Ad Hoc Networks最新文献

Mobile Crowd Sensing/Souring (MCS) is a novel sensing approach that leverages the collective participation of users and their mobile devices to collect sensing data. As large volumes of data get stored and processed by the MCS platform, Artificial Intelligence (AI) techniques are being deployed to make informed decisions that help optimize the system performance. Despite their effectiveness in solving many of the challenges, incorporating AI models in the system introduces many concerns, which could adversely affect its performance. This includes exploiting the vulnerabilities of the models by an adversary to manipulate the data and cause harm to the system. Adversarial Machine Learning (AML) is a field of research that studies attacks and defences against machine learning models. In this study, we conduct a systematic literature review to comprehensively analyze state-of-the-art works that address various aspects of AI-based MCS systems. The review focuses mainly on the applications of AI in different components of MCS, including task allocation and data aggregation, to improve its performance and enhance its security. This work also proposes a novel classification framework that can be adapted to compare works in this domain. This framework can help study AML in the context of MCS, as it facilitates identifying the attack surfaces that adversaries can exploit, and hence highlights the potential vulnerabilities of AI-based MCS systems to adversarial attacks, motivating future research to focus on designing resilient systems.

The Internet of Things (IoT) is an emerging field that encompasses several heterogeneous devices and smart objects that are integrated with the network. In open platforms, these objects are deployed to present advanced services in numerous applications. Innumerable security-sensitive data is generated by the IoT device and therefore, the security of these devices is an important task. This work formulates a secure data transfer technique in IoT, named Authentication enabled Privacy Protection (APPS) scheme for resource-constrained IoT devices. The proposed scheme demonstrates resilience against various attacks; such as resisting reply attacks, device anonymity, untracebility, session key establishment, quantum attacks and resisting MITM attacks. For the privacy protection scheme, the secured data transfer is initiated between the entities, like IoT devices, servers, and registration centers, by using various phases, namely registration phase, key generation phase, data encryption, authentication, verification, and data retrieval phase. Here, a mathematical model is designed for protecting data privacy using hashing, encryption, secret keys, etc. Finally, performance of proposed APPS model is analyzed; wherein the outcomes reveal that the proposed APPS model attained the maximum detection rate of 0.85, minimal memory usage of 0.497MB, and minimal computational time of 112. 79 sec and minimal turnaround time 131.91 sec.

The deployment of Roadside Units (RSUs) in the Cellular-Vehicle to Everything enabled Internet of Vehicles is crucial for the transition from individual intelligence of vehicles to collective intelligence of vehicle-road collaboration. In this paper, we focus on improving the adaptability of RSU deployment to real scenarios, and optimizing deployment costs and vehicle-oriented service performance. The RSU deployment problem is modeled as a Multi-objective Optimization Problem (MOP), with a cost model integrating the purchase and installation costs, and a service-oriented Quality of Service (QoS) model adopting the total time the RSUs cover the vehicles as the evaluation metric. Specifically, we propose an RSU reliable coverage analysis method based on Packet Delivery Ratio model to estimate the coverage distances in different scenarios, which will be used in QoS calculation. Then, an evolutionary RSU deployment algorithm is designed to solve the MOP. The performance of the proposed method is simulated and discussed in real road network and dynamic scenarios. The results prove that our method outperforms the baseline method in terms of significant cost reduction and total coverage time improvement.

The advent of wireless technologies has led to the development of novel services for end-users, with stringent needs and requirements. High availability, very high throughput, low latency, and reliability are all of them crucial performance parameters. To address these demands, emerging technologies, such as non-terrestrial networks or millimeter wave (mmWave), are being included in 5G and Beyond 5G (B5G) specifications. mmWave enables massive data transmissions, at the expense of a more hostile propagation, typical for high frequency bands. Consequently, the inherent instability of the physical channel significantly affects the upper layers of the protocol stack, resulting in congestion and data losses, which might strongly hinder the overall communication performance. These challenges can be addressed not only at the link layer, but at any affected layer. QUIC is a new transport protocol designed to reduce communications latency in many ways. Among other features, it enables the use of multiple streams to effectively manage data flows sent through its underlying UDP socket. This paper introduces an implementation of priority-based stream schedulers along with the design of a flexible interface. Exploiting the proposed approach, applications are able to set the required scheduling scheme, as well as the stream priorities. The feasibility of the proposed approach is validated through an extensive experiment campaign, which combines Docker containers, the ns-3 simulator and the Mahimahi framework, which is exploited to introduce realistic mmWave channel traces. The results evince that an appropriate stream scheduler can indeed yield lower delays for time-sensitive applications by up to 36% under unreliable conditions.

Efficient energy consumption is crucial in Wireless Sensor Networks (WSNs). Uncontrolled energy usage can lead to the hotspot issue, hindering network lifetime and successful packet delivery. Sink mobility has been suggested as a solution, but it comes with challenges such as high data gathering delay and poor packet reception. These problems stem from the short contact time of nodes with the Mobile Sink (MS). To tackle these issues, we present an MS-based heterogeneous WSN with super and normal nodes. Most previous studies only considered the energy heterogeneity of sensors. These methods also suffered from different issues such as fixed MS tours, including inappropriate criteria in cluster construction, proposing greedy schemes, and employing basic metaheuristic algorithms. In our proposed model, super nodes are richer in initial energy and transmission range than normal sensors. In each round, the nodes are organized into clusters, and the MS visits the Cluster Heads (CHs) to gather data packets. Super nodes, owing to their elevated initial energy, are more adept at executing energy-sensitive tasks compared to normal sensors. Additionally, as CH, super nodes extend the contact time with the MS due to their longer transmission range, delivering more packets. The clusters are constructed using a variant of Particle Swarm Optimization (PSO), namely PSO-TVAC. We empower this method with effective initialization and decoding methods. Furthermore, we propose a heuristic intra-cluster multi-hop routing algorithm to enhance network lifetime. Our other contribution is to propose an efficient algorithm to determine the time to reconfigure the network, while the other algorithms mainly reconfigure the WSN periodically. Simulation results demonstrate superior performance compared to state-of-the-art algorithms, showcasing lower energy consumption, higher energy efficiency, higher lifetime, reduced packet delivery delay, and higher number of received packets by 30%, 38.2%, four times, 20.6%, and 22.6%, respectively.

Machine learning (ML) techniques have demonstrated considerable effectiveness when integrated into routing protocols to enhance the performance of Smart Grid Networks. However, their performance across diverse real-world scenarios remains a topic of exploration. In this study, we evaluate the performance and transferability of four ML models—Long Short-Term Memory (LSTM), Feedforward Neural Network (FF), Decision Trees, and Naive Bayes—across three distinct scenarios: Barcelona, Montreal, and Rome. Through rigorous experimentation and analysis, we analyze the varying efficacy of these models in different scenarios. Our results demonstrate that LSTM outperforms other models in the Montreal and Rome scenarios, highlighting its effectiveness in predicting the optimal forwarding node for packet transmission. In contrast, Ensemble of Bagged Decision Trees emerge as the optimal model for the Barcelona scenario, exhibiting strong performance in selecting the most suitable forwarding node for packet transmission. However, the transferability of these models to scenarios where they were not trained is notably limited, as evidenced by their decreased performance on datasets from other scenarios. This observation underscores the importance of considering the data characteristics when selecting ML models for real-world applications. Furthermore, we identify that the distribution of nodes within datasets significantly influences model performance, highlighting its critical role in determining model efficacy. These insights contribute to a deeper understanding of the challenges inherent in transferring ML models between real-world scenarios, providing valuable guidance for practitioners and researchers alike in optimizing ML applications in Smart Grid Networks.

In the context of rapidly urbanizing smart cities reliant on IoT networks, efficient load management is critical for sustainable energy use. This paper proposes an AI-enhanced Multi-Stage Learning-to-Learning (MSLL) approach tailored for secure load management in IoT networks. The proposed approach leverages MMStransformer, a transformer-based model designed to handle multivariate, correlated data, and to capture long-range dependencies inherent in load forecasting. MMStransformer employs a multi-mask learning-to-learning strategy, optimizing computational efficiency without compromising prediction accuracy. The study addresses the dynamic and complex nature of smart city data by integrating diverse environmental and operational variables. Security and privacy concerns inherent in IoT networks are also addressed, ensuring secure data handling and communication. Experimental results demonstrate the efficacy of the proposed approach, achieving competitive performance compared to traditional methods and baseline models. The findings highlight the potential of AI-driven solutions in enhancing load forecasting accuracy while ensuring robust security measures in smart city infrastructures. This research contributes to advancing the state-of-the-art in AI applications for sustainable urban development and energy management.

Strip-shaped Wireless Sensor Networks (WSNs) with branches are commonly used in various long and narrow applications, such as mines, factories, subways, and pipelines, and they face serious energy hole problems caused by multi-hop communication. The Mobile Data Collector (MDC) can alleviate the energy hole problem. Current solutions have two limitations: one is the balance between energy consumption and delay, and the other is the overly ideal network model, e.g., the square region or circular area. This paper focuses on strip-shaped networks and proposes a novel mobile data collection method to find a trade-off between energy preservation and data delivery delay. Firstly, the MDC path is planned by solving the diameter of the tree in the network, resulting in reduced delay. Secondly, the network energy consumption is further reduced by clustering and optimal transmission distance adjustment. Then, a network lifetime balancing mechanism is designed to balance network energy between backbone and branches. Finally, the performance of the algorithm proposed in this paper has been studied in four types of strip-shaped WSNs and compared with four existing MDC methods with evaluation metrics of maximum node energy consumption, network delay and weighted sum of both. The simulation results demonstrate that the proposed algorithm is applicable to different types of strip-shaped WSNs with branches and achieves excellent network performance, which can effectively balance network energy consumption and data collection delay.

Smartphones or similar WiFi-enabled devices regularly discover nearby access points by broadcasting management frames known as probe-requests. Probe-request frames relay, as information, the MAC addresses of sending devices, which act as the device identifiers. To protect the user’s privacy and location, probe-requests use a randomized MAC address generated according to the MAC address randomization protocol. Unfortunately, MAC randomization greatly limits any studies on trajectory inference, flow estimation, crowd counting, etc. To overcome this limitation while respecting users’ privacy, we propose Bleach, a novel, efficient, and comprehensive approach allowing randomized MAC addresses to device association from probe-requests. Bleach models the frame association as a resolution of MAC conflicts in small time intervals. We use time and frame content-based signatures to resolve and associate MACs inside a conflict. We propose a novel MAC association algorithm involving logistic regression using signatures and our introduced time metric. To the best of our knowledge, this is the first work that formulates the probe-request association problem as a generic resolution of conflicts and benchmarks the association concerning several datasets. Our results show that Bleach outperforms the state-of-the-art schemes in terms of accuracy (as high as 99%) and robustness to a wide range of input probe-request datasets.