Deep Pipeline Architecture for Fast Fractal Color Image Compression Utilizing Inter-Color Correlation

Fractal compression technique is a well-known technique that encodes an image by mapping the image into itself and this requires performing a massive and repetitive search. Thus, the encoding time is too long, which is the main problem of the fractal algorithm. To reduce the encoding time, several hardware implementations have been developed. However, they are generally developed for grayscale images, and using them to encode colour images leads to doubling the encoding time 3× at least. Therefore, in this paper, new high-speed hardware architecture is proposed for encoding RGB images in a short time. Unlike the conventional approach of encoding the colour components similarly and individually as a grayscale image, the proposed method encodes two of the colour components by mapping them directly to the most correlated component with a searchless encoding scheme, while the third component is encoded with a search-based scheme. This results in reducing the encoding time and also in increasing the compression rate. The parallel and deep-pipelining approaches have been utilized to improve the processing time significantly. Furthermore, to reduce the memory access to the half, the image is partitioned in such a way that half of the matching operations utilize the same data fetched for processing the other half of the matching operations. Consequently, the proposed architecture can encode a 1024×1024 RGB image within a minimal time of 12.2 ms, and a compression ratio of 46.5. Accordingly, the proposed architecture is further superior to the state-of-the-art architectures.©2022 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

Energy-Harvesting for IoT-based Wireless Nodes: A Progress Study

Energy-harvesting technology is a promising renewable energy source for many applications through deriving energy from ambient environments. In IoT-based devices, this plays a significant role in providing sustainable energy and overcomes the need for battery maintenance and replacement, leading to increased efficiency, reliability, and operation time. This paper focuses on energy- harvesting in the context of IoT-based wireless nodes, its current progress, and future expectations. The main components, energy dissipations, and powering sources of IoT-based nodes are, first, identified. Then, the various ambient energy-harvesting sources and general architecture of the IoT-based energy-harvesting node are presented and concisely discussed. Finally, a progress discussion on the current and future trends of energy-harvesting technology for IoT devices is provided from different aspects, while shedding light on the key challenges facing the growth of energy harvesting for IoT-based systems. Overall, the paper provides a contemporary study that helps researchers who are working in this area and aiming to participate in its developments

TEMSEP: threshold-oriented and energy-harvesting enabled multilevel SEP protocol for improving energy-efficiency of heterogeneous WSNs

Energy-saving in WSN-based monitoring systems has drawn considerable interest lately. Further investigations and real efforts are needed to reduce the rapid energy consumption in such networks that commonly use battery-operated nodes. In this paper, we propose TEMSEP (Threshold-oriented and Energy-harvesting enabled Multi-level Stable Election Protocol) for improving the energy of large-scale WSNs. TEMSEP is a reactive protocol basing on hierarchical clustering, energy-harvesting relay nodes, and multilevel sensor nodes' heterogeneity that supports unlimited levels of battery initial energy. Instead of continuous data transmission, the network nodes in TEMSEP send their data only when it is necessary by responding reactively to the changes in relevant parameters or events of interest. We introduce a new thresholding model that provides an ideal mechanism for such reactive behaviour in detecting events, based on the values of heterogeneous thresholds and the sliding window formulated. This efficiently regulates the data reporting frequency, and hence, directly achieves significant reductions in the network traffic-load, optimizes the energy consumption of battery-powered nodes, and maximizes the network lifetime. The extensive simulations show that TEMSEP highly improves the network performance by reducing up to 53% of the network traffic-load and save up to 73% of the total dissipated energy, on average. The stability period and overall network lifetime are increased, at least, by 69% and 56% respectively, compared to other tested protocols

A Robust Tuned K-Nearest Neighbours Classifier for Software Defect Prediction

If the software fails to perform its function, serious consequences may result. Software defect prediction is one of the most useful tasks in the Software Development Life Cycle (SDLC) process where it can determine which modules of the software are prone to defects and need to be tested. Owing to its efficiency, machine learning techniques are growing rapidly in software defect prediction. K-Nearest Neighbors (KNN) classifier, a supervised classification technique, has been widely used for this problem. The number of neighbors, which measure by calculating the distance between the new data and its neighbors, has a significant impact on KNN performance. Therefore, the KNN’s classifier will perform better if the k hyperparameters are properly tuned and the independent inputs are rescaled. In order to improve the performance of KNN, this paper aims to presents a robust tuned machine learning approach based on K-Nearest Neighbors classifier for software defect prediction, called Robust-Tuned-KNN(RT-KNN). The RT-KNN aims to address the two abovementioned problems by (1) tuning KNN and finding the optimal value for k in both the training and testing phases that can lead to good prediction results, and (2) using the Robust scaler to rescale the different independent inputs. The experiment results demonstrate that RT-KNN is able to give sufficiently competitive results compared with original KNN and other existing works.©2022 Springer. This is a post-peer-review, pre-copyedit version of an article published in Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems: ICETIS 2022, Volume 2. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-20429-6fi=vertaisarvioitu|en=peerReviewed

An enhanced energy efficient protocol for large-scale IoT-based heterogeneous WSNs

There is increasing attention, recently, to optimizing energy consumption in IoT-based large-scale networks. Extending the lifetime of battery-powered nodes is a key challenge in such systems and their various application scenarios. This paper proposes a new zone-based and event-driven protocol for saving energy in large-scale heterogeneous WSNs called TESEES (Threshold Enabled Scalable and Energy Efficient Scheme). The proposed protocol is designed to support network scenarios deploying higher levels of heterogeneity with more than three types of sensor nodes (i.e., four, five, and more). TESEES is a reactive version of the proactive SEES protocol, in which we leverage a novel state-of-the-art thresholding model on the zone-based hierarchical deployments of heterogeneous nodes to regulate the data reporting process, avoiding unnecessary frequent data transmission and reducing the amount of energy dissipation of the sensing nodes and the entire system. With this model, we present a general technique for formulating distinct thresholds for network nodes in each established zone. This mechanism allows for individually configuring the nodes with transmission settings tailored to their respective roles, independent of the heterogeneity levels, total node count, or initial energy. This approach ensures that each node operates optimally within the network. In addition, we present an improved hybrid TMCCT (Threshold-based Minimum Cost Cross-layer Transmission) algorithm that operates at the node level and ensures effective data transmission control by considering current sensor values, heterogeneous event thresholds, and previous data records. Instead of periodical data transmission, this hybridization mechanism, integrated with a grid of energy-harvesting relay nodes, keeps the zone member nodes in the energy-saving mode for maximum time and allows for reactive data transmission only when necessary. This results in a reduced data-reporting frequency, less traffic load, minimized energy consumption, and thus a greater extension of the network’s lifetime. Moreover, unlike the traditional cluster-head election in the weighted probability-based protocols, TESEES relies on an efficient mechanism for zone aggregators’ election that runs at the zone level in multiple stages and employs various static and dynamic parameters based on their generated weights of importance. This leads to selecting the best candidate nodes for the aggregation task and, hence, fairly rotating the role among the zones’ alive nodes. The simulation results show significant improvements in the total energy saving, the lifetime extension, and the transmitted data reduction, reaching 29%, 68%, and 26% respectively, compared to the traditional SEES protocol. Also, the average energy consumption per single round has decreased by 36%