Antar Shaddad Hamed Abdul-Qawy

The continuous scaling of silicon technology has enabled many-core systems to become ubiquitous, offering enormous computational power for various applications spanning from high-performance computing to mobile devices. However, this advancement resulted in increased power density that exacerbated the thermal challenges of dark silicon, where certain cores are turned off or become dark due to thermal constraints. While various methods have been put forward to enhance the performance of thermally constrained 2D many-core systems, 3D designs introduce more serious thermal issues due to heightened power density and challenges with heat dissipation in vertically stacked configurations. This paper introduces a dynamic neighbor-aware performance enhancement for thermally constrained 3D many-core systems (3D-DNaPE). 3D-DNaPE is a technique that improves the performance of a thermally constrained 3D many-core system where only a limited number of cores can be activated. Initially, it uses the proposed neighbor-aware pattern (NaP) algorithm to select the coldest core among the four adjacent dark cores suitable for task migration. Subsequently, it uses the proposed 3D dynamic thermal management (3D-DTM) algorithm to optimize system performance by considering the core and memory bank temperatures. A static non-uniform cache access (S-NUCA) configuration mitigates cache misses resulting from task migration. Comprehensive evaluations indicate that 3D-DNaPE performs better than its contemporaries, showing improvements reaching up to 43% in execution time, a 34% decrease in performance slowdown, and an up to 51% enhancement in energy efficiency. This research not only underscores the challenges faced by 3D many-core systems but also provides a robust solution with promising implications for future 3D many-core designs.

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 3x 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 correlation 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 1024x1024 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.

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.

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 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.

In this paper, we propose an improved hierarchical clustering protocol called EH-mulSEP (Energy-Harvesting enabled Multi-level Stable Election Protocol) for green IoT-based heterogeneous WSNs. We study the impact of energy-harvesting techniques in the large-scale systems by adding a number of energy-harvesting relay nodes that receive the aggregated data from the elected cluster heads and transmit to the base stations layer. Besides, we introduce a general formula for multi-level weighted election probability that can support up to n levels of heterogeneous nodes with their respective amounts of initial energies. For this, we leverage a state of the art heterogeneity method that considers more than three types of heterogeneous nodes and provides general models for higher levels of initial energy in WSNs. EH-mulSEP aims to minimize the total energy usage of battery-powered sensor nodes in IoT-based systems so as to maximize energy conservation, extend the network lifetime and ensure a higher level of scalability. The simulation results show that EH-mulSEP highly enhances the network performance in terms of stability, network lifetime, energy consumption, and throughput compared to other versions of SEP protocol in all conditions and scenarios tested.

Networking devices in ISP networks and data centers have been deployed in an over-provisioning and redundant manner to meet the worst case traffic loads and to quickly recover from failures. These devices are idle or semi-idle most of the time with full power consumption. Today, there is widespread interest in techniques that help in reducing energy waste and achieving high levels of energy-efficiency. Solutions proposed in literature are either topology-oriented (ESTOP) or traffic-oriented (ESTA). In this paper, we propose a Hybrid Heuristic for Green Networking (HHGN) that exploits the parameters considered in both ESTOP and ESTA to identify a subset of network elements that can be switched off such that the power saving is maximized under network connectivity and edge bandwidth utilization constraints. We introduce two new heuristics, w-BFLP (weighted Betweenness, Flow, Links, Power) and w-BFP (weighted Betweenness, Flow, Power), that sort the nodes and the edges respectively in increasing order of their importance in the topology. Nodes and edges are then switched off from the least important until the connectivity threshold or maximum bandwidth utilization constraint on the edges as specified by the user is reached. We compare our approach with ESTOP and ESTA for edge optimization in terms of power gain, mean utilization of edge bandwidth, percentage of sleeping edges, fairness index, trade-off between power gain and edge utilization, and increase in the length of re-computed paths for real ISP and FatTree topologies using different power models. Experimental results show that HHGN gives the best performance independent of power models, traffic matrices and topologies tested.