Wireless Networks最新文献

Due to the complexity and security requirements of edge computing environments and the limited resources of terminals, secure offloading in multi-access edge computing (MEC) networks has emerged as a critical and urgent research area. However, many studies on task offloading often ignore the necessary balance between security requirements and efficiency. To address this issue, we propose a Secure Offloading Strategy Considering Computational Acceleration, named SOCA, designed to bolster security while preserving offloading efficiency. Specifically, the secure offloading problem is modeled as a multi-objective optimization problem by achieving a composite function of latency mitigation and security metrics as the optimization objective, which is solved by the ChaCha20-based offloading decision algorithm (ChaCha20-ODA). The algorithm employs the ChaCha20 encryption protocol as its security mechanism. By executing a quarter-round function to generate a keystream, it provides robust protection for data tasks, ensuring that the data remains impervious to malevolent interception by adversaries throughout the transmission process. Furthermore, to improve the computational efficiency of task offloading, the algorithm simultaneously leverages both edge and local computing resources, achieving computational acceleration by optimizing the appropriate offload ratio. The experimental results illustrate that as compared with baselines, our approach achieves remarkable improvement in the balance between latency and safety benchmarks, which demonstrates the superiority of our method.

In order to solve the complex task scheduling problem of fog computing processing big data in the industrial Internet of Things, a task scheduling strategy based on ant colony algorithm called TSSAC (task scheduling strategy with ant colony)is proposed. Tasks with dependencies are modeled as a directed acyclic graph. The performance indices including makespan, load balancing and energy consumption of fog server are optimized simultaneously, and the ant colony algorithm is used to solve the multi-objective optimization problem. The pheromone heuristic factor and pheromone evaporation coefficient of the ant colony algorithm are updated in a linear increasing way, so that the ants are less affected by pheromones in the early stage and obtain a larger search range. During the later stage, it is greatly affected by pheromone and quickly converges to the optimal solution. Furthermore, during the task execution container migration mechanism is introduced to solve the overload problem caused by high server utilization and energy loss caused by low server utilization simultaneously. The simulation results show that the proposed task scheduling strategy TSSAC reduces energy consumption by 23.5% compared with the traditional algorithm, meanwhile, achieves a compromise between task makespan and load balancing index compared with the traditional algorithm.

This study proposes a blockchain-based solution to enhance the efficiency and security of Healthcare Knowledge Management Systems for Industry 4.0. With the widespread adoption of network internet platforms, managing knowledge data safely and effectively has become a critical challenge. Traditional Healthcare Knowledge Management Systems rely on centralized storage methods, which can lead to knowledge monopolies, resulting in a crisis of trust between knowledge management and sharing subjects. This paper proposes the Dumbo algorithm, which enhances the adaptability of asynchronous environments and improves the security of shared blockchains in knowledge-sharing environments. The Dumbo algorithm ensures traditional knowledge management platforms’ trust, security, and efficiency by eliminating data tampering through a unique workload-proof and consensus mechanism in blockchain technology. Additionally, distributed accounting solves the problems of low efficiency and poor security of central systems while dispersing the risk of damage to the central database and guaranteeing data security. This study aims to realize knowledge sharing based on blockchain and solve the challenges associated with conventional Healthcare Knowledge Management Systems. The proposed solution has the potential to significantly improve the management, sharing, and security of knowledge in Industry 4.0. We expect to see major improvements in the way that healthcare data is shared, saved, and used thanks to this blockchain-based strategy, which will open the door to better patient care and more operational efficiency in the healthcare sector.

Cloud computing represents an evolved form of cluster, client server, and grid computing, enabling users to seamlessly access resources over the internet. The quality and reliability of the cloud computing services are depends on the specific tasks undertaken by the users. Task Scheduling emerges as a pivotal factor in enhancing the efficiency and reliability of a cloud environment, aiming to optimize resource utilization. Furthermore, efficient task scheduling holds a prime importance in achieving superior performance, minimizing response time, reducing energy consumption and maximizing throughput. Assigning work to essential resources is a challenging process to achieve better performance. However, this paper plans to propose a novel workload prioritization and optimal task scheduling in the cloud with two steps. At first, the ranks are allotted to the tasks with Analytical Hierarchy Process based ranking process that uses a k-means clustering strategy to group the workloads. Then, the tasks are scheduled under the consideration of constraints like makespan, utilization cost, and migration cost and risk probability; based on priority. Accordingly, the task scheduling is done optimally by the proposed hybrid optimization Blue Updated Jellyfish Search Optimization that combines algorithms like Blue Monkey Optimization and Jelly fish Search Optimization algorithms. The performance of the proposed scheduling process is validated and proved over the conventional methods.

Enhancing the task offload performance of UAV-assisted Vehicular Edge Computing Networks (VECNs) is complex, especially in vehicle-to-everything (V2X) applications. These networks rely on UAVs and roadside units (RSUs) to offload heavy computational tasks and reduce the load on the on-board systems. However, UAV-assisted VECNs face severe challenges from heterogeneous offload node resources and dynamic edge network environments in providing low-latency and high-response task offloading, especially during traffic congestion or infrastructure failures. In this paper, we propose a digital twin (DT)-driven task offloading framework for UAV-assisted VECNs. The aim of the proposed framework is to improve the global performance of VECN task offloading under limited computational and communication resource constraints. Firstly, we construct a decentralized offloading decision-centralized evaluation task offloading framework for UAV-assisted VECNs based on the asynchronous advantage actor-critic (A3C) algorithm. Secondly, we integrate the graph attention networks (GAT) into the framework to incorporate the dynamically changing DT network topology information into the state evaluation of VECNs. By simulating a DT-driven multi-UAV cooperative system and comprehensive evaluation of real-world task request datasets. The framework has a better task throughput rate and stability when performing task offloading in local resource overload and dynamic edge environment scenarios.

In this paper, we present a comprehensive investigation on security-reliability trade-off (SRT) in cooperative wireless networks in which full-duplex intermediate nodes act as jammer and/or relay. Various cooperative schemes named as opportunistic relay selection (ORS), opportunistic jammer selection (OJS), hybrid relay-jammer selection (HRJS), and joint relay-jammer selection (JRJS) are considered. In these schemes, relay and jammer sets are distinguished by comparing the instantaneous and average fading properties of the main and wiretap links, respectively. So that, one or two helpers from these sets are opportunistically employed for cooperation regarding the applied scheme. We analyze the outage and intercept probabilities and derive closed-form expressions for the SRT of the considered schemes. The results show that the cooperative schemes deliver different behaviors by varying parameters such as number of helpers, link quality and data rate. In details, ORS outperforms the other schemes for high main to eavesdropper ratio (MER), whereas JRJS delivers better performance for lower MERs. By improving the relay-to-destination link compared to source-to-destination, both HRJS and JRJS outperform the ORS while HRJS delivers better SRT than JRJS. Moreover, HRJS outperforms JRJS at higher data rates. The results also indicate that SRT of all schemes is improved by increasing the number of helpers.

In order to improve the stability and fidelity of 5G multi-band communication, an anti-jamming design method based on NFC and RFID technology is proposed to develop the anti-jamming ability of 5G multi-band communication antenna. Combine NFC technology with mobile phone technology and RFID identification technology, then construct a 5G multi-band communication channel model based on NFC technology. A unique signal attenuation technique is used to suppress intercode interference between 5G multiband communication channels. At the same time, extract the symbolic feature components of 5G multi-band communication antenna, and the compatible design of NFC and existing contactless smart card technology is used to guide the Bluetooth matching process. In order to realize the channel equalization design of 5G multi-band communication antenna, adopt the adaptive sampling decision equalization adjustment technique. According to the beamforming and delay scheduling methods, establish the transmission delay control of the multi-band communication antenna. According to the spatial beamforming principle, realize the anti-interference design of the 5G multi-band communication antenna. This method has a better channel balance and a lower bit error rate in the anti-jamming design of 5G multi-band communication antenna supported by NFC technology. Therefore, this technology has good potential and value for promoting NFC technology in mobile communication equipment. The lowest bit error rate is 0.006, and the shortest delay is 0.251.

Affected by limited computing resources and energy, intelligent terminal devices in edge computing systems cannot perform computationally intensive mining tasks in blockchains based on the PoW (proof-of-work) protocol. Therefore, rational terminal devices, as miners, choose to offload mining tasks to other devices or edge computing servers. Aiming at the problem that lightweight devices cannot complete the blockchain mining tasks, this paper firstly proposes a blockchain mining task offloading strategy based on D2D-EC (Device to Device Communication Assisted Edge Computing). Miners offload mining tasks to CMN (Collaborative Mining Network) integrated by mining devices or edge computing server. Secondly, the mobility of devices increases the risk of failure in the blockchain consensus process. Therefore, we develop a prediction method based on Lagrange interpolation to predict the track of devices. The mobility prediction of devices enable miners to make rational offloading strategy, that is, offload fewer tasks to devices with strong mobility to reduce consensus failure costs. In this paper, the interaction between miners and resource suppliers is modeled as a two-stage multi-leader multi-follower Stackelberg game to obtain the best resource requests of miners and best pricing of resource suppliers. To find the NE (Nash Equilibrium) of the Stackelberg game, this paper develops a gradient search-based best response distributed algorithm (BRD). Simulation results show that the algorithm can optimize miners’ utilities and suppliers’ profits quickly, and the proposed prediction method can effectively enable miners to optimize allocation of mining tasks.

Hospital facilities were limited in rural areas and there is no awareness about disease infection and so on. Hence, the Internet of Things (IoT) technology was designed in the health care industry to treat and save illiterate people from the harmful diseases. Recently, the health care system based on IoT technology became a huge demand in the online and medical industry. However, offering the protection frame for gathered data in cloud becomes a challenging task, because the cloud contains a lot of different patient data. To overcome this issue, the current research has designed a novel Elapid Encryption in cloud frame to secure the gathered data. Moreover, the security function is executed by encrypting the collected information in the cloud storage. Also, a novel generalized fuzzy intelligence and ant lion optimization model was developed for disease prediction and severity calculation. Hence, the developed design is implemented using MATLAB and its efficiency is compared with the existing approaches such as H-DT, DNN, and DTNNN. From the comparison, proposed model has finest and highest performance like high accuracy, precision, recall and confidential rate then lower error rate and processing time. Consequently, AUC value by the developed model is 89.8%, sensitivity rate as 99% and specificity rate as 97.8%, less error rate as 0.08, accuracy rate as 99.92% and 99.9% of precision, high recall measure as 99.92%, time consumption of the proposed model is 10 s.

The recent upsurge of data-demanding applications has necessitated a paradigm shift in deployment scenario in the direction of Multi-tier Ultra-Dense Heterogeneous networks (UDHN), which involve the dense deployment of more than one tier of small cells under-laying traditional macro cellular networks. However, higher data rates and the dense deployment of Small cell eNodeBs (SeNBs) elicit a possible escalation of network energy consumption which stirs up the mobile operators' operating expenditure. To deal with this, primarily, in this work, we present the Strategic Sleeping Policy of the SeNBs based on M/M/1 queuing theory and investigate its impact in reducing the power consumption of the proposed three-tier UDHN which consists of one tier of Macro eNodeB and two tiers of SeNBs based on performance metrics like Energy Efficiency and Area Energy Consumption Ratio. Further, we also introduce a novel Sleep Cycle Modulated Energy Harvesting Technique for SeNBs to ensure proper utilization of energy resources. An analytical model based on Continuous Time Markov Chain is also developed to evaluate the Energy Utilization of the proposed SCMEH method. The comprehensive performance analysis reveals that the implementation of integrated SCMEH enabled SeNBs under HetNet can not only guarantee QoS requirements under concurrent time-varying urban tele-traffic conditions but also ensure Sustainable Green Communication by radically controlling the estimated power consumption per hour basis throughout a day.