Automated Guided Vehicles (AGVs) are integral to modern industrial automation, streamlining material handling and logistics operations. However, the increasing computational demands of AGVs, including real-time navigation, decision-making, and task scheduling, necessitate efficient offloading mechanisms. Vehicular Fog Networks (VFNs), a specialized form of fog computing, offer a promising solution by leveraging distributed computing resources at the network edge. Despite their advantages, VFNs introduce challenges such as fluctuating connectivity, variable node availability, and unpredictable computational loads, making traditional offloading approaches inadequate. This paper proposes a novel optimization framework that integrates profit maximization and resource-aware task offloading for AGVs in VFNs. The framework incorporates a dynamic fee-charging model that adjusts service charges based on user-experienced Quality of Service (QoS), incentivizing service providers to enhance performance. Additionally, an Integer Linear Programming (ILP) model is developed to balance profit optimization with delay-aware QoS constraints. To ensure scalability, resource expansion strategies are introduced, dynamically allocating computing resources based on real-time task demands. Extensive performance evaluations demonstrate the effectiveness of the proposed framework in optimizing task offloading for AGVs in VFN-based Industrial Internet of Things (IIoT) environments. Results indicate significant improvements in service profitability, resource utilization, and delay-sensitive task execution. By addressing key IoT challenges in AGV-driven IIoT systems, this study contributes to advancing intelligent resource allocation strategies in fog computing and IoT ecosystems. The proposed approach enhances AGV efficiency and service quality, reinforcing the role of IoT-enabled VFNs in next-generation industrial automation.

This article introduces an innovative decision-making framework for optimizing task offloading in cloud radio access networks (CRANs), specifically designed to overcome the constraints of battery power and computing resources in Internet of Things (IoT) devices. While traditional optimization methods have made notable advancements in addressing offloading challenges, their effectiveness is often compromised by fluctuating network conditions and uncertainties. Recent machine learning approaches offer adaptive solutions; however, they demand extensive real-time data, which can impede efficiency and result in local optima. To address these issues, we propose a hybrid framework that synergizes optimization techniques with case-based reasoning (CBR). Initially, we establish a comprehensive decision database through offline optimization to secure global optimal solutions for task offloading. This database serves as a critical resource for the CBR method, which selects the most pertinent decision script based on the current network state, thereby facilitating effective real-time task offloading decisions. Our simulation results demonstrate that this framework not only significantly enhances decision-making efficiency but also consistently yields near-optimal offloading strategies. This improvement directly contributes to the performance of IoT systems operating within CRANs, showcasing the potential of our approach to elevate resource management in complex network environments. By integrating optimization with adaptive reasoning, this work advances state-of-the-art IoT task-offloading solutions.

The rapid proliferation of mobile applications and data-intensive services, such as augmented reality and real-time analytics, necessitates efficient content delivery mechanisms in Mobile Edge Computing (MEC) environments. MEC enhances service responsiveness by caching content closer to end users; however, the constrained storage capacities of edge servers pose challenges in maintaining optimal Quality of Service (QoS). This paper presents a novel service-oriented content caching framework that optimizes resource allocation and revenue generation while ensuring QoS compliance. We introduce a dynamic fee-based pricing model that adapts service charges based on content retrieval latency, incentivizing improved service performance. The caching optimization problem is formulated as an Integer Linear Programming (ILP) model, and a computationally efficient approximation algorithm leveraging Linear Programming (LP) relaxation and rounding techniques is proposed to derive near-optimal solutions. Additionally, a resource expansion model is integrated to dynamically extend storage capacity in response to evolving service demands, ensuring scalable and cost-effective content provisioning. Extensive theoretical analysis and simulations validate the proposed approach, demonstrating a 22.4% increase in total service revenue and a 31.7% reduction in average content access delay compared to baseline strategies. This work contributes to services computing by providing a mathematically rigorous and computationally efficient framework for dynamic content management in edge networks.

With the continuous development of the communication service industry, users pay more attention to the quality of network service. Previous studies on offloading problems, especially in the Cloud Radio Access Network (C-RAN) architecture with Mobile Edge Computing (MEC), are primarily focused on the economic perspective, with little consideration given to user-oriented satisfaction problems. To fill this gap, this article proposes a mathematical model for maximizing user satisfaction in the C-RAN architecture with multi-layer MEC. The problem is divided into two stages for solution. The first stage addresses the optimal connection problem between users and Remote Radio Heads (RRHs). The second stage then schedules user tasks reasonably based on the solution obtained in the first stage. The two-stage problems are all proved to be NP-Hard. Two efficient approximation algorithms, namely User-to-RRH Association Algorithm (URAA) and Maximum Satisfaction Algorithm (MSA), are proposed to solve the problems in different stages. This article proves and analyzes the theoretical performance of the two algorithms. Finally, the performance of the proposed algorithms is verified by simulation experiments. The experimental results demonstrate that the two proposed algorithms can achieve reasonable solutions to the problems, and the user satisfaction level can be maintained at a high level.

In Cloud Radio Access Networks (C-RANs), the performance of real-time Internet of Things (IoT) content retrieval is improved by placing/requesting the contents into/from remote radio heads (RRHs) and baseband units (BBUs). In the previous studies for the problem in jointly placing user contents and allocating storage allocation of RRHs and BBUs, a strategy of minimizing system resource consumption or transmission delay was used. In this article, we adopt a distinct strategy to maximize the profit of content retrieval services under the constraints of meeting the real-time requirements of users and the limited system resources in C-RANs. The problem is formulated as integer linear programming (ILP). Then, an algorithm for solving the ILP is proposed, and it can provide an approximate solution close to the optimal one with a bounded factor. In the simulation conducted, the results verified the above claims. Further, another algorithm is proposed to effectively expand the storage budgets in C-RANs. By controlling a bounded factor, not only the system performance is guaranteed, but also the upper limit of the expanded storage budget is provided. The smaller the bounding factor, the stricter the performance guarantee, but the expected storage budget will increase.

In cloud radio access networks (C-RANs), a local controller controls a set of lightweight remote radio heads (RRHs) for connecting users nearby, and creates virtual machines (VMs) for executing user tasks. In view of previous works, the strategies of minimizing system resource consumption or transmission delay are adopted in the issue of joint task scheduling and resource allocation. Different from them, this work puts forward the strategy of maximizing the profit of the joint issue. The problem is formulated as integer linear programming (ILP), and then an algorithm with bounded approximation ratio for solving the ILP is proposed. The simulation results show that the solution of the algorithm is very close to the optimal one of the ILP. In addition, another algorithm is proposed to control the tradeoff between performance and robustness of the solution under the assumption that the system resource can be expanded by a bounded factor.

To leverage the scalability of Media-on-Demand (MoD) Peer to Peer (P2P) streaming services, it's a critical challenge to propose a segment scheduling algorithm for ensuring that a packet must be successfully received before its decoding deadline for the packet to contribute to the reconstructed media quality, and efficiently making use of limited network bandwidth. Most of previous works adopt a greedy approach for maximizing the received number of segments by making full use of the available network bandwidth to make that a peer can receive its requested segments as soon as possible. However, the greedy approach will cause the traffic burst on the network and adversely decrease the network capacity. They also suffer from needing larger disk memory for storing the received segments which are not played yet. To address the two issues for efficiently using the limited network bandwidth and saving disk memory, this paper proposes a distinct segment scheduling algorithm by scheduling the segments evenly transmitted into the network according to the playback rate of the MoD streaming service. The proposed algorithm only schedules an enough amount of segments for satisfying the playback rate of the requested streaming service. Further, to disseminate segments quickly, the rare segments available at few peers will be also scheduled.

SUMMARY In two‐tier high‐performance networks (HPNs), some facilities are constructed to form a powerful supercomputing environment, and to alleviate server load. Then, the applications are provided by them in co‐operated, parallel and distributed manners. A proper way to select facilities is crucial to the performance of two‐tier HPNs The problem of selecting facilities can be regarded as a kind of the facility location problem, which is to determine an optimal subset of facilities that will be open to serve users. The traditional facility location problem aims to minimize the incurred costs between the users/servers and their assigned facilities. In two‐tier HPNs, the incurred costs can be regarded as the transmission costs, e.g. transmission latency, bandwidth overhead. We observe that most of the packets are transmitted among the facilities for application servicing and framework maintaining. In this paper, we address the problem of selecting facilities in two‐tier HPNs by minimizing the transmission costs from servers to users by passing through the selected facilities. Our problem is different from the traditional facility location problem, which only considers the transmission costs between the users/servers and their assigned facilities. In our problem, the transmission costs between the selected facilities are further considered. The problem is formulated as a 0/1 integer non‐linear programming (0/1 INLP) and 0/1 integer linear programming (0/1 ILP). Further, a simple heuristic algorithm is proposed for obtaining a feasible solution when the network sizes increase, since solving INLPs and ILPs for large‐scale problems takes long time. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Previous quality‐of‐service (QoS) routing protocols in mobile ad hoc networks (MANETs) determined bandwidth‐satisfied routes for QoS applications. Since the multi‐rate enhancements have been implemented in MANETs, QoS routing protocols should be adapted to exploit them fully. However, existing works suffer from one bandwidth‐violation problem, named the hidden route problem (HRP), which may arise when a new flow is permitted and only the bandwidth consumption of the hosts in the neighborhood of the route is computed. Without considering the bandwidth consumption to ongoing flows is the reason the problem is introduced. This work proposes a routing protocol that can avoid HRP for data rate selection and bandwidth‐satisfied route determination with an efficient cross‐layer design based on the integration of PHY and MAC layers into the network layer. To use bandwidth efficiently, we aim to select the combination of data rates and a route with minimal bandwidth consumption to the network, instead of the strategy adopted in the most previous works by selecting the combination with the shortest total transmission time. Using bandwidth efficiently can increase the number of flows supported by a network. Copyright 2010 John Wiley & Sons, Ltd.

This study designs and implements a cross-platform, cross-domain P2P multimedia sharing system in the Taiwan Advance Research and Education Network. The system allows users to easily access the multimedia resources of the entire network from any network node, and all multimedia providers can share local multimedia resources from any location on the entire network. Moreover, this is a Peer-to-Peer based transmission architecture model, thus, users can easily maintain the network, and enhance the service quality and efficiency of the entire network through relevant mechanisms.

Since the multi-rate enhancements have been implemented in wireless ad hoc networks (MANETs), QoS-constrained multicast protocols for multimedia communication should be adapted to exploit them fully for using more efficiently the limited resources. To build a multicast tree with delay-guaranteed, one-hop delay and end-to-end delay must be known. The one-hop delay is the transmission time at a given link connecting two neighboring hosts, and the end-to-end delay is the time taken for a data packet from a specific source to reach the destination node. However, how to calculate the above two delays using the IEEE 802.11 MAC is still a challenging problem, because the radio channel is shared among neighbors. Further, the multi-rate enhancements make it more difficult since the neighboring relation among hosts is varied with the transmission rates. In this paper, we first propose a method to estimate the one-hop delay based on varied transmission rates by monitoring the sensed busy/idle ratio of shared channel. Then, by its aid, another method is proposed to compute the end-to-end delay. Finally, we integrate the above two methods with a typical multicast routing protocol, ODMRP, for constructing a delay-guaranteed multicast protocol. Simulation results show that the proposed method obtains more precise one-hop delay than a very recently work. Besides, the integrated protocol provides better delay guarantee than the existing protocol when the multicast traffic has delay requirement.

Previous quality-of-service (QoS) routing/multicasting protocols in mobile ad hoc networks determined bandwidth-satisfied routes for QoS applications. However, they suffer from two bandwidth-violation problems, namely, the hidden route problem (HRP) and the hidden multicast route problem (HMRP). HRP may arise when a new flow is permitted and only the bandwidth consumption of the hosts in the neighborhood of the route is computed. Similarly, HMRP may arise when multiple flows are permitted concurrently. Not considering the bandwidth consumption of two-hop neighbors is the reason that the two problems are introduced. In this paper, a novel algorithm that can avoid the two problems is proposed to construct bandwidth-satisfied multicast trees for QoS applications. Furthermore, it also aims at minimizing the number of forwarders so as to reduce bandwidth and power consumption. Simulation results show that the proposed algorithm can improve the network throughput.

In the existing mobile ad hoc network (MANET) QoS routing and multicasting protocols, the methods of bandwidth calculation and allocation were proposed to determine routes with bandwidth guaranteed for QoS applications. As our observations, two bandwidth-violation problems is incurred in the above protocols. First: When a new bandwidth-requirement flow starts, the existing methods determine a bandwidth-satisfied route and reserve the bandwidth for the flow accordingly by considering the nodes' status on the route and network configuration. However, the reservation might violate the bandwidth capacities of other ongoing bandwidth-consuming flows. Second: Another bandwidth-violation problem would mislead the bandwidth reservation for QoS multicast applications when the multiple routes from a server to all clients were determined concurrently. Our simulation results exhibit that the two problems have high possibilities to be incurred so as to cause serious performance declination while the network traffic is heavy. In this paper, the problem of determining a bandwidth-satisfied tree is formulated as a 0/1 integer linear programming (ILP) for the theoretical studies. We minimize the number of forwarders for reducing the number of hosts participating in packet forwarding so as to lower bandwidth and power consumption that are crucial to MANET performance.

Recent routing protocols and multicast protocols in ad-hoc networks adopt two-tier architecture to accommodate the effectiveness of the flooding scheme and the efficiency of the tree-based scheme. Some hosts with maximum neighbor degree are chosen as RPs (rendezvous point) to forward data. However, these hosts will higher possibility to be the traffic concentration and bottleneck of the network, and RPs will spend more time forwarding data due to maximum neighbor degree. In this paper, we propose a multicast protocol for ad-hoc network, called OGHAM, with shorter relay and less concentration via selecting RPs from the hosts with minimum hop distance between them and from other normal hosts to these selected RPs rather than the hosts with maximum neighbor degree.