With the rise of smart wearables and growing demand for longer battery life, wireless charging has become essential, especially when wired options are unavailable. Current wireless energy charging methods for wearable devices are mainly based on radio frequency wireless charging technology. However, due to the low energy transmission efficiency of individual wireless charging devices, multiple wireless transmission devices are combined to meet the energy demands of wearable devices. Consequently, efficiently combining multiple wireless energy transmission devices to supply power to a wearable device has emerged as a critical issue. To address this challenge, we propose an approach for optimizing the combination of wireless energy charging devices based on a deep reinforcement learning-based ant colony optimization algorithm. We leverage deep reinforcement learning to automatically adjust the heuristic parameters and strategies of the ant colony algorithm, thereby enhancing its search capability. This effectively solves the energy transmission combination optimization problem. Experimental results demonstrate that the energy combination plans of the proposed algorithm are more efficient than those generated by baseline and state-of-the-art algorithms. Additionally, we explore the combination scheme with minimal energy transmission loss and demonstrate that our approach consistently yields solutions with lower energy loss compared to other algorithms.

With the in-depth study of taxi services, mobile crowdsourcing has become an emerging paradigm for solving location-based assignment tasks. However, only considering the task completion and regional coverage balance may reduce user’s satisfaction. Therefore, designing effective methods that not only address coverage balance but also take into account user’s satisfaction is a problem that needs to be addressed. We investigate the problem of regional coverage balance with users’ satisfaction and propose a service selection method based on transfer learning. This method comprises user’s trajectory prediction and service’s reputation to recommend services to users. In the user’s trajectory prediction part, an incentive mechanism is considered to ensure an even distribution of taxi services in each sub-region. Taking the service providers’ moving as the input, we design an adaptive ant colony algorithm that incorporates transfer learning to provide users with the optimal services. We validate the effectiveness of this method with datasets collected from the real world and compare the performance with existing regional coverage balance strategies. The experimental results show that user’s satisfaction increases by 53%, and the imbalance reduces by 24%.

Service recommendations are crucial for developers to create mashups such as mobile applications, workflows, e-business solutions, etc. Existing methods based on collaborative filtering or content analysis are manual and cannot automatically acquire services that align with the requirements of mashup creation. A possible solution to automatically acquiring necessary services for mashups is the seq2seq (sequence to sequence) generation model, which has demonstrated promising performance in automatic text and program code generation. However, two main challenges must be tackled in service acquisition based on the seq2seq model. First, the seq2seq model can only acquire a set of services without inter-service dependencies, but such dependencies are crucial in the generation of sequences for services. Second, external knowledge must be leveraged to recommend services more accurately that fulfill developers' requirements, such as similar historical user requirements and combining mashup category information, due to the incomplete description of user requirements. To tackle these challenges, this paper proposes GSR (Generation ofServiceRecommendations), an approach that can automatically acquire services based on user requirements. Specifically, GSR employs reinforcement learning to learn the inter-dependencies among services and integrate dependencies into service recommendations. To further improve the quality of the acquired services, GSR retrieves relevant user requirements based on BERT (Bidirectional Encoder Representation from Transformers) to help identify potential services. Experiment results conducted on real-world datasets show the superior performance of GSR. Compared with the existing recommendation approaches, the precision metric is increased by up to 1.99x, and the recall metric is increased by up to 12%.

Cloud computing users may obtain great flexibility with low cost by outsourcing their data and services to the cloud. Services are composed together as a solution when no individual services meet the goal. Unfortunately, searching an optimal composition requires significant time due to the high number of available services in the cloud. In this paper, we develop an approach that solves the composition problem with a graph database. This approach uses the shortest bidirectional breadth-first and Dijkstra algorithms to find solutions with either fewest services or the optimal QoS values. Firstly, we preprocess service compositions and store them as paths in a directed bipartite graph in graph database. Compared with using a relational database, the join operator can be avoided and the search speed increases. Secondly, this approach utilize existing resources and can be easily migrated to a cloud database. Correctness and feasibility of this system are verified with experimental results, which shows this system leads to good performance in finding users' satisfying solutions.