International Journal of Embedded Systems (5 papers in press)

Regular Issues

• Incentive-based resource management in pervasive mobile cloud computing  
  by Yuanhao Ma, Jigang Wen, Yuxiang Chen 
  Abstract: oud computing is a promising technique to conquer the resource limitations of a single mobile device. To relieve the work load of mobile users, computation-intensive tasks are proposed to be offloaded to the remote cloud or local Cloudlet. However, these solutions also face some challenges. It is difficult to support data intensive and delay-sensitive applications in the remote cloud, while the local Cloudlets often have limited coverage. When both of these methods cannot be supported, another option is to relieve the load of a single device by taking advantage of resources of surrounding smart-phones or other wireless devices. To facilitate the efficient operation of the third option, we propose a novel pervasive mobile cloud framework to provide an incentive mechanism to motivate mobile users to contribute their sources for others to borrow and an efficient mechanism to enable multi-site computation partition. More specifically, we formulate the problem as a Stackelberg game, and prove that there exists a unique Nash equilibrium for the game. Based on the unique Nash equilibrium, we propose an offloading protocol to derive the mobile users strategies. Through extensive simulations, we evaluate the performance and validate the theoretical properties of the proposed economy-based incentive mechanism.
  Keywords: cloud computing; resource management.
  DOI: 10.1504/IJES.2025.10070247
   
  
• Intelligent workload optimisation based on a protocol-fused cloud robotics physical framework with integrated multi-sensors  
  by Songshuang Li, Shengyu Zhu, Kui Qian, Nannan Dong 
  Abstract: Cloud computing significantly improves the performance of robots in data processing and storage, but still faces problems such as high computational loads and high energy requirements for local robots. To address these issues, a protocol-fused physical framework integrating multiple sensors is proposed to simplify sensoring data integration and device deployment. Cloud robotics intelligent workload optimization has also been achieved through accurate sensoring data collection based on this framework. First, a middleware called ProtoFusion is introduced to manage the robot’s local services, facilitating protocol conversion and transmission of multimodal sensory information. Next, the cloud robot’s physical framework, based on ProtoFusion, enables sensing, perception, and control. Finally, ProtoFusion’s task division (e.g., receiving, sending, and controlling) is scheduled using uC/OS-III, optimizing system resource utilization. The effectiveness of the optimisation is verified experimentally. Resource efficiency was improved, energy consumption was reduced and system reliability was enhanced.
  Keywords: cloud computing; ProtoFusion; data driven; robotics physical framework; intelligent workload optimisation.
  DOI: 10.1504/IJES.2025.10070812
   
  
• An AI knowledge base system for the recognition and personalised treatment of SARS-CoV-2  
  by Beniamino Di Martino, Gennaro Junior  Pezzullo, Alessandro Magliacane, Hung-Wei Li, Meng-Yen Hsieh 
  Abstract: Due to its rapid spread and associated symptoms, conventional methods of prevention and treatment have often proven inadequate to manage SARS-CoV-2, as conventional approaches show limited effectiveness in many scenarios. The ability of the virus to transmit rapidly, even to asymptomatic individuals, and the severity of symptoms in some patients have put significant pressure on healthcare systems. Against this backdrop, the work described aimed to develop an expert system for personalised recognition and treatment of SARS-CoV-2. A clear methodology was defined, supported by conceptual diagrams to outline the logical flow of the problem and proposed solutions. This methodology is based on key components: a Bayesian network to calculate the probability of infection by analysing the patients symptoms, contacts, and geographic context; and a semantic component to determine the most appropriate treatment using the patients clinical and personal information, such as allergies or individual risk factors. Once the solution was defined logically, we moved on to formalising the components and designing the workflows, which were implemented using appropriate technologies and open data. Testing of the system carried out through real data simulations, confirmed the systems ability to provide customised patient responses.
  Keywords: semantic; ontology; e-health; expert system.
  DOI: 10.1504/IJES.2025.10073047
   
  
• Privacy-preserved federated learning for internet of things with multi-round sparse aggregation  
  by Jiao Zhang, Xueting Huang, Xiong Li, Kai Jin, Dacheng He, Wei Liang 
  Abstract: This paper addresses the challenges of Privacy-Preserved Federated Learning (PPFL) in the Internet of Things (IoT) by introducing Multi-SparseAgg, an innovative framework designed for efficient, secure multi-round aggregation. Existing secure aggregation protocols in PPFL struggle with scalability, latency, and the communication overhead associated with frequent model updates across numerous, resource-limited IoT devices. Multi-SparseAgg tackles these challenges by employing sparse neural networks optimized with binary masks, significantly reducing communication costs without sacrificing model accuracy. A one-time setup phase generates reusable secrets, eliminating the need for costly reinitialization in each round and enabling robust aggregation even with intermittent client participation. Experimental results on a benchmark dataset demonstrate that Multi-SparseAgg significantly lowers communication costs on the client side by 8.2% to 69.1% and on the server side by 7.1% to 28.6%, compared to baseline methods. It also reduces computational overhead by 21.1% to 77.6% while preserving model accuracy and ensuring fast convergence.
  Keywords: privacy-preserved federated learning; PPFL; sparse subnetwork; secure aggregation; multi-round aggregation.
  DOI: 10.1504/IJES.2025.10073336
   
  
• Development of an intelligent mathematical model for n-channel FinFET using improved MobileNet architecture  
  by Vijayalaxmi Kumbar, Manisha Waje 
  Abstract: This work proposes a novel sophisticated mathematical model for the device. Initially, pre-acquired experimental data are collected, and a database is constructed. A variety of factors, such as gate length, electric field, gate width, drain-source voltage, and gate-source voltage, are included in the previously obtained experimental data. The B-Spline Interpolation technique is used for the data augmentation process. An Improved MobileNet model that defines three distinct models is then used to train and evaluate the supplemented data. The Improved MobileNet model helps the network improve the training speed and accuracy. The training is executed iteratively till a minimal error is obtained between actual and predicted values. This work improves predictive accuracy by utilizing experimental enabling more dependable simulations that are essential for enhancing device performance and design. This extends beyond improved FinFET modeling; it sets a precedent for integrating advanced machine learning techniques in semiconductor technology.
  Keywords: FinFET characterisation; B-Spline interpolation; improved MobileNet architecture; short channel effects; depth-wise separable convolution.