The Internet of Things (IoT) has facilitated the generation of vast amounts of data, enabling advanced personalized healthcare services such as Human Activity Recognition (HAR) systems. Privacy concerns have driven the adoption of federated learning (FL) across multiple distributed healthcare devices. However, the lack of model adaptation for device-specific data, particularly in non-i.i.d. settings and the limited resource capabilities of these devices, presents an ongoing challenge for FL implementation on-devices. In this study, we introduce a new Personalized Orthogonal Low-Rank Adaptation (ProLoRA) method which provides efficient personalized HAR system. ProLoRA uses low-rank orthogonal transformations of the fully connected layers to mitigating interference with previously acquired personalized knowledge. Our method demonstrates superior personalized model performance and competitive global model accuracy while significantly reducing computational and memory overhead compared to existing state-of-the-art personalized FL techniques. Comprehensive empirical evaluations on HAR and PAMAP2 datasets validate the superior performance of ProLoRA in both accuracy and resource efficiency.

Task graphs are widely used to represent data-intensive applications. To efficiently execute these applications on heterogeneous systems, each task must be properly scheduled on the processors of the system. The NP-completeness of the task scheduling problem has motivated researchers to propose various heuristic methods. Recently, Quality of Service (QoS) aware scheduling is becoming an active research area in heterogeneous systems because the end-user has different QoS requirements. Generally, time and cost are the most relevant user concerns. However, it is challenging to find a feasible scheduling plan which minimizes the total execution time of the user’s application (makespan) while satisfying both budget and deadline constraints. In this paper, we present a novel heuristic algorithm called Budget-Deadline-Aware-Scheduling (BUDA) that addresses task graphs scheduling under budget and deadline constraints in heterogeneous systems. The novelty of the BUDA algorithm is based on a Heterogeneous Time-Cost Matrix (HTCM) that is used to prioritize tasks and for processor selection. In addition, we introduce a new Heterogeneous Time-Cost Trade-off factor (HTCT) that tries to adjust the time and cost for the current task among all processors. The experiments based on randomly generated graphs and real-world applications graphs show that the BUDA algorithm outperforms the state-of-the-art algorithms in terms of makespan, time efficiency, and success rate.

Efficient scheduling algorithms are key for attaining high performance in heterogeneous computing systems. In this article, we propose a new list scheduling algorithm for assigning task graphs to fully connected heterogeneous processors with an aim to minimize the scheduling length. The proposed algorithm, called Improved Predict Priority Task Scheduling (IPPTS) algorithm has two phases: task prioritization phase, which gives priority to tasks, and processor selection phase, which selects a processor for a task. The IPPTS algorithm has a quadratic time complexity as the related algorithms for the same goal, that is$O(t^{2} \times p)$, for$t$tasks and$p$processors. Our algorithm reduces the scheduling length significantly by looking ahead in both task prioritization phase and processor selection phase. In this way, the algorithm is looking ahead to schedule a task and its heaviest successor task to the optimistic processor, i.e., the processor that minimizes their computation and communication costs. The experiments based on both randomly generated graphs and graphs of real-world applications show that the IPPTS algorithm significantly outperforms previous list scheduling algorithms in terms of makespan, speedup, makespan standard deviation, efficiency, and frequency of best results.

Efficient workflow scheduling algorithms are crucial for attaining high performance in large-scale heterogeneous distributed infrastructures, such as cloud computing. List scheduling algorithms are one of the most efficient heuristic methods for assigning task graphs to fully connected heterogeneous systems. However, most existing list-based scheduling algorithms do not consider the applications' security requirements and the security services offered by cloud providers. In this paper, we extend four list scheduling algorithms for security-aware workflow scheduling in the IaaS cloud. The idea of the extension is to consider the security overheads in both tasks prioritizing phase and virtual machine selection phase of the four original algorithms. Based on real-world applications, we evaluate the performance of the proposed algorithms in terms of scheduling length, speedup and efficiency.

With the recent advancements in the information and communication technologies, large number of devices are connecting to the Internet, hence large volumes of data in different formats and from different sources are generating. Consequently, on one hand dynamic and heterogeneous data sharing and management, in the ecosystem of Internet of Things (IoT), where every smart object is connected to Internet, presents new research challenges. On the other hand, citizen privacy preserving is another challenge, because he/she has to send his/her information to a service provider, to obtain the required information. This information is sensitive since it can reveal information about an individual. An attacker or a malicious service provider can utilize this sensitive information for their own business or something else. This paper presents a Secure Framework for Future Smart City (SEFSCITY), for better city living and governance, based on Cloud Computing IoT and Distributed Computing. We first present the architecture of SEFSCITY, which is based on Multi-Cloud and Cloud Federation approach; then we propose a security protocol for our framework. In our security model, we use Zero-Knowledge Protocol based on Elliptic Curve Discrete Logarithm Problem. Finally, we validate our architecture by conducting several scenarios that we have implemented using Cloud Analyst tool. The results show that in all scenarios, the cost infrastructure remains the same for the cloud customer, and our approach is benefic for the cloud provider in term of revenues and data processing time