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Nowadays, the blockchain, Internet of Things, and artificial intelligence technology revolutionize the traditional way of data mining with the enhanced data preprocessing, and analytics approaches, including improved service platforms. Nevertheless, one of the main challenges is designing a combined approach that provides the analytics functionality for diverse data and sustains IoT applications with robust and modular blockchain-enabled services in a diverse environment. Improved data analytics model not only provides support insights in IoT data but also fosters process productivity. Designing a robust IoT-based secure analytic model is challenging for several purposes, such as data from diverse sources, increasing data size, and monolithic service designing techniques. This article proposed an intelligent blockchain-enabled microservice to support predictive analytics for personalized fitness data in an IoT environment. The designed system support microservice-based analytic functionalities to provide secure and reliable services for IoT. To demonstrate the proposed model effectiveness, we have used the IoT fitness application as a case study. Based on the designed predictive analytic model, a recommendation model is developed to recommend daily and weekly diet and workout plans for improved body fitness. Moreover, the recommendation model objective is to help trainers make future health decisions of trainees in terms of workout and diet plan. Finally, the proposed model is evaluated using Hyperledger Caliper in terms of latency, throughput, and resource utilization with varying peers and orderer nodes. The experimental result shows that the proposed model is applicable for diverse resourceconstrained blockchain-enabled IoT applications and extensible for several IoT scenarios.

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With the emergence of various new Internet of Things (IoT) devices and the rapid increase in the number of users, enormous services and complex applications are growing rapidly. However, these services and applications are resource-intensive and data-hungry, requiring satisfactory quality-of-service (QoS) and network coverage density guarantees in sparsely populated areas, whereas the limited battery life and computing resources of IoT devices will inevitably become insufficient. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is one of the most promising solutions that ensures the stability and expansion of the network coverage area for these applications and provides them with computational capabilities. In this paper, computation offloading and resource allocation are jointly considered for multi-user multi-UAV-enabled mobile edge-cloud computing systems. First, we propose an efficient resource allocation and computation offloading model for a multi-user multi-UAV-enabled mobile edge-cloud computing system. Our proposed system is scalable and can support increases in network traffic without performance degradation. In addition, the network deploys multi-level mobile edge computing (MEC) technology to provide the computational capabilities at the edge of the radio access network (RAN). The core network is based on software-defined networking (SDN) technology to manage network traffic. Experimental results demonstrate that the proposed model can dramatically boost the system performance of the system in terms of time and energy.

Underwater wireless sensor networks (UWSNs) enable various oceanic applications which require effective packet transmission. In this case, sparse node distribution, imbalance in terms of overall energy consumption between the different sensor nodes, dynamic network topology, and inappropriate selection of relay nodes cause void holes. Addressing this problem, we present a relay-based void hole prevention and repair (ReVOHPR) protocol by multiple autonomous underwater vehicles (AUVs) for UWSN. ReVOHPR is a global solution that implements different phases of operations that act mutually in order to efficiently reduce and identify void holes and trap relay nodes to avoid it. ReVOHPR adopts the following operations as ocean depth (levels)-based equal cluster formation, dynamic sleep scheduling, virtual graph-based routing, and relay-assisted void hole repair. For energy-efficient cluster forming, entropy-based eligibility ranking (E2R) is presented, which elects stable cluster heads (CHs). Then, dynamic sleep scheduling is implemented by the dynamic kernel Kalman filter (DK2F) algorithm in which sleep and active modes are based on the node’s current status. Intercluster routing is performed by maximum matching nodes that are selected by dual criteria, and also the data are transmitted to AUV. Finally, void holes are detected and repaired by the bicriteria mayfly optimization (BiCMO) algorithm. The BiCMO focuses on reducing the number of holes and data packet loss and maximizes the quality of service (QoS) and energy efficiency of the network. This protocol is timely dealing with node failures in packet transmission via multihop routing. Simulation is implemented by the NS3 (AquaSim module) simulator that evaluates the performance in the network according to the following metrics: average energy consumption, delay, packet delivery rate, and throughput. The simulation results of the proposed REVOHPR protocol comparing to the previous protocols allowed to conclude that the REVOHPR has considerable advantages. Due to the development of a new protocol with a set of phases for data transmission, energy consumption minimization, and void hole avoidance and mitigation in UWSN, the number of active nodes rate increases with the improvement in overall QoS.