ABSTRACT Integration of fog and cloud has become increasingly important in the age of IoT, where everything is connected to the Internet. The cloud‐only models face many challenges when serving the requests from IoT devices due to several factors such as latency, network congestion, data privacy, and security. Despite the popularity and numerous advantages of hybrid models, task scheduling is still an unsolvable multiobjective optimization problem. This research uses an improved bioinspired jellyfish search algorithm to solve the nonlinear np‐hard task scheduling optimization problem. The work proposes a multiobjective improved jellyfish search (MOIJS) framework using a multiobjective adaptation function to minimize the make‐span, cost, and power consumption that benefit customers and providers by considering the expenses associated with execution and power consumption. The performance of MOIJS is evaluated by comparing it with the discrete nondominated sorting genetic algorithm II using a MATLAB simulator. The experimental outcomes demonstrate the proposed work's efficacy in reducing the make‐span, cost, and energy in cloud‐fog environments in different batches of tasks.

Abstract In this study, we propose a novel Federated Learning Based Multi‐Head Attention (FBMA) framework for image classification problems considering the Independent and Identically Distributed (IID) and Non‐Independent and Identically Distributed (Non‐IID) medical data. The FBMA architecture integrates FL principles with the Multi‐Head Attention mechanism, optimizing the model performance and ensuring privacy. Using Multi‐Head Attention, the FBMA framework allows the model to selectively focus on important regions of the image for feature extraction, and using FL, FBMA leverages decentralized medical institutions to facilitate collaborative model training while maintaining data privacy. Through rigorous experimentation on medical image datasets: MedMNIST Dataset, MedicalMNIST Dataset, and LC25000 Dataset, each partitioned into Non‐IID data distribution, the proposed FBMA framework exhibits high‐performance metrics. The results highlight the efficacy of our proposed FBMA framework, indicating its potential for real‐world applications where image classification demands both high accuracy and data privacy.

Abstract Fog computing has become an effective platform for computing delay‐sensitive IoT tasks. However, the increased scalability of IoT devices () makes it difficult for fog nodes to perform better. Volunteer computing (VC) has emerged as a supportive technology in which resource‐capable s, such as computers and laptops, share their idle resources to compute the IoT tasks. However, in VC‐based approaches, improper selection of volunteer nodes (VNs) may result in an increased failure rate and delay. To address these challenges, this work proposes a Smart Admission Control strategy utilizing volunteer‐enabled Fog‐Cloud computing (SAC‐VFC). The VNs are selected based on grey TOPSIS ranking. The incoming tasks are classified based on priority and delay and then scheduled using the Improved Jellyfish Algorithm (IJFA). Smart gateway (SGW) and fog manager (FM) act as mediators for allocating tasks among voluntary, fog, and cloud resources. FM performs a similarity‐based clustering of fog nodes using the enhanced Fuzzy C Means clustering (EFCM) algorithm to manage resources. Simulation study suggests the superior performance of SAC‐VFC over peers under comparison in terms of average delay, average makespan, success rate of tasks and tasks satisfying the deadline metrics.

Summary The worldwide food supply chain network firms have embraced digitalization and have changed customers' everyday lives in serval viewpoints. This work proposes a framework for IoT and Blockchain based smart Food Chain Management System (IBFS) aiming to regulate the food inventory systems in an organization focusing on the safety and nature of the items conveyed to the end consumers. The proposed IBFS framework is an IoT‐based blockchain with smart contract architecture for trust, reliability, and transparency. A Message Queue Telemetry Transport (MQTT) (Message Queue Telemetry Transport) broker or server is deployed on the broadcast to record all data collected from the IoT sensors. The data provided by IoT‐sensors or devices is pooled and gathered by MQTT based cloud brokers and the shipment's geolocation, movement, and temperature are logged. The work uses two types of smart contracts based on Ethereum‐based blockchain technology (BCT) environment as the operational backbone written in the Solidity language. The first smart contract deals with custody and the second smart contract deals with shipment or transportation to track the produce. The food supply chain process evaluates the collected data from the IoT sensors managed with the help of blockchain and MQTT servers. The quality of food is measured by evaluating by temperature and humidity function, transition time, final item and for validation of smart contracts. Performance evaluation of IBFS in terms of Gas cost under various test conditions prove the effectiveness of the work.

Summary Scheduling on dynamic voltage and frequency scaling enabled processors to determine the Pareto‐optimal solutions with optimized makespan and energy consumption demands faster multi‐objective scheduling algorithms. In general, the problem of multi‐objective optimization, ie, finding the Pareto‐optimal solutions to optimize two or more QoS parameters, has been proven to be an NP‐complete problem. In this work, we propose a novel energy‐efficient quantum‐inspired stochastic Q‐HypE algorithm to schedule the batch‐of‐stochastic‐tasks (BoT) on DVFS‐enabled processors with the aim to optimize the makespan of BoT as well as the energy consumption of processors. The stochastic processing times of tasks are drawn from independent probability distributions. The proposed Q‐HypE algorithm evolves from combined characteristics of quantum computing and a hypervolume based multi‐objective optimization HypE algorithm. The proposed Q‐HypE algorithm simultaneously minimizes the makespan and energy consumption of the Pareto‐optimal solutions whereas the dynamics of quantum computing accelerate the process of HypE to further minimize the overheads of hypervolume estimation. Experimental results reveal the effectiveness of the proposed Q‐HypE algorithm both in terms of the number and quality of solutions offered.

Summary Owing to the advancements in low‐power consumption processors and high‐power storage in a small‐sized battery, the cost of handheld mobile devices, eg, mobiles, tabs, or personal digital assistants, have reduced to a great extent. This has enabled people to have at least 1 smartphone in general with this number increasing exponentially. However, the increasing use of these mobile devices results in an equal increase in the underused processing capacity of these devices too. This encourages the research aiming to use this processing power by forming a mobile computational grid. Because of the inherent limitations of bandwidth, battery, and computational power, job scheduling on these devices demands an efficient scheduling approach to harness the true potential of the grid. The problem becomes even more challenging considering the dynamic nature of these mobile devices. Job scheduling being nondeterministic polynomial time–complete allows the use of evolutionary approaches by exploring and exploiting the search space efficiently. The exploration gets boosted even more with the use of quantum‐computing concepts. This work proposes a quantum‐inspired Newtonian approach of attraction based on gravitational search algorithm for scheduling the jobs on mobile computational grid. Simulation study has been performed to evaluate the performance of the model over various dimensions. A comparative study has been performed with quantum‐genetic algorithm. Simulation result establishes the effectiveness of model under various test conditions.

Summary The primary objective of load balancing for distributed systems is to minimize the job execution time while maximizing the resource utilization. Load balancing on decentralized systems need effective information exchange policy so that with minimum amount of communication the nodes have up to date information about other nodes in the system. Periodic, event‐based and on‐demand information exchange are some important policies used for the same. All these approaches involve a lot of overhead and even sometime leading toward obsolete data with the nodes if there is a delay in the updation. This work presents an adaptive threshold‐based hybrid load balancing scheme with sender and receiver initiated approach (HLBWSR) using random information exchange (RIE). RIE ensures that the information is exchanged in such a way that each node in the system has up‐to‐date state of the other nodes with much reduced communication overhead. Further, the adaptive threshold ensures that almost an average numbers of jobs are executed by all the nodes in the system. The study of the effect of the use of RIE on sender initiated, receiver initiated and hybrid of sender and receiver initiated load balancing approach establishes the superior performance of HLBWSR among its RIE‐based peers. A comparative analysis of HLBWSR, with periodic information exchange strategy, modified estimated load information scheduling algorithm and load balancing on arrival reveals its effectiveness under various test conditions. Copyright © 2016 John Wiley & Sons, Ltd.

Summary One of the major design constraints of a heterogeneous computing system is optimal scheduling, that is, mapping of tasks on the processing nodes in order to optimize the QoS parameters. Because of the huge energy consumption by computing resources, negative environmental effects and reduced system reliability, energy has unavoidably been added as a new parameter to the list of QoS parameters. Energy optimization in scheduling strategies along with makespan makes it an even more challenging combinatorial optimization problem. This work proposes two energy‐aware scheduling algorithms G1 and G2 to schedule a batch‐of‐tasks, made of a collection of independent tasks, on heterogeneous processors in order to minimize the makespan and the energy consumption. The proposed algorithms schedule tasks based on weighted aggregation cost function to the appropriate processors followed by task migration phase designed to further minimize the makespan and the energy consumption. The study evaluates the performance of the proposed algorithms with some of the peers, that is, MinMin, MINSuff on account of makespan, energy consumption, flowtime, and utilization. An experimental study reveals that the proposed algorithm (G2) consistently performs better under various test conditions. Copyright © 2015 John Wiley & Sons, Ltd.