BCHealth: A Novel Blockchain-based Privacy-Preserving Architecture for IoT Healthcare Applications

The Internet of Health Things (IoHT), which is an extension of the Internet of Things (IoT) in healthcare, has provided a new type of telemedicine approach. In IoHT, wearable sensors are used to collect patient health data, and information is transmitted remotely to doctors who can develop accurate treatment plans and provide timely telemedicine services to patients. However, patient health data are transmitted over a public channel, which means that the privacy and medical data of patients are at significant risk of leakage and can be confronted by serious security problems. We proposed a lightweight authentication protocol known as LAP-IoHT for IoHT environments to overcome the various threats that are currently faced by IoHT. We verified the security of LAP-IoHT using a Real-or-Random model and demonstrated its significant performance advantage by conducting a comparative analysis with other similar protocols for a better adaptation to the IoHT environment.

/pdf/lap-ioht-a-lightweight-authentication-protocol-for-the-35u7socf.pdf

LAP-IoHT: A Lightweight Authentication Protocol for the Internet of Health Things

Customized Industrial Internet of Things (IoT) presents the needs of strong pertinence, energy saving, and simplified design. However, the services of process-oriented and chainoriented were not fully considered and addressed in traditional schemes. First, a spatial probabilistic model is established that can be used to enhance the location services of devices. Second, we construct a spatio-temporal relationship graph of different devices and describe the rules for generating association index among devices. Third, we create virtual groups with computing expressions for the problem solving. Through systematically evaluating the multiple indicators of different candidates for network generation, we demonstrate the superiority and stability of the smart collaborative evolvement. 

Smart Collaborative Evolvement for Virtual Group Creation in Customized Industrial IoT

The job scheduling paradigms include dispatching Internet of Things (IoT) critical services onto processing nodes. Here most energy is consumed in finding suitable virtual machines (VMs) that can execute IoT tasks without resource fragments. Therefore, a significant problem is minimizing energy consumption through efficient task placement that leads to load balance and minimizes resource leakage. To resolve this problem, we proposed a dual-phase metaheuristic algorithm called CSSA-DE. First, we conduct a clustering approach to group computing nodes into effective clusters. Each node is trained at different utilization levels, and the one that can yield the highest Performance-to-Power Ratio (PPR) is selected as the mega cluster head (MCH). Then, we integrated the sparrow search algorithm (SSA) with the differential evolution (DE) algorithm to expand the high search efficiency of finding an appropriate pair task-VM combination. Further, the integration phase can exploit the count of overloaded and underloaded VMs, reducing resource fragments. The performance of CSSA-DE is highly competitive and relatively better in multiple cases compared to state-of-the-art algorithms. 

Efficient job scheduling paradigm based on hybrid sparrow search algorithm and differential evolution optimization for heterogeneous cloud computing platforms

Recently, cloud computing resources have become one of the trending technologies that permit the user to manage diverse resources and a huge amount of data in the cloud. Task scheduling is considered one of the most significant challenges and ineffective management results in performance degradation. It is necessary to schedule the task effectively with maximum resource utilization and minimum execution time. Therefore, this paper proposes a novel technique for effective task scheduling with enhanced security in the cloud computing environment. A novel convolutional neural network optimized modified butterfly optimization (CNN-MBO) algorithm is proposed for scheduling the tasks, thereby maximizing the throughput and minimizing the makespan. Secondly, a modified RSA algorithm is employed to encrypt the data, thereby providing secure data transmission. Finally, our proposed approach is simulated under a cloudlet simulator and the evaluation results are analyzed to determine its performance. In addition to this, the proposed approach is compared with various other task scheduling-based approaches for various performance metrics, namely, resource utilization, response time, as well as energy consumption. The experimental results revealed that the proposed approach achieved minimum energy consumption of 180 kWh, a minimum response time of the 20 s, a minimum execution time of 0.43 s, and maximum utilization of 98% for task size 100.

/pdf/an-efficient-and-secure-model-using-adaptive-optimal-deep-2baah5g1.pdf

An Efficient and Secure Model Using Adaptive Optimal Deep Learning for Task Scheduling in Cloud Computing

The development of the smart medical industry and equipment has made great progress due to the fusion of the IoT, cloud computing, and big data. In smart IoT healthcare, patients can collect vital parameters from various medical sensors attached to them to detect diseases and make initial diagnoses by themselves. With the powerful storage and computing functions of cloud computing, medical sensor devices deployed in a cloud environment can effectively solve the problems that the devices are highly dispersed, heterogeneous, and have limited processing capabilities. As a result, this method can effectively provide customized and scalable smart medical services for patients. However, because these medical resources share computing resources on the cloud platform, changes in equipment workloads will lead to service performance competition among tenants. Therefore, determining how to achieve performance isolation between tenants and guarantee the service-level agreements (SLAs) of the tenants has become the most concerning issue for cloud service providers. In this article, we propose a performance isolation algorithm for multitenant IoT clouds, which can effectively provide performance isolation between tenants. Experiments show that the message processing delay of tenants working within the allocated quota can be reduced by 82%.

High-Performance Isolation Computing Technology for Smart IoT Healthcare in Cloud Environments

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High-Performance Isolation Computing Technology for Smart IoT Healthcare in Cloud Environments