Mobile Edge Computing (MEC) has been proposed to bring content and computation closer to users, which can reduce latency and network load. However, using only MEC is neither scalable nor cost-effective for large-scale services. An alternative solution, namely Mobile Device Clouds (MDCs), was proposed. MDC is a cluster of mobile devices that can share idle computation and storage resources, using device-to-device (D2D) communication. While promising, performance for accessing cached content remains a key challenge in MDC.We propose CAG (Content-Aware Gossip), a lightweight, fully distributed protocol that improves content dissemination through context-aware gossiping, without relying on fixed node identifiers. By decoupling content from specific devices, CAG enables adaptive replication tailored to the dynamic and infrastructure-free nature of in MDC.To guide caching under mobility and resource constraints, we develop a fluid-based system model and formulate an optimization problem balancing replication cost and access latency. Extensive simulations in MATLAB show that CAG reduces average content access latency by over 20% compared to state-of-the-art protocols, demonstrating its effectiveness for scalable and low-latency caching in MDC.

Modern financial networks, particularly those supporting High-Frequency Trading (HFT) systems, are characterized by intense data flows and high packet rates, where even minor variations in latency can impact trading outcomes. The problem of queuing and latency management in such networks has become increasingly critical. Dynamic load balancing comes as a solution to this by splitting traffic and sending it on multiple paths. Usually, the problem observed is re-ordering. In response to this challenge, we present a novel Software-Defined Load Balancing Framework for splitting traffic at the packet level without causing packet reordering, which is especially suited for High-Frequency Trading Networks. We propose SOLT+ a traffic splitting algorithm that inspects bursts of packets specifically choosing packets to avoid re-ordering. We perform MATLAB simulations to show the accuracy and compare SOLT + with state-of-the-art algorithms.

In this paper, we introduce a novel framework designed specifically for federated reinforcement learning in IoT-driven networks, focusing on flow management in autonomous systems. Our framework optimizes flow table matching by monitoring IoT network traffic and ensuring efficient flow management across connected devices. By considering the specific flow requirements of IoT traffic, our framework enables an intelligent agent to make informed decisions regarding flow table entries, thereby improving the performance and management of autonomous systems. To gather essential data for decision-making, our framework utilizes an IoT-based SDN module that collects traffic statistics and relevant information from the network’s data plane. By leveraging SDN, our system enhances the learning and decision-making capabilities of IoT devices within autonomous systems. We introduce an optimization model called Software-Defined Network Assisted Federated Reinforcement Learning (SORE), based on the Markov decision process. SORE capitalizes on the advantages of SDN to boost the overall performance of IoT-based autonomous systems. By applying reinforcement learning techniques, our framework demonstrates significant improvements over existing models. Extensive simulations validate the effectiveness of our proposed system, showcasing superior performance and efficiency in IoT-enabled wireless networks within autonomous systems. The results underscore the potential of our approach in real-world IoT deployments.

As autonomous vehicles, smart infrastructure, and connected devices become integral components of our daily lives, the need to protect communication and establish trust among entities is fundamental. This problem necessitates the development of robust authentication mechanisms tailored for autonomous systems. In this paper, we address the critical challenge of designing authentication methods to verify the legitimacy of messages and participants in autonomous systems. The proposed methods aim to validate the origin of messages and the identity of participants, ensuring that only authorized entities interact with the system. Achieving this requires cryptographic techniques, digital signatures, and secure key management. To that end, we developed an algorithmic framework that includes message digest generation, digital signature creation, and recipient-side verification. Additionally, participant authentication and message integrity checks are incorporated to fortify the authentication process. The algorithm leverages public key cryptography to verify digital signatures and ensure the message's integrity. Second, we develop a simulation by harnessing Federated Learning (FL) which provides a dynamic and self-improving authentication mechanism that aligns with the high-reliability demands of modern autonomous applications on MATLAB. We elaborate on how addressing this problem is essential to bolster the security of autonomous systems, safeguard against cyber threats, and instill trust in the reliability and authenticity of communication within these systems. The proposed framework has been tested and validated on MATLAB.

Motivated by the need to provide a precisely determined delay between source and sink nodes in time-sensitive networks, we propose an architecture that provisions near-zero queuing delay in new Quality-of-Service frameworks, e.g., those of$\mathbf{5G}$solutions. To this end, various studies have shown how load balancing can reduce delay. Most of these studies consider$N$parallel processing queues with exponential service rates and Poisson arrivals with mean rate$\lambda$. These queues are handled by a single controller that assigns a new task to the shortest queue. The so-called power-of-d-servers or power-of-d-choices approach was proven to provide necessary delay improvements. In this strategy, the controller allocates the request to the least-loaded server among$d(N), 1\leq d(N)\leq N$randomly selected servers. However, none of these studies have considered realistic scenarios of fractional resource assignment to flow requests. To address this key shortcoming, we make the following contributions: (1) We design a software-defined network (SDN) controller framework called SOLT that considers the keys aspects of available resources in a time-sensitive network (TSN) setting, (2) We prove theoretically, how these bounds can be achieved and show the necessary conditions for achieving asymptotically zero delays in such networks, and (3) Through simulations, we demonstrate the improvements achieved by SOLT in comparison with state-of-the-art algorithms.

Recently, the benefits of moving computation to the edge were researched and proven. The paradigm of Mobile Edge Computing (MEC) provides a clear view of how offloading core network burden leads to the shortening of data access latencies, which is primal for supporting services at the edge. Increasing the density of edge data centers for supporting such requests is uneconomical. However, it is important to have a clear resource allocation strategy in such a way that user scheduling is considered. Therefore, in this paper, we study and prove how resource scheduling at the edge can substantially affect network performance. To this end, we focus on the stochastic nature of the wireless links and reduce this problem to a stochastic game. We design a software edge controller called Edge-Boss that resides at one base station and controls the wireless links available at each base station. In addition, the model ensures that Edge-Boss achieves an equilibrium that enables the Base stations (BS) to provide a higher performance (as this equilibrium acts as an incentive for the BS). Using simulations we show that Edge-Boss achieves latency reductions and a high network throughput.

Industrial Internet of Things (IIoT) applications have diverse network session requirements. Certain critical applications, such as emergency alert relays, as well as industrial floor evacuation and surveillance systems, require fresh updates that can maintain the most recently delivered packets. This requires high reconfigurability to an extent where the system can measure the impact of an event and adapt the network accordingly. Recent research has demonstrated that network failures can undermine the sustainability of Industry 4.0 or Industrial IoT in general. In this paper, we design an intelligent Federated learning based Time-Sensitive Networking (Fed-TSN) controller framework to optimize the failure recovery. In industrial IoT scenarios, such as emergency evacuations on factory floors due to natural disasters, there can be multiple link failures with no disjoint paths which require a sustainable recovery solution. Accordingly, we consider multiple simultaneous link failures, both for networks with and without disjoint paths. The typically probabilistic network failures on a factory floor call for designing a mechanism that can search for routes with minimum joint failure probability (JFP). We formulate the JFP minimization problem as a non-linear integer program. We design a Software Defined Networking (SDN) controller that runs an application to produce near-optimal solutions for providing enhanced sustainability in a wide range of Industry 4.0 scenarios. We employ this non-linear integer program solution as input to our intelligent Fed-TSN fault recovery strategy that predicts the migration location based on the changes in the TSN gate schedule. We conduct simulations to quantify the improvements achieved with Fed-TSN compared to state-of-the-art approaches.

Network slicing is a key enabler of multi-tenancy in 5G-and-beyond networks that satisfies the distinct requirements of different use-cases. As the density of tenants increases over time, admission requests may be put in waiting queues leading to impatient tenant behaviors. Due to such behaviors, tenants may frequently leave-and-join the slice admission queues in search for an alternate mobile network provider (MNO). This can be a severe problem when slices are leased and released on a short-term basis. In this paper, we argue that the instant behavior of a slice may deviate considerably from the predicted average behavior known to the tenant through a slice controller and thus gives rise to impatient tenant behaviors. To address this problem, we propose Mutes, a multi-tenant switching algorithm, that aids tenants in finding the best MNO. For a fixed number of tenants, we show that Mutes attains a Nash Equilibrium. We also show that Mutes stabilizes the system under strict admission conditions in scenarios where tenants are allowed to randomly move between MNOs. Through simulations, we justify that the proposed Mutes algorithm significantly improves the resource assignment performance and converges faster than state-of-the-art policies.

Emerging applications of the Internet of Vehicles (IoV) require the wireless transmission of growing amounts of data, e.g., vehicle location and sensor data, over unreliable and increasingly congested wireless links between the mobile vehicles and the Road Side Units (RSUs); also, urban areas are becoming increasingly congested with vehicle road traffic. Road traffic management and data network traffic management to address these challenges require accurate representations of the road and network traffic, which are difficult due to the wide temporal and spatial correlations in the road and network traffic. We address this representation problem by designing, implementing, and evaluating the VeNet deep learning system to exploit the wirelessly transmitted data to predict future vehicle locations and network traffic. We develop the novel VeNet hybrid learning system that employs a stacked autoencoder (AE) consisting of a central AE and multiple local AEs that jointly feed into a Long-Short Term Memory (LSTM). We propose a new training algorithm for the hybrid VeNet learning system. The novel VeNet hybrid learning system conducts spatial learning that accounts for the spatial and temporal correlations in the dataset gathered from the mobile vehicles. Evaluations that involve measurements with custom-made Raspberry Pi vehicles indicate that the VeNet learning model significantly reduces the required signalling network traffic and prediction errors (down to approx. three quarters) compared to existing prediction models. At the same time, VeNet reduces the energy consumption on the vehicles as well as the learning delay.

Managing cache content at the edge is one of the many use cases of 5G-and-beyond networks. However, increasing the density of Edge Data Centers (EDCs) to service requests is a crucial problem. To overcome this problem, recent research has advanced mobile device architecture paradigms and the content caching in a Mobile Device Cloud (MDC). These two service locations (EDCs and MDC) are registered with the Mobile Network Operator (MNO), enabling the MNO to control the content placement for profit maximization. As the user demands for content items are directly related to the QoS perceived by the user, it is important to understand the future popularity of the content items and to place them appropriately. Additionally, privacy issues have increased over time because of sensitive user information being divulged at the MDC. To preserve privacy, a branch of machine learning called Federated Learning (FL) can train machine learning models leaving the data in the end user devices. The paper contributions are as follows: (1) We introduce an FL algorithm called FedCo, that trains a deep-neural network (DNN) to predict the user demand of a specific content, so as to manage the content files placement at EDC and MDC sites. (2) We then conduct a theoretical evaluation of user demand behavior via prospect theory to justify revenue maximization for an MNO. (3) We show numerically via a multimedia content delivery use-case how the proposed model compares favorably with two state-of-the-art designs.

In recent years, the amounts of network traffic targeted towards cloud data centers have fluctuated based on user requests. This traffic is bursty and requires a high degree of attention. Due to the variable nature of this traffic, some requests need to be re-allocated on-the-fly. Such circumstances result in performance degradations due to resource management. As appropriate solutions can be proposed only based on understanding the workload and the environment, Reinforcement Learning (RL) is a strategy that is predominantly used. Further, it has been shown that the Poisson arrival rates do not capture real-world burstiness. Thus, we mainly have a two-fold problem to address: (i) the traffic requires a new modelling approach that can characterize the burstiness, and (ii) balancing the load that can maximize the reward to the provider in such circumstances. In this paper, we propose a novel, yet simple traffic modelling that enables burst detection based on an index policy. We show that the throughput constraints play a crucial role in scheduling and our proposed RL technique produces reliable results in such a scenario. Our RL algorithm decides what instance of the request traffic needs to be processed so that the cloud provider can maximize its profit and the decisions made in hindsight are non-biased. We compare the proposed policy with two state-of-the-art approaches and draw key inferences as to why an index policy performs better in scenarios that demand RL. We observe over five times shorter average wait times while bursty workload crosses a saturation limit of 150% compared to conventional policies.

Mobile Edge Computing (MEC) and Network Slicing techniques have a potential to augment 5G-IoT network services. Telecommunication operators use a diverse set of radio access technologies to provide services for users. Mobility management is one such service that needs attention for new 5G deployments. The QoS requirements in 5G networks are user specific. Network slicing along with MEC has been promoted as a key enabler for such on-demand service schemes. This paper focuses on radio resource access across heterogeneous networks for mobile roaming users. A unified service architecture is proposed enabling seamless handover between a 5G (New Generation Core) service and a 4G (Evolved Packet Core) service via the network slicing paradigm. An identifier-locator (I-L) concept that allows active source-IP sessions is used to handle the seamless hand-over. Signaling costs, service disruptions and other resource reservation requirements are considered in the evaluation to assure that profit for mobile edge operators is achieved. Simulation experiments are considered to provide performance comparisons against the state-of-the-art Distributed Mobility Management Protocol (DMM).

By moving computation and caching to the network edge, Mobile Edge Computing (MEC) offloads core networks and shortens data access latencies, which is important for large scale mobile multimedia services. Increasing the density of edge data centers to service these multimedia requests is uneconomical. Recent research has proven the benefits of involving devices in the delivery of multimedia services. This is done by exploiting the idle computation and storage resources via device-to-device (D2D) communication, i.e., by forming a so-called Mobile Device Cloud (MDC). Despite the flexibility and cost efficiency of this MDC paradigm, the timely allocation of caching resources to satisfy the dynamic user demands is challenging. This is mainly due to the uncertainty in resource availability of mobile devices. To this end, we propose Edge-Boost, a novel MDC caching architecture for lowlatency multimedia streaming services. We develop a novel fluid-based model to capture the dynamically changing network status. Additionally, we propose a dynamic caching allocation to jointly minimize caching cost and service latency. Edge-Boost achieves over 20% higher average cache utilization and 15% shorter average access latency than the state-of-the-art MDC approach.

In order to address the ossification of the traditional network, there have been many studies that show the benefits of the orthogonality offered by the principles of Software Defined Networking (SDN). Therefore, as the concept of SDN saw wide spread acceptance, it's adaptability in wireless networks began to emerge. Many proposals in the literature have addressed the issues that are related to the Mobile Ad-hoc Networks (MANETs). A computing environment formed atop a MANET that is closely linked to the rigidity of the underlying network is called Mobile Ad-hoc Cloud. In this paper we show how a seamless disruption tolerant mobile ad-hoc cloud can be maintained with the assistance of the adaptive principles offered by the SDN framework. Further, we demonstrate how a selection of mobile ad-hoc cloud composition traffic can reduce the latency in task computation and result collection in comparison with the traditional non-SDN ecosystem.