Federated unlearning (FUL) is a solution aimed at addressing the problem of removing data contributions from trained federated learning (FL) models. Existing FUL methods only focus on iterative unlearning of clients’ contributions and fail to perform unlearning in scenarios where multiple clients request to remove their data at a time. Additionally, FUL still needs to address issues, including convergence speed, maintaining the global model’s performance, and parallel unlearning to expedite the unlearning process. To fill this gap, we introduce Federated Clients Forgetting (FedCF), a fast and accurate FUL method that can eliminate single client contributions similar to existing methods, eliminate multiple clients’ contributions on the global model parallelly, ensure the performance of the unlearned global model, and reduce the unlearning time. The key idea is to construct a temporary model by extracting knowledge from the remaining clients’ updates and adding it to the corresponding parameters of the initial global model and then leverage a temporary model to reconstruct the unlearned global model. Extensive experiments on three benchmark datasets, FedCF demonstrates its efficiency and effectiveness for single client contribution unlearning, achieving an average time efficiency of 8.3x, 6.5x, and 4.1x over existing methods FedRetrain, FedEraser, and FUL with knowledge distillation, respectively. Additionally, FedCF showcases the time efficiency and performance guarantee after unlearning the contributions of multiple clients in parallel.

The rapid expansion of social media has notably transformed political communication, with politicians and activists increasingly adopting multimedia formats to disseminate their ideologies and policy proposals. This transformation poses a significant risk of propaganda through coordinated campaigns that leverage template-based imagery to spread political messages. To tackle this challenge, our research focuses on developing a detection framework for identifying political images crafted from similar templates, which are a common tool in such propaganda efforts. During a national referendum held in 2021 in Taiwan, we collected visual content from various social networks and implemented a hybrid approach that combines object recognition, textual analysis, and pixel-level information. This methodology is specifically designed to detect patterns and similarities within propaganda images, enabling us to trace and analyze the potentially manipulative content. Our hybrid feature combination technique has demonstrated superior performance compared to several established baseline methods in identifying template-based images. This advancement in detection technology not only enhances the efficiency of researchers studying political communication but also serves as a crucial tool in uncovering and understanding the mechanisms behind potential political propaganda and coordinated efforts to shape public opinion on social media platforms.

Energy harvesting wireless sensor networks (EHWSNs) are useful for ambient monitoring, especially in hazardous and hard-to-reach environments, where sensor nodes sense and transmit data to a remote sink via multi-hop communication, enabling informed decision making. Accurate decisions require a global snapshot that comprises environmental parameters simultaneously sensed from all nodes. To realize an efficient snapshot collection scheme, all nodes should send an equal number of messages where each message contributes to the snapshot; excessive messages from some nodes are ineffective and in-turn increase the network traffic and contribute to the wasteful work done by nodes. Reducing such ineffectual messages is challenging due to the variable ambient energy supply at nodes, network congestion, limited information about the overall state of the system and varying node distances from the sink. To achieve this objective, we introduce WaWoR, a novel distributed system where nodes dynamically decide when to sense and transmit snapshot messages based on their physical locations, energy availability and perceived network congestion. WaWoR outperforms two theoretical hypothetical baselines and two state-of-the-art-systems. It captures [90−175]% of the snapshots compared to the theoretical baselines and [1.02 − 4.26]× more snapshots than state-of-the-art systems. Additionally, it reduces wasted messages, which further reduces the total energy consumption by [1.6 − 9.9]×.

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data from Industrial Internet of Things (IIoT) devices, have highlighted the critical need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. We defined a storage model and data format for private data through the design and deployment of smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in the real-world simulations, Hyper-IIoT shows well-controlled contract execution time, stable system throughput and fast consensus process, and is capable of handling high throughput and effective consensus in distributed systems even in large-scale request scenarios.

Despite continuous improvements, cloud physical resources remain underused, hence severely impacting the efficiency of these infrastructures at large. To overcome this inefficiency, Infrastructure-as-a-Service (IaaS) providers usually compensate for oversized Virtual Machines (VMs) by offering more virtual resources than are physically available on a host. However, this technique-known as oversubscription-may hinder performances when a statically-defined oversubscription ratio results in resource contention of hosted VMs. Therefore, instead of setting a static and cluster-wide ratio, this article studies how a greedy increase of the oversubscription ratio per Physical Machine (PM) and resources type can preserve performance goals. Keeping performance unchanged allows our contribution to be more realistically adopted by production-scale IaaS infrastructures. This contribution, named SCROOGEVM, leverages the detection of PM stability to carefully increase the associated oversubscription ratios. Based on metrics shared by public cloud providers, we investigate the impact of resource oversubscription on performance degradation. Subsequently, we conduct a comparative analysis of SCROOGEVM with state-ofthe-art oversubscription computations. The results demonstrate that our approach outperforms existing methods by leveraging the presence of long-lasting VMs, while avoiding live migration penalties and performance impacts for stakeholders.

Among the available processor-level energy savings schemes, dynamic voltage and frequency scaling (DVFS) is very popular and effective due to its widespread cross-platform use in designing energy-efficient scheduling algorithms. However, rapid frequency switching by DVFS based algorithms while minimizing the energy consumptions may result transient failures in the system. To avoid such failures and their catastrophic consequences, energy-efficient scheduling algorithms with the capabilities to provide more reliable task schedules are always in demand. Therefore, this paper introduces two novel low complexity energy-efficient task scheduling algorithms for heterogeneous computing environments. We term the first algorithm as 'binary search-based energy-efficient scheduling with reliability goal (BSESRG)' for running parallel task graphs in heterogeneous computing systems. We show that the proposed BSESRG has the capability to reduce energy consumption, and shorten the total schedule length by meeting the reliability goals upto a certain threshold. Then, we present our second algorithm, the 'binary search-based energy-efficient fault-tolerant scheduling with reliability goal (BSESRG-FT), which ensures meeting the reliability goals with simultaneous consideration of fault tolerance. The proposed BSESRG-FT is able to reach higher reliability goals, reduce energy consumption, and shorten the total schedule length of a parallel task graph on heterogeneous platforms. We demonstrate the working of both BSESRG and BSESRG-FT through simulation experiments considering real-world task graphs, and show the supremacy of the two proposed algorithms over their respective peers (viz., ESRG and EFSRG) in terms of energy savings, schedule lengths, run times and reliability goals. The superiority of the proposed BSESRG and BSESRG-FT over their respective competitors are also validated on the real benchmark MiBench. Moreover, from the complexity analysis, we respectively find the time complexities of BSESRG and BSESRG-FT as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O\mathbf {(|\mathcal {X}|\times |P| \times log_{2}|F|)}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O\mathbf {(|\mathcal {X}|\times |P|^{2}\times log_{2}|F|)}$</tex-math></inline-formula> confirming their better computational efficiency than the respective peers.

A wide variety of Mobile Devices (MDs) are adopted in Internet of Things (IoT) environments, resulting in a dramatic increase in the volume of task data and greenhouse gas emissions. However, due to the limited battery power and computing resources of MD, it is critical to process more data with less energy. This paper studies the Wireless Power Transfer-based Mobile Edge Computing (WPT-MEC) network system assisted by Intelligent Reflective Surface (IRS) to enhance communication performance while improving the battery life of MD. In order to maximize the Computation Energy Efficiency (CEE) of the system and reduce the carbon footprint of the MEC server, we jointly optimize the CPU frequencies of MDs and MEC server, the transmit power of Power Beacon (PB), the processing time of MEC server, the offloading time and the energy harvesting time of MDs, the local processing time and the offloading power of MD and the phase shift coefficient matrix of Intelligent Reflecting Surface (IRS). Moreover, we transform this joint optimization problem into a fractional programming problem. We then propose the Dinkelbach Iterative Algorithm with Gradient Updates (DIA-GU) to solve this problem effectively. With the help of convex optimization theory, we can obtain closed-form solutions, revealing the correlation between different variables. Compared to other algorithms, the DIA-GU algorithm not only exhibits superior performance in enhancing the system's CEE but also demonstrates significant reductions in carbon emissions.

With the swift development of express delivery industry, the increasingly attention has been shifted to express delivery mechanism design. Generally, the revenue of the courier is the difference between the users' express fee and the courier's pickup cost. In order to improve the revenue of courier without increasing the user's express fee, this paper presents a low-cost package pickup covering system to find an optimal Hamiltonian pickup tour for the courier over a subset of packages, where packages who are not on the tour should be covered exactly by one package on the tour. A billing rule discounting the express fee to incentivize users to deliver their packages is also proposed. We formulate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Low-cost Package Pickup Covering (LPPC)</i> problem to maximize the revenue of the courier. Considering the complexity of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LPPC</i> , we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Low-cost Package Pickup Covering Mechanism (LPPCM)</i> to solve the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LPPC</i> problem including problem transformation, hardness analyzing, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Attention Model based on Encoder-Decoder Architecture (AMEDA)</i> model design and model training. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AMEDA</i> is trained by a deep reinforcement learning algorithm in an unsupervised manner and it can directly output the solution based on the given instances. Through extensive simulations, we demonstrate that the average revenue of courier for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AMEDA</i> is at least 10.1% higher than the traditional heuristic local search and is 18.5% lower than the optimal solution on average. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AMEDA</i> provides a desired trade-off between the execution time and solution quality, which is well suited for the large-scale tasks which require quick decisions.

In this paper, we study the performance analysis of a cooperative terrestrial-underwater visible light communication (UWVLC) system, considering energy harvesting at a decode and forward (DF)relay onthe ship.The transmitter tothe shiplink is modelled by shadowed Rician fading channel, while the UWVLC link is characterised by mixture exponential generalized gamma (EGG) distribution. The energy constrained relay harvests energy from the received signal based on power splitting (PS) scheme, assuming a part of received power is used for energy harvesting. Energy harvesting at the relay node makes the system sustainable by reducing the carbon footprints. We derive novel closed form expressions for bit error rate (BER), outage probability and ergodic capacity for the considered system at the underwater node, considering the effects of various parameters that include bubble level, temperature and salinity

Integrating larger shares of renewables in data centers' electrical mix is mandatory to reduce their carbon footprint. However, as they are intermittent and fluctuating, renewable energies alone cannot provide a 24/7 supply and should be combined with a secondary source. Finding the optimal infrastructure configuration for both renewable production and financial costs remains difficult. In this paper, we examine three scenarios with on-site renewable energy sources combined respectively with the electrical grid, batteries alone and batteries with hydrogen storage systems. The objectives are first, to size optimally the electric infrastructure using combinations of standard microgrids approaches, secondly to quantify the level of grid utilization when data centers consume/ export electricity from/to the grid, to determine the level of effort required from the grid operator, and finally to analyze the cost of 100% autonomy provided by the battery-based configurations and to discuss their economical viability. Our results show that in the grid-dependent mode, 63.1% of the generated electricity has to be injected into the grid and retrieved later. In the autonomous configurations, the cheapest one including hydrogen storage leads to a unit cost significantly more expensive than the electricity supplied from a national power system in many countries.