Ad Hoc Networks最新文献

{"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}

{"title":"Analysis of the computational costs of an evolutionary fuzzy rule-based internet-of-things energy management approach","authors":"M. Mikus ,&nbsp;Ja. Konecny ,&nbsp;P. Krömer ,&nbsp;K. Bancik ,&nbsp;Ji. Konecny ,&nbsp;J. Choutka ,&nbsp;M. Prauzek","doi":"10.1016/j.adhoc.2024.103715","DOIUrl":"10.1016/j.adhoc.2024.103715","url":null,"abstract":"<div><div>This study presents an in-depth analysis of the computational costs associated with the application of an Evolutionary Fuzzy Rule-based (EFR) energy management system for Internet of Things (IoT) devices. In energy-harvesting IoT nodes, energy management is critical for sustaining long-term operation. The proposed EFR approach integrates fuzzy logic and genetic programming to autonomously control energy consumption based on available resources. The study evaluates the system’s computational performance, particularly focusing on processing time, RAM and flash memory usage across various hardware configurations. Different compiler optimization levels and floating-point unit (FPU) settings were also explored, comparing standard and pre-compiled algorithms. The results reveal computational times ranging from 2.43 to 5.23 ms, RAM usage peaking at 6.23 kB, and flash memory consumption between 19 kB and 32 kB. A significant reduction in computational overhead is achieved with optimized compiler settings and hardware FPU, highlighting the feasibility of deploying EFR-based energy management systems in low-power, resource-constrained IoT environments. The findings demonstrate the trade-offs between computational efficiency and energy management, with particular benefits observed in scenarios requiring real-time control in remote and energy-limited environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"168 ","pages":"Article 103715"},"PeriodicalIF":4.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient slicing scheme and cache optimization strategy for structured dependent tasks in intelligent transportation scenarios","authors":"Zhu Sifeng ,&nbsp;Song Zhaowei ,&nbsp;Zhu Hai ,&nbsp;Qiao Rui","doi":"10.1016/j.adhoc.2024.103699","DOIUrl":"10.1016/j.adhoc.2024.103699","url":null,"abstract":"<div><div>The challenges posed by structured large-scale tasks to resource-sensitive intelligent transportation systems have been acknowledged, particularly regarding the need to reduce delay and energy consumption during the caching and offloading processes. To address these challenges and improve the quality of service for vehicular users, a cloud–edge-end collaboration caching strategy (CACCSC) based on structured task content awareness was proposed in this paper. The dependencies among task fragments were modeled through fuzzy judgment criteria. In addition, a system delay model, an energy consumption model, and an edge server load balancing model were developed, along with a multi-objective optimization model that integrates system delay, energy consumption, and edge server load balancing variance. To solve this multi-objective optimization problem, an adaptive multi-objective optimization algorithm (MDE-NSGA-III) was developed, which combines an enhanced version of the Differential Evolution algorithm with improvements to the NSGA-III algorithm. Finally, it has been demonstrated through simulation experiments that when the number of users in the system reaches 35, the system delay, energy consumption, and load balancing variance of the MDE-NSGA-III optimization scheme proposed in this paper are 6.1%, 6.6%, and 25% lower than those of the NSGA-III scheme, 15.8%, 10%, and 41.7% lower than those of the NSGA-II scheme, and 62.7%, 20.7%, and 8.3% lower than those of the PeEA scheme.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"168 ","pages":"Article 103699"},"PeriodicalIF":4.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719625","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":"Impact of urban environments on FANET communication: A comparative study of propagation models","authors":"Henok Gashaw ,&nbsp;Jamie Wubben ,&nbsp;Carlos T. Calafate ,&nbsp;Fabrizio Granelli","doi":"10.1016/j.adhoc.2024.103695","DOIUrl":"10.1016/j.adhoc.2024.103695","url":null,"abstract":"<div><div>The steady rise in the use of unmanned aerial vehicles (UAVs) is leading to the development of an ever-growing number of applications. In urban settings, efforts like the U-Space initiative in Europe are striving to standardize and regulate the operations of UAVs. To support these applications and further UAV research, it is essential to thoroughly understand UAV communication, both among and between UAVs. Nonetheless, we have identified a lack of studies on communication models, especially in urban areas where obstacles like tall buildings can disrupt communication. This study offers a comprehensive review of current measurement campaigns on channel models for aerial communication. In addition, we conducted experiments on (i) the separation distance between two UAVs, (ii) Multi-UAV communication and (iii) Multi-UAV to ground communication using three different city profiles in Spain (Valencia, Barcelona, and Madrid). To accomplish this, we utilized an advanced co-simulation framework that accurately models both UAV mobility (Ardusim) and communication (OMNeT++). Our results regarding UAV-to-UAV communication in a city environment indicate that: (i) the communication range, in our specific experiments, is limited to around 400 meters. Afterward, the Packet Delivery Ratio (PDR) declines significantly. (ii) Different communication models yield similar results. (iii) UAV-to-UAV communication becomes feasible at higher altitudes (e.g., 120 m), particularly in the presence of tall buildings. With respect to the Multi-UAV to ground communications, we can conclude that again, the altitude of the UAVs is paramount. Furthermore, increasing the number of UAVs providing service to the ground does increase the PDR, but only ever so slightly.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"168 ","pages":"Article 103695"},"PeriodicalIF":4.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747819","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}