A novel approach for dynamic task scheduling for IOT in fog-cloud environment

Hybrid heuristic algorithm for cost-efficient QoS aware task scheduling in fog–cloud environment

Fog computing bridges IoT applications and cloud computing, providing low latency services through local computation and storage. Despite its advantages, challenges such as efficient scheduling and placement of IoT applications on fog cloud nodes hinder its widespread adoption. This manuscript presents a Performance Enhancement Algorithm for scheduling IoT applications in a fog cloud environment. The algorithm comprises four key procedures that schedule application modules across the available infrastructure devices. Merge sort along with the heterogeneous shortest module first strategy is used in fusion as the key to improve the performance. The effectiveness of this proposed algorithm was evaluated using the iFogSim simulator and the results demonstrate significant improvements, with total network usage improvement of 66.31% over HSMF, 88.40% over edge-wards, and 98.66% over the cloud-only method. Also, it improves the execution time significantly in most network configurations. Our research contributes to providing a very reliable means for placing applications in the fog-cloud infrastructure as our fusion algorithm makes it a very stable and scalable for CPU bound tasks in fog and cloud computing environment.

With the continuous integration of IoT technology and information technology, edge computing, as an emerging computing paradigm, makes full use of terminals to process and analyse real-time data. The explosion of Internet of Things (IoT) devices has created challenges for traditional cloud-based data processing models due to high latency and availability requirements. This paper proposes a new edge computation-based framework for iot data processing and scheduling using deep reinforcement learning. The system architecture incorporates distributed iot data access, realtime processing, and an intelligent scheduler based on Deep q networks (DQN). A large number of experiments show that compared with traditional scheduling methods, the average task completion time is reduced by 20% and resource utilization is increased by 15%. The unique integration of edge computing and deep reinforcement learning provides a flexible and efficient platform for lowlatency iot applications. Key results obtained from testing the proposed system, such as reduced task completion time and increased resource utilization.

This paper presents a dynamic multi-application scheduling approach for running and scheduling parallel adaptive applications in Networks of Workstations (NOWs) and Clusters of Processors (COPs). Parallel adaptive applications have the property of varying their parallelism degree dynamically following availability of resources and changes in the underlying environment state. In our model, each parallel adaptive application is controlled by its own scheduler responsible for optimizing resources which it uses. Multi-application scheduling consists of sharing resources among applications fairly and uses a combined (time-sharing and space-sharing) scheduling approach. The scheduling approach is characterized especially by dynamic arrivals of applications, and remapping of allocation in order to handle dynamic arrivals and departures of applications and underlying environment state changes. Therefore, resources are fairly shared among applications. The multi-application scheduler interacts with the application schedulers in order to optimize the scheduling approach. The application schedulers are responsible for applying the multi-application scheduler decisions and internally optimizing the resources exploited by the application. Beyond the adaptive aspect which allows parallel applications to exploit idle cycles in NOWs with respect to the personal character of workstations, the proposed model provides a multi-application scheduling support which globally ensures better resource utilization and fair resource sharing.

Complications and mortality in patients with chronic kidney disease undergoing total shoulder arthroplasty

In this paper, the problem of energy-efficient data gathering in an Internet of Things (IoT) based remote area surveillance application is addressed by designing a suitable MAC layer uplink solution. We follow the 3GPP specified scheduled access scheme for narrowband IoT (NB-IoT) and consider presence of both delay sensitive and delay tolerant traffic in the network. First, we present a mixed integer non-linear programming (MINLP) based optimization framework to minimize the IoT devices’ cumulative transmission energy per uplink frame under the constraints of finite resource blocks, mean queue stability and residual energy levels of IoT nodes. A cross layer approach has been adopted. Precisely, using Lyapunov optimization, we propose a dynamic, distributed transmit power allocation for IoT nodes and a centralized node scheduling scheme. Further, assuming Poisson model for traffic generation at delay tolerant traffic generating nodes, we suggest a distributed probabilistic sleep scheduling scheme to improve the average delay experience of delay sensitive traffic while improving the overall energy conservation. Simulation results suggest impressive performance of our proposed solution over the existing uplink solution for NB-IoT, in terms of delay experience of delay sensitive traffic, buffer length requirement and nodes’ total energy consumption at high traffic load.

The concept of the Internet of Things (IoT) has received much attention over the last five years. It is predicted that the IoT will influence every aspect of our lifestyles in the near future. Wireless Sensor Networks are one of the key enablers of the operation of IoTs, allowing data to be collected from the surrounding environment. However, due to limited resources, nature of deployment and unattended operation, a WSN is vulnerable to various types of attack. Security is paramount for reliable and safe communication between IoT embedded devices, but it does, however, come at a cost to resources. Nodes are usually equipped with small batteries, which makes energy conservation crucial to IoT devices. Nevertheless, security cost in terms of energy consumption has not been studied sufficiently. Previous research has used a security specification of 802.15.4 for IoT applications, but the energy cost of each security level and the impact on quality of services (QoS) parameters remain unknown. This research focuses on the cost of security at the IoT media access control (MAC) layer. It begins by studying the energy consumption of IEEE 802.15.4 security levels, which is followed by an evaluation for the impact of security on data latency and throughput, and then presents the impact of transmission power on security overhead, and finally shows the effects of security on memory footprint. The results show that security overhead in terms of energy consumption with a payload of 24 bytes fluctuates between 31.5% at minimum level over non-secure packets and 60.4% at the top security level of 802.15.4 security specification. Also, it shows that security cost has less impact at longer packet lengths, and more with smaller packet size. In addition, the results depicts a significant impact on data latency and throughput. Overall, maximum authentication length decreases throughput by almost 53%, and encryption and authentication together by almost 62%.

Multi-objective optimization for scientific workflow scheduling based on Performance-to-Power Ratio in fog–cloud environments

The consolidation of the Fog Computing paradigm and the ever-increasing diffusion of Internet of Things (IoT) and smart objects are paving the way toward new integrated solutions to efficiently provide services via short-mid range wireless connectivity. Being the most of the nodes mobile, the node discovery process assumes a crucial role for service seekers and providers, especially in IoT-fog environments where most of the devices run on battery. This paper proposes an original model and a fog-driven architecture for efficient node discovery in IoT environments. Our novel architecture exploits the location awareness provided by the fog paradigm to significantly reduce the power drain of the default baseline IoT discovery process. To this purpose, we propose a deterministic and competitive adaptive strategy to dynamically adjust our energy-saving techniques by deciding when to switch BLE interfaces ON/OFF based on the expected frequency of node approaching. Finally, the paper presents a thorough performance assessment that confirms the applicability of the proposed solution in several different applications scenarios. This evaluation aims also to highlight the impact of the nodes' dynamic arrival on discovery process performance.

Task scheduling is one of the major problems with Internet of Things (IoT) cloud computing. The need for cloud storage has skyrocketed due to recent advancements in IoT-based technology. Sophisticated planning approaches are needed to load the IoT services onto cloud resources professionally while meeting application necessities. This is significant because, in order to optimise resource utilisation and reduce waiting times, several procedures must be properly configured on various virtual machines. Because of the diverse nature of IoT, scheduling various IoT application activities in a cloud-based computing architecture can be challenging. Fog cloud computing is projected for the integration of fog besides cloud networks to address these expectations, given the proliferation of IoT sensors and the requirement for fast and dependable information access. Given the complexity of job scheduling, it can be difficult to determine the best course of action, particularly for big data systems. The behaviour of zebras in the wild serves as the primary basis of stimulus for the development of the Zebra Optimisation Algorithm (ZOA), a novel bio-inspired metaheuristic procedure presented in this study. ZOA mimics zebras' feeding habits and their defence mechanisms against predators. Various activities are analysed and processed using an optimised scheduling model based on ZOA to minimise energy expenditures and end-to-end delay. To reduce makespan and increase resource consumption, the technique uses a multi-objective strategy. By using a regional exploratory search strategy, the optimisation algorithm may better utilise data and stays out of local optimisation ruts. The analysis revealed that the suggested ZOA outperformed other well-known algorithms. It was advantageous for big data task scheduling scenarios since it converged more quickly than other techniques. It also produced improvements of 18.43% in several outcomes, including resource utilisation, energy consumption, and make span.

The environmental impacts that are associated with the interconnected sensor devices in the Internet of Things (IoT) network are minimized with the utilization of green communication. This is because, when the IoT devices tend to grow, the IoT system becomes more prone to issues regarding the utilization of resources, sustainability, and energy consumption within the communication system. Tuning the communication protocols, lowering the IoT device’s energy usage rate, and establishing an effective technique for the transmission of data are performed. This is achieved by the green communication technique, which aids in the establishment of significant energy savings in the IoT network. For devices with limited power sources, the green communication technology offers an extended battery life. Despite the enormous potential of IoT technology, a number of obstacles need to be overcome, including those related to load balancing, security, storage, privacy, energy management, and device heterogeneity. In response to the challenges posed by conventional models, an innovative solution for the selection of Cluster Head (CH) using a hybrid approach is devised here. The energy consumption of sensor nodes is influenced by various factors, such asCH load, temperature, distance, residual energy, the number of alive nodes, delay, and so on. To address these challenges in IoT networks, a hybrid approach that combines the Squid Game Optimizer (SGO) and the Artificial Gorilla Troops Optimizer (AGTO) for the optimal selection of CH is suggested. The implemented approach is named Hybrid Squid Game with Artificial Gorilla Troops Optimizer (HSG-AGTO). This approach aims to optimize the above-mentioned factors and overcome energy consumption challenges in IoT networks. The effectiveness of the model is validated, and the results demonstrate the superior performance of the suggested model.

In every successful organization, the critical factors of time, cost, and quality preservation are paramount. Effectively managing and controlling these factors necessitates the implementation of strategic measures. Specifically, the reduction of wastage emerges as a key approach to conserving time, cost, and quality. Achieving this goal hinges on the optimal utilization of organizational resources, which entails precise planning, allocation, monitoring, and control. While various methods for planning and resource tracking exist across organizations, this study focuses on strategies employed within the manufacturing industry. These strategies have demonstrated greater efficiency compared to traditional methods. Moreover, the study proposes the integration of Internet of Things (IoT) technology to address this challenge effectively. The research recommends the use of IoT technology as a comprehensive solution. Prior studies have often utilized the JIT method solely for resource utilization or TPM method solely for resource management. In contrast, this research advocates for the individual application of these methods to plan each resource meticulously. Specifically, the JIT method is proposed for material utilization, the TPM method for equipment utilization, and the Kaizen method for labor allocation. Furthermore, it emphasizes the integration of IoT with these lean methods. While some researchers have explored IoT, they have not fully integrated it with lean methods and techniques. The synergy of lean production methods and IoT technology offers an ideal opportunity for optimizing the utilization of organizational resources. Through these techniques, organizational resources can be efficiently planned and allocated to the production process. IoT provides valuable tools such as sensors, which can be installed at various resources, facilitating real-time data transmission to managers. This enables remote monitoring from office settings and timely data acquisition, thus addressing the challenge of optimal organizational resource utilization effectively.

Internet of things (IoT) is a smart technology that connects all the things anywhere at any time. Such multiuse nature of IoT is responsible for draining out energy from its resources. Therefore, the energy efficiency of IoT resources has emerged as a major research topic currently. This chapter presents an energy-efficient architecture for IoT, which consists of three layers, namely, sensing and control, information processing, and presentation. The architectural design of IoT allows the system to ensure the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for any particular application. The obtained value can be used to boost the utilization of cloud resources by reprovisioning the allocated resources it has when the corresponding sensory nodes are in sleep mode. This mechanism allows the energy-efficient utilization of all the IoT resources in nature. Wireless sensor network (WSN) and low-power smart device network IoT are fast-growing technologies used in a wide range of applications. In IoT, nodes are defined as sensor/actuators, which are highly constrained in nature (limited in energy and CPU usage). Designing and analyzing energy-efficient network architecture and routing mechanism is a major challenge in IoT networks. The network is said as energy-efficient based on node survivability. Energy-efficient network architecture is used to prolong the network lifetime. Sensor nodes and relay nodes are placed in a hierarchical manner to avoid uneven energy drainage. Given the architecture, sensors do the sensing and relay nodes handle the communication (data transmission from the sensor to sink), which reduces the complexity of device. Relay node is placed based on data traffic of the network. Energy-efficient routing mechanism is implemented in a given architecture to achieve balanced energy consumption.

The Internet is a global system of interconnected computers and computer networks where semi-structured data has been successfully applied for exchanging information. In nowadays Internet the huge range of actors, the large diversity of the associated device classes and domains, and the enormous amount of resource-restricted controllers in this system created new requirements and coined also a new term. Internet of Things (IoT), in this regard, refers to identifiable objects (things) and their virtual representations in an Internet-like structure. The fundamental question the thesis tries to answer is whether and how the same semi-structured data can be also applied to the IoT and the embedded domain in spite of resource-limited controllers. In order to discuss this question properties and requirements of embedded networks with regard to the IoT domain have been collected and evaluated. Thereafter the omnipresent semi-structured data exchange format in the Web, the Extensible Markup Language (XML), has been validated. The result was a list of missing requirements such as a compact representation, a representation that can be generated and consumed fast and also allows a small footprint implementation. To address the compiled requirements a binary representation of XML which nowadays is known as W3Cs Efficient XML Interchange (EXI) format has been accomplished which simultaneously optimizes performance and the utilization of computational resources and is designed to be compatible with XML. Moreover, in this work the format has been practically validated and tested. Addressing the needs of the embedded domain one result of this analyzes were optimizations to constrain runtime memory usage and to predict memory growth at runtime. A concept introduced in this thesis is LazyDOM which reduces memory requirements when processing and querying data. By means of a newly proposed code generation technique processing of EXI on ultra-constrained device classes has been enabled and resulting format modifications have been adopted by the W3C standardization. The research work described in this thesis on efficiently exchanging and processing semi-structured data on constrained embedded devices has not only triggered modifications in the W3C EXI format but even is already adopted in domain specific application standards and implementations. The above mentioned optimizations such as predictably limit the memory growth at runtime have been contributed, discussed and evaluated by the W3C experts and become a core part of the EXI specification. Even more significantly from the IoT perspective these optimizations provide the basis for the adoption of this technology in ISO and IEC standardization which is the first time for automotive and power industry to use IoT in the control plane. The implementation of EXI to conduct the evaluation as part of this thesis has become the de-facto open source reference implementation of EXI and became the basis of a number of other reference implementations such as the OpenV2G project that provides the reference implementation of the communication interface in ISO/IEC 15118. In summary the conducted research work has evaluated the options to adapt semi-structured data for the constrained embedded domain, proposed modifications and evaluated those under realistic conditions. This made it relevant for the technology as well as for application standardization despite the short period of this work. As such the research can now be taken as a basis for further challenges in the IoT field namely adopting concepts of the Semantic Web and adapting those to stimulate the quickly expanding eco-system of embedded devices.

With the proliferation of ubiquitous Internet of things (IoT) sensors and smart devices in a wide array of industries, involving transportation, agriculture, and healthcare, a huge amount of data is being generated, which requires ever-increasing calculations and services both at the cloud and at the edge of the network. Because cloud servers are specifically located a significant distance from IoT devices, time-sensitive Internet of things applications must be extended to the cloud architecture for the timely delivery of important services. Although IoT services can be allocated to the right edge nodes, ensuring low latency and efficient resource use is still challenging. To overcome these challenges, a novel resource allocation using clustering enabled optimization in IoT (RACE-IoT) has been proposed in this paper. This work introduces a novel Shannon–dragonfly (Sha-Dragon) optimization algorithm, which combines the dragonfly algorithm with the Shannon entropy for resource scheduling. The proposed RACE-IoT balances the load by reallocating the resources to maintain the same load ratio over all servers. The developed technique has been compared with the previous strategies involving PBSM, HRL-Edge-Cloud, and ERAM-EE in terms of specific parameters such as energy consumption, throughput, response time, end-to-end delay, resource utilization, makespan, and success rate. The outcomes demonstrate the efficiency of the developed RACE-IoT technique. The proposed technique achieves higher success rates of 35%, 17%, and 18% than existing techniques such as PBSM, HRL-Edge-Cloud, and ERAM-EE, respectively.