We present a blockchain-assisted mobile edge computing architecture for adaptive resource distribution in wireless communication systems, where the blockchain acts as an overhead system that provide command and control functionalities. In this context, achieving consensus across nodes while also ensuring the functionality of both MEC and blockchain systems is a big difficulty. Furthermore, resource distribution, frame size, and the number of sequential blocks generated by each contributor are important to Blockchain aided MEC functionality. As a result, a strategy for dynamic resource distribution and block creation is presented. To strengthen the efficiency of the overlapped blockchain system and enhance the quality of services (QoS) of the clients in the technologies to facilitate MEC system, spectrum allocation, frame size, and number of developing blocks for each distributor are framed as a joint optimization method that takes into account time-varying communication channels and MEC server saturation is defined. We use deep reinforcement learning (RAMBAN) to address this issue because standard approaches are ineffective. The simulation findings demonstrate that the efficacy of the suggested strategy when compared to different baseline approaches.

Satellite-terrestrial integrated networks have recently gained substantial interest due to their exceptional coverage, lower transmission delay, robust storage, and computing power. However, existing task offloading schemes often fail to effectively manage task dependencies, resulting in incorrect execution sequences, increased end-to-end delay, and excessive energy consumption. To address these challenges, we propose a dependency-aware task offloading framework for jointly optimizing delay and energy consumption in satellite-terrestrial collaborative networks with mobile-edge computing (MEC). First, we construct a directed acyclic graph (DAG) based dependency-aware task offloading framework aimed at reducing delay and energy consumption. Second, to reduce the frequency of low Earth orbit (LEO) satellite access, we design a cluster head selection strategy (CHSS), which leverages DAG-based task dependencies to optimize the association between Internet of Things (IoT) devices and LEO satellites. Finally, we formulate system delay and energy consumption as a cost-minimization problem, modeling it as a Markov decision process (MDP). We also propose a novel hybrid deep reinforcement learning (DRL) algorithm to effectively handle DAG structures and optimize task offloading decisions, thereby minimizing the total cost. Extensive simulation results confirm the effectiveness of the proposed method, demonstrating that the proposed algorithm significantly outperforms others by reducing system delay by 18.07% and decreasing energy consumption by 21.15% on average, respectively.

Vehicle trajectory prediction, as a key enabler of the intelligent transportation system, has attracted considerable attention from academia and industry in recent years. However, the variability and dynamism of traffic conditions pose significant challenges to current vehicle trajectory prediction methods, particularly in the form of domain bias. Domain bias occurs when a model trained on one traffic domain, such as one segment of a highway, underperforms when applied to another segment with different traffic patterns. To address this challenge and advance the field, we propose a new transfer learning-based collaborative vehicle trajectory prediction framework called TRACER, designed to provide reliable and accurate traffic predictions with high adaptability for cross-domain highway traffic scenarios. The core of our framework lies in an adaptive interactive extraction module and a trajectory generation module based on Bidirectional Long Short-Term Memory (BiLSTM), further strengthened by a pre-task of intention recognition for vehicle operation types. To improve model robustness, consistency regularization is applied by injecting disturbances into the target data, and a one-dimensional Convolution (Conv1D)-based intention extraction module is integrated into the BiLSTM-based trajectory generation process, leading to notable improvements in prediction accuracy. Our framework is first trained on source domain data, followed by the transfer of a small amount of labeled data from the target domain, and the overall model is further refined using unlabeled data. By effectively mitigating domain bias, TRACER significantly enhances trajectory prediction accuracy while maintaining high adaptability. The results underscore the importance of addressing domain shift challenges in trajectory prediction tasks and demonstrate the potential of domain adaptation techniques to improve the prediction accuracy of vehicle trajectories across different domains in highway scenarios.

Next generation networks will involve huge number of industrial internet of things (IIoT) sensors which require reliable connectivity with low latency to manage the data transmission and processing. The design of these networks entails a lot of challenges. This article describes the 3D placement of multiple unmanned aerial vehicles (UAVs) in an IIoT network that supports non-orthogonal multiple access (NOMA). UAVs act as decode and forward (DF) relays. The 3D UAV placement problem is formulated which is highly non-convex in the coordinates. Therefore, we employ an improved adaptive whale optimization algorithm (IAWOA) to handle the problem. Even with its improved performance, IAWOA is not suitable for real-time application. Hence, we propose path aggregation network (PANet) to handle the 3D UAV placement. The simulation results show that PANet is more suitable for the online-learning.

The air transportation system has attracted due attention from researchers due to its fast expansion over the last decade. Past research has focused on air transportation networks (ATN), but this work considers the resilience of the air route network. This research work proposes a modified approach based on GRA-WSM, named MA (Modified Approach based on GRA-WSM) for the identification of critical edges that form the backbone of the Chinese air route network. MA is a two-step process: Initially, important nodes are identified using the proposed GRA-WSM, and second, a novel approach is used for the computation of critical edges. Previously, researchers have used edge betweenness centrality measure to identify vital edges. But it took into account the global information of a node. This research work considers different centrality measures as the multi-attribute of the network, to take advantage of each centrality measure. The proposed MA approach aims to minimize the robustness of the network after the removal of some edges and the result is the set of critical edges. The critical edges found by the proposed MA approach are different from the edges that are topologically more important. These findings provide new perspectives on how to better understand other real-world networks.

The recent expansion of mobile IoT devices (MIoTDs) along with the exposure of many compute-intensive and latency-critical applications, have given a step rise to the mobile edge computing (MEC) platform to process computational microservices at the edge. The paramount importance of designing an effective incentive mechanism is a very important topic for such systems to get a fair amount of resources and provide incentives to MIoDs. Hence, we design a MEC platform with heterogeneous MIoTDs participating in a computational microservice offloading scheme. Here, we propose an incentive approach applying a double auction mechanism to incentivize the involvement of MIoTDs. In practice, the incentive mechanism typically interacts with the demand estimation scheme that estimates the demand profile of MIoTDs. As a result, we design a novel mechanism for microservices –microservice Incentive Service Offloading (mISO), which comprises an incentive approach and a demand estimation scheme. The mISO mechanism holds truthfulness, rationality, and low computational complexity while guaranteeing positive social welfare and generating the optimal demand profiles for MIoTDs. Simulation results showed that mISO provides 18–21$\%$and 25–30$\%$improvements in terms of average latency and resource utilization compared to existing works.

Wireless communication networks and technologies are witnessed a huge improvement which gain a large number of users. In addition, the choice and using methods of the network are depending on the environment in which it is created. Although the network in each case is unique, many of them share a lot of common. To this end, we propose a new approach for planning the structure of the Internet of Things (IoT) network based on fractals, where fractal figures are utilized to describe the structure of the target environment. Moreover, fractal dimension’s estimation, fraction area occupied by the target environment, and network model are used in the planning process. This approach allows you to choose a model that accurately describes the properties of the environment. Finally, the results proved the suitability of this approach for the IoT network structure planning in an urban or other environment based on the target environment’s data.

Smart cities provide citizens with smart and advanced services to improve their quality of life. However, it has been observed that the collection, storage, processing, and analysis of heterogeneous data that are usually borne by citizens will bear certain difficulties. The development of the Internet of Things, cloud computing, social media, and other Industry 4.0 influencers pushed technology into a smart society’s framework, bringing potential vulnerabilities to sensor data, services, and smart city applications. These vulnerabilities lead to data security problems. We propose a decentralized data management system for smart and secure transportation that uses blockchain and the Internet of Things in a sustainable smart city environment to solve the data vulnerability problem. A smart transportation mobility system demands creating an interconnected transit system to ensure flexibility and efficiency. This article introduces prior knowledge and then provides a Hyperledger Fabric-based data architecture that supports a secure, trusted, smart transportation system. The simulation results show the balance between the blockchain mining time and the number of blocks created. We also use the average transaction delay evaluation model to evaluate the model and to test the proposed system’s performance. The system will address residents’ and authorities’ security challenges of the transportation system in smart, sustainable cities and lead to better governance.

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.

Ultrareliable and low‐latency connection (URLLC) is one of the novel features in 5G networks and subsequent generations, in which it targets to fulfill stringent requirements on data rates, reliability, and availability. Moreover, the multiconnectivity concept is introduced to meet these requirements, where multiple different technologies are connected simultaneously, and the data packet is duplicated and transmitted from multiple transmitters. To this end, in this paper, we present an analysis, model, and method to ensure the reliability of data delivery when organizing URLLC in 5G networks. In addition, a new approach based on the organization of multiple connections (multiconnectivity) and duplication of transmitted data is considered. Further, an analytical model is presented for assessing the probability of failure, taking into account the traffic intensity, the probability of failure of elements, and the number of used connections. Moreover, an efficient method is proposed for increasing the reliability of data delivery by optimizing the number of connections. Further, a multiconnectivity‐based URLLC model has been built for evaluating the proposed method and verifies that the optimal number of routes for data delivery between the user and the point of service can be obtained, where the probability of losses and equipment reliability are jointly considered. Finally, detailed analysis of results shown that with “equal” routes in terms of load (with an equally probable traffic distribution) and the probability of equipment failure, the optimal number of routes can be found, at which the minimum probability of losses is achieved.