Recently, companies have focused on developing new technologies for air mobility using flying cars to alleviate road congestion in urban areas. A critical aspect to consider is the seamless integration of flying cars with their ground-based counterparts in the 5G network, where ground-based cars can provide transit functions, including access to vertiports and urban amenities. Additionally, flying and ground-based cars require various services with different requirements, such as path planning, remote diagnosis, and autonomous driving/piloting. Supporting these services in 5G networks is challenging due to the high mobility and stringent network latency requirements. Network slicing can be a promising solution to meet these requirements. However, the literature lacks comprehensive research on combining flying and ground-based cars in network slicing, where resource under-provisioning can cause the violation of service requirements. We propose three-level closed-loops for sliced resource block management to satisfy the delay budget constraint of flying and ground-based cars while avoiding resource under-provisioning. We present a reward function and continual learning that links these closed-loops. Furthermore, we use Ape-X as distributed deep reinforcement learning to maximize reward and continual learning to improve resource allocation via prediction. The simulation results demonstrate that the proposed approach maximizes delay requirement satisfaction.

Internet access in rural areas should be improved to support digital inclusion and 5G services. Due to the high deployment costs of fiber optics in these areas, Fixed Wireless Access (FWA) has become a preferable alternative. Additionally, the Open Radio Access Network (O-RAN) can facilitate the interoperability of FWA elements, allowing some FWA functions to be deployed at the edge cloud. However, deploying edge clouds in rural areas can increase network and energy costs. To address these challenges, we propose a closed-loop system assisted by a Digital Twin (DT) to automate energy-aware O-RAN based FWA resource management in rural areas. We consider the FWA and edge cloud as the Physical Twin (PT) and design a closed-loop that distributes radio resources to edge cloud instances for scheduling. We develop another closed-loop for intra-slice resource allocation to houses. We design an energy model that integrates radio resource allocation and formulate ultra-small and small-timescale optimizations for the PT to maximize slice requirement satisfaction while minimizing energy costs. We then design a reinforcement learning approach and successive convex approximation to address the formulated problems. We present a DT that replicates the PT by incorporating solution experiences into future states. The results show that our approach efficiently uses radio and energy resources.

Recently, companies have increasingly developed new technologies for urban air mobility using flying cars to alleviate road congestion. Unfortunately, the seamless integration of flying cars with their ground-based counterparts in the 5G network, where ground-based cars can support flying cars in proving transit functions, has not yet been fully investigated. Flying and ground-based cars require various services, such as autonomous driving/plot, path planning, and remote di-agnosis. Supporting these services in 5G networks is challenging due to the high mobility and stringent network latency requirements. Network slicing is a promising solution. However, a comprehensive research on combining flying and ground-based cars in network slicing is still missing in the literature. Under-provisioning of radio resources can result in the violation of service requirements, while radio resource over-provisioning can cause resource under-utilization. We propose two-level closed-loops for Resource Block (RB) management to satisfy the delay budget constraint of flying and ground-based cars simultaneously while avoiding radio resource under/over provisioning. We design two closed-loops to map slices and services to Open RAN elements for radio resource scheduling and to allocate RB to cars. We propose a zero-touch RB adjustment approach and link these two closed-loops through the reward function of a deep reinforcement learning algorithm that optimizes slice resources in real time. Results show that our approach maximizes delay requirement satisfaction while preventing RB under/over-provisioning.

In state-of-the-art autonomous vehicles, data from the vehicle's sensors is often processed using fast and expensive onboard hardware. Such an onboard processing scheme quickly drains the vehicle's battery and consumes computing resources. Recent research proposed to offload parts of processing tasks onto cloud. However, offloading tasks to the cloud is challenging because of the low latency needed for reliable and safe autonomous driving decisions. To address this issue, we propose an Age of Processing (AoP)-aware offloading mechanism for autonomous vehicles. First, we develop a collaboration space of edge clouds to process data closely as possible to the vehicles. Second, we reveal a new communication planning model that allows the vehicle to find suitable open radio units available in route to offload tasks to edge clouds and reduce variation in offloading delay. Third, we formulate an optimization problem that minimizes AoP, i.e., elapsed time from generating tasks and getting computation results. Our AoP-based approach allows a status update to be available to the vehicle after computation. To solve the formulated non-convex problem, we apply dual decomposition and design an AoP-aware algorithm to compute the solution in near real-time. The results demonstrate that our approach meets computation deadlines while minimizing AoP.

For digital inclusion, Internet quality in Low-Density and Rural Areas (LDRAs) should be enhanced to satisfy QoS requirements of various services and applications. Due to the high operating costs of fiber optic deployment in LDRAs, 5G Fixed Wireless Access (5G FWA) is becoming a more attractive solution. Furthermore, 5G services require edge cloud deployment for proximity computation, which increases both required network and energy resources. Therefore, we propose closed-loops assisted by Digital Twin (DT) for energy-efficient Open RAN-based FWA provisioning in LDRAs. We consider a 5G FWA and edge cloud system as Physical Twin (PT) and design a closed-loop that distributes radio resources to edge cloud instances that manage network slices for scheduling purposes. We propose another closed-loop for intra-slice resource allocation to LDRAs. We develop an energy model and join radio resource allocation with the energy model. Then, we design reinforcement learning and optimization approaches to maximize delay requirement satisfaction while minimizing energy cost. Finally, we present DT replicating PT by incorporating solution experiences into future states. The results show that our approach uses energy resources efficiently while satisfying delay requirements of slices.

Flying and ground-based cars require various services such as autonomous driving, remote piloting, infotainment, and remote diagnosis. Each service requires specific Quality of Service (QoS) and network features. Therefore, network slicing can be a solution to fulfill the requirements of various services. Some services, such as infotainment, may have similar requirements to serve flying and ground-based cars. Therefore, some slices can serve both kinds of cars. However, when network slice resource sharing is too aggressive, slices can not meet QoS requirements, where resource under-provisioning causes the violation of QoS, and resource over-provisioning causes resources under-utilization. We propose two closed loops for managing RAN slice resources for cars to address these challenges. First, we present an auction mechanism for allocating Resource Block (RB) to the tenants who provide services to the cars using slices. Second, we design one closed loop that maps slices and services of tenants to Open Distributed Units (vO-DUs) and assigns RB to vO-DUs for management purposes. Third, we design another closed loop for intra-slices RB scheduling to serve cars. Fourth, we present a reward function that interconnects these two closed loops to satisfy the time-varying demands of cars at each slice while meeting QoS requirements in terms of delay. Finally, we design distributed deep reinforcement learning approach to maximize the formulated reward function. The simulation results show that our approach satisfies more than 90% vODUs resource constraints and network slice requirements.

Offloading computation-intensive tasks to edge clouds has become an efficient way to support resource constraint edge devices. However, task offloading delay is an issue largely due to the networks with limited capacities between edge clouds and edge devices. In this paper, we consider task offloading in Open Radio Access Network (O-RAN), which is a new 5G RAN architecture allowing Open Central Unit (O-CU) to be co-located with Open Distributed Unit (DU) at the edge cloud for low-latency services. O-RAN relies on fronthaul network to connect O-RAN Radio Units (O-RUs) and edge clouds that host O-DUs. Consequently, tasks are offloaded onto the edge clouds via wireless and fronthaul networks, which requires routing. Since edge clouds do not have the same available computation resources and tasks’ computation deadlines are different, we need a task distribution approach to multiple edge clouds. Prior work has never addressed this joint problem of task offloading, fronthaul routing, and edge computing. To this end, using segment routing, O-RAN intelligent controllers, and multiple edge clouds, we formulate an optimization problem to minimize offloading, fronthaul routing, and computation delays in O-RAN. To determine the solution of this NP-hard problem, we use Deep Q-Learning assisted by federated learning with a reward function that reduces the Cost of Delay (CoD). The simulation results show that our solution maximizes the reward in minimizing CoD.

Today, vehicles use smart sensors to collect data from the road environment. This data is often processed onboard of the vehicles, using expensive hardware. Such onboard processing increases the vehicle’s cost, quickly drains its battery, and exhausts its computing resources. Therefore, offloading tasks onto the cloud is required. Still, data offloading is challenging due to low latency requirements for safe and reliable vehicle driving decisions. Moreover, age of processing was not considered in prior research dealing with low-latency offloading for autonomous vehicles. This paper proposes an age of processing-based offloading approach for autonomous vehicles using unsupervised machine learning, Multi-Radio Access Technologies (multi-RATs), and Edge Computing in Open Radio Access Network (O-RAN). We design a collaboration space of edge clouds to process data in proximity to autonomous vehicles. To reduce the variation in offloading delay, we propose a new communication planning approach that enables the vehicle to optimally preselect the available RATs such as Wi-Fi, LTE, or 5G to offload tasks to edge clouds when its local resources are insufficient. We formulate an optimization problem for age-based offloading that minimizes elapsed time from generating tasks and receiving computation output. To handle this non-convex problem, we develop a surrogate problem. Then, we use the Lagrangian method to transform the surrogate problem to unconstrained optimization problem and apply the dual decomposition method. The simulation results show that our approach significantly minimizes the age of processing in data offloading with 90.34% improvement over similar method.

Without steering wheel and driver's seat, the self-driving cars will have new interior outlook and spaces that can be used for enhanced infotainment services. For traveling people, self-driving cars will be new places for engaging in infotainment services. Therefore, self-driving cars should determine themselves the infotainment contents that are likely to entertain their passengers. However, the choice of infotainment contents depends on passengers' features such as age, emotion, and gender. Also, retrieving infotainment contents at data center can hinder infotainment services due to high end-to-end delay. To address these challenges, we propose infotainment caching in self-driving cars, where caching decisions are based on passengers' features obtained using deep learning. First, we proposed deep learning models to predict the contents need to be cached in self-driving cars and close proximity of self-driving cars in multi-access edge computing servers attached to roadside units. Second, we proposed a communication model for retrieving infotainment contents to cache. Third, we proposed a caching model for retrieved contents. Fourth, we proposed a computation model for the cached contents, where cached contents can be served in different formats/qualities based on demands. Finally, we proposed an optimization problem whose goal is to link the proposed models into one optimization problem that minimizes the content downloading delay. To solve the formulated problem, a block successive majorization-minimization technique is applied. The simulation results show that the accuracy of prediction for the contents that need to be cached is 97.82% and our approach can minimize the delay.

The concept of Multi-access Edge Computing (MEC) has been recently introduced to supplement cloud computing by deploying MEC servers to the network edge so as to reduce the network delay and alleviate the load on cloud data centers. However, compared to the resourceful cloud, MEC server has limited resources. When each MEC server operates independently, it cannot handle all computational and big data demands stemming from users devices. Consequently, the MEC server cannot provide significant gains in overhead reduction of data exchange between users devices and remote cloud. Therefore, joint Computing, Caching, Communication, and Control (4C) at the edge with MEC server collaboration is needed. To address these challenges, in this paper, the problem of joint 4C in big data MEC is formulated as an optimization problem whose goal is to jointly optimize a linear combination of the bandwidth consumption and network latency. However, the formulated problem is shown to be non-convex. As a result, a proximal upper bound problem of the original formulated problem is proposed. To solve the proximal upper bound problem, the block successive upper bound minimization method is applied. Simulation results show that the proposed approach satisfies computation deadlines and minimizes bandwidth consumption and network latency.

Mobile data traffic is increasing astronomically. This enormous extent was not only caused by the increasing in the number of mobile devices, but also the growing number of applications running on clouds. Most of these mobile devices have limited resources for computing, they are relying on cloud computing, which is inadequate to realize millisecond-scale latency in the 5G network. To deal with this issue, Mobile Edge Computing (MEC) has been introduced to supplement cloud computing by pushing Computing, Caching, Communication, and Control (4C) to the edges. However, the proposed MEC server at each base station is not enough to deal with 4C, when it operates independently, and without any collaboration with other MEC servers. This results in increasing the delay and making backhaul to continue suffering from huge data exchange between end-users and remote clouds. To address this challenge, we propose collaborative cache allocation and computation offloading, where the MEC servers collaborate for executing computation tasks and data caching. We formulate an optimization problem that aims at maximizing the resource utilization. The simulation results show that our proposal is easy to be implemented in production network.

Modern day's User Equipments (UEs) are equipped with rich resources which encourage them to be used for more sophisticated applications. On the other hand, with these equipments in hand, users demand for high-quality video on the move is increasing day-by-day. Moreover, Information/Content Centric Networking (ICN/CCN) has changed the network dynamics by getting the desired contents regardless of the location. Unused memory in UEs can be used to cache the contents and provide it to the other nearby users on demand. In this paper, we propose to provide the requested video to users from other users cache, using D2D link, if it is present there. Our objective is to reduce the download delay for the users' requested video. We formulate the problem as a matching game in which the resources are assigned to the users in the uplink period. The UEs select the content node for D2D communication and the suitable channel. We have evaluated the proposed mechanism by implementing it in Matlab and have compared it with greedy approach and no D2D communication scheme. The experimental results show the effectiveness of our proposed mechanism.

Caching is the key feature of Content Centric Networking (CCN) that allows the Internet Service Provider (ISP) to reduce network traffic crossing its network, and save bandwidth usage cost. On the other hand, it is also on benefit of the Content Providers (CPs) to cache the contents within the ISP network near the consumers. However, caching paid contents (the contents that only paying consumers can access), which are the main source of income for CP, in the ISP network complicates the CP's task of controlling content access and payment. Thus, ISP manages content placement inside its cache-enabled routers and serves content based on user demands, without any coordination with CP. There is no profit sharing mechanism between both ISP and CPs. Therefore, a payment mechanism between ISP and CPs that considers paid content caching and distribution inside the ISP network is needed. To address this challenge, we propose a new incentive mechanism for paid content caching that satisfies both ISP and CPs through the use of reverse auction. The ISP monetizes its cache storage through caching contents from multiple CPs and selling them to its customers. The reverse auction helps the ISP to get prices from multiple CPs, and to select the price that minimize its total payment. The simulation results show that our proposal satisfies all network players involved in in- network caching through increasing their utilities.

Content Centric Networking (CCN) is one of the Future Internet architectures that aims at improving content distribution and retrieval, where content is requested by name rather than IP address. Each content is partitioned into small units, namely chunks. In CCN, the consumer sends Interest packet in order to get a chunk of Data. Upon successful reception of the requested chunk, the consumer sends the next Interest packet. This policy of send Interest, wait for the Data to reach and generate next Interest makes the situation worst in case of large sized content. The result is uplink underutilization. To overcome the above highlighted issue, we propose Interest forwarding in CCN, which is based on packet aggregation through combining multiple chunk requests in one Interest packet, and content name aggregation. The Interest packet aggregation will reduce the number of Interest packets need to be sent in the network and downsize Pending Interest Table (PIT), while organizing content based on aggregated name reduces Content Store (CS) entries. The simulation results show that our proposal achieves high performance with throughput improvement, reduced number of Interest packets in network, PIT and CS entries over other existing proposal in the literature.

Internet has grown very rapidly in the last couple of decades and still growing because of the expansion and utilization of various services and applications. Consequently, demand of delay and throughput sensitive services, like audio/video is also increasing. Information Centric Networking (ICN) is proposed as an architecture for the future Internet to meet the modern users and application requirements. In ICN users send requests (Interest Packets) for the Data they need. Interest packet is assigned a lifetime, which greatly affects the Quality of Experience (QoE) because user needs to resend the Interest, when the lifetime expires. Interest lifetime may be expired because of congestion, or Interest lifetime is shorter than the network delay, etc. Waiting for the expiration of an Interest lifetime to resend it is merely appropriate for best effort traffic, rather than services which require high throughput and are delay sensitive. In this paper, we propose Network-Assisted Congestion Control mechanism in ICN, which detects the congestion before it happens, and provides notification to downstream node. On reception of the notification, downstream node continuously reduces the traffic rate. However, when the downstream node fails to adjust the sending rate, the same procedure continues, until the sending node reduces the traffic rate through adjusting its congestion window. We have intensively evaluated our proposal by comparing it with similar proposal using ndnSIM. The experimental results show that our proposal achieves up to 59 percent performance improvement over other proposal in the literature.