Journal of Network and Computer Applications最新文献

With the wide application of big data technology, a large number of data geographically stored in data centers across various regions are generated everyday, waiting to be analyzed by big data tasks. Examples of such data analysis tasks include weather prediction and intelligent healthcare applications. Clouds are being used by more and more enterprises due to their nearly infinite resources, ease of scaling, and other characteristics. Organizations often rent StaaS (Storage-as-a-Service) storage products offered by cloud providers, such as OSS (Object Storage Service), for massive data storage, while building a big data cluster in cloud environments to analyze the collected data from various regions. However, when the data to be analyzed are not located in the rented cluster, how to efficiently and economically process the distributed input data stored in clouds becomes an urgent problem to be solved. A simple approach is to only cache frequently accessed data rather than all data from other regions into the cluster to reduce total traffic costs. However, it is generally very hard to predict future data access curves. Thus, a rash caching decision may incur more costs. To address this problem, in this paper we propose an online algorithm for guiding cloud users to make cost-effective caching decisions properly, while not requiring any future information. We prove theoretically that the competitive ratio of our online algorithm is less than 2. Finally we verify the effectiveness of our proposed algorithm through extensive experiments based on the real price of Alibaba’s public IaaS cloud products using both real-world Yahoo S2 data and synthesized datasets.

As blockchain technology continues to evolve, it has fostered an extensive ecosystem of applications and platforms. This dynamic landscape is characterized by a myriad of innovative solutions, ranging from decentralized finance and supply chain management to digital identity and voting systems, each contributing to the ongoing advancement and adoption of blockchain technology across various sectors. Achieving interoperability among these applications and platforms poses a significant challenge due to their use of distinct protocols, and remains a bottleneck hindering the widespread adoption of blockchain technologies. Addressing this challenge requires designing a universal interoperability protocol while ensuring compliance with the security and privacy constraints specific to each blockchain, thus adding complexity. We propose an agnostic interoperability protocol designed for seamless asset movement across independent private blockchain networks, regardless of their individual protocols. This protocol leverages incentive-driven smart contract and Zero-Knowledge Proofs to establish a decentralized, secure, and privacy-focused framework for interoperability. We conduct a security analysis using game theory and provide both theoretical and empirical evaluations of the protocol end-to-end delay. Through a comprehensive use case, we demonstrate the secure deployment of the proposed protocol for asset movement between two private blockchains. We also discuss the trade-offs between cost and delay in cross-blockchain transactions. Furthermore, a comparative analysis with existing interoperability schemes showcases the proposed interoperability scheme superiority in terms of robustness, privacy preservation, and verifiability.

Cloud, Edge, and Fog computing provide computational services to different end users. A federation among these computing paradigms is beneficial, as it enhances the capability, capacity, coverage, and services of cloud, edge, and fog. An authentication method is needed to realize such a federation among cloud, edge, and fog so that a user belonging to one of these computing paradigms can use the services offered by other computing paradigms in the federation without creating a new account. This paper proposes a standard-compliant universal federator that transparently provides third-party authentication among different protocols, used by cloud, edge, and fog, such as 3GPP EPS-AKA, OpenID Connect (OIDC), and 802.1x. The federator provides transparency by using a controller and modules that act as virtual counterparts of the authentication entities in EPS-AKA, OIDC, and 802.1x. These virtual counterparts play multiple roles, depending upon the involved protocols. We deployed a testbed, published our implementation on GitHub, and tested third-party authentication for 16 scenarios across EPS-AKA, OIDC, and 802.1x. The results show that our federator successfully provides third-party authentication while taking 4.07–51.8% of the total authentication time, which ranges between 1.193–3.825 s for 16 scenarios. Some scenarios involving 802.1x take considerably longer due to the bottleneck caused by the 802.1x switch. We also conducted a security analysis to show that our proposed federator fulfills multiple security requirements.

With the advancement of wireless energy transfer, Wireless Rechargeable Sensor Networks (WRSNs) have become increasingly popular for efficiently charging sensor nodes. In WRSNs, determining the charging schedule for Mobile Chargers (MCs) is critical for reducing maintenance costs and improving charging efficiency. This is termed the Charging Scheduling Problem (CSP), which is proven to be NP-hard in nature. The existing schemes lack a comprehensive approach to determine the optimal number of MCs and often set a fixed charging threshold for the sensor nodes, degrading charging efficiency in dynamic networks. Additionally, relying on a single MC is unrealistic and impractical for large-scale networks, necessitating a more advanced charging strategy. Thus, this study proposes a dynamic and multi-node charging scheduling scheme named Partitioning-based Charging Schedule for Multiple Mobile Chargers (PCSMMC). The PCSMMC utilizes the traffic load of sensor nodes and energy load of MCs to estimate the optimal number of MCs and computes the progressive threshold for sensor nodes to improve the charging efficiency. Moreover, potential sojourn locations are determined and multiple network factors are integrated into a multi-attribute decision-making process to achieve an efficient charging scheduling and path planning scheme for multiple MCs. The objective of PCSMMC is to enhance the survivability rate of sensor nodes and decrease the traveling path followed by MCs to charge the sensor nodes within the network. Empirical simulation results confirm the superiority of PCSMMC in terms of charging response time, survivability rate, energy utilization efficiency, and network lifetime by a significant margin when compared to alternative approaches.

The Internet of things (IoT) is highly exposed to various attacks due to its sensitive applications, but it is very vulnerable in dealing with these attacks. So, various studies have been introduced to improve IoT security. Most of methods have focused on improving the security of the RPL protocol (Low-Power and Lossy Networks routing protocol) based on the development of trust models. However, most of these researches have considered good and bad behaviors to calculate the value of trust. This way of evaluating trust alone is not enough and it is vulnerable in dealing with some attacks, especially smart attacks (such as on-off attack, selective sending). To improve this issue, in this paper, a method called multi-context trust-based efficient RPL for IoT (MCTE-RPL) is proposed based on RPL development utilizing trust models with intrusion detection system. MCTE-RPL focuses on three important principles, including establishing secure and reliable routing topology, evaluating trust, and detecting malicious nodes. In the first step, the network routing topology is formed based on the trust and conditions of the nodes. In the second step, in accordance with the data exchanges, the trust of the nodes is evaluated and malicious factors are identified. The simulation results using cooja indicated the superiority of MCTE-RPL in improving routing reliability and data exchange compared to previous studies.

The multi-controller scheme is widely adopted in Software-Defined Wide Area Networks (SDWANs), where a WAN is segmented into multiple domains, each controlled by one controller. These controllers communicate with each other in-band, necessitating authentication before exchanging control messages. However, relying solely on identification of a single node for authentication exposes the network to spoofing attacks, jeopardizing its security. To address this issue, we present Seraph, an innovative $(t,n)$-threshold signature-based authentication scheme that verifies not only the node itself but also its “endorsement” nodes to establish its identity. We have investigated the best practice for defining the “endorsement” relationships concerning security and overheads, formulating the problem as an integer programming problem. We have demonstrated the polynomial-time hardness (NP-hardness) of the problem and proposed an efficient Seraph algorithm. Through our rigorous simulation analysis, we show that Seraph can provide comparative performance with Optimal and reduce time usage by over 90%.

The Internet has existed since the 1970s as a means of data exchange between network devices in small networks. In the early stage, there was a small number of devices, but today there is an ever-increasing number of devices, leading to congestion in the network. Therefore, congestion control has attracted so much attention in the academic community and the industry for the past 30 years. Recently, Google has developed BBR (Bottleneck Bandwidth and Round-Trip Time), a rate-based congestion control algorithm. BBR controls transmission rates based on delivery rate and round-trip time (RTT). However, such a static congestion control algorithm (e.g., BBR, etc.) cannot achieve high performance in various network conditions (e.g., low bandwidth, etc.). Concretely, these static algorithms cannot adapt to the dynamic changes of the network environment. Therefore, in this paper, we propose an adaptive algorithm (called ABBR) for congestion control in next-generation networks. ABBR takes into account the reinforcement learning algorithm to learn relevant policies to change the transmission rate corresponding to each congestion control algorithm to optimize long-term performance. The experimental results show that our proposal can achieve good performance in terms of throughput, RTT, and fairness compared to the benchmarks.

In this paper, we design a hybrid kitchen safety guarding framework using embedded devices and onboard sensors to detect abnormal events and block gas sources in time through the Internet of Things (IoT). According to the relevant literature we studied, this is the first framework for kitchen safety guarding that provides the following features: (1) the deep learning based model integrating densely connected convolutional networks with neural architecture search networks is developed to accurately recognize abnormal stove fire, (2) the acceleration correction method is designed to correct the sensed accelerometer values for estimating the actual earthquake level, and (3) the proper gas leakage threshold is defined to precisely detect the gas leakage for automatic gas blocking, and remote surveillance and control are provided to monitor the kitchen environment and control the gas source anytime and anywhere. In particular, an Android-based prototype consisting of the IoT device, diverse sensors, dedicated server, and smartphones is implemented to verify the feasibility and superiority of our framework. Experimental results show that our framework outperforms existing methods and can precisely recognize stove fire intensity and detect earthquake levels for kitchen safety guarding.

The significant advancements in sensors and other resource-constrained devices, capable of collecting data and communicating wirelessly, are poised to revolutionize numerous industries through the Internet of Things (IoT). Sectors such as healthcare, energy, education, transportation, manufacturing, military, and agriculture stand to benefit. IoT is expected to play a crucial role in implementing both Industry 4.0 and its successor, Industry 5.0. IoT relies on data collected by sensors from various points, shared over wireless or wired networks, making it more vulnerable to attacks. Consequently, addressing privacy and security concerns is of paramount importance for the widespread adoption of IoT across industries. Recognizing the pivotal role of IoT security, recent years have witnessed a marked upswing in publications dedicated to leveraging Machine Learning techniques for intrusion detection within the IoT framework. This paper embarks on a comprehensive endeavor to classify and characterize the myriad of intrusion detection methodologies that have emerged through the fusion of Machine Learning and IoT security. Serving as a timely and insightful review, this survey is not only of immense value to seasoned researchers immersed in this dynamic field but also serves as an invaluable resource for newcomers eager to contribute to the enhancement of IoT security. This paper sets itself apart from existing surveys by placing particular emphasis on recent advancements in machine learning-based intrusion detection across various IoT domains. Unlike previous surveys, it comprehensively explores papers published within the past five years, encompassing a wide range of dimensions within this field. These dimensions include, but are not limited to, medical IoT, agricultural IoT, industrial IoT, Fog/Edge IoT, Intelligent Transportation Systems, Smart Home Networks, and more.

By meticulously outlining the diverse machine learning-based intrusion detection methods found in the literature, this survey not only captures the current landscape but also provides a roadmap for future research endeavors. This roadmap aims to strengthen the security framework of the rapidly expanding IoT ecosystem.

To tackle task offloading and path planning challenges in multi-UAV-assisted mobile edge computing, this paper proposes a task offloading and path optimization approach via multi-agent deep reinforcement learning. The primary goal is to minimize the overall energy consumption of the system and improve computational performance. Initially, we established a model for a multi-UAV-assisted mobile edge computing system that centrally manages the UAV network through software-defined networking technology. Subsequently, considering UAV load and fairness in user equipment-related services, we employ the multi-agent deep deterministic policy gradient algorithm to optimize task offloading and UAV path management, aiming at load balancing and reducing overall system energy consumption. Simulation results demonstrate our method’s effectiveness in reducing energy consumption and computation latency compared to benchmark algorithms. It ensures system efficiency, stability, and reliability, meeting mobile edge users’ service requests while utilizing computing resources efficiently.