Ad Hoc Networks最新文献

{"title":"Task offloading and resource allocation in cellular heterogeneous networks for NOMA-based mobile edge computing","authors":"Guowei Wu,&nbsp;Guifen Chen","doi":"10.1016/j.adhoc.2024.103742","DOIUrl":"10.1016/j.adhoc.2024.103742","url":null,"abstract":"<div><div>Mobile edge computing (MEC) is an effective strategy for real-time data processing and lowering data transmission latency between end devices and the cloud, which can minimize network congestion and improve user experience. This paper examines the problem of system energy consumption minimization in a cellular heterogeneous network (HetNets) MEC architecture with non-orthogonal multiple access (NOMA). The NOMA protocol is used by each base station to serve users within its small cell, and each base station is fitted with an edge computing server. Because of the parameter coupling, energy consumption minimization is a difficult non-convex optimization problem. As a result, the problem is divided into three subproblems, the computational and communication resource allocation subproblem and the task offloading subproblem being optimally solved by their convexity, while the subchannel allocation and power control subproblems are solved by sequential convex programming. Then, for each of the three subproblems, an efficient iterative technique is presented. Simulation results show that the partial offloading strategy under NOMA (PO-NOMA) proposed in this paper outperforms several baseline strategy, including all local computation, complete offloading, random offloading strategy, and greedy offloading strategy, and outperforms Orthogonal Multiple Access (OMA)-based MEC in cellular heterogeneous networks in a cellular heterogeneous network scenario.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"169 ","pages":"Article 103742"},"PeriodicalIF":4.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven cell-free scheduler","authors":"Yara Huleihel ,&nbsp;Gil Maman ,&nbsp;Zion Hadad ,&nbsp;Eli Shasha ,&nbsp;Haim H. Permuter","doi":"10.1016/j.adhoc.2024.103738","DOIUrl":"10.1016/j.adhoc.2024.103738","url":null,"abstract":"<div><div>Efficient scheduling is essential in cell-free (CF) networks, where user equipments (UEs) communicate with multiple distributed transceivers (radio units (RUs)) linked to a centralized base station (BS) that coordinates and processes the received or transmitted signals. Unlike traditional cellular networks, CF networks operate without cell boundaries, allowing UEs to seamlessly connect to multiple RUs, and thus eliminating the conventional necessity for handoffs between transceivers. In this paper, we introduce a novel CF scheduler designed to enhance data quality of service (QoS) parameters, including throughput, and latency. The scheduler employs a neural network (NN) algorithm to autonomously manage interactions with users across a distributed network of transceivers. This approach utilizes both model and data driven methods to optimize user communication. To mitigate the high computational complexity of traditional model-driven algorithms, we propose a supervised NN that learns from the model-driven approach. We assess its performance using simulated data from orthogonal frequency division multiple access (OFDMA) waveforms in frequency, time, space, and polarization (e.g., resource blocks, OFDM symbols, beam ID), within multi-transceiver RU environments. Our results indicate that the model-driven algorithms exhibit competitive performance compared to the exhaustive search method, while the supervised NN demonstrates comparable efficiency after offline learning. Consequently, our NN-based scheduler emerges as a viable, efficient solution for optimizing CF network scheduling.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"169 ","pages":"Article 103738"},"PeriodicalIF":4.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing real-time intrusion detection system for in-vehicle networks by employing novel feature engineering techniques and lightweight modeling","authors":"Wael Aljabri,&nbsp;Md. Abdul Hamid,&nbsp;Rayan Mosli","doi":"10.1016/j.adhoc.2024.103737","DOIUrl":"10.1016/j.adhoc.2024.103737","url":null,"abstract":"<div><div>Autonomous vehicles are built using a variety of electronic control units (ECUs) that communicate over a controller area network (CAN). A CAN enables the communication of data between ECUs to guarantee safety, assist drivers, and perform different functions. Nevertheless, a CAN lacks built-in security measures, which makes it susceptible to cyberattacks. A significant amount of existing research on intrusion detection systems (IDSs) is aimed at enhancing the security of a CAN by identifying and detecting unauthorized packet injections. However, the majority of machine/deep learning-based IDSs have difficulty sufficiently addressing latency. To address this issue, we propose a novel IDS framework that introduces two distinctive features. The first feature is the utility of data entropy, which is dynamically recalculated as new data arrives to capture unpredictable variations in the data payload. The second feature is an anomaly score, combining data entropy and time interval entropy to detect abnormal patterns in CAN communication. We validated the significance of these features using SHapley Additive exPlanations (SHAP) analysis. These features are integrated into a lightweight deep learning-based IDS model, specifically designed for resource-constrained environments. This integration significantly improves detection accuracy and operational efficiency. Our approach is validated using two well-known public datasets, car hacking: attack &amp; defense challenge and car-hacking datasets. It shows significant detection capabilities with accuracies of 0.9946 and 0.9995 and F1 scores of 0.9945 and 0.9995, respectively. Also, our IDS achieves an effectively low inference latency of only 0.17 milliseconds, surpassing the performance of existing machine/deep learning-based IDSs.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"169 ","pages":"Article 103737"},"PeriodicalIF":4.4,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent edge–fog interplay for healthcare informatics: A blockchain perspective","authors":"Nitin Rathore ,&nbsp;Rajesh Gupta ,&nbsp;Nihar Thakkar ,&nbsp;Keyaba Gohil ,&nbsp;Sudeep Tanwar ,&nbsp;Gagangeet Singh Aujla ,&nbsp;Fayez Alqahtani ,&nbsp;Amr Tolba","doi":"10.1016/j.adhoc.2024.103727","DOIUrl":"10.1016/j.adhoc.2024.103727","url":null,"abstract":"<div><div>This paper explores artificial intelligence (AI) and edge–fog interplay to strengthen healthcare informatics (HCI), while also considering the blockchain perspective for securing HCI to transform cloud-based HCI to edge–fog-based HCI to serve real-time responses for critical healthcare applications. This article discusses that AI is vital in providing better healthcare, precision medicine, and personalized treatments. A comprehensive review of edge–fog interplay-based HCI and blockchain-based HCI demonstrated the need for integrating blockchain and edge–fog interplay for successful HCI. Subsequently, some current research projects explore edge computing, fog computing, and blockchain in the healthcare sector to securely store and share patient data, thereby providing real-time data analysis, which is also highlighted. The latter part reflects some essential aspects of blockchain in healthcare, such as immutability, trustworthiness, and traceability. We also present a case study on AI-edge–fog interplay, and blockchain on heart stroke prediction for HCI that examines the practical application of the amalgamation of such technologies required to refine the healthcare sector to support the proposed analysis. Finally, this work discusses the future challenges of integrating AI, edge–fog interplay, and blockchain in the field of HCI.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"169 ","pages":"Article 103727"},"PeriodicalIF":4.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient multi-hop LoRa broadcasting with reinforcement learning for IoT networks","authors":"Xueshuo Chen,&nbsp;Yuxing Mao,&nbsp;Yihang Xu,&nbsp;Wenchao Yang,&nbsp;Chunxu Chen,&nbsp;Bozheng Lei","doi":"10.1016/j.adhoc.2024.103729","DOIUrl":"10.1016/j.adhoc.2024.103729","url":null,"abstract":"<div><div>Low power wide area networks (LPWAN) have grown significantly in popularity recently, and long-range (LoRa) technologies have drawn notice as a branch of LPWAN. Nevertheless, most current research primarily concentrates on optimizing communication protocols or mechanisms for the LoRa uplink. Considering the demand for large-scale data distribution in the IoT environment, we propose a novel mechanism for LoRa broadcasting with formula derivation and parameter analysis. This scheme adopts the advantages of both LoRa protocols and multi-hop technology that make the data quickly spread to all devices from the center of an area.This scheme optimizes transmission energy consumption by selecting proper relays to alleviate the problem of power shortage in LoRa devices. In this paper, we design an algorithm based on machine learning and reinforcement learning to reduce transmission costs for LoRa devices. The superiority of the proposed scheme in saving communication resources has been demonstrated compared to traditional methods. When broadcasting data downstream, it can save approximately 87.4% of the time. Moreover, through simulation analysis, the proposed algorithm can save at least 12.61% transmitting energy under constraints comparing with the benchmark algorithms.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"169 ","pages":"Article 103729"},"PeriodicalIF":4.4,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PopFL: A scalable Federated Learning model in serverless edge computing integrating with dynamic pop-up network","authors":"Neha Singh ,&nbsp;Mainak Adhikari","doi":"10.1016/j.adhoc.2024.103728","DOIUrl":"10.1016/j.adhoc.2024.103728","url":null,"abstract":"<div><div>With the rapid increase in the number of Internet-of-Things (IoT) devices, the massive volume of data creates significant challenges for traditional cloud-based solutions. These solutions often lead to high latency, increased operational costs, and limited scalability, making them unsuitable for real-time applications and resource-constrained environments. As a result, edge, and fog computing have emerged as viable alternatives, reducing latency and costs by processing data closer to its source. However, managing the flow of such vast and distributed data streams requires well-structured data pipelines to control the complete lifecycle—from data acquisition at the source to processing at the edge and fog layers, and finally storage and analytics in the cloud. To dynamically handle data analytics at varying distances from the source, often on heterogeneous hardware devices with collaborative learning techniques such as Federated Learning (FL). FL enables decentralized model training by leveraging the local data on Edge Devices (EDs), thereby preserving data privacy and reducing communication overhead with the cloud. However, FL faces critical challenges, including data heterogeneity, where the non-independent and identically distributed (non-IID) nature of data degrades model performance, and resource limitations on EDs, which lead to inefficiencies in training and biases in the aggregated models.</div><div>To address these issues, we propose a novel FL solution, called Pop-Up Federated Learning (PopFL) in edge networks. This solution introduces hierarchical aggregation to reduce network congestion by distributing the aggregation tasks across multiple Fog Servers (FSs), rather than relying solely on centralized cloud aggregation. To further enhance participation and resource utilization at the edge, we incorporate the Stackelberg game model, which incentivizes EDs based on their contribution and resource availability. Additionally, PopFL employs a pop-up ad-hoc network for scalable and efficient communication between EDs and FSs, ensuring robust data transmission in dynamic network conditions. Extensive experiments conducted on three diverse datasets highlight the superior performance of PopFL compared to state-of-the-art FL techniques. The results show significant improvements in model accuracy, robustness, and fairness across various scenarios, effectively addressing the challenges of data heterogeneity and resource limitations. Through these innovations, PopFL paves the way for more reliable and efficient distributed learning systems, unlocking the full potential of FL in real-world applications where low latency and scalable solutions are critical.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"169 ","pages":"Article 103728"},"PeriodicalIF":4.4,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hyper-heuristic optimization multi-task allocation in mobile crowdsensing based on inherent attributes","authors":"Heng Cao,&nbsp;Yantao Yu,&nbsp;Guojin Liu,&nbsp;Yucheng Wu","doi":"10.1016/j.adhoc.2024.103717","DOIUrl":"10.1016/j.adhoc.2024.103717","url":null,"abstract":"<div><div>Task allocation is a critical issue in mobile crowdsensing (MCS) that significantly impacts the overall sensing quality of the system. However, previous research has often focused on improving sensing quality through single indicators such as user coverage or user reliability, neglecting the inherent attributes of users and tasks as well as the variability in user abilities. This oversight can lead to unreliable sensing abilities among recruited users, thereby affecting the system’s overall sensing quality. In this paper, we first analyze the intrinsic attributes of users and tasks and propose an aggregative indicator and user enhancement model for better assessment and description of user sensing abilities. To improve the system’s overall sensing quality, the task allocation problem is modeled as a multi-constraint single-objective optimization problem. To address this problem, a Simulated Annealing-based Random Selection Hyper-Heuristic Optimization Algorithm (SARSHHOA) has been developed. This algorithm begins by generating an initial allocation scheme using a greedy approach, then applies randomly selected search operators to various allocation schemes and utilizes simulated annealing to selectively accept solutions. Finally, the effectiveness of the proposed aggregative indicator and task allocation algorithm is validated through simulation experiments on real datasets.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"168 ","pages":"Article 103717"},"PeriodicalIF":4.4,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A self-contained emulator for the forensic examination of IoE scenarios","authors":"Sergio Ruiz-Villafranca ,&nbsp;Juan Manuel Castelo Gómez ,&nbsp;Javier Carrillo-Mondéjar ,&nbsp;José Roldán-Gómez ,&nbsp;José Luis Martínez Martínez","doi":"10.1016/j.adhoc.2024.103718","DOIUrl":"10.1016/j.adhoc.2024.103718","url":null,"abstract":"<div><div>With the number of cyber incidents on the Internet of Everything (IoE) increasing every year, so does the amount of forensic investigations that are carried out in this environment. As the research community is avidly working on the development of solutions that can assist in the examination process, it is crucial to, firstly, have access to a resource that can facilitate the process of learning the characteristics of these investigations, and, secondly, to have a testbed that allows evaluating the effectiveness and feasibility of new solutions. Likewise, from an educational standpoint, having access to assets that allow interacting with these devices in a simple and efficient way can lead to learners getting a better understanding of the forensic characteristics and requirements of this environment. In view of this, a self-contained emulator for the forensic examination of these scenarios is presented in this article that mirrors their static and dynamic by emulating both the firmware of the devices that comprise them and the multiple network protocols used in them. Additionally, the emulator offers the capability to deploy digital twins within IoE scenarios, enhancing its utility for cybersecurity forensic investigations and training sets. To demonstrate its feasibility and convenience, two case studies are presented that emulate different IoE forensic contexts, showing that the proposal is capable of emulating their static and dynamic behaviour, and that it can be used to perform different forensic tasks.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"168 ","pages":"Article 103718"},"PeriodicalIF":4.4,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance evaluation for Q-learning based anycast routing protocol in unmanned aerial vehicle networks with multiple base stations","authors":"Yuhong Xiang,&nbsp;Shuai Gao,&nbsp;Hongchao Wang,&nbsp;Dong Yang,&nbsp;Yuming Zhang,&nbsp;Hongke Zhang","doi":"10.1016/j.adhoc.2024.103719","DOIUrl":"10.1016/j.adhoc.2024.103719","url":null,"abstract":"<div><div>Unmanned Aerial Vehicle (UAV) networks can be used for data transmission in emergency scenarios, relaying data from ground users to base stations (BSs). While UAV networks collaborating with multi-BSs can significantly enhance performance, existing UAV routing protocols predominantly focus on unicast routing and often neglect critical aspects such as base station discovery. In addition, the high mobility of UAVs and rapid changes in network topology also pose great challenges for existing multi-BS routing protocols to maintain efficient data transmission. Aiming at the above problems, this paper abstracts the routing of multi-base station UAV networks as anycast routing for dynamic networks and proposes a distributed anycast routing protocol called QARP to improve the data transmission performance. In QARP, base stations can be discovered automatically and parameters of Q-learning are dynamically adjusted to improve the efficiency of data transmission. The Link Duration Estimation is used to influence routing decision and dynamically adjust the hello message interval. A multiple base stations transmission value function is designed to indicate the performance of data transmission and is used to calculate the reward and update Q-table. The experimental results show that the QARP proposed in this paper outperforms existing multi-BS routing and Q-learning based routing protocols in terms of delay, packet delivery ratio and throughput in single base station and multiple base stations scenarios.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"168 ","pages":"Article 103719"},"PeriodicalIF":4.4,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliable and cost-efficient session provisioning in CRNs using spectrum sensing as a service","authors":"Hisham M. Almasaeid","doi":"10.1016/j.adhoc.2024.103716","DOIUrl":"10.1016/j.adhoc.2024.103716","url":null,"abstract":"<div><div>With the advancement of wireless communication technologies, and the growing number of wireless and IoT applications that demand various types and volumes of data, Sensing as a Service (SaaS) has emerged as a necessary enabling business model for many of those applications. Spectrum Sensing as a Service (SSaaS) has also emerged as a form of SaaS that is concerned with the monitoring of wireless spectrum to facilitate its safe reuse by cognitive radio-enabled wireless users. SSaaS was primarily motivated by the need for a low-cost, accurate, and reliable spectrum sensing service to support a plethora of heterogeneous wireless devices and applications. Under the SSaaS model, clients need to pay the service provider for the sensing service they receive. In this paper, we address the problem of allocating spectrum channels to links of a given communication session in a cognitive radio network (CRN) that utilizes SSaaS. The objective is to allocate channels such that the worst link availability among the session is maximized and the spectrum access cost is minimized. A number of multi-objective evolutionary optimization algorithms (MOEAs) were used to solve this multi-objective optimization problem. Extensive experimentation was conducted to compare between these algorithms and identify the best ones to use. We also propose a post-processing greedy algorithm to further enhance the solution obtained by a MOEA algorithm. Results show that an improvement of up to 20% can be achieved using the proposed greedy algorithm under some network settings.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"168 ","pages":"Article 103716"},"PeriodicalIF":4.4,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}