Ali Bohlooli is an Associate Professor at the Faculty of Computer Engineering, University of Isfahan. His areas of expertise include the design and implementation of microcontroller- and FPGA-based systems, quality control using image processing, and the application of artificial intelligence in embedded systems (Embedded AI) and IoT. With over 20 years of experience in troubleshooting, optimizing, and upgrading industrial hardware, he is focused on developing intelligent, low-power, and efficient solutions for real-world problems. He believes that technology is only valuable when it can solve real problems; therefore, he pursues his projects with a problem-driven approach, emphasizing performance optimization, energy efficiency, and precision.
🔸 Cyber-Physical Systems
🔸 Internet of Things
🔸 Embedded Artificial intelligence
🔸 IIoT Security
🔸 Design and implementation of microcontroller- and FPGA-based digital systems
🔸 Quality control of products using image processing
🔸 Updating and troubleshooting hardware systems
- Ph.D., Ph.D., Isfahan University [Isfahan - Iran]
- Master's degree, MSc, Isfahan University of technology [Isfahan - Iran]
- Bachelor, BSc, Isfahan University of technology [Isfahan - Iran]
Advanced Optimization
Artificial Intelligence for Embedded Systems
Programming for IoT Devices
Articles
Publication Date: 2026
International Journal of Engineering, Transactions A: Basics (17281431)39(10)pp. 2529-2545
This study proposes the Bermuda Weed Optimization (BWO) algorithm; a novel and scalable metaheuristic algorithm inspired by the invasive growth of Bermuda grass. This algorithm has been developed as an enhanced version of the Invasive Weed Optimization (IWO) algorithm and, by imitating the plant's robust propagation strategies, achieves a better balance between global exploration and local exploitation. The algorithm's performance was rigorously evaluated against four previous IWO-based versions, and its superior scalability was demonstrated through the lowest average error and stable performance across diverse scenarios. Furthermore, BWO was compared with the new Gray Squirrel Search Algorithm (GSFA)—which falls outside the IWO category—to assess its performance against a novel method unrelated to the IWO family; this comparison highlighted BWO's competitive superiority and achieved an average 64.43% improvement in best-cost results. The strong convergence and scalability of BWO make it highly suitable for real-time applications—particularly in automotive systems. In a practical implementation using a cloud-based cruise control (CC) framework, BWO significantly outperformed the RPO-based method (the latest approach) by reducing overshoot by 45.92%, settling time by 29.38%, ISE speed by 8.92%, and maximum jerk by 20.09%. By achieving near-optimal convergence and leveraging cloud deployment with high scalability, BWO can effectively adapt to diverse automotive system requirements and achieve high efficiency across multiple operating modes. © 2026 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.
Publication Date: 2025
Digital Communications and Networks (23528648)11(2)pp. 574-586
In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, commonly used in data transmission protocols, increases transmission delay and consumes excessive bandwidth. To overcome this issue, forward error correction techniques, e.g., Random Linear Network Coding (RLNC) can be used in data transmission. The primary challenge in RLNC-based methodologies is sustaining a consistent coding ratio during data transmission, leading to notable bandwidth usage and transmission delay in dynamic network conditions. Therefore, this study proposes a new block-based RLNC strategy known as Adjustable RLNC (ARLNC), which dynamically adjusts the coding ratio and transmission window during runtime based on the estimated network error rate calculated via receiver feedback. The calculations in this approach are performed using a Galois field with the order of 256. Furthermore, we assessed ARLNC's performance by subjecting it to various error models such as Gilbert Elliott, exponential, and constant rates and compared it with the standard RLNC. The results show that dynamically adjusting the coding ratio and transmission window size based on network conditions significantly enhances network throughput and reduces total transmission delay in most scenarios. In contrast to the conventional RLNC method employing a fixed coding ratio, the presented approach has demonstrated significant enhancements, resulting in a 73% decrease in transmission delay and a 4 times augmentation in throughput. However, in dynamic computational environments, ARLNC generally incurs higher computational costs than the standard RLNC but excels in high-performance networks. © 2024 Chongqing University of Posts and Telecommunications
Publication Date: 2025
Computing (14365057)107(6)
With the advancement of the Internet of Things (IoT) and the changing needs of edge computing applications within the TCP/IP architecture, several challenges have emerged. One solution to these challenges is to integrate edge computing with information-centric networks (ICN). In ICN-based edge computing, there is a high level of similarity in request computing due to the proximity of users, which is leveraged to improve the efficiency of computation reuse. Computation reuse occurs through naming, caching, and forwarding. Computation reuse through forwarding means that similar requests are directed to the same compute node (CN). In many past works, forwarding algorithms for computation reuse have been used with high overhead for resource discovery or did not consider the important criterion of assessing the capacity of CNs. In this paper, we propose two forwarding algorithms, named TLCF) Trade-Off Between Load Balancing and Computation Reuse Forwarding) and AFCT (Adaptive Forwarding Considering Capacity Threshold), that measures criteria for selecting the best CN, the trade-off between computation reuse and load balancing, while also considering capacity. These two aspects lead to a reduction in completion time. Computation reuse inherently disrupts load balancing. The evaluation was conducted using the ndnSIM simulation. Through simulations, we have shown that our method significantly reduces completion time compared to the default method, achieving an improvement of approximately 22%. These findings highlight the efficiency and potential of our proposed method in optimizing edge computing performance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025.
Publication Date: 2024
IEEE Internet of Things Journal (23274662)11(7)pp. 12815-12822
Edge-Cloud Computing Industrial Internet of Things (ECIIoT) is composed of edge and cloud nodes with Industrial Internet of Things (IIoT) devices to get the service function chain (SFC). The service function chaining placement refers to a series of virtual network functions (VNFs) that are run at edge or cloud nodes in the form of software instances. In the problem of ECIIoT service embedding, the multiple VNFs must be placed for IIoT devices, so how these virtual functions are placed at cloud or edge nodes to minimize the delay is challenging to achieve. In this article, the placement of virtual functions with considering the edge and cloud nodes is proposed. In our model, the cloud server with edge nodes can run the required functions of IIoT devices in the SFC to decrease the imposed delay and use the computation resource in an efficient way. This is formed as an optimization problem to minimize the delay and residual computing resource consumption and reuse the previous functions. The exact solution of this problem is not available in polynomial time, therefore an efficient approximation algorithm is proposed which solves the problem in three stages. First, it linearizes the nonlinear objective function and constraint and approximates them by the convexity of these functions. Then, it solves the relaxed linear problem and finally, it rounds the decision variables in a heuristic way. This solution not only has polynomial time computational complexity but also obtains the near-optimal solution. The simulation results confirm the effectiveness of this approach. © 2014 IEEE.
Publication Date: 2024
Computing (14365057)106(9)pp. 2949-2969
In edge computing, repetitive computations are a common occurrence. However, the traditional TCP/IP architecture used in edge computing fails to identify these repetitions, resulting in redundant computations being recomputed by edge resources. To address this issue and enhance the efficiency of edge computing, Information-Centric Networking (ICN)-based edge computing is employed. The ICN architecture leverages its forwarding and naming convention features to recognize repetitive computations and direct them to the appropriate edge resources, thereby promoting “computation reuse”. This approach significantly improves the overall effectiveness of edge computing. In the realm of edge computing, dynamically generated computations often experience prolonged response times. To establish and track connections between input requests and the edge, naming conventions become crucial. By incorporating unique IDs within these naming conventions, each computing request with identical input data is treated as distinct, rendering ICN’s aggregation feature unusable. In this study, we propose a novel approach that modifies the Content Store (CS) table, treating computing requests with the same input data and unique IDs, resulting in identical outcomes, as equivalent. The benefits of this approach include reducing distance and completion time, and increasing hit ratio, as duplicate computations are no longer routed to edge resources or utilized cache. Through simulations, we demonstrate that our method significantly enhances cache reuse compared to the default method with no reuse, achieving an average improvement of over 57%. Furthermore, the speed up ratio of enhancement amounts to 15%. Notably, our method surpasses previous approaches by exhibiting the lowest average completion time, particularly when dealing with lower request frequencies. These findings highlight the efficacy and potential of our proposed method in optimizing edge computing performance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.
