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This paper proposes an Enhanced Edge-Linked Caching (EELC) scheme for Internet of Things (IoT) environments under Information-Centric Networking (ICN), employing an advanced use of Proximal Policy Optimization (PPO), a form of deep reinforcement learning, to inform the caching decisions of edge nodes. The rapid proliferation of IoT devices has led to significant challenges in managing content efficiently within networks, particularly in terms of scalability, latency, and energy consumption. Traditional IP-based architectures are inadequate in addressing these challenges, necessitating a shift towards a content-centric approach provided by ICN. By leveraging PPO, EELC dynamically adapts to changing IoT network conditions, optimizing caching strategies to enhance energy efficiency and improve network responsiveness. In our simulation, we verify the performance of EELC in comparison to the Edge-Linked Caching (ELC) and Leave Copy Everywhere (LCE) approaches under different cache sizes and Zipf-distributed content requests. EELC performs far better than ELC under energy efficiency, cache hit ratios, and server hit reduction in all tested scenarios. This indicates that EELC could be a potential approach for significantly improving network efficiency and responsiveness in IoT networks.

Information-centric networking (ICN) is one of promising networking architectures to replace IP because its notable feature, in-network caching, is expected to reduce about a one-third of the forever increasing Internet traffic. However, the existing ICN router implementations with caches cannot come with the rapid increase of terabit-scale network bandwidth. An implementation on hardware-based router platforms like programmable switches is promising, but due to their limited memory capacity, cache stores should be located at external devices like computers. This makes packets being sent from and to the switch to cache them at the computers, which increases the number of ports for connecting them called external ports. That is, the number of ports for connecting the switch and external networks is reduced, and as a result, the packet forwarding rate is degraded. We analytically and experimentally show that a naive implementation decreases both the packet forwarding rate and the number external ports to almost half in the condition that the cache hit ratio is about 30%. To solve this bottleneck, this paper proposes the two algorithms, the cache admission and lazy response ones, to reduce the number of packets sent between the switch and the computers. In order to validate them, we evaluate the forwarding rate improvement by developing an analytical model and a prototype ICN router implementation on a programmable switch with computers. The evaluation shows that the proposed algorithms almost double the forwarding rate compared to a naive implementation without the proposed algorithms under realistic request patterns to data packets. The paper’s contributions are two-fold: realistic implementation and comprehensive performance evaluation. First, we implement a terabit-class ICN router with cache functionality on a Tofino switch which provides 3.2 terabit/s (Tbps) forwarding rate with two pipelines. The implemented router achieves 1.075 Tbps forwarding rate in the condition that just one pipeline of the Tofino switch is used and that the cache hit ratio is about 30%. As far as we know, this prototype implantation is one of the first full-fledged ICN routers on a Tofino switch. Second, the performance of the proposed router is both analytically and experimentally evaluated, and the both results show the similar packet forwarding rate under realistic request patterns following the Zipf distribution, which the Internet traffic is believed to follow. Finally, the paper is extended from the conference paper so that the proposed algorithms are evaluated both analytically and experimentally, whereas they are mainly evaluated by simulation in that paper.

The fundamental objective of the Internet of Things (IoT) and Named Data Networking (NDN) architectures is to facilitate the provision of communication services. The existing Internet infrastructure presents various challenges associated with its location-based architecture, including those related to latency, bandwidth, and power consumption. This paper explains the NDN-based IoT caching architecture and discusses the caching module, focusing on the selection of an optimal caching approach to address the issues above. This study selected two distinct caching categories, namely centrality-based and probability-based approaches. The selection of these caching strategies was based on their prominence within the research community. In order to determine the most optimal caching strategies, the Icarus network simulator is employed to conduct a comprehensive evaluation of the selected strategies. The evaluation of the performance is conducted based on several key metrics, including the hit ratio, content retrieval latency, and average hop count. The Popularity-Aware Closeness Centrality strategy and the Efficient Popularity-Aware Probabilistic Caching strategy demonstrated superior performance when employing centrality-based caching and probabilistic-aware caching categories, respectively, in order to enhance network performance.

Caching plays an important role in reducing latency and increasing the overall performance of fog computing systems. With the rise of the Internet of Things (IoT) and edge computing, fog computing has become an essential paradigm for addressing the challenges related to latency-sensitive and bandwidth-intensive applications. The integration of Information-Centric Networking (ICN) and Fog Computing (ICN-Fog) has emerged as a promising solution for meeting the demands of low-latency and high-throughput applications in rapidly growing IoT devices. A step was taken by ICN-Fog to reduce latency and achieve higher data communication and information gathering for fog computing. Using Artificial Intelligence (AI) methods, the Firefly Optimization Technique was introduced as an effective optimization algorithm to enhance the caching technique. In this study, we aim to improve several performance metrics, such as the cache hit ratio, internal link load, and average query duration. To achieve these enhancements, we propose a unique solution inspired by the Firefly Optimization Technique. This technique applies to the ICN-Fog caching model, which was utilized to intelligently determine cache placement and network topology adaptations. CloudSim was employed to execute a simulation of the proposed strategy. The results of the experiments suggest that the Multi-Objective Firefly Algorithm (MOFA) outperforms the compared algorithms in terms of both efficiency and effectiveness in identifying the optimal caching technique.

Caching technology is essential for enhancing content transmission rates and reducing data transmission delays in heterogeneous networks, making it a crucial component of the Internet of Things (IoT). However, during data transmission and caching model training, the security of information is destroyed by untrusted third parties. In addition, the flexibility of storage locations presents another bottleneck in heterogeneous network caching technology. Deep learning (DL) is an important method for improving caching performance due to its powerful learning capabilities. Nonetheless, the DL process is vulnerable to various attacks, including white-box and black-box attacks, disclosing private information. Therefore, this study proposes a DL-based caching framework aimed to enhance security in heterogeneous networks based on differential privacy-preserving technology. Moreover, we utilize a boosting integrated method to improve caching accuracy. Simulated experiments demonstrate that the proposed framework ensures security and accuracy in the heterogeneous network caching process, outperforming existing solutions.

The deployment of 5G has exposed capacity constraints in realizing the key vision of the Internet of Everything (IoE). Therefore, the researchers are exploring potentials of Digital Twin Network (DTN) in wireless networks. DTN is a novel technology to create virtual replicas of physical environment for testing, optimizing, and managing wireless networks. The integration of Artificial Intelligence (AI) and DTN appears to be a promising approach to address communication systems by providing an efficient environment for testing and improving AI models before deployment in real networks for effective network management, optimal resource allocation, and precise behavior prediction. Therefore, AI-enabled DTN in 6G represents a compelling avenue to address multifaceted challenges faced by wireless networks. In this comprehensive work, we offer a holistic survey that delves into the state-of-the-art approaches for AI-enabled DTNs in 6G. Firstly, we discuss the evolution of wireless networks and concept of AI-enabled DTN in 6G. Secondly, we discuss the role of AI-enabled DTN in 6G and driving advancements in fundamental components of 6G including resource allocation, caching, data offloading, and data security. Thirdly, we conduct a detailed discussion on key enabling technologies for realizing the capabilities of AI-enabled DTN in 6G. Fourthly, several applications of AI-enabled DTN in 6G are discussed for the practical relevance and significance in various industries such as smart cities, healthcare, and transportation etc. Finally, we provide lessons learned and highlight existing challenges and research directions to embark on further research efforts in the realm of AI-enabled DTN in 6G.

Vehicular Edge Computing (VEC) technology holds great promise, but also poses significant challenges to the limited computing power of in-vehicle devices and the capacity of Roadside Units (RSUs). At the same time, the highly mobile nature of vehicles and the frequent changes in the content of requests from vehicles make it critical to offload applications to edge servers and to effectively predict and cache the most popular content, so that the most popular content can be cached in advance in the RSU. And also considering protecting the privacy of vehicle user vehicular users (VUs), traditional data-sharing methods may not be suitable for this work, so we use an asynchronous Federated learning (FL) approach to update the global model in time and at the same time can protect the personal privacy of VUs. Unlike the traditional synchronous FL, asynchronous FL no longer needs to wait for all vehicles to finish training and uploading local models before updating the global model, which avoids the problem of long training time. In this paper, we propose an in-vehicle edge computing caching scheme based on asynchronous federated learning and deep reinforcement learning(AFLR), which prefetches possible popular contents in advance and caches them in the edge nodes or vehicle nodes according to the vehicle’s location and moving direction while reducing the latency of the content requests. After extensive experimental comparisons, the AFLR scheme outperforms other benchmark caching schemes.

Named Data Networking (NDN) is considered the future of Internet architecture, providing a realistic solution for data delivery using a caching module in an Internet of Things (IoT) based environment. However, a major challenge of the caching module is data redundancy, which decreases the overall caching performance by caching similar data at numerous locations in an NDN-based IoT scenario. Moreover, the latency and stretch are maximized due to high redundant caching operations. Several attempts have been made by the research community to provide an enhanced solution to overcome such issues. However, the caching module still requires efficient enhancement. This study provides critical insights into earlier caching strategies. To solve the problems of these caching strategies, an enhanced caching strategy is proposed, named Priority-based Content Popularity-Aware (PCPA) Caching Strategy, which is evaluated by comparing its performance with some of the novel NDN-based IoT caching strategies. The proposed caching strategy outperforms the comparing strategies in terms of latency, hop count, cache hit ratio and energy consumption.

One of the important ultimatums in enhancing the fifth generation (5G) network’s capacity is the performance limit due to spectrum occupancy. The mmWave technology overlay on 5G heterogeneous network (HetNet) and caching the contents are proposed as the solutions to this problem. By reducing the backhaul links occupancy, increasing the access link utilization and caching, the impact of spectrum occupancy problem can be minimized. In our earlier work, differential Quality of Service (QoS) was provisioned by managing the cache and backhaul resources using machine learning techniques. In this work, an integrated solution combining content, cache and user management is proposed to maximize network utilization and QoS. The problem of effective utilization of the network at the same time ensuring the QoS for users are solved as a multi objective optimization problem with the aid of hybrid meta heuristics with complementary exploration and exploitation capability. The user association to base station is made adaptive to load and cache hit ratio at the base station. By increasing the content proximity to the users, the load of backhaul links is minimized. Through these integrated management strategies, the proposed solution is able to provide higher QoS compared to existing works in terms of reduction in packet drop by 6%, reduction in delay by 39%, increase in network throughput by 8% and a consistent cache hit ratio more than 85%.

Efficient content caching is essential to address the explosive growth of multimedia contents and most works on content cache placement have been proposed mainly based on the popularity of content. Since the priority of content is also an important attribute of content, we consider both popularity and priority of content together for content caching in information-centric networking (ICN). We define weighted delivery cost of content as content delivery cost multiplied by a weighted sum of popularity and priority of the content. Then, we formulate optimized content cache placement problem to minimize weighted content delivery cost for all content requests in a hierarchical network architecture with multi-access edge computing (MEC) and software-defined networking (SDN) controller. Average quality of experience (QoE), i.e., average content delivery cost, for contents with each priority is imposed as constraint. The number of content requests is predicted based on seq2seq long short-term memory (LSTM) model in MECs and this is delivered to SDN controller. Then, SDN controller obtains predicted popularity of contents and decides content placement in MECs and core routers by solving the content cache placement optimization problem based on binary particle swarm optimization (BPSO). Performance of the proposed content caching scheme is compared with conventional popularity-based, popularity prediction-based, and popularity prediction-based optimization schemes, from the aspect of QoE satisfaction ratio, average cost, weighted average cost, total cost, and weighted total cost. Numerical results show the effectiveness of the proposed scheme at caching content with high priority efficiently, at the expense of caching content with low priority.

Driven by the urgent requirement of ubiquitous and reliable coverage for global users, the low earth orbit (LEO) satellite network has attracted numerous attentions from the academic and industry circles. By deploying multi-access edge computing (MEC) servers in LEO satellites, computation offloading and content caching services can be provided for remote Internet-of-Things (IoT) devices without proximal servers. In this paper, the joint optimization of computation offloading, radio resource allocation and caching placement in LEO satellite MEC networks are investigated. The problem is formulated to minimize the total delay of all ground IoT devices while ensuring the energy, computing and caching constraints. To solve this mixed-integer and non-convex problem, a Lagrange dual decomposition (LDD)-based algorithm is proposed to obtain the closed-form optimal solution. Then, a heuristic algorithm is proposed to further reduce the computation complexity. Numerical results validate that both algorithms are effective compared to the optimal exhaustive search, the full local computing and the full MEC methods. Besides, the offloading ratio and the average delay of all IoT devices with different numbers and computing capacities of devices and satellites are also demonstrated.

This paper studies the problem of optimal cache placement to maximize the offloading probability in a device-to-device (D2D) enabled cellular network with small base stations (SBSs). Different from most existing works, we consider unequal users’ equipment (UE) cache memory sizes and all wireless links are modeled as Nakagami- $m$ fading. User preference for each UE and global popularity for SBS, as well as the higher priority of content request from neighboring UEs vs. SBS, are the main factors that make the problem formulation of our work different from that of existing works. It is assumed that each UE caches its desired content with the order of searching its cache, neighboring UEs’ cache via D2D communications, and its serving SBS’ cache. A close to optimal low complexity heuristic cache placement policy is proposed and it is shown that its performance reaches the optimal caching strategy.

Cache-enabled Fog radio access network (F-RAN) is a promising technology to alleviate the traffic congestion and boost the contents delivery success rate. The efficiency of disseminating the cached contents in F-RAN can be boosted by enabling multicast service at the fog access points (F-APs). This paper proposes a joint random caching and multicasting optimization scheme for wireless backhauled F-RAN. Using tools from stochastic geometry, the expression of the successful transmission probability (STP) is derived by carefully analyzing the different types of serving F-APs and interferers. Then, a closed-form expression of the asymptotic STP in the high SNR region is derived to reduce the complexity. The joint caching and multicasting optimization problem is formulated to maximize the STP. The optimization problem is complex and non-convex in general. A novel projected cuckoo search algorithm (PCSA) is proposed to obtain the optimal content placement that maximizes the STP. The numerical simulation results show that PCSA outperforms the original cuckoo search algorithm (CSA) and the proposed asymptotically joint caching and multicasting scheme outperforms the benchmark caching schemes by up to 15% higher STPs.

Highly immersive content in the form of extended reality (XR) is attracting attention as an alternative to conventional video services such as YouTube and Facebook. Many galleries and museums already offer online virtual reality (VR) tours where users are free to choose the spot they want to move to, beyond merely looking around. Although the ease of implementation, this key-spot hopping is still far from giving the real feeling of walking. Meanwhile, in recent volumetric or light-field-based studies, view rendering that supports free and continuous viewpoint movements has been attempted. With online services in mind, however, the high data volume and computational complexity are a big obstacle to practical applications. Path-walking VR, the target system of this paper, can be a good compromise, where the viewer can enjoy the virtual space while walking along the route. The interactive path-walking VR service is entry-level immersive video, but streaming over the network is still challenging. One of the main problems to be tackled is that the movement patterns of viewers need to be reflected in the streaming strategy to improve the quality of experience. Unlike unidirectional video, the movement of the viewer determines which images and how many images should be transmitted. This paper proposes schemes to reduce streaming delays by reflecting the viewer’s movement characteristics. It is differentiated from existing studies for omnidirectional video in that the proposed schemes control not only image quality but also view update rate. The first is a caching strategy which takes advantage of the geometrical locality of the virtual space that the viewer will soon reach a position close to the current position. This not only reduces the communication delay from the server, but also decreases the burden of server-side request handling. The second scheme uses the relationship between the viewer’s speed and the field of vision. The image quality is adjusted according to the viewer’s speed and head direction. Experimental results show that the proposed schemes achieve stable viewer’s experience by considering walking characteristics in virtual space. It is expected that the results of this paper will provide insight to those who design interactive streaming systems for immersive media applications.

Effective networking over wireless media has become extremely essential today as communication between massive Internet of things (IoT) devices is on an increase, thereby leading to a limited spectrum resource for utilisation. Specifically, for healthcare infrastructure in a remote or critical situation, providing uninterrupted communication between the macro base station and IoT devices or user nodes is imperative. However, owing to their limited spectral capacity, unmanned aerial vehicles (UAVs)-based networks can provide an efficient solution and utilise both licensed and unlicensed bands for communication among users or devices. In this paper, our focus is on cache-enabled cognitive networking for secondary users (SUs) that accredits precise communication delivery for critical healthcare systems that are performed by the cognitive UAV (CUAV). In addition, we develop a caching strategy wherein a CUAV is capable of caching relevant information from high-power (HP) and moderate-power (MP) devices in its local and cloud storage by applying a non-orthogonal multiple-access method. In the downlink scenario, the CUAV proactively transmits the requested HP and MP information to the designated SUs considering this entire model over two states, namely effectual state and interference state, which we can realise by any presence or absence of interference. To maximise this system’s energy efficiency, we formulate an optimisation problem to minimise the transmission power and satisfy the target performance in terms of throughput for SUs. We solve the optimisation issue using the Lagrangian approach and the Karush-Kuhn-Tucker conditions. In all simulations, the energy efficiency during the effectual state renders an average performance of approximately 400% better than that of the interference state.

Caching in a network of caches has been widely investigated for improving information/content delivery efficiency (e.g., for reducing content delivery latency, server load and bandwidth utilization). In this work, we look into another dimension of network of caches – enhancing resilience in information dissemination rather than improving delivery efficiency. The underlying premise is that when information is cached at more locations, its availability is increased and thus, in turn, improve information delivery resiliency. This is especially important for networks with perturbations (e.g., node failures). Considering a general network of caches, we present a collaborative caching framework for maximizing the availability of the information. Specifically, we formulate an optimization problem for maximizing the joint utility of caching nodes in serving content requests in perturbed networks. We first solve the centralized version of the problem and then propose a distributed caching algorithm that approximates the centralized solution. We compare our proposal against different caching schemes under a range of parameters, using both real-world and synthetic network topologies. The results show that our algorithm can significantly improve the joint utility of caching nodes. With our distributed caching algorithm, the achieved caching utility is up to five times higher than greedy caching scheme. Furthermore, our scheme is found to be robust against increasing node failure rate, even for networks with a high number of vulnerable nodes.

The paper is motivated by the urgent need, imposed by the COVID-19 pandemic, for trustworthy access to secure communication systems with the highest achievable availability and minimum latency. In this regard, we focus on an ultra-dense wireless network consisting of Femto Access Points (FAPs) and Unmanned Aerial Vehicles (UAVs), known as caching nodes, where there are more than one possible caching node to handle user's request. To efficiently cope with the dynamic topology of wireless networks and time-varying behavior of ground users, our focus is to develop an efficient connection scheduling framework, where ground users are autonomously trained to determine the optimal caching node, i.e., UAV or FAP. Our aim is to minimize users' access delay by maintaining a trade-off between the energy consumption of UAVs and the occurrence of handovers. To achieve these objectives, we formulate a multi-objective optimization problem and propose the Convolutional Neural Network (CNN) and Q-Network-based Connection Scheduling (CQN-CS) framework. More specifically, to solve the constructed multi-objective connection scheduling problem, a deep Q-Network model is developed as an efficient Reinforcement Learning (RL) approach to train ground users to handle their requests in an optimal and trustworthy fashion within the coupled UAV-based femtocaching network. The effectiveness of the proposed CQN-CS framework is evaluated in terms of the cache-hit ratio, user's access delay, energy consumption of UAVs, handover, lifetime of the network, and cumulative rewards. Based on the simulation results, the proposed CQN-CS framework illustrates significant performance improvements in companion to Q-learning and Deep Q-Network (DQN) schemes across all the aforementioned aspects.

In this paper, we analyze the impact of caching on the performance of a cache enabled system with heterogeneous traffic where one of the users need to be served with confidential data. In this setup, a wireless helper system always serves a dedicated user and it can also serve a user requesting cached content. A cellular network access point is also available to serve the latter user if it cannot retrieve the requested data from the helper’s cache. The impact of caching and secrecy on throughput and delay performance for each user is then examined when the access point can deploy superposition coding to serve both users simultaneously. Two decoding schemes are considered in this work. The first decoding scheme treats interference from parallel transmissions as noise while the second one utilizes the parallel transmission to apply successive decoding for the intended data. Furthermore, network and cache related factors are identified and their impact on the overall performance of the system are analyzed. In order to find the optimal transmission power allocations, two distinct optimization problems are set in this context comparing the two decoding schemes. This will assist to identify the benefits of the considered decoding schemes for each user satisfying the secrecy requirements of the dedicated user and reducing its impact on the overall performance of the system.

Information centric networking (ICN) shifts the focus of existing internet architecture from host-oriented to content-oriented model by enabling in-network caching and content-based forwarding. These ICN features help to increase network performance by decreasing content discovery delay, content server load, and network congestion. To route a content interest inside network such that content can be fetched with minimal time is a challenging task in ICN. The performance of the ICN routing protocol can be significantly improved if the decisions related to content chunk placement and request forwarding are taken in a cooperative fashion. This paper describes a novel strategy for co-operative caching joint request forwarding in ICN, focusing on decreasing content retrieval latency. To do so, the caching strategy leverages the concept of connected dominating set (CDS) for creating a virtual backbone network to eliminate caching redundancy and reduce content discovery delay. It considers content chunk placement and request forwarding tasks as strongly co-related procedures. It exploits the caching information so that the request is forwarded to a content router (CR) with the maximum likelihood of carrying needed data using the betweenness centrality (BC) of CR. The proposed approach also uses the Markov chain-based model to estimate CS hit likelihood and use it as decisional parameter while forwarding the interest packet. This mechanism helps to fetch the content within the shortest possible time duration. The CCJRF-ICN adopts the Dijkstra’s shortest path routing and works in collaboration with the CDS-driven caching joint forwarding mechanism. The simulation study of CCJRF-ICN is done inside ns-3 based ndnSIM-2.0 simulator with performance measures like content store (CS) hit ratio, content discovery delay, mean hop distance, network load, and network overhead. The simulation outcomes demonstrate that CCJRF-ICN outperforms the state-of-the-art strategies for realistic topologies (GEANT, US-26, Euro-28) and shows improvement up to 5-35% against stated performance measures.

The fifth generation cellular networks are required to provide very fast and reliable communications while dealing with the increase of users traffic. In heterogeneous networks (HetNets) assisted with device-to-device (D2D) communications, traffic can be offloaded to small base stations or to users to make content delivery faster and alleviate the traffic burden from the core network. In this paper, we aim to maximize the probability of successfully delivering files to users, referred to as the successful delivery probability, by jointly optimizing the caching placement and channel allocation in cache-enabled D2D-assisted HetNets. First, an analytical expression of the average content delivery delay is derived. The latter is subsequently used to formulate the joint optimization problem of cache placement and channel allocation. Due to the problem’s non-convexity, a linearization transformation is proposed, which allows to find the optimal solution. However, given the high complexity of the problem, we propose a low-complex heuristic approach for channel allocation and caching. Numerical results illustrate the efficacy of the proposed solutions and compare them to the conventional HetNet. Finally, the impact of several key parameters, e.g., transmit power, caching capacity, and QoS requirements, is investigated, which provides design guidelines for D2D-assisted HetNets.

Fog radio access networks (F-RANs) can effectively alleviate fronthaul loads and reduce content transmission delay by migrating cloud services to the network edge. This paper addresses a cooperative caching scenario in F-RAN, where each mobile user can acquire the requested contents from any one of its associated fog-computing-based access points (F-APs). However, caching disparate contents in different F-APs will lead to different content delivery delays, since mobile users suffer from diverse channel fadings and interferences when they download contents from different F-APs. Considering limited caching storage in each F-AP, diverse user preferences, unpredictable user mobility and time-varying channel states, an average transmission delay minimization problem is formulated. With the aid of dueling deep-Q-network framework, a delay-aware cache update policy is proposed for mobile users in F-RAN. The proposed cache update policy will decide to replace the stored contents in F-APs with the proper contents at each time slot. Compared with first in first out, least recently used and least frequently used caching policies, simulation experiments are performed to evaluate the performance of the proposed algorithm. Simulation results illustrate that the proposed caching policy yields better average hit ratio and lower average transmission delay than other traditional caching policies.

The ever-increasing number of diverse and computation-intensive Internet of things (IoT) applications is bringing phenomenal growth in global Internet traffic. Mobile devices with limited resource capacity (i.e., computation and storage resources) and battery lifetime are experiencing technical challenges to satisfy the task requirements. Mobile edge computing (MEC) integrated with IoT applications offloads computation-intensive tasks to the MEC servers at the network edge. This technique shows remarkable potential in reducing energy consumption and delay. Furthermore, caching popular task input data at the edge servers reduces duplicate content transmission, which eventually saves associated energy and time. However, the offloaded tasks are exposed to multiple users and vulnerable to malicious attacks and eavesdropping. Therefore, the assignment of security services to the offloaded tasks is a major requirement to ensure confidentiality and privacy. In this article, we propose a green and secure MEC technique combining caching, cooperative task offloading, and security service assignment for IoT networks. The study not only investigates the synergy between energy and security issues, but also offloads IoT tasks to the edge servers without violating delay requirements. A resource-constrained optimization model is formulated, which minimizes the overall cost combining energy consumption and probable security-breach cost. We also develop a two-stage heuristic algorithm and find an acceptable solution in polynomial time. Simulation results prove that the proposed technique achieves notable improvement over other existing strategies.

With the development of emerging applications such as augmented reality, more and more computing tasks are sensitive to delay. Caching popular task computation results on the mobile edge computing (MEC) server is an effective solution to meet the latency requirements. When multiple users request the same task, if the computation result is cached on the MEC server, it will return the computation result directly to the user to reduce the delay for repeated computation. In this paper, we use the caching to assist the calculation. Non-orthogonal multiple access (NOMA) is used to further reduce the delay for computation offloading. The optimization problem is formulated as how to make caching and offloading decision to minimize the delay of whole system. In the case of unknown popularity, we use Gated Recurrent Unit (GRU) algorithm to predict the task popularity in time-varying system, and place the computing results of tasks with high popularity on the corresponding server. Based on the predicted popularity, a multi-agent Deep-Q-network (MADQN) algorithm is used to solve the caching and offloading problem. The simulation results show that the prediction error of GRU algorithm can be reduced by increasing the learning rate. Meanwhile, the proposed MADQN can effectively reduce the delay compared with other methods.

The IoT devices produce a massive amount of content compared to the host-centric (IP-based) network. The IP-based network has no strengthened capabilities to handle the growing traffic of the IoT network. The existing research is focusing on proposing Information Centric Network (ICN) framework with potential replacement of IP-based network. Using the caching technique in ICN based IoT significantly strengthen the resource-constrained IoT devices, by reducing the access time with optimized energy consumption. Thus, this paper proposes a caching scheme for IoT contents, known as Central Control Caching (CCC) Scheme. This scheme positions a central entity between different autonomous systems that manage caching and updates the content, by keeping information in a table regarding different contents present on different autonomous systems. This information is utilized when cached contents are exchanged among different autonomous systems. Furthermore, when contents becomes stale, the same information is used to identify the autonomous systems with a copy of the outdated content. The simulation results show that the proposed scheme has outperformed in terms of reduced energy consumption, response latency, and cache-hit compared to state-of-the-art works.

By caching the most popular content into mobile devices, users can retrieve content directly from nearby devices through Device to Device (D2D) communications, which can significantly reduce backhaul traffic and improve network performance. Most existing D2D cache placement strategies are proactive approaches, which cannot deal with the problem of timely cache updating. In this paper, we propose a network coding-based socially-aware D2D caching strategy, which takes geographical proximity and the social relationships of users into consideration. First, a physical D2D network with high communication reliability is built according to the geographical proximity, composed of devices with high probability of communicating to each other through stable D2D communications. According to the social relationship between users within the physical D2D network, we partition the devices into communities and rank the devices within same community by their influence degree. Within a community, each caching decision is made independently according to the user-content contribution degree. To calculate the contribution degree, the impact between devices on requesting same content is modeled using an Indian Buffet Process. Devices cache coded blocks instead of the whole content to improve caching efficiency. Simulation results show that the proposed strategy achieves higher cache hit and sum rates compared to other schemes.

Various advanced and mission-critical applications are enabled by the emerging technologies in fifth-generation (5G) mobile communication systems. To ensure improved quality of experience (QoE) of users, 5G and beyond networks require ultra-reliable low-latency communications (URLLC). The successful realization of the URLLC entails the advent of new technological concepts. Therefore, this article presents an overview of the enabling techniques for the URLLC. Classification of the enabling techniques is done and an extensive review of the literature is presented to identify the state-of-the-art techniques, limitations, and the potential approaches for alleviating the limitations. It is observed that artificial intelligence (AI)-enabled edge computing and caching solutions are widely explored as promising techniques to effectively guarantee low latency and reliable content acquisition while reducing redundant network traffic and improving the QoE. Therefore, we present a classification of the AI-enabled edge caching solutions and discuss various mechanisms of the caching agents. In particular, we investigate the use of deep learning (DL), deep reinforcement learning (DRL), and federated learning (FL) algorithms. Subsequently, we analyze the performance of the state-of-the-art edge caching schemes and demonstrate the performance gains of FL frameworks over conventional centralized and decentralized DL and DRL frameworks. We confirm that FL edge caching is a viable mechanism in 5G and beyond networks. On the other hand, it is shown that the IEEE 802.1 time sensitive networking and the emerging IETF deterministic networking standards present effective mechanisms when deterministic networks with bounded ultra-low latency are considered. Finally, we present the open issues and opportunities for further research.

In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching.

Autoencoders implemented by artificial neural networks (ANNs) are utilized to learn the latent space representation of data in an unsupervised manner, and they have been widely used in recommender systems. For instance, several collaborative denoising autoencoder (CDAE) models have shown that their performance gains outperform that of the collaborative filtering based (CF-based) models. In this work, a near-optimal Top-K forecasting solution is proposed for our advanced autoencoder recommender systems. We propose a method which utilizes CDAE model in predicting the Top-K popular videos in an upcoming time period. In order to improve the prediction accuracy, we also propose an autoencoder based recommendation algorithm with the help of K-means clustering that upgrades the performance of the original autoencoder model. The experimental results show that our method increases significantly the Average Precision (AP) and Recall values by nearly 30%. We then further utilize our proposed autoencoder model with clustering in predicting Top-K popular videos. The applications of predicting Top-K popular videos can be used in the video delivery for the Mobile Edge Computing (MEC) environment to avoid bottleneck in the constricted capacity of backhaul link. Namely, the performance gain will be upgraded if our proposed method precisely predicts and caches the Top-K popular videos in advance with the help of a better forecasting model.

Data communication in the present Internet paradigm is dependent on fixed locations that disseminate similar data several times. As a result, the number of problems has been generated in which location dependency is the most crucial for communication. Therefore, Named Data Networking (NDN) is a new network architecture that revolutionized the handling gigantic amount of data generated from diverse locations. The NDN offers in-network cache which is the most beneficial feature to reduce the difficulties of location-based Internet paradigms. Moreover, it mitigates network congestion and provides a short stretch path in the data downloading procedure. The current study explores a new comparative analysis of popularity-based cache management strategies for NDN to find the optimal caching scheme to enhance the overall network performance. Therefore, the content popularity-based caching strategies are comparatively and extensively studied in an NDN-based simulation environment in terms of most significant metrics such as hit ratio, content diversity ratio, content redundancy, and stretch ratio. In this analysis, the Compound Popular Content Caching Strategy (CPCCS) has performed better in terms to enhance the overall NDN-based caching performance. Therefore, it is suggested that the CPCCS will perform better to achieve enhanced performance in emerging environments such as, Internet of Things (IoT), Fog computing, Edge computing, 5G, and Software Defined Network (SDN).

Proactive content caching at the wireless network edge, such as users and small base stations (SBSs), is an effective way to deal with high mobile traffic. In this paper, based on the user mobility and social relationships, we investigate the optimal caching strategy in device-to-device (D2D) communications underlying heterogeneous networks, where several SBSs are within the coverage of a macrobase station (MBS). Except for SBSs, important users (IUs) hired by an operator also cache files. First, assuming that user preference, i.e., the probability distribution of different file requests of users, are unknown, we cluster users and predict each user preference based on their history file requests by fitting to the Zipf distribution. Second, we derive the closed-form expression of the average system cost by jointly considering the mobility of users and the social relationship between them. With the purpose of minimizing the average system cost, we optimize the SBSs and IU caching strategies. The optimization problem is NP-complete. To solve the problem, we demonstrate that this problem belongs to the minimization of a supermodular function over a partition matroid, and thus, we provide a locally greedy caching algorithm with an approximation ratio of 2 to obtain the sub-optimal solution in polynomial time. Finally, since the operator can reduce the system cost by hiring more IUs, requiring higher payments, we reach a tradeoff by determining the number of IUs. The simulation results show that the proposed caching strategy outperforms the traditional caching strategy, and the suboptimal solution obtained by proposing the greedy algorithm is close to the optimal solution.

The proliferation of network devices and novel bandwidth hungry applications over the existing network imposes novel challenges in terms of fulfilling the users’ requirements. Dense deployment of small cells is thought to be a promising solution to fulfill these requirements. However, the user association in such dense networks becomes challenging and can greatly affect the network performance, as a user in such dense deployments can be connected to any of the available base stations. Traditionally, the user association has been performed based on the signal strength, however, such an approach does not apply when taking into account novel bandwidth hungry applications. Moreover, in recent years, a successful paradigm has been proposed to handle such bandwidth hungry applications, i.e., caching at small cell base stations. In this paper, we aim to solve this joint problem of user association and content caching in a dense small cell setting. To solve this problem, we present a novel iterative scheme that uses matching theory and a learning approach to find a suboptimal solution of the joint NP hard problem. Note that the user association and cache placement are strongly coupled, i.e., the association of users at a base station will determine the cache placement at base stations and the availability of cache at base stations will force the users to change their associations. Simulation results show that the proposed scheme (i.e., cache aware user association) significantly outperforms the cache unaware scheme and achieves a performance gain of up to 31% in terms of normalized utility and saves up to twice the backhaul bandwidth. Moreover, the proposed scheme also achieves up to 82% of the utility obtained by the optimal solution.

Named data networking (NDN) and edge cloud computing (ECC) are emerging technologies that are considered as the most representative technologies for the future Internet. Both technologies are the promising enabler for the future Internet such as fifth generation (5G) and beyond which requires fast information response time. We believe that clear benefits can be achieved from the interplay of NDN and ECC and enables future network technology to be much more flexible, secure and efficient. In this paper, therefore, we integrate NDN with ECC in order to achieve fast information response time. Our framework is based on N-Tier architecture and comprises of three main Tiers. The NDN is located at the Tier1 (Things/end devices) and comprises of all the basic functionalities that connect Internet of Things (IoT) devices with Tier 2 (Edge Computing), where we have deployed our Edge node application. The Tier 2 is then further connected with Tier 3 (Cloud Computing), where our Cloud node application is deployed at multiple hops on the Microsoft Azure Cloud machine located in Virginia, WA, USA. We implement an NDN-based ECC framework and the outcomes are evaluated through testbed and simulations in terms of interest aggregation, round trip time (RTT), service lookup time with single query lookup time and with various traffic loads (load-based lookup time) from the IoT devices. Our measurements show that enabling NDN with edge computing is a promising approach to reduce latency and the backbone network traffic and capable of processing large amounts of data quickly and delivering the results to the users in real time.

Content-centric mobile hybrid Internet-of-Things (IoT) networks consisting of mobile devices and static femto access points (FAPs) are studied, where each device moves according to the random walk mobility model and requests a content object from the library independently at random according to a Zipf popularity distribution. Instead of allowing access to content objects at macro base stations via costly backhaul providing connection to the core network, we consider a more practical scenario where mobile devices and static FAPs, each having a finite-size cache space, are able to cache a subset of content objects so that each request is served by other mobile devices or static FAPs. Under a general multihop-based content delivery protocol, we analyze the order-optimal throughput-delay trade-off by presenting a new cache allocation strategy. In particular, under a given caching strategy, we first characterize a throughput-delay trade-off in terms of scaling laws along with the general content delivery multihop routing protocol. Then, the order-optimal throughput-delay trade-off is characterized by presenting the order-optimal cache allocation strategy, which jointly finds the replication sets at mobile devices and static FAPs via a novel variable decoupling approach. In our mobile IoT network, an interesting observation is that highly popular content objects are mainly served by mobile devices while the rest of content objects are served by static FAPs. We perform numerical evaluation to validate our analytical results. We also show that the order-optimal strategy strictly outperforms a baseline approach, where the replication sets at mobile devices and static FAPs are optimized separately.

Proactive caching at the base station (BS) is a promising way to leverage the user-behavior-related information to boost network throughput and improve user experience. However, the gain of caching at the mobile edge highly depends on random user behavior and is largely compromised by the uncertainty in predicting behavior-related information. First, the local file popularity in each cell may not be skewed. Second, the local file popularity varies quickly due to user mobility even if the lifetime of each file is long. Furthermore, considering the small population of users that initiate requests in each cell, the local popularity in the next cache update period is not easy to predict accurately, because users may not request their interested files in this period, despite that the popularity can be indirectly obtained by predicting the mobility and preference of each individual user in a cell. To address such issue, in this paper, we integrate recommendation with caching at BS, aiming at improving cache efficiency whereas not violating user preference. In particular, we propose a temporal-spatial recommendation policy, which can guide mobile users to request their preferred files in proper time and place, so as to make local popularity peakier. We do not assume that the user preference, the impact of the recommendation on request probability, and the mobility pattern are known. Hence, we resort to deep reinforcement learning to optimize recommendation and caching policy. To deal with the difficulty in predicting local popularity in the next cache replacement period, we model the user preference and request probability with Bernoulli mixture distribution and hence can estimate them separately. The simulation results demonstrate that the proposed policy can reduce the cache miss number, compared to the policies without any recommendation and without temporal-spatial recommendation.

To address the drastic growth of data traffic dominated by streaming of video-on-demand files, mobile edge caching/computing (MEC) can be exploited to develop intelligent content caching at mobile network edges to alleviate redundant traffic and improve content delivery efficiency. Under the MEC architecture, content providers (CPs) can deploy popular video files at MEC servers to improve users' quality of experience (QoE). Designing an efficient content caching policy is crucial for CPs due to the content dynamics, unknown spatial-temporal traffic demands, and limited service capacity. The knowledge of users' preference is very useful and important for efficient content caching, yet often unavailable in advance. Under this circumstance, machine learning can be used to learn the users' preference based on historical demand information and decide the video files to be cached at the MEC servers. In this paper, we propose a multi-agent reinforcement learning (MARL)-based cooperative content caching policy for the MEC architecture when the users' preference is unknown and only the historical content demands can be observed. We formulate the cooperative content caching problem as a multi-agent multi-armed bandit problem and propose a MARL-based algorithm to solve the problem. The simulation experiments are conducted based on a real dataset from MovieLens and the numerical results show that the proposed MARL-based cooperative content caching scheme can significantly reduce content downloading latency and improve content cache hit rate when compared with other popular caching schemes.

Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute- and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions.

Pushing contents to users with device-to-device (D2D) data sharing is considered as a promising solution to overcome backhaul congestion. However, this model is associated with critical issues which are user selfishness in terms of sharing personal resources and security concern when connecting with strange devices. Therefore, in this paper, we propose the notion of random D2D connection where the probabilities that the devices are connected for receiving or sharing data are manipulated by users themselves. Moreover, these probability values can also be treated as variables to be optimized. Based on that, we address the backhaul congestion issue and formulate it as a non-convex optimization problem. Two solving schemes are proposed based on primal decomposition and the alternating direction method of multipliers algorithm, respectively. In addition, these methods are designed in both centralized and distributed manners. Besides that, the congestion probability expression is derived in the special case giving an upper bound for the performance of our presented solution. The numerical results are provided to illustrate the effectiveness of the proposed method.

An emerging trend of next generation communication systems is to deploy caches at network edges to reduce file delivery latency. To investigate this aspect, we study the fundamental limits of a cache-aided broadcast-relay wireless network consisting of one central base station, $M$ cache-equipped transceivers and $K$ receivers from a latency-centric perspective. We use the normalized delivery time (NDT) to capture the per-bit latency at high signal-to-noise ratio (SNR). The objective is to jointly design the cache placement and file delivery in order to minimize the NDT. To this end, we establish two converse results and two achievability schemes. The first converse result is restricted to one-shot delivery schemes, while the second excludes this restriction. Similarly, the first achievable scheme is a general NDT-optimal one-shot scheme that synergistically exploits both multicasting and distributed zero-forcing opportunities. With respect to the second converse result, this scheme performs well for various parameter settings, particularly at higher cache sizes. The second scheme, effective at lower cache sizes, designs beamformers to facilitate both subspace interference alignment and zero-forcing. Exploiting both schemes, we are able to characterize the optimal tradeoff between cache storage and latency in networks satisfying $K+M\leq 4$ . The tradeoff illustrates that the NDT is the preferred choice to capture the latency of a system rather than the commonly used sum degrees of freedom (DoF). In fact, our optimal tradeoff refutes the popular belief that increasing cache sizes translates to increasing the achievable sum DoF. As such, we discuss cases where increasing cache sizes decreases both the delivery time and the achievable DoF.

As a promising architecture in the future networks, fog radio access network (F-RAN) can effectively improve network performance due to the advantages in both communication and computation aspects. In this paper, the data caching scheme is studied in a device-to-device (D2D) enabled the F-RAN from the social point of view. First, we help each access point (AP) to ascertain its best belonging community in terms of the content preference based on the projective adaptive resonance theory neural network (PART NN). The APs within identical community cache data cooperatively, which can reduce the caching redundancy. Considering the content preference of the AP may change dynamically, a community update scheme is also studied after the community construction is completed. In order to further reduce the content delivery delay, the D2D technology is resorted to and the most appropriate user equipment (UE) is selected to cache data for other UEs within each AP's coverage. After that, an ant colony optimization scheme is proposed to find out the most requested contents needed to be cached by such most appropriate UE. The effectiveness of the proposed scheme is finally validated by the simulation experiments.

In this paper, a cache-enabled wireless network with circuit power consumption is considered, and the strategy of joint transmission and local caching is studied. The optimization problem of energy consumption minimization is formulated, where the optimized variables include the transmission rate and the local caching rate. The constraint conditions contain the maximum data departure curve, the minimum data departure curve, and the range of the local caching rate. The original problem is intractable and is transformed into a two-layer optimization one by variable substitution. Under given caching policy, the sufficient and necessary conditions for the optimal offline transmission strategy are deduced by using the transmission characteristics. Then, an energy-efficient offline transmission strategy is proposed, and its optimality is proved. The simulation results are provided to verify the performance of the proposed strategy. The simulation results reveal the impact of the cache capacity and the file popularity on energy consumption. Under the same conditions, the performance of the proposed strategy is better than that of the other known algorithms. The proposed strategy to energy-efficient communication is helpful for future green communication.

Current video streaming services use a conventional, client-server network topology that puts a heavy load on content servers. Previous work has shown that Peer-to-Peer (P2P) assisted streaming solutions can potentially reduce this load. However, implementing P2P-assisted streaming poses several challenges in modern networks. Users tend to stream videos on the go, using their mobile devices. This mobility makes the network difficult to orchestrate. Furthermore, peers have to contribute their storage to the network, which is challenging, since mobile devices have limited resources compared to desktop machines. In this paper, we introduce an analytical framework for mobile P2P-assisted streaming to estimate the server load that we define as the minimum required server upload rate. Using our framework, we evaluate four caching strategies: infinite cache as a baseline, first in first out (FIFO), random, and Random Linear Network Coded (RLNC) cache. We verify our analytical results with empirical data that was obtained by carrying out extensive measurements on our working P2P system. Our results show that when employing FIFO, random, and RLNC caching strategies, the server load converges to that of the infinite cache as the cache size increases. With a limit of 5 P2P connections per peer, we show that using the random caching, peers can store 40% fewer packets and still achieve the same benefit as with FIFO caching. When using the RLNC caching, it is enough to store 50% fewer packets to achieve the same benefit.

The spinal cord plays a key role for big data processing in the central nervous system, which is composed of the brain and spinal cord. A close look to the spinal cord reveals that the main functions of fog nodes such as communication, computation, and storage capability define what spinal cord does in the central nervous system. Based on this analogy, a new network architecture is described dubbed brain-spinal cord-nerve network that bears a striking resemblance to cloud-fog-thing network architecture under consideration for 5G networks. A stochastic geometry analysis is performed for this network to specify the optimum number of special neurons at the spinal cord responsible for learning. Additionally, to provide an alternative model for some fundamental motor skills in our daily life such as driving, swimming, dancing, and the brain-spinal cord-nerve network is modeled as a coded cache. These findings can be quite useful for neuroscientists who may want to apply the fog network model to the central nervous system with the ultimate aim of treating serious central nervous system diseases. Lastly, a novel coded caching structure is developed for fog networks inspired by the central nervous system.

In this paper, we investigate the joint design of transmit beamforming over physical layer and file dissemination strategy at base stations (BSs) over transport layer in the cache-enabled cloud radio access networks. Our objective is to minimize the total user file download delay while satisfying each BS's power constraint and file dissemination proportion constraints. This problem is very challenging because of the highly coupled optimization variables in the objective function. To address the untractable non-convex problem, the original problem is decomposed into two subproblems. The first subproblem is a non-convex fractional program problem, and by transforming it into a second-order cone program, an iterative algorithm based on an inner approximation method is proposed to achieve the optimal beamforming vectors. For the other subproblem, we propose a sparse optimization algorithm and prove that there exists a closed-form solution in terms of l0-norm. Then, the original non-convex problem is addressed in an iterative manner by employing the two proposed sub-algorithms. Numerical results demonstrate that the proposed cross-layer optimization algorithms can efficiently reduce the total user download delay.

In this paper, we investigate the effect of bursty traffic and random availability of caching helpers in a wireless caching system. More explicitly, we consider a general system consisting of a caching helper with its dedicated user in proximity and another non-dedicated user requesting for content. Both the non-dedicated user and the helper have limited storage capabilities. When the user is not able to locate the requested content in its own cache, then its request shall be served either by the caching helper or by a large data center. Assuming bursty request arrivals at the caching helper from its dedicated destination, its availability to serve other users is affected by the request rate, which will further affect the system throughput and the delay experienced by the non-dedicated user. We characterize the maximum weighted throughput and the average delay per packet of the considered system, taking into account the request arrival rate of the caching helper, the request probability of the user, and the availability of the data center. Our results provide fundamental insights in the throughput and delay behavior of such systems, which are essential for further investigation in larger topologies.

While augment reality applications are becoming popular, more and more data-hungry and computation-intensive tasks are delay-sensitive. Mobile edge computing is expected to an effective solution to meet the low latency demand. In contrast to previous work on mobile edge computing, which mainly focus on computation offloading, this paper introduces a new concept of task caching. Task caching refers to the caching of completed task application and their related data in edge cloud. Then, we investigate the problem of joint optimization of task caching and offloading on edge cloud with the computing and storage resource constraint. We formulate this problem as mixed integer programming which is hard to solve. To solve the problem, we propose efficient algorithm, called task caching and offloading (TCO), based on alternating iterative algorithm. Finally, the simulation experimental results show that our proposed TCO algorithm outperforms others in terms of less energy cost.

In-network caching is a key feature of information-centric networking (ICN). However, challenges still exist in ICN caching such as how to place content replicas among cache nodes to maximize cache system benefits without introducing too much overhead. In this paper, we formulate the content placement problem and propose a distributed probabilistic caching strategy to enhance cache efficiency. Each node makes cache decision individually and caches passing content with certain probability, which is proportional to content popularity and content placement benefit. As a component of our scheme, we also propose an accurate method to predict the variations of content popularity. Besides, a global-popularity-based caching scheme is proposed to be used as a benchmark for performance evaluation. We conduct extensive simulations based on ndnSIM and evaluate our scheme on tree, intra-AS, and inter-AS topologies. Results indicate it outperforms the state-of-art schemes in terms of cache hit ratio, access latency, cache operation cost, and link bandwidth savings. It can achieve dramatic performance improvement, even in the case of a small cache size. In particular, the reduction of caching operation can reach up to two orders of magnitude. We also examine the impacts of various replacement policies on cache performance and perform overhead analysis. Finally, we give a simplified implementation of our scheme and validate it via simulations.

Cloud computing is a paradigm that offers storage, computation, and software services on demand in the Cloud and far from end users. These services and computations are then extended to the Edge of the network with the Edge computing paradigm. This paradigm offers computation, data, and application services in close proximity of end users. Future Internet architectures will result in a fast information response time, and low latency will be a main feature of evolving 5th generation (5G) radio networks. To ensure the widespread adoption of 5G applications, low latency, security, mobility, and scalable content distribution support is required. Information-centric networking (ICN) is a newly proposed future Internet paradigm in which communication is based on content names irrespective of their locations. At the same time, ICN promises efficient content delivery, mobility support, scalability, and security for content. The Edge computing and ICN provide an opportunity to reduce latency, support mobility, security, and scalability. In this paper, discussions on Edge computing with ICN are provided. In detail, the Edge computing proposals, use cases, differences among Edge computing proposals and drivers for Edge computing are investigated. The Edge computing standardization, research, and industry/vendors collaboration overview are studied. Applications of ICN integration in Edge computing and their advantages and limitations are highlighted. We conclude our paper by describing potential directions for future research in the field of ICN over Edge computing.

Millimeter-wave (mmWave) communications are a key enabler of gigabit access using large spectrum resources. Most relevant works focus on the design of radio access networks (RANs), but a transport layer design tends to be overlooked despite its importance as a data supply to the RAN. Recent studies reported that transmission control protocol (TCP)-the de facto standard for the transport layer-is problematic in mm-Wave communications because the sending rate cannot be controlled due to the drastic channel status change between line-of-sight (LoS) and non-LoS (NLoS); this is known as TCP performance collapse problem (TPCP). The TPCP can be alleviated if a cache is deployed in a base station, such that TCP packets from servers are stored in the cache during NLoS and forwarded to the RAN immediately when the channel becomes LoS. This paper overcomes the TPCP by making full use of the mmWave spectrum via efficient cache utilization. Here, a caching gain is analyzed and defined as the end-to-end rate ratio between TCPs with and without cache. This results show that the gain is maximized by forwarding packets to the RAN whenever the wireless channel is available. Based on this, we propose a novel cache-enabled TCP framework called mmWave TCP (mmTCP) with two key functionalities. The first is a batch retransmission, where packets likely to be lost during NLoS are simultaneously retransmitted once the channel becomes LoS. The second is an online cache management, where the cache is being reallocated to different users given the current channel and cache status. The performance of the mmTCP is evaluated by a realistic network simulator-version 3 under different environments, such that 48% and 10% end-to-end rate improvements are made in singleand multi-user cases, respectively.

Caching popular contents is a promising way to offload the mobile data traffic in wireless networks, but so far the potential advantage of caching in improving physical layer security (PLS) is rarely considered. In this paper, we contribute to the design and theoretical understanding of exploiting the caching ability to improve the transmission security in a wireless heterogeneous network. Two transmission schemes, namely Symbol-level scheme and Bit-level scheme, are considered in such network to improve PLS by utilizing the content property of cached files. In Symbol-level scheme, the base station (BS) ensures the secrecy of communication by transmitting the requested file along with a pre-cached file of the user such that the eavesdropper's channel is degraded. In Bit-level scheme, the BS encodes the requested file with a pre-cached file to fully resist the overhearing of eavesdroppers in bit level, e.g., network coding. Accordingly, the node locations of BSs, users and eavesdroppers are first modeled as mutually independent Poisson point processes and the corresponding file access protocol is developed. We consider two scenarios where each scheme is employed respectively, and then derive analytical expressions of two secrecy metrics in terms of average secrecy rate and secrecy coverage probability. Numerical results are provided to show the significant security advantages of the proposed schemes and to characterize the impacts of the cache ability and network resources on the security performance.

As an extension of cloud computing, fog computing at the edge of networks provides low latency, location awareness, and real-time interactions. At the same time, millimeter-wave communications are able to provide directional multi-gigabit transmission rates with large available bandwidth. Based on the user mobile trajectories in a region, several activity hotspots that users pass by frequently can be obtained. By caching popular content at the edge nodes near the hotspots, users can download the cached content directly at a short distance, and the user experience can be significantly improved. Considering multiple hotspots in a region, how to efficiently schedule the transmission for the caching at edge nodes becomes a key problem. In this paper, we focus on the problem of mobility-aware transmission scheduling for caching at edge nodes near hotspots and utilize the multi-hop relaying and concurrent transmissions to achieve better performance. After formulating the optimal scheduling problem as a stochastic nonlinear mixed integer program, we propose a mobility-aware caching scheduling scheme, called Multi-Hop Relaying-based Caching, where multi-hop D2D paths are established for edge nodes, and concurrent transmissions are exploited in the scheduling of caching at edge nodes. Extensive performance evaluation demonstrates that MHRC achieves more than $1\times $ higher expected cached data amount compared with the state-of-the-art schemes.