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Encryption technology has a great influence on data security. There are many encryptions have been proposed. In general, the encryptions with higher complexity will provide higher security, but it will also consume a lot of computing resources. In some cases, these high complexity method may not suitable such as Internet of Thing devices. This is because Internet of Thing device has lower computing power due to the size and the limited battery. Therefore, most of Internet of Thing (IoT) devices are used symmetric encryption as the main method. However, this kind of symmetric encryption algorithms are easy to occur weak keys such as data encryption algorithm (DES). This will expose IoT devices to high risk environments. In this study, a new fitness function has given and then a Genetic Algorithm-based symmetric key generation is proposed. As the simulation results, our proposed method can provide higher randomness and very low probability to occur the weak keys so that the data security in IoT environment will be increased significantly.

Recently, the 5G as the next‐generation network is a popular research and discussed widely. The architecture of 5G is a heterogeneous network, and it can support more networked types, like the ultra‐dense network, traditional cellular network, and Machine to Machine communication. Although the high frequency and larger bandwidth have been using in 5G, resource allocation is still a critical issue that needs to be discussed and solved. Consider the spectrum resource is limited, but almost all users hope that equipment can get a better quality of services. Therefore, how to manage the spectrum resource and allocation is a big problem. Consider the fast‐growing devices and traffic in the future; hence, task scheduling for UEs to reduce energy consumption will be focused on. To solve resource allocation and minimise energy consumption, the Markov decision chain is proposed to be used to predict the channel state. The modified particle swarm optimization (MPSO) is also used in this paper to find the best task scheduling. The simulation will be used to verify the performance of the mechanism that is used and compare it with PSO and first‐in‐first‐service (FIFS). The result shows the method used can be scheduled for the task efficiently.

In high-mobility non-terrestrial networks (NTN), the reference signal received power (RSRP)-based handover (RBH) mechanism is often unsuitable due to its limitations in handling dynamic satellite movements. RSRP, a key metric in cellular networks, measures the received power of reference signals from a base station or satellite and is widely used for handover decision-making. However, in NTN environments, the high mobility of satellites causes frequent RSRP fluctuations, making RBH ineffective in managing handovers, often leading to excessive ping-pong handovers and a high handover failure rate. To address this challenge, we propose an innovative approach called location-based handover with particle filter and reinforcement learning (LBH-PRL). This approach integrates a particle filter to estimate the distance between user equipment (UE) and NTN satellites, combined with reinforcement learning (RL), to dynamically adjust hysteresis, time-to-trigger (TTT), and handover decisions to better adapt to the mobility characteristics of NTN. Unlike the location-based handover (LBH) approach, LBH-PRL introduces adaptive parameter tuning based on environmental dynamics, significantly improving handover decision-making robustness and adaptability, thereby reducing unnecessary handovers. Simulation results demonstrate that the proposed LBH-PRL approach significantly outperforms conventional LBH and RBH mechanisms in key performance metrics, including reducing the average number of handovers, lowering the ping-pong rate, and minimizing the handover failure rate. These improvements highlight the effectiveness of LBH-PRL in enhancing handover efficiency and service continuity in NTN environments, providing a robust solution for intelligent mobility management in high-mobility NTN scenarios.

Recently, the healthcare technologies continue to develop rapidly, especially various wearable Internet of Things (IoT) devices for body network have been invented one after another. The relevant products can already be easily purchased in the market such as the smart bracelet, smart blood pressure monitor and so on. These healthcare devices not only make users able to understand their own body information more in more detail but also provide a communication way to the hospital. It means that patients can obtain the professional medical prescription advice without going to the hospital in person because the health information can transmit to the medical cloud through any network interfaces. Additionally, both medical records of patients and prescription advice from doctors are stored in the cloud. In order to provide the better service quality, the use of fog in the network edge can quickly response the requests from the patients. The computing power of the fog node is less than the cloud. Therefore, balancing the trade-off between cloud and fog is very important. In this paper, we formulate an optimization problem about offloading then use the metaheuristic to find out the best policy. Moreover, we also design an emergency supporting measure. Simulation results show that the proposed methods can provide a more efficient healthcare service.

To extend a network’s lifetime, wireless rechargeable sensor networks are promising solutions. Chargers can be deployed to replenish energy for the sensors. However, deployment cost will increase when the number of chargers increases. Many metrics may affect the final policy for charger deployment, such as distance, the power requirement of the sensors and transmission radius, which makes the charger deployment problem very complex and difficult to solve. In this paper, we propose an efficient method for determining the field of interest (FoI) in which to find suitable candidate positions of chargers with lower computational costs. In addition, we designed four metaheuristic algorithms to address the local optima problem. Since we know that metaheuristic algorithms always require more computational costs for escaping local optima, we designed a new framework to reduce the searching space effectively. The simulation results show that the proposed method can achieve the best price–performance ratio.

Underwater wireless sensor networks (UWSNs) have recently received a significant amount of attention. Since they have delay/disruption-tolerant networks (DTNs) characteristics, the design of any UWSN scheme must take DTN influences into account, especially in routing protocols. Many researchers have proposed various DTN routing techniques for different types of DTN routing schemes in UWSNs. The authors survey state-of-the-art DTN routing protocols, and use the definition of DTN to classify these proposals into scheduled contact, opportunistic contact and predicted contact. Furthermore, the authors analyse the detailed information in order to draw up a comparison table and also expect to inspire more research into this topic in the future.

Content cache as well as data cache is vital to Content Centric Network (CCN). A sophisticated cache scheme is necessary but unsatisfied currently. Existing content cache scheme wastes router’s cache capacity due to redundant replica data in CCN routers. The paper presents an intelligent data cache scheme, viz content popularity and user location (CPUL) scheme. It tackles the cache problem of CCN routers for pursuing better hit rate and storage utilization. The proposed CPUL scheme not only considers the location where user sends request but also classifies data into popular and normal content with correspond to different cache policies. Simulation results showed that the CPUL scheme yields the highest cache hit rate and the lowest total size of cache data with compared to the original cache scheme in CCN and the Most Popular Content (MPC) scheme. The CPUL scheme is superior to both compared schemes in terms of around 8% to 13% higher hit rate and around 4% to 16% lower cache size. In addition, the CPUL scheme achieves more than 20% and 10% higher cache utilization when the released cache size increases and the categories of requested data increases, respectively.

With the rapid development of healthcare, mobile cloud computing can improve medical efficiency by capturing and analyzing patient data. Fog computing, as an emerging paradigm to complement cloud computing, has significant advantages in local wireless signal processing, resource management and distributed storage capabilities to potentially meet future healthcare demands. However, performance is limited by the capacity of fronthaul links. In this paper, we propose a novel fog radio access network (F-RAN) model, where cooperation caching strategy and content transmission are jointly optimized. We formulate a mixed integer nonlinear programming problem in order to achieve an ultra-low delay for the proposed F-RAN. We also propose a novel matching algorithm based on the student project allocation (SPA) algorithm instead of the traditional optimization algorithm to solve the formulated problem. Numerical results reveal that the proposed joint optimization design can significantly improve the performance of the considered F-RAN.

Currently, almost every family has at least one car; thus, vehicle density is increasing annually. However, road capacity is finite; consequently, traffic accident frequency may increase due to increasing vehicle density. Typically, car accidents result in traffic congestion because vehicles behind the accident are not aware of the event and continue to follow the front queue. To address this problem, some emergency services, such as emergency message broadcasting, have been proposed. However, not all drivers want to receive such messages because they intend to exit the route prior to the accident scene, which means that communication resources may be wasted. In this paper, we propose a prediction model to forecast vehicles behavior based on a Markov chain and identify which vehicles require the emergency message. In addition, the proposed model includes an efficient policy based on the shortest path for police cars and ambulances such that they can attend the accident scene quickly and relieve traffic congestion. Simulation results show that the proposed method reduces unnecessary message transmission and increases road utilization efficiently.

A mature cloud system needs a complete resource allocation policy which includes internal and external allocation. They not only enable users to have better experiences, but also allows the cloud provider to cut costs. In the other words, internal and external allocation are indispensable since a combination of them is only a total solution for whole cloud system. In this paper, we clearly explain the difference between internal allocation (IA) and external allocation (EA) as well as defining the explicit IA and EA problem for the follow up research. Although many researchers have proposed resource allocation methods, they are just based on subjective observations which lead to an imbalance of the overall cloud architecture, and cloud computing resources to operate se-quentially. In order to avoid an imbalanced situation, in previous work, we proposed Data Envelopment Analysis (DEA) to solve this problem; it considers all of a user’s demands to evaluate the overall cloud parameters. However, although DEA can provide a higher quality solution, it requires more time. So we use the Q-learning and Data Envelopment Analysis (DEA) to solve the imbalance problem and reduce computing time. As our simulation results show, the proposed DEA+Qlearning will provide almost best quality but too much calculating time.