The Mobile Internet of Things (MIoT) represents a significant evolution of traditional IoT by enabling seamless connectivity for mobile devices and sensors in dynamic environments. Given the resource constraints and mobility challenges in MIoT networks, developing adaptive and energy-efficient routing strategies is important. This paper proposes a novel routing protocol that integrates Grey Wolf Optimization (GWO) and Recurrent Neural Networks (RNNs) to enhance energy efficiency, reliability, and responsiveness in MIoT systems. The protocol features dynamic clustering, predictive traffic load balancing, and multi-objective optimization for Cluster Head (CH) selection, where RNNs forecast traffic trends and GWO optimizes routing paths. Simulation results demonstrate that the proposed method reduces energy consumption, lowers end-to-end delays, and improves packet delivery ratio (PDR) and network reliability under both static and mobile conditions. Compared to existing methods such as the Krill Herd (KH) algorithm, Dynamic Multi-Sink Routing Protocol (DMS-RP), and Evolutionary Fuzzy Rule-based (EFR) models, the proposed solution exhibits superior performance, validating its scalability and effectiveness for real-world MIoT applications.

As smart cities nowadays use many Internet of Things (IoT) devices, the need for service composition methods that are both efficient and flexible has become unavoidable. Methodologies should be designed to provide flexibility and sustain high quality of service (QoS) under dynamic urban conditions. Deterministic techniques often have poor scalability and require complete, accurate QoS information. In contrast, non-deterministic approaches can handle uncertainty but tend to be computationally expensive and less reliable under dynamic conditions. Service composition is considered an NP-hard problem due to the exponential complexity involved in selecting and combining optimal services under multiple QoS constraints. This paper proposes a novel hybrid framework called Bayesian Network–Grey Wolf Optimizer (BN-GWO), which integrates Bayesian Networks (BN) with the Grey Wolf Optimizer (GWO) algorithm. The BN captures conditional dependencies among QoS attributes and accurately estimates missing values, while the GWO algorithm efficiently explores the compositional space to optimize QoS metrics. Experimental evaluations conducted using QoS traces synthetically generated via the iFogSim2 simulation platform demonstrate that the proposed BN-GWO framework significantly outperforms state-of-the-art methods across multiple performance metrics.

Abstract Efficient and intelligent task scheduling in heterogeneous multi-cloud environments remains a complex challenge due to conflicting objectives such as energy consumption, delay minimization, service-level agreement (SLA) compliance, and host utilization. This paper proposes a hybrid framework that integrates Long Short-Term Memory (LSTM) networks for temporal workload forecasting with Elephant Herding Optimization with Memory (EHOM) for multi-objective task-to-host allocation. The framework is implemented as containerized microservices on an OpenStack Yoga private cloud with Prometheus telemetry, Kafka streaming, TensorFlow Serving for LSTM-based forecasting, and a Python-based EHOM optimizer orchestrated through OpenStack. Performance is benchmarked against Trust-Aware Spring Swarm Optimization (TSSO), Auto Clipped Double Deep Q-Learning (Auto-CDDQL), Self-Adaptive Flower Pollination-based RSA (SA-FPRSA), Elephant Herding Lion Optimizer (EHLO), and a Markov-based scheduler. Experimental results across workloads of 200–1000 tasks show that the proposed method reduces total energy consumption by 12–22%, decreases normalized delay by 8–15%, and improves deadline satisfaction ratio (DSR) by 2.5–5.3 percentage oints, while consistently maintaining availability and reliability above 97%. These improvements confirm the robustness, scalability, and real-time applicability of the proposed framework for SLA-sensitive multi-cloud environments. The system links LSTM forecasts with an EHOM-based allocator in a closed loop.

Abstract Efficient load balancing stands out as a crucial challenge in multi-cloud environments, particularly for applications that demand ultra-reliable, low-latency communications (URLLC). This paper proposes a novel approach integrating Decision Functions with Normal Distributions (DFND) for precise probabilistic modeling of task-to-cloud compatibility. Multivariate normal distributions capture interdependencies between resource features such as CPU, memory, bandwidth, and latency, ensuring accurate resource compatibility evaluation. Additionally, the Tasmanian Devil Optimization (TDO) algorithm employs dynamic exploration and exploitation strategies inspired by natural behaviors, providing rigorous optimization to improve task assignment in dynamic, multi-cloud environments. It uses flexible methods to ensure the optimization process is both efficient and scalable. Simulation results using CloudSim demonstrate significant improvements over state-of-the-art methods in terms of makespan reduction, response time minimization, resource utilization, and cost efficiency. The proposed framework effectively supports latency-sensitive, large-scale applications in dynamic, heterogeneous multi-cloud environments.

A recent technology trend known as the Internet of Things (IoT) involves using devices like smartphones, smart TVs, medical and healthcare equipment, and home appliances to generate data. This paper introduces a novel framework, Reinforcement Learning with Black Widow Optimization (RL-BWO), to enhance IoT service recommendations through responsiveness to evolving service requests and optimized resource usage. Unlike prior hybrid approaches that rely on static recommendation strategies or single-pass learning, RL-BWO uniquely integrates incremental Reinforcement Learning (RL) with evolutionary optimization, enabling continuous policy refinement in dynamic environments. The framework features a multi-batch data partitioning mechanism, and a service-request interactive simulator based on Markov Decision Processes (MDP) to support real-time adaptation. The Black Widow Optimization (BWO) algorithm is used to fine-tune service selection through fitness-based ranking, ensuring high-quality recommendations under resource constraints. Experimental results in a smart city simulation show that RL-BWO improves the solved request rate by up to 12.8%, reduces latency by 17%, and enhances reliability by 9.6% compared to leading methods such as Genetic Algorithm–Simulated Annealing–Particle Swarm Optimization (GASAPSO), Time Correlation Coefficient with Cuckoo Search–K-means (TCCF), and Artificial Bee Colony with Genetic Algorithm (ABCGA). These results demonstrate RL-BWO’s superior scalability, accuracy, and responsiveness, making it a robust solution for large-scale, real-time IoT service recommendation.

Despite significant advancements in Internet of Things (IoT)-based smart cities, service discovery and composition continue to pose challenges. Current methodologies face limitations in optimizing Quality of Service (QoS) in diverse network conditions, thus creating a critical research gap. This study presents an original and innovative solution to this issue by introducing a novel three-layered Recurrent Neural Network (RNN) algorithm. Aimed at optimizing QoS in the context of IoT service discovery, our method incorporates user requirements into its evaluation matrix. It also integrates Long Short-Term Memory (LSTM) networks and a unique Black Widow Optimization (BWO) algorithm, collectively facilitating the selection and composition of optimal services for specific tasks. This approach allows the RNN algorithm to identify the top-K services based on QoS under varying network conditions. Our methodology’s novelty lies in implementing LSTM in the hidden layer and employing backpropagation through time (BPTT) for parameter updates, which enables the RNN to capture temporal patterns and intricate relationships between devices and services. Further, we use the BWO algorithm, which simulates the behavior of black widow spiders, to find the optimal combination of services to meet system requirements. This algorithm factors in both the attractive and repulsive forces between services to isolate the best candidate solutions. In comparison with existing methods, our approach shows superior performance in terms of latency, availability, and reliability. Thus, it provides an efficient and effective solution for service discovery and composition in IoT-based smart cities, bridging a significant gap in current research.

Recently, a new technology topic has been known as the Internet of Things (IoT), where all devices like smartphones, smart TVs, medical and healthcare ones, and home appliances have been applied for data generating. Due to the variety of services, the numerous service composition problems, mostly related to the Quality-of-Service (QoS) parameters, are recognized in the IoT domain. Since this issue is an NP-hard obstacle, different metaheuristic approaches have been utilized up until now to solve it. Many varieties of services can be brought into the IoT, depending on users’ demands. In this research, we have proposed an effective way based on a hidden Markov model (HMM) and an ant colony optimization (ACO) to answer the service composition issue by enhancing the QoS. The HMM has been trained to predict QoS. The emission and transition matrices have been improved using the Viterbi algorithm. We have executed the QoS estimation using the ACO algorithm and found a suitable path. The outcomes have illustrated the efficacy of the introduced method regarding availability, response time, cost, reliability, and energy consumption compared to the previous methods.