducing the energy consumption.