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.

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.

mmWave communication is considered a major player for 5 G and beyond networks due to its wide unlicensed bandwidth. However, it comes with some challenges such as propagation and penetration losses. To overcome these issues, antenna beamforming is adopted. But it is difficult to estimate massive mmWave multiple input multiple output channel for active beamforming, so training using switched antenna arrays with structured codebook is used to perform mmWave beamforming training (BT). The conventional exhaustive search BT scheme wastes a long time and power, especially in pencil beams scenarios, hence, for a better communication, these issues have to be handled. Depending on available out off band information and motivated by the idea that mmWave is a location driven communication network, several mmWave BT schemes had been proposed in literature based on positioning information (PI). In this paper, we will make a survey on those works, clarifying how different positioning services contribute to reduce and relax BT complexity in mmWave communication. In this context, we will divide those studies into two main categories: (1) Straightforward positioning based BT schemes, which use PI for minimizing the searching space of a predefined codebook or reducing the complexity of channel estimation, (2) Positioning-based BT schemes using machine learning approaches, which classified as, first, statistical and probabilistic learning based schemes, secondly, other ML approaches based schemes. Moreover, we will discuss the effect of positioning and orientation errors, existence of obstacles, user mobility and storing information on the performance of BT. In addition, a comparison between all studies, will be presented considering several aspects such as the implementation cost. Finally, challenges facing these schemes will be discussed and several solutions will be suggested as possible future works.

The paper proposes a Machine Learning methodology for Android malware detection and recognition, including crypto-mining applications using the blockchain. The design is based on a hierarchical classification method, with several decision stages. A combination of functional and statistical features is proposed to be applied for data classification in order to provide a high-performance malware recognition process. The specific contribution of this design methodology is the hierarchical classifier with detection and discrimination stages, respectively. Further works should be done for various features sets in order to achieve an optimized and high-accuracy modeling process supporting an innovative Machine Learning-based solution for Android malware detection.

Current M2M communication platforms are being integrated in cloud IoT applications for providing remote sensing and actuating. Nevertheless, requirements for energy efficiency and resilience in severe operating environments are driving the development of new algorithms and infrastructures. This paper presents a survey of the measurement results for the winegrowing season 2014, as it was seen by an M2M remote telemetry station in cooperation with a big data processing platform and several sensors. We demonstrate the use of recent technologies such as Cloud IoT systems and Big Data processing in order to implement disease prediction and alerting application for viticulture. Finally, the extension of the proposed system for other agriculture applications is discussed.

A discrete time implementation of the RAKE receiver (DTR) is proposed. The suggested approach works without knowledge of the path delays and their number. It is based on a global estimation of the multipath channel convolutioned with the shaping filter. Since the DTR performance is very sensitive to the channel estimation quality, we suggest a disregarding method for the least significant estimated channel coefficients while taking into account the known impulse response of the shaping filter. The performance of the DTR and the conventional RAKE are then compared.