Ad Hoc Networks最新文献_第6页

Computational Offloading and Resource Allocation for IoT applications using Decision Tree based Reinforcement Learning

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-01-18 DOI: 10.1016/j.adhoc.2024.103751

Guneet Kaur Walia, Mohit Kumar

{"title":"Computational Offloading and Resource Allocation for IoT applications using Decision Tree based Reinforcement Learning","authors":"Guneet Kaur Walia, Mohit Kumar","doi":"10.1016/j.adhoc.2024.103751","DOIUrl":"10.1016/j.adhoc.2024.103751","url":null,"abstract":"<div><div>The pervasive penetration of IoT devices in various domains such as autonomous vehicles, supply chain management, video surveillance, healthcare, industrial automation etc. necessitates for advanced computing paradigms to achieve real time response delivery. Edge computing offers prompt service response via its competent decentralized platform for catering disseminate workload, hence serving as front-runner for competently handling a wide spectrum of IoT applications. However, optimal distribution of workload in the form of incoming tasks to appropriate destinations remains a challenging issue due to multiple factors such as dynamic offloading decision, optimal resource allocation, heterogeneity of devices, unbalanced workload etc in collaborative Cloud-Edge layered architecture. Employing advanced Artificial Intelligence (AI)-based techniques, provides promising solutions to address the complex task assignment problem. However, existing solutions encounter significant challenges, including prolonged convergence time, extended learning periods for agents and inability to adapt to a stochastic environment. Hence, our work aims to design a unified framework for performing computational offloading and resource allocation in diverse IoT applications using Decision Tree Empowered Reinforcement Learning (DTRL) technique. The proposed work formulates the optimization problem for offloading decisions at runtime and allocates the optimal resources for incoming tasks to improve the Quality-of-Service parameters (QoS). The computational results conducted over a simulation environment proved that the proposed approach has the high convergence ability, exploration and exploitation capability and outperforms the existing state-of-the-art approaches in terms of delay, energy consumption, waiting time, task acceptance ratio and service cost.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103751"},"PeriodicalIF":4.4,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133752","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}

引用次数: 0

Anchor nodes selection and placement strategy for node positioning in wireless sensor networks

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-01-18 DOI: 10.1016/j.adhoc.2025.103766

Xiaobo Gu , Jiale Liu , Yuan Liu , Yuan Chi

引用次数: 0

A hybrid and efficient Federated Learning for privacy preservation in IoT devices

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-01-15 DOI: 10.1016/j.adhoc.2025.103761

Shaohua Cao, Shangru Liu, Yansheng Yang, Wenjie Du, Zijun Zhan, Danxin Wang, Weishan Zhang

{"title":"A hybrid and efficient Federated Learning for privacy preservation in IoT devices","authors":"Shaohua Cao, Shangru Liu, Yansheng Yang, Wenjie Du, Zijun Zhan, Danxin Wang, Weishan Zhang","doi":"10.1016/j.adhoc.2025.103761","DOIUrl":"10.1016/j.adhoc.2025.103761","url":null,"abstract":"<div><div>Federated learning (FL) allows multiple participants to collaborate to train a machine learning model while ensuring that the data remain local. This approach has seen extensive application in the Internet of Things (IoT). Compared to traditional centralized training methods, FL indeed protects the raw data, but it is difficult to defend against inference attacks and other data reconstruction methods. To address this issue, existing research has introduced a variety of cryptographic techniques, mainly encompassing secure multi-party Computation (SMC), homomorphic encryption (HE), and differential privacy (DP). However, approaches reliant on HE and SMC do not provide sufficient protection for the model data itself and often lead to significant communication and computation overhead; exclusively employing DP necessitates the incorporation of substantial noise, which harms model performance. In this paper, we propose an efficient and privacy-preserving dual-key black-box aggregation method that uses Paillier threshold homomorphic encryption (TPHE), which ensures the protection of the model parameters during the transmission and aggregation phases via a two-step decryption process. To defend various data reconstruction attacks, we also achieve a node-level DP to effectively eliminate the possibility of recovering raw data from the aggregated parameters. Through experiments on MNIST, CIFAR-10, and SVHN, we have shown that our method has up to a 11% smaller reduction in model accuracy compared to other schemes. Furthermore, compared to SMC-based FL schemes, our scheme significantly reduces communication overhead from 60% to 80%, depending on the number of participating nodes. We also conduct comparative experiments on the defense against GAN attacks and membership inference attacks, proving that our method provides effective protection for data privacy.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103761"},"PeriodicalIF":4.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133753","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}

引用次数: 0

BFL-SC: A blockchain-enabled federated learning framework, with smart contracts, for securing social media-integrated internet of things systems

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-01-14 DOI: 10.1016/j.adhoc.2025.103760

Sara Salim, Nour Moustafa, Benjamin Turnbull

{"title":"BFL-SC: A blockchain-enabled federated learning framework, with smart contracts, for securing social media-integrated internet of things systems","authors":"Sara Salim, Nour Moustafa, Benjamin Turnbull","doi":"10.1016/j.adhoc.2025.103760","DOIUrl":"10.1016/j.adhoc.2025.103760","url":null,"abstract":"<div><div>The integration of Social Media (SM) and the Internet of Things (IoT) is gradually transforming the activities of SM users into valuable data streams that can be analyzed using Machine Learning (ML) algorithms. Federated Learning (FL) has been widely employed to predict user and anomaly behaviors from distributed systems. However, FL encounters substantial security challenges, particularly within the context of SM-integrated IoT systems, known as SM 3.0 systems. These challenges encompass issues of accountability and vulnerabilities that render them susceptible to various cyberattacks, including single-point-of-failure, free-riding, model inversion, and poisoning attacks. We propose a Blockchain-enabled FL with Smart Contracts (SC) (BFL-SC) framework. To coordinate the learning process, track participants’ contributions and reward the participants transparently, an SC-based FL is constructed as an incentive mechanism that combats free-riding attacks and enables automated and auditable rewarding of the participants. Also, to conceal the original data points and mitigate the impact of model inversion attacks, a Differentially Privacy-based Perturbation (DPP) mechanism is proposed. To address potential poisoning attacks, a thorough verification protocol is suggested. The experimental results obtained from two datasets, namely SM 3.0 and Human Activity Recognition (HAR), show that the BFL-SC framework can achieve high utility with a precision of 96.95% over the SM 3.0 dataset and 90.14% over the HAR dataset while adhering to privacy and efficiency standards, compared with compelling techniques.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"169 ","pages":"Article 103760"},"PeriodicalIF":4.4,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137696","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}

引用次数: 0

Multi-user motion state task offloading strategy for load balancing in mobile edge computing networks

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-01-11 DOI: 10.1016/j.adhoc.2025.103759

Shanchen Pang, Yuanzhao Cheng, Xiao He, Yanxiang Zhang

{"title":"Multi-user motion state task offloading strategy for load balancing in mobile edge computing networks","authors":"Shanchen Pang, Yuanzhao Cheng, Xiao He, Yanxiang Zhang","doi":"10.1016/j.adhoc.2025.103759","DOIUrl":"10.1016/j.adhoc.2025.103759","url":null,"abstract":"<div><div>In mobile edge computing (MEC) networks, users can offload computational tasks from their devices to nearby mobile edge servers, reducing their computational loads and improving user experience quality. However, users exhibit various movement patterns with inherent random mobility in practice. Additionally, data that needs processing arrives randomly over continuous periods. To stabilize data and energy consumption in complex real-world environments and maximize the network system’s data processing capacity, we propose a User Trajectory Prediction-Lyapunov-guided Deep Reinforcement Learning (UTP-LyDRL) algorithm. This algorithm first predicts the movement trajectories of mobile users (MUs) using a Mobility-aware Offloading (MO) mechanism. It then formulates the problem of both MUs and fixed users (FUs) as a Mixed Integer Nonlinear Programming (MINLP) problem. Through Lyapunov optimization, the multi-stage MINLP problem is decomposed into deterministic MINLP sub-problems for each time frame, ensuring long-term constraint satisfaction. Subsequently, combining model-free training with DRL, the algorithm addresses the binary offloading of FUs across sequential time frames and overall system resource allocation. Simulation results indicate that the proposed UTP-LyDRL algorithm optimizes computational performance and ensures the stability of all data and energy queues within the system.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"169 ","pages":"Article 103759"},"PeriodicalIF":4.4,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137656","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}

引用次数: 0

Energy-efficient clustering and path planning for UAV-assisted D2D cellular networks

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-01-11 DOI: 10.1016/j.adhoc.2025.103757

Kanhu Charan Gouda, Rahul Thakur

{"title":"Energy-efficient clustering and path planning for UAV-assisted D2D cellular networks","authors":"Kanhu Charan Gouda, Rahul Thakur","doi":"10.1016/j.adhoc.2025.103757","DOIUrl":"10.1016/j.adhoc.2025.103757","url":null,"abstract":"<div><div>The integration of Device-to-Device (D2D) communication and Unmanned Aerial Vehicles (UAVs) into advanced cellular networks is essential for effectively addressing the growing data demands. However, long-range communication in cellular and D2D networks typically requires higher transmission power, leading to increased energy consumption and reduced energy efficiency. To address this, we propose an innovative technique that combines hypergraph-based clustering with UAV path planning to minimize energy consumption in UAV-assisted D2D cellular networks. Our technique utilizes hypergraph theory to group UEs into clusters based on proximity and communication needs. The Particle Swarm Optimization (PSO) algorithm is employed to select a central User Equipment (UE) in each cluster, considering factors such as distance, residual energy, and degree centrality. Once the central UEs are chosen, the UAV’s path is optimized using the Ant Colony System (ACS) algorithm, addressing the Generalized Traveling Salesman Problem (GTSP) to minimize travel distance and energy consumption. We also analyze the computational complexity of the proposed technique, demonstrating its efficiency over existing techniques. Simulation results show significant improvements in system throughput, energy consumption, energy efficiency, and UAV path length.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103757"},"PeriodicalIF":4.4,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133754","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}

引用次数: 0

Multi-sensor system deployment planning method for underwater surveillance based on formation characteristics

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-01-11 DOI: 10.1016/j.adhoc.2025.103763

Zheping Yan , Sijia Cai , Shuping Hou , Mingyao Zhang

{"title":"Multi-sensor system deployment planning method for underwater surveillance based on formation characteristics","authors":"Zheping Yan , Sijia Cai , Shuping Hou , Mingyao Zhang","doi":"10.1016/j.adhoc.2025.103763","DOIUrl":"10.1016/j.adhoc.2025.103763","url":null,"abstract":"<div><div>The deployment planning issue for a multi-sensor system comprising a limited number of sensors designed to detect underwater intrusion targets is defined as a multi-objective NP-hard problem. This problem is constituted by two competing and incommensurable optimization objectives: \"larger sensor coverage\" and \"higher probability of detecting intrusion targets\". The map of the mission area is transformed into a topological map through the application of polygon fitting and segmentation based on Delaunay triangulation. This study employs a characteristics-based non-dominated sorting genetic algorithm (CBNSGA) to address the deployment planning issue of the multi-sensor system. In this algorithm, Mean-Shift clustering is employed to yield characteristics information through the clustering of the multi-sensor system formation. Subsequently, this information is employed to enhance the crossover, mutation, and selection strategies. Adaptive parameters are designed to accelerate convergence and avoid local optima. Additionally, the Cauchy inverse cumulative distribution operator is employed to enhance the mutation step. The feasibility and effectiveness of the CBNSGA in multi-sensor system deployment planning are demonstrated through simulation and comparison with other algorithms.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103763"},"PeriodicalIF":4.4,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133829","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}

引用次数: 0

Designing Deep Reinforcement Learning enhanced edge-terminal collaborative AIoT for Intelligent Visitor Management System

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-01-09 DOI: 10.1016/j.adhoc.2025.103756

Yong Liao, Zhiyuan Zhu, Tong Tang, Dapeng Wu, Ruyan Wang

{"title":"Designing Deep Reinforcement Learning enhanced edge-terminal collaborative AIoT for Intelligent Visitor Management System","authors":"Yong Liao, Zhiyuan Zhu, Tong Tang, Dapeng Wu, Ruyan Wang","doi":"10.1016/j.adhoc.2025.103756","DOIUrl":"10.1016/j.adhoc.2025.103756","url":null,"abstract":"<div><div>Intelligent Visitor Management System (IVMS) is crucial for enhancing security and operational efficiency in smart factories and intelligent office buildings. Leveraging AIoT-driven image analysis will facilitate real-time visitor authentication and access control. However, the growing volume of interactions and the limited processing power of local terminals complicate the delivery of timely and accurate image analysis. To address these challenges, we propose an edge-terminal collaborative AIoT framework for real-time visitor management. The framework solves the limitations of traditional approaches, where local terminals are unable to handle the computational load and edge solutions experience high latency due to transmission delays. Specifically, it integrates three key components to improve system performance: a local analysis module for initial processing, an image communication module for efficient data transmission, and an edge analysis module for advanced processing. Moreover, the framework jointly optimizes image task offloading, wireless channel allocation, and image compression, all formulated as an optimization problem to ensure fast and accurate analysis. Additionally, a novel multi-level Deep Reinforcement Learning (DRL) method is further designed to dynamically refine the selection of compression and offloading strategies. By learning in real-time, the DRL model adapts to network variations, addressing the scalability and adaptability limitations of existing methods. Simulation results show that our proposed edge-terminal collaborative AIoT framework significantly outperforms both edge-only and terminal-only methods in terms of latency and accuracy.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"169 ","pages":"Article 103756"},"PeriodicalIF":4.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137655","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}

引用次数: 0

TemsRoute: A temporally and socially aware routing framework for delay-tolerant networks

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-01-09 DOI: 10.1016/j.adhoc.2025.103755

Tianming Zhang , Renbo Zhang , Zhengyi Yang , Lu Chen , Yunjun Gao , Xiaochun Yang

{"title":"TemsRoute: A temporally and socially aware routing framework for delay-tolerant networks","authors":"Tianming Zhang , Renbo Zhang , Zhengyi Yang , Lu Chen , Yunjun Gao , Xiaochun Yang","doi":"10.1016/j.adhoc.2025.103755","DOIUrl":"10.1016/j.adhoc.2025.103755","url":null,"abstract":"<div><div>In the realm of delay-tolerant networks (DTNs), designing a routing strategy that optimizes relay vertex selection for faster message dissemination and lower network overhead is an important and challenging topic. DTNs inherently exhibit temporal variations, characterized by mobility and intermittent connectivity. In addressing this, in the paper, we model DTNs as temporal networks and propose a temporally and socially aware routing framework, called TemsRoute, which takes both the temporal betweenness centrality and the social information into consideration to intelligently identify optimal relay vertices. Within the TemsRoute, we devise exact and approximate heuristic sorting-based label propagation methods, together with two pruning lemmas, to efficiently compute the temporal betweenness centrality. We also introduce four metrics to calculate social relevance between pairs of vertices. Additionally, we explore how to incrementally update the approximate temporal betweenness centrality within the context of temporal graph streams. Extensive experiments conducted on real-world DTNs underscore the superior performance of TemsRoute. It achieves the highest message delivery rate and the lowest message average delay when compared to six other routing methods. This underscores the potential of TemsRoute to improve message dissemination efficiency in certain dynamic and challenging DTN scenarios, particularly those involving sporadic connectivity, frequent topology changes, and limited resources.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"169 ","pages":"Article 103755"},"PeriodicalIF":4.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137697","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}

引用次数: 0

A Hybrid Charging Scheme for Non-deterministic Mobile Nodes aimed at minimizing the number of dead nodes

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-01-09 DOI: 10.1016/j.adhoc.2025.103758

Shuqi Tang, Yue Chen, Tingdan Deng, Feng Lin

{"title":"A Hybrid Charging Scheme for Non-deterministic Mobile Nodes aimed at minimizing the number of dead nodes","authors":"Shuqi Tang, Yue Chen, Tingdan Deng, Feng Lin","doi":"10.1016/j.adhoc.2025.103758","DOIUrl":"10.1016/j.adhoc.2025.103758","url":null,"abstract":"<div><div>Wireless Rechargeable Sensor Networks (WRSNs) are able to solve the energy scarcity issue in wireless sensor networks by employing static chargers or mobile chargers to recharge sensor nodes. Charging static nodes or mobile nodes with certain mobility patterns has been the primary focus of most WRSN research. However, non-deterministic mobile nodes whose mobility patterns are unknown or whose movement cannot be controlled have received limited attention. Due to the uncertainties of nodes’ movement, the chargers are unable to determine the locations where they charge the nodes, which makes charging non-deterministic mobile nodes a uniquely challenging problem. In this work, aiming at minimizing the number of dead nodes, we explore the hybrid charging problem for non-deterministic mobile nodes, i.e., the problem of charging non-deterministic mobile nodes by static chargers and mobile chargers simultaneously. We deduce the problem into two sub-problems: the static charger deployment problem and the mobile charger scheduling problem, and present their definitions. Then, we propose a Hybrid Charging Scheme for Non-deterministic Mobile Nodes (HCSNMN) that consists of two sub-algorithms, named Hybrid Charging-Static Charger Deployment Algorithm (HC-SCDA) and Hybrid Charging-MC Scheduling Algorithm (HC-MCSA), to address these two sub-problems, respectively. Simulation results demonstrate that the proposed HCSNMN consistently outperforms all the existing charging schemes in node death ratio by 3%–13%.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"169 ","pages":"Article 103758"},"PeriodicalIF":4.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143138179","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}

引用次数: 0