LoRa, a leading wireless technology for Low Power Wide Area Networks (LPWAN), is well-known for its long transmission range and low power consumption. The extended range is primarily attributed to the Chirp Spread Spectrum technique. However, the LoRa physical layer (LoRa PHY) contributes only marginally to this advantage, as it employs an inefficient Forward Error Correction (FEC) strategy for error recovery. In this paper, we introduce Sym-FEC, a symbol-level FEC decoder designed to link the received signals' spectrum with the coding correlations inherent in LoRa PHY, thereby enhancing error recovery. The key enabler of Sym-FEC is signal copy retrieval. We begin by facilitating signal copy conversion between two symbols and extend this to the general case, where signal copy conversions can be performed between any symbols in a coding block. Approaches are also introduced to assess the validity of the block-wide decoding results. Extensive hardware evaluations demonstrate that Sym-FEC provides Signal-to-Noise-Ratio (SNR) improvement of 2.3dB to 3dB compared to the traditional decoder in LoRa PHY. Sym-FEC requires no modifications at the transmitter while incurs low storage and computational complexity at the gateway, thus can be easily integrated into gateway nodes.

In the Industrial Internet of Things (IIoT), the latency-sensitive task can be efficiently performed based on real-time data collection, transmission, and computation. In this article, we thus propose a hybrid nonorthogonal multiple access-rate splitting multiple access (NOMA-RSMA) edge-cloud service computing framework for the IIoT consisting of end devices (EDs), edge servers (ESs), and cloud servers (CSs), in which the tasks are allowed to be computed at the EDs, the ESs, or a CS. When the task computation takes place at the ESs or the CS, tasks would be first compressed at EDs, and then be offloaded to the ESs via nonorthogonal multiple access (NOMA). After that, the ESs offload tasks to the CS via rate splitting multiple access (RSMA) if tasks are intended to be processed at the CS. Otherwise, the ESs perform computation locally. Specifically, under the delay constraints, we aim to minimize the total system energy consumption by jointly optimizing the transmit power of the EDs and the ESs, the transmission delays of each NOMA group and RSMA, the signal splitting ratio of the ESs, the computation power of the EDs, the ESs, and the CS, the compression delay, the offloading decisions of the EDs and the ESs. To address the formulated nonconvex problem, we employ a hierarchical decomposition approach to layer it into a top-level offloading decision problem and a bottom-level resource allocation problem. We design a block coordinate descent (BCD)-based method to solve the bottom-level problem. Additionally, we propose an algorithm based on deep reinforcement learning and online offloading (DROO) to obtain the suboptimal offloading decisions for EDs and ESs in the top-level problem. Numerical results validate the accuracy and effectiveness of our algorithms in terms of the total energy consumption, compared with three heuristic algorithms, i.e., the genetic algorithm, and the cross-entropy algorithm, the Deep Q-Network algorithm.

LoRaWAN is a prominent technology for Low Power Wide Area Networks (LPWAN). However, the increasing network size has introduced a significant challenge: packet collisions resulting from concurrent transmissions in LoRaWAN. Previous studies either overlooked the issue by examining limited features or tackled it with intricate receivers employing up to eight antennas. To achieve a more favorable balance between implementation cost and system performance, we introduce$\text{Hi}^{2}\text{LoRa}$—a solution utilizing highly dimensional and accurate features for LoRa concurrent decoding, implemented with only two receiving antennas. The feature dimensions are expanded through an exploration of various hardware imperfections and inherent channel state information specific to each transceiver pair. To enhance feature accuracy, low pass filters and BiLSTM networks are applied to capture and learn their temporal patterns. Additionally, an efficient collision suppression strategy is introduced to mitigate feature corruption from concurrently transmitted packets. Extensive real-world testbed evaluations demonstrate that the achievable concurrency in$\text{Hi}^{2}\text{LoRa}$approaches that of state-of-the-art approaches with significantly higher complexity (e.g., utilizing eight antennas) or exceeds prior work by a factor of 2.7 with comparable complexity (e.g., using two antennas).

Fandom crowdfunding, where fans collectively raise funds for idols, fosters dynamic interactions within fandom communities, evoking a range of emotions. Despite the prevalence of such activities, the specific emotions involved and their effects on participant behavior remain underexplored. Addressing this, our mixed-methods study—encompassing observations, interviews, and analysis of crowdfunding data—investigated emotions during fandom crowdfunding and their influence on behavior across crowdfunding stages: planning, support, encouragement, realization, and auditing. We identified 10 key emotions related to idols and the community, finding these emotions crucial in shaping participant actions. Our findings highlight the dual impact of fandom crowdfunding on the community’s internal dynamics and its relationships with idols and broader society. We propose design recommendations for enhancing fandom crowdfunding and suggest how general crowdfunding can benefit from insights gained from the fandom context, offering a novel understanding of emotions in collaborative monetary activities.

With the massive social media data available online, the conventional single modality emotion classification has developed into more complex models of multimodal sentiment analysis. Most existing works simply extracted image features at a coarse level, resulting in the absence of partially detailed visual features. Besides, social media data usually contain multiple images, while existing works considered a single image case and used only one image for representing visual features. In fact, it is nontrivial to extend the single image case to the multiple images case, due to the complex relations among multiple images. To solve the above issues, in this paper, we propose aGatedFusionSemanticRelation (GFSR) network to explore semantic relations for social media sentiment analysis. In addition to inter-relations between visual and textual modalities, we also exploit intra-relations among multiple images, potentially improving the sentiment analysis performance. Specifically, we design a gated fusion network to fuse global image embeddings and the corresponding local Adjective Noun Pair (ANP) embeddings. Then, apart from textual relations and cross-modal relations, we employ the multi-head cross attention mechanism between images and ANPs to capture similar semantic contents. Eventually, the updated textual and visual representations are concatenated for the final sentiment prediction. Extensive experiments are conducted on real-worldYelpandFlickr30kdatasets, showing that our GFSR can improve about 0.10% to 3.66% in terms of accuracy on theYelpdataset with multiple images, and achieve the best accuracy for two classes and the best macro F1 for three classes on theFlickr30kdataset with a single image.