Ad Hoc Networks最新文献

Sensor data exchanges in IoT applications can experience a variable delay due to changes in the communication environment and sharing of processing capabilities. This variability can impact the performance and effectiveness of the systems being controlled, and is especially reflected on Age of Information (AoI), a performance metric that quantifies the freshness of updates in remote sensing. In this work, we discuss the quantitative impact of activation and propagation delays, both taken as random variables, on AoI. In our analysis we consider an offline scheduling over a finite horizon, we derive a closed form solution to evaluate the average AoI, and we validate our results through numerical simulation. We also provide further analysis on which type of delay has more influence on the system, as well as the probability that the system fails to deliver all the scheduled updates due to excessive delays of either kind.

Cellular Vehicle-to-Everything (C-V2X) networks provide critical support for intelligently connected vehicles (ICVs) and intelligent transport systems (ITS). C-V2X utilizes vehicle-to-vehicle (V2V) communication technology to exchange safety–critical information among neighbors. V2V communication has stringent high-reliability and low-latency requirements. The existing solutions on resource allocation for V2V communications mainly rely on channel states to optimize resource utilization but fail to consider vehicle safety requirements, which cannot satisfy safety application performance. In this paper, we focus on application-driven channel resource allocation strategy for V2V communications. First, we propose an inter-packet reception model to represent the delay between two consecutive and successful reception packets at a receiver. We then design an application-specific utility function where the utility depends on the packet reception performance and vehicle safety context. Finally, we formulate the channel resource allocation problem as a non-cooperative game model. The game model can guide each node to cooperate and achieve the trade-off between fairness and efficiency in channel resource allocation. The simulation results show that our work can significantly improve the reliability of V2V communications and guarantee the vehicle safety application performance.

Online computing offloading is an effective method to enhance the performance of mobile edge computing (MEC). However, existing research ignores the impact of system stability and device priority on system performance during task processing.To address the problem of computing offloading for computing-intensive tasks, an online partial offloading algorithm combining task queue length and energy consumption is proposed without any prior information. Firstly, a queue model of IoT devices is created to describe their workload backlogs and reflect the system stability. Then, using Lyapunov optimization, computing offloading problem is decoupled into two sub-problems by calculating the optimal CPU computing rate and device priority, which can determine the task offloading amount and offloading location to complete resource allocation. Finally, the online partial offloading algorithm based on devices priority is solved by minimizing the value of the drift-plus-penalty function’s upper bound to ensure system stability and reduce energy consumption. Theoretical analysis and the outcomes of numerous experiments demonstrate the effectiveness of the proposed algorithm in minimizing system energy consumption while adhering to system constraints, even in dealing with dynamically varying task arrival rates.

Wireless Sensor Networks (WSNs) are vulnerable to attacks during data transmission, and many techniques have been proposed to detect and secure routing data. In this paper, we introduce a novel stochastic predictive machine learning approach designed to discern untrustworthy events and unreliable routing attributes, aiming to establish an artificial intelligence-based attack detection system for WSNs. Our methodology leverages real-time analysis of the features of simulated WSN routing data. By integrating Hidden Markov Models (HMM) with Gaussian Mixture Models (GMM), we develop a robust classification framework. This framework effectively identifies outliers, pinpoints malicious network behaviors from their origins, and categorizes them as either trusted or untrusted network activities. In addition, dimensionality reduction techniques are used to improve interpretability, reduce computation and processing time, extract uncorrelated features from network data, and optimize performances. The main advantage of our approach is to establish an efficient stochastic machine learning method capable of analyzing and filtering WSN traffic to prevent suspicious and unsafe data, reduce the large dissimilarity in the collected routing features, and rapidly detect attacks before they occur. In this work, we exploit a well-tuned data set that provides a lot of routing information without losing any data. The experimental results show that the proposed stochastic attack detection system can effectively identify and categorize anomalies in wireless sensor networks with high accuracy. The classification rates of the system were found to be around 83.65%, 84.94% and 94.55%, which is significantly better than the existing classification approaches. Furthermore, the proposed system showed a positive prediction value of 11.84% higher than the existing approaches.

Analyzing packet loss, whether resulting from communication challenges or malicious attacks, is vital for broadcast authentication protocols. It ensures legitimate and continuous authentication across networks. While previous studies have mainly focused on countering Denial of Service (DoS) attacks' impact on packet loss, our research introduces an innovative investigation into packet loss and develops data recovery within variant TESLA protocols. We highlight the efficacy of our proposed hybrid TLI-µTESLA protocol in maintaining continuous and robust connections among network members, while maximizing data recovery in adverse communication conditions. The study examines the unique packet structures associated with each TESLA protocol variant, emphasizing the implications of losing each type on the network performance. We also introduce modifications to variant TESLA protocols to improve data recovery and alleviate the effects of packet loss. Using Java programming language, we conducted simulation analyses that illustrate the adaptability of variant TESLA protocols in recovering lost packet keys and authenticating previously buffered packets, all while maintaining continuous and robust authentication between network members. Our findings also underscore the superiority of the hybrid TLI-µTESLA protocol in terms of packet loss performance and data recovery, alongside its robust cybersecurity features, including confidentiality, integrity, availability, and accessibility. Additionally, we demonstrated the efficiency of our proposed protocol in terms of low computational and communication requirements compared to earlier TESLA protocol variants, as outlined in previous publications.

The Internet of Things (IoT) has recently gained significance as a means of connecting various physical devices to the Internet, enabling various innovative applications. However, the security of IoT networks is a significant concern due to the large volume of data generated and transmitted over them. The limited resources of IoT devices, along with their mobility and diverse characteristics, pose significant challenges for maintaining security in routing protocols, such as the Routing Protocol for Low-Power and Lossy Networks (RPL). This lacks effective defense mechanisms against routing attacks, including Sybil and rank attacks. Various techniques have been proposed to address this issue, including cryptography and intrusion-detection systems. The use of these techniques on IoT nodes is limited by their low power and lossy nature, primarily due to the significant computational overhead they involve. In addition, conventional trust-management systems for addressing security concerns need to be improved due to their high computation, memory, and energy costs. Therefore, this paper presents a novel, Lightweight, and Efficient Trust-based Mechanism (LETM-IoT) for resource-limited IoT networks to mitigate Sybil attacks. We conducted extensive simulations in Cooja, the Contiki OS simulator, to assess the efficacy of the proposed LETM-IoT against three types of Sybil attack (A, B, and C). A comparison was also made with standard RPL and state-of-the-art approaches. The experimental findings show that LETM-IoT outperforms both of these in terms of average packet-delivery ratio by 0.20 percentage points, true-positive ratio by 1.34 percentage points, energy consumption by 2.5%, and memory utilization by 19.42%. The obtained results also show that LETM-IoT consumes increased storage by 5.02% compared to the standard RPL due to the existence of an embedded security module.

The trust system is widely used to prevent malicious behaviors, and it is a key element for vehicles to establish interactions in the Internet of Vehicles (IoV). Nevertheless, trust and privacy remain unresolved concerns stemming from the distinctive features of the IoV. The IoV must thwart malicious attackers from spreading false data while ensuring that the vehicle’s evaluation data is not leaked, which is of utmost importance. In this paper, we propose a blockchain-based trust model (BPS-V), which supports ciphertext computation of trust evaluation data submitted by different vehicles. Design a cooperative update method for vehicle trust, which utilizes an improved distributed two-trapdoor public-key cryptography algorithm to achieve cooperative computing of trust and reduce the risk of privacy leakage of evaluation data. On this basis, BPS-V introduces blockchain sharding technology to realize cross-domain storage and sharing of the trust. Simulation results show that our scheme can effectively protect the privacy of evaluation data and maintain a high detection rate and low false alarm rate in different road environments. Compared with traditional schemes, BPS-V can improve the efficiency of trust updates and the detection of malicious vehicles by 9.5% and 32%.

With the evolution of Space-based backbone networks, the demand for enhanced efficiency and stability in network resource allocation has become increasingly critical, presenting a substantial challenge to conventional allocation methods. In response, we introduce an innovative resource allocation algorithm for space-based backbone networks. This algorithm represents a synergistic fusion of Deep Reinforcement Learning (DRL) and Local Search (LS) methodologies. It is specifically designed to reduce the extensive training duration associated with traditional policy networks, a crucial aspect in assuring optimal service quality. Our algorithm is structured within a two-stage framework that seamlessly integrates DRL and LS. A distinctive feature of our approach is the incorporation of link reliability into the algorithmic design. This element is meticulously tailored to address the dynamic and heterogeneous nature of space-based networks, ensuring effective resource management. The effectiveness of our approach is substantiated through extensive simulation results. These results demonstrate that the integration of DRL with LS not only enhances training efficiency but also exhibits significant improvements in resource allocation outcomes. Our work represents a noteworthy contribution to the development of practical optimization strategies in space-based networks, merging DRL with traditional methodologies for improved performance.

As the Internet of Things(IoT) and its intelligent applications continue to proliferate, forthcoming 6G networks will confront the dual challenge of heightened communication and computing capacity demands. To address this, D2D collaborative computing is being explored. However, the current D2D collaborative computing ignores the integrity of computing and communication. For a single-task device, offloading operations intertwine computing and communication, internal coupling causes due to parallel executed between local and D2D offloading. In addition, external coupling arises among devices competing for limited radio and computing resources. Worse, internal coupling and external coupling interact, exacerbating the situation. To address these challenges, a novel D2D offloading framework is proposed based on hyper-graph matching in this paper. Our goal is to minimize both delay and energy costs while ensuring service quality for all users by jointly optimizing task scheduling, offload policies and resource allocation. The original problem is formulated as a nonlinear integer programming problem. Then, by three-stage strategy optimization decomposition, it is separated into several sub-problems. In the first stage, a polynomial-time algorithm has been developed to optimize the task offloading ratio, taking into account both its upper and lower bounds. In the second stage, a geometric programming algorithm has been created to address power allocation. In the third stage, a three-dimensional hyper-graph matching model is employed to derive the optimal offloading and channel allocation policies. This is based on analyzing the conflict graph and applying the claw theorem. Simulation results demonstrate that the proposed scheme outperforms other algorithms by approximately 12%, 20%, 28%, 40%, respectively. Moreover, it enhances both spectral efficiency and computational efficiency.

When designing smart cities’ building blocks, mobility data plays a fundamental role in applications and services. However, mobility data usually comes with unrestricted location of its corresponding entities (e.g., citizens and vehicles) and poses privacy concerns, among them recovering the identity of those entities with linking attacks. Location Privacy Protection Mechanisms (LPPMs) based on anonymization, such as mix-zones, have been proposed to address the privacy of users’ identity. Once the data is protected, a comprehensive discussion about the trade-off between privacy and utility happens. However, issues still arise about the application of anonymized data to smart city development: what are the smart cities applications and services that can best leverage mobility data anonymized by mix-zones? To answer this question, we propose the Utility Analysis Framework of Anonymized Trajectories for Smart Cities-Application Domains (UAFAT). This characterization framework measures the utility through twelve metrics related to privacy, mobility, and social, including mix-zones performance metrics from anonymized trajectories produced by mix-zones. This framework aims to identify applications and services where the anonymized data will provide more or less utility in various aspects. The results evaluated with cabs and privacy cars datasets showed that further characterizing it by distortion level, UAFAT ranked the smart cities application domains that best leverage mobility data anonymized by mix-zones. Also, it identified which one of the four case studies of smart city applications had more utility. Additionally, different datasets present different behaviors in terms of utility. These insights can contribute significantly to the utility of both open and private data markets for smart cities.