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In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models. [ABSTRACT FROM AUTHOR]

Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost [ABSTRACT FROM AUTHOR]

The search algorithm based on symbiotic organisms' interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61–20.08% and 1.92–25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as the most favorable computing platform for researchers and industry. The load balanced task scheduling has emerged as an important and challenging research problem in the Cloud computing. Swarm intelligence-based meta-heuristic algorithms are considered more suitable for Cloud scheduling and load balancing. The optimization procedure of swarm intelligence-based meta-heuristics consists of two major components that are the local and global search. These algorithms find the best position through the local and global search. To achieve an optimized mapping strategy for tasks to the resources, a balance between local and global search plays an effective role. The inertia weight is an important control attribute to effectively adjust the local and global search process. There are many inertia weight strategies; however, the existing approaches still require fine-tuning to achieve optimum scheduling. The selection of a suitable inertia weight strategy is also an important factor. This paper contributed an adaptive Particle Swarm Optimisation (PSO) based task scheduling approach that reduces the task execution time, and increases throughput and Average Resource Utilization Ratio (ARUR). Moreover, an adaptive inertia weight strategy namely Linearly Descending and Adaptive Inertia Weight (LDAIW) is introduced. The proposed scheduling approach provides a better balance between local and global search leading to an optimized task scheduling. The performance of the proposed approach has been evaluated and compared against five renown PSO based inertia weight strategies concerning makespan and throughput. The experiments are then extended and compared the proposed approach against the other four renowned meta-heuristic scheduling approaches. Analysis of the simulated experimentation reveals that the proposed approach attained up to 10%, 12% and 60% improvement for makespan, throughput and ARUR respectively. [ABSTRACT FROM AUTHOR]

With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices' trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach. [ABSTRACT FROM AUTHOR]