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Smart cities are driven by huge amount of data collected from sensors deployed across the city. Sensors typically form a multi-hop network with a base station (BS ) in order to send their data to the command and control center. However, sparse deployment of sensors can leave subsets of the network partitioned from the rest of the network. In such a case, isolated partitions cannot forward their data to the BS . Consequently, network coverage and data fidelity decline. A possible solution to link partitions and provide connectivity is employing mobile data collectors (MDCs). A smart vehicle supporting wireless communication can act as an MDC and carry data between sensors and the BS . Using a single MDC extends the average tour length. To minimize the maximum tour length, multiple MDCs can be employed. To identify sensors to be visited by each MDC, this paper clusters partitions as many as the number of MDCs and assigns an MDC for each cluster. Then two different cooperative data collection schemes are considered based on the availability of inter-MDC data exchange. If MDCs collaborate in data delivery, they meet at certain meeting points for data exchange. Such a cooperation avoids the requirement of visiting the BS for some MDCs and reduces tour lengths. On the other hand, MDCs closer to the BS can experience data loss due to buffer overflow given the higher volume of the accumulated data. Presented approaches are evaluated in terms of maximum tour length, data latency, and data loss. The smart city application is simulated with deployment of sensors on certain amenity types. Geographic data is obtained from a volunteered geographic information system and MDC mobility is restricted with the road network. Obtained results indicate that MDC cooperation decreases maximum tour length at the expense of increased rate of data loss and data latency.

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).

Information-centric networking (ICN) as an emerging networking paradigm has recently gained significant attention, due to the improvement of content delivery efficiency. The built-in network storage for caching is a key component in ICN to provide low latency service and reduce high backhaul traffic by caching popular content. However, users’ content preference contains individual sensitive characteristics which is distinguishable from others. Therefore, in this work, we propose a data-driven caching revenue maximization problem with the considerations of users’ local differential privacy. Specifically, we employ dBitFlip, a local differential privacy (LDP) mechanism, to locally add differential private noise to the users’ preference content information. We leverage data-driven approach to predict the content popularity based on the reference distribution constructed by the reported noisy preference content data from users, mathematically present the distance between the noisy reference distribution and the true distribution by the tolerance level, and prove the relationship among the tolerance level, differential privacy budget and the confidence level. We provide feasible solutions to the proposed revenue maximization problem, and conduct simulations to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Caching content in small-cell networks can reduce the traffic congestion in backhaul. In this article, we develop a hybrid caching network comprising of unmanned aerial vehicles (UAVs) and ground small-cell base stations (SBSs), where UAVs are preferred because of their flexibility and elevated platform for line of sight. First, we derive the association probability for the ground user affiliated with a UAV and ground SBS. Then, we derive the successful content delivery probability by considering both the intercell and intracell interference. We also analyze the energy efficiency of the hybrid network and compare it with the separate UAV and ground networks. We further propose the caching scheme to improve the successful content delivery by managing the content popularity, where the part of the caching capacity in each UAV and ground SBS is reserved to store the most popular content (MPC), while the remaining stores less popular contents. Numerical results unveil that the proposed caching scheme has an improvement of 26.6% in content delivery performance over the MPC caching, which overlooks the impact of content diversity during caching. [ABSTRACT FROM AUTHOR]

Many specific problems ranging from theoretical probability to applications in statistical physics, combinatorial optimization and communications can be formulated as an optimal tuning of local parameters in large systems of interacting particles. Using the framework of stationary point processes in the Euclidean space, we pose it as a problem of an optimal stationary marking of a given stationary point process. The quality of a given marking is evaluated in terms of scores calculated in a covariant manner for all points as a function of the proposed marked configuration. In the absence of a total order of the configurations of scores, we identify intensity-optimality and local optimality as two natural ways for defining optimal stationary marking. We derive tightness and integrability conditions under which intensity-optimal markings exist and further stabilization conditions making them equivalent to locally optimal ones. We present examples motivating the proposed, general framework. Finally, we discuss various possible approaches leading to uniqueness results. [ABSTRACT FROM AUTHOR]

In this paper, a file popularity and size-aware (PSA) caching scheme is proposed for a hybrid heterogeneous network (HetNet) comprising a file server, macro base stations (BSs) operating in the microwave frequency bands, and pico BSs operating in the millimeter wave frequency wave bands. Different than the state-of-the-art size-weighted popularity (SWP) and popularity-based caching schemes that assume equal file size, the file PSA caching scheme utilizes knowledge of the file popularity and size distributions to maximize the network average cache hit probability and the network average success probability subject to the available cache capacity. Moreover, the maximization of the network average success probability is shown to be non-convex and dynamic programming and branch and bound algorithms are employed to circumvent its convexity. New expressions for the optimal file caching probabilities that maximize the network average success probability in noise-limited and interference-limited HetNet scenarios are provided which are evaluated using numerical techniques. The results demonstrate that the proposed file PSA caching scheme provides up to 7% gain in the network average success probability compared to the SWP-based scheme. Lastly, the combination of coded caching with cooperative transmission achieves up to 40% gain compared to a non-cooperative transmission scheme without coded caching. [ABSTRACT FROM AUTHOR]

Today, with the worldwide offer and rapid increment in multimedia applications on the web, the demands of users to get them accessed are also increasing prominently. The users in vehicular environment too expect efficient multimedia streaming while travelling on the road. However, the high mobility of vehicles as well as the limited transmission range of infrastructure components in IP based network provides low performance by offering high delay and additional network overhead. To provide better Quality of Experience (QoE) with high performance, Information Centric Networking (ICN) is blended with vehicular environment. Caching the content inside network nodes is inherent feature of ICN with various associated benefits such as low content retrieval delay, less network traffic, path reduction and so on. However, challenges still exists for caching the content due to resource constrained network environment (such as limited cache capacity, node battery) as well as for secure delivery of cached data. To solve these challenges and to enhance network performance, we propose a cooperative caching scheme in hierarchical network architecture that jointly considers cache location as well as combined content popularity and predicted future rating score while making caching decision. The proposed approach uses two layer hierarchical architecture where nodes in edge layer are divided into clusters. The proposed scheme uses modified Weighted Clustering Algorithms (WCA) for selection of cluster heads which are then used to decide cache location. A probability matrix is used to compute content caching probability which considers both popularity and predicted future rating of content. The proposed approach dynamically predict the user’s preferences using non-negative matrix factorization (NMF) - a machine learning technique which eventually provides prediction of future rating. Based on the selection of both cache location and content to cache, the proposed scheme can effectively cache the content in the network. Further, to deal with the secure delivery of cached content, this work supports legitimate user authorization at edge nodes. The performance of the proposed scheme is evaluated in MATLAB parallel computing toolkit. The results prove significant caching improvement in terms of cache hit, hop reduction and average delay using our proposed scheme. [ABSTRACT FROM AUTHOR]

Summary: The demand for mobile broadband services is increasing exponentially alongside with user expectations regarding the reachability of these services and their prices. This paper presents an integrated satellite and fifth generation (5G) network for providing inflight connectivity and evaluates the economic viability of offering broadband connectivity to passengers on commercial airplanes by the development of a techno‐economic framework, which considers both capital and operational expenditures to compute the total cost of ownership (TCO) and an average cost per user and per Megabyte. Results show high operational costs mainly due to satellite bandwidth usage. Therefore, caching popular content on the network onboard is beneficial to reduce the traffic carried over the satellite link, thus lowering the operational costs as well. Furthermore, the framework is used to compare the identified business models for Inflight Entertainment and Connectivity offerings and their pricing strategies, alongside a benchmark against the current inflight connectivity pricing. Finally, a sensitivity analysis is elaborated in order to mitigate the uncertainty of inputs (e.g., rate of caching) used to feed the TCO model. Following concrete recommendations are the main result of this research: (1) Providing inflight broadband services with a 2–5 Mbps throughput per user is feasible if satellite communications is integrated into the 5G network. (2) Caching popular data reduces the operational costs and hence the average cost per user (from 25% to 32% depending on the caching rate adopted). (3) This framework allows to provide recommendations on the best suited business models and related pricing schemes. [ABSTRACT FROM AUTHOR]

Summary: The first true multitechnology communication system is 5G, which is expected to have a large impact on society and industry. The European Commission funded H2020 5G‐PPP Phase 2 project SaT5G addressed the plug‐and‐play integration of satellite communication into 5G. One of the SaT5G use cases corresponds to the delivery of 5G connectivity services to moving platforms such as aircraft via GEO/MEO satellite backhauling. With focus on this use case, this paper elaborates on the practical implementation and measurement results obtained within the 5G Aero testbed developed as part of the SaT5G project. The 5G Aero testbed activities focus on the next generation of connectivity and content distribution services to airplanes through satellite and terrestrial integration in 5G at the user, control and management planes. Software‐defined networking (SDN) and network functions virtualisation (NFV) are key enablers to develop a powerful end‐to‐end testbed that can accelerate the adoption of multi‐access edge computing (MEC) for the next‐generation in‐flight entertainment and connectivity (IFEC) services, which use geostationary (GEO) and medium Earth orbit (MEO) satellite backhauling technologies. Hence, measurement results obtained from both over‐the‐air demonstration over the O3b MEO satellite constellation and in‐lab validation over an emulated GEO satellite link are presented, towards the next‐generation 5G‐enabled IFEC services. [ABSTRACT FROM AUTHOR]

As one key enabler of Intelligent Transportation System (ITS), Vehicular Ad Hoc Network (VANET) has received remarkable interest from academia and industry. The emerging vehicular applications and the exponential growing data have naturally led to the increased needs of communication, computation and storage resources, and also to strict performance requirements on response time and network bandwidth. In order to deal with these challenges, Mobile Edge Computing (MEC) is regarded as a promising solution. MEC pushes powerful computational and storage capacities from the remote cloud to the edge of networks in close proximity of vehicular users, which enables low latency and reduced bandwidth consumption. Driven by the benefits of MEC, many efforts have been devoted to integrating vehicular networks into MEC, thereby forming a novel paradigm named as Vehicular Edge Computing (VEC). In this paper, we provide a comprehensive survey of state-of-art research on VEC. First of all, we provide an overview of VEC, including the introduction, architecture, key enablers, advantages, challenges as well as several attractive application scenarios. Then, we describe several typical research topics where VEC is applied. After that, we present a careful literature review on existing research work in VEC by classification. Finally, we identify open research issues and discuss future research directions. [ABSTRACT FROM AUTHOR]

We report an observation at Jhalana Leopard Reserve (JLR), Jaipur, India. On 16 March 2019, we saw a female walking up the mountain while calling her two, 4-month-old, male and female cubs. This allowed several safari jeeps to park in the shade of nearby Acacia trees. Two Striped Hyenas (Hyaena hyaena) approached and sniffed at the base of one of the trees near the parked jeeps. Upon seeing the Hyaenas, the female ran down the mountain, passed between the jeeps, and climbed into the tree. That is when we noticed the body of the female cub at a height of approximately 4.5 m. The female licked the body of the cub for several minutes and then picked it up in her mouth, climbed down from the tree, passed again between the jeeps, and walked up the mountain towards a dense stand of Thor (Euphorbia caducifolia), a thorny cactus. Our observations display how a mother leopard that has lost a cub refuses to abandon the carcass, cached it in a tree, and when discovered by scavengers removed it to a thicket of cacti. We consider the behavior towards her dead cub, and subsequent caching when discovered by other animals, to be the first evidence for thanatological expression in leopards. [ABSTRACT FROM AUTHOR]

This paper comprehensively studies a content-centric mobile network based on a preference learning framework, where each mobile user is equipped with a finite-size cache. We consider a practical scenario where each user requests a content file according to its own preferences, which is motivated by the existence of heterogeneity in file preferences among different users. Under our model, we consider a single-hop-based device-to-device (D2D) content delivery protocol and characterize the average hit ratio for the following two file preference cases: the personalized file preferences and the common file preferences. By assuming that the model parameters such as user activity levels, user file preferences, and file popularity are unknown and thus need to be inferred, we present a collaborative filtering (CF)-based approach to learn these parameters. Then, we reformulate the hit ratio maximization problems into a submodular function maximization and propose two computationally efficient algorithms including a greedy approach to efficiently solve the cache allocation problems. We analyze the computational complexity of each algorithm. Moreover, we analyze the corresponding level of the approximation that our greedy algorithm can achieve compared to the optimal solution. Using a real-world dataset, we demonstrate that the proposed framework employing the personalized file preferences brings substantial gains over its counterpart for various system parameters. [ABSTRACT FROM AUTHOR]

The reach on the applications of internet is growing faster than any other technology. Mobile phones are becoming powerful and accessible to everyone. With the wider reach of Internet and smart phones, information explosion is happening in the field of information technology. It is very important to make required information available instantly for smart phone users. Hence, in this work, few enhancements are made by introducing cache sharing and pre-fetching together with noise reduction to improve overall performance of the mobile phone for optimal cache memory utilisation, network traffic reduction, efficient bandwidth utilization and latency reduction. Caching combined with prefetching is used to get frequent and recently used contents in cache of mobile phone without much delay. Whenever a new content is searched it is invariably fetched from the server. If users keep searching for new information more than reusing the content, then cache utilisation will be less and network traffic will be more resulting in more server hits and latency. In order to reduce server hits, sharing of cache contents amongst mobile users is effective. In the proposed method, the junk/noise contents are identified and removed from cache/pre-fetch area, to improve the overall performance. The experimental results revealed that the hybrid method with noise reduction has improved the cache performance much better than any other methods in the mobile phone environment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-tiered segment-based Device-to-Device (S-D2D) caching approach to decrease the startup and playback delay experienced by Video-on-Demand (VoD) users in a cellular network. In the S-D2D caching approach cache space of each mobile device is divided into two cache-blocks. The first cache-block reserve for caching and delivering the beginning portion of the most popular video les and the second cacheblock caches the latter portion of the requested video les 'fully or partially' depending on the users' video watching behaviour and popularity of videos. In this approach before caching, video is divided and grouped in a sequence of xed-sized fragments called segments. To control the admission to both cache blocks and improve the system throughput, we further propose and evaluate three cache admission control algorithms. We also propose a video segment access protocol to elaborate on how to cache and share the video segments in a segmentation based D2D caching architecture. We formulate an optimisation problem and find the optimal cache probability and beginning-segment size that maximise the cache-throughput probability of beginning-segments. To solve the non-convex cache-throughout maximisation problem, we derive an iterative algorithm, where the optimal solution is derived in each step. We used extensive simulations to evaluate the performance of our proposed S-D2D caching system. [ABSTRACT FROM AUTHOR]

We consider distributed and dynamic caching of coded content at small base stations (SBSs) in an area served by a macro base station (MBS). Specifically, content is encoded using a maximum distance separable code and cached according to a time-to-live (TTL) cache eviction policy, which allows coded packets to be removed from the caches at periodic times. Mobile users requesting a particular content download coded packets from SBSs within communication range. If additional packets are required to decode the file, these are downloaded from the MBS. We formulate an optimization problem that is efficiently solved numerically, providing TTL caching policies minimizing the overall network load. We demonstrate that distributed coded caching using TTL caching policies can offer significant reductions in terms of network load when request arrivals are bursty. We show how the distributed coded caching problem utilizing TTL caching policies can be analyzed as a specific single cache, convex optimization problem. Our problem encompasses static caching and the single cache as special cases. We prove that, interestingly, static caching is optimal under a Poisson request process, and that for a single cache the optimization problem has a surprisingly simple solution. [ABSTRACT FROM AUTHOR]

Content caching is recognized as a promising solution to release the heavy burden of backhaul links and decrease the content transmission latency in Fog radio access networks (Fog-RANs). However, the content caching design is still a challenging problem with considering the user request patterns, the content delivery strategies, and the limited caching capacity. Recommendation has the capability of reshaping users’ content requests for further prompting caching gain. The joint recommendation, caching, beamforming holds the potential to improve the system performance of Fog-RANs. In this paper, a joint recommendation, caching, and beamforming scheme is proposed for multi-cell multi-antenna recommendation aware Fog-RANs. Aiming at minimizing the content transmission latency, we formulate a joint recommendation, caching, and beamforming optimization problem. The minimization problem is a very challenging two-timescale mixed integer nonlinear programming problem, which is hard to solve in general. By exploring structural properties of the problem, we propose an alternative optimization algorithm with low complexity through decomposing the original problem into three sub-problems. Extensive simulations show that our proposed method can significantly reduce the content transmission delay. [ABSTRACT FROM AUTHOR]

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Information Centric Network (ICN) is a content-based information dissemination approach that improves the content delivery and latency. We introduce a modified architecture for ICN which can reduce the traffic by combining multiple messages and facilitate the data distribution in the network. By observing the similarities between index coding and ICN architectures, we propose to combine these two techniques to arrive at a new architecture that enhances data delivery in networks beyond original ICN scheme. By taking advantage of some concepts such as linear network coding, caching and index coding, we demonstrate that we can reduce the traffic and increase the capacity of ICN architecture by combining (network encoding) multiple messages requested by different nodes and sending them in one transmission. We demonstrate that each node will be able to extract its desired message from the combined encoded messages. To achieve this goal, we first define a modified version of index coding in order to apply index coding for both wired and wireless networks. Further, we introduce a hybrid caching scheme that includes both central and distributed caching to support two different goals. Our hybrid caching approach is a combination of conventional caching in ICN that caches the content in various network locations in order to make the content readily available to nodes and a new distributed caching scheme across nodes in the network to improve the performance of the entire system. The purpose of the second caching scheme is to allow central cache system to combine multiple contents in order to serve several client nodes simultaneously with a single transmission of encoded messages. The focus of this paper is to describe the new ICN architecture and demonstrate the advantages of the new architecture. © 2014 IEEE.

Information Centric Network (ICN) is a content-based information dissemination approach that improves the content delivery and latency. We introduce a modified architecture for ICN which can reduce the traffic by combining multiple messages and facilitate the data distribution in the network. By observing the similarities between index coding and ICN architectures, we propose to combine these two techniques to arrive at a new architecture that enhances data delivery in networks beyond original ICN scheme. By taking advantage of some concepts such as linear network coding, caching and index coding, we demonstrate that we can reduce the traffic and increase the capacity of ICN architecture by combining (network encoding) multiple messages requested by different nodes and sending them in one transmission. We demonstrate that each node will be able to extract its desired message from the combined encoded messages. To achieve this goal, we first define a modified version of index coding in order to apply index coding for both wired and wireless networks. Further, we introduce a hybrid caching scheme that includes both central and distributed caching to support two different goals. Our hybrid caching approach is a combination of conventional caching in ICN that caches the content in various network locations in order to make the content readily available to nodes and a new distributed caching scheme across nodes in the network to improve the performance of the entire system. The purpose of the second caching scheme is to allow central cache system to combine multiple contents in order to serve several client nodes simultaneously with a single transmission of encoded messages. The focus of this paper is to describe the new ICN architecture and demonstrate the advantages of the new architecture. © 2014 IEEE.

Mobile Edge Caching (MEC) can be exploited for reducing redundant data transmissions and improving content delivery performance in mobile networks. However, under the MEC architecture, dynamic user preference is challenging the delivery efficiency due to the imperfect match between users' demands and cached content. In this paper, we propose a learning-based cooperative content caching policy to predict the content popularity and cache the desired content proactively. We formulate the optimal cooperative content caching problem as a 0-1 integer programming for minimizing the average downloading latency. After using an artificial neural network to learn content popularity, we use a greedy algorithm for its approximate solution. Numerical results validate that the proposed policy can significantly increase content cache hit rate and reduce content delivery latency when compared with popular caching strategies.

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.

Data caching has emerged as a promising technique to handle growing data traffic and backhaul congestion of wireless networks. However, there is a concern regarding how and where to place contents to optimize data access by the users. Data caching can be exploited close to users by deploying cache entities at Small Base Stations (SBSs). In this approach, SBSs cache contents through the core network during off-peak traffic hours. Then, SBSs provide cached contents to content-demanding users during peak traffic hours with low latency. In this paper, we exploit the potential of data caching at the SBS level to minimize data access delay. We propose an intelligence-based data caching mechanism inspired by an artificial intelligence approach known as Reinforcement Learning (RL). Our proposed RL-based data caching mechanism is adaptive to dynamic learning and tracks network states to capture users’ diverse and varying data demands. Our proposed approach optimizes data caching at the SBS level by observing users’ data demands and locations to efficiently utilize the limited cache resources of SBS. Extensive simulations are performed to evaluate the performance of proposed caching mechanism based on various factors such as caching capacity, data library size, etc. The obtained results demonstrate that our proposed caching mechanism achieves 4% performance gain in terms of delay vs. contents, 3.5% performance gain in terms of delay vs. users, 2.6% performance gain in terms of delay vs. cache capacity, 18% performance gain in terms of percentage traffic offloading vs. popularity skewness (γ), and 6% performance gain in terms of backhaul saving vs. cache capacity.

In the internet of vehicles (IoVs), vehicle users should provide location information continuously when they want to acquire continuous location-based services (LBS), which may disclose the vehicle trajectory privacy. To solve the vehicle trajectory privacy leakage problem in the continuous LBS, we propose a vehicle trajectory privacy preservation method based on caching and dummy locations, abbreviated as TPPCD, in IoVs. In the proposed method, when a vehicle user wants to acquire a continuous LBS, the dummy locations-based location privacy preservation method under road constraint is used. Moreover, the cache is deployed at the roadside unit (RSU) to reduce the information interaction between vehicle users covered by the RSU and the LBS server. Two cache update mechanisms, the active cache update mechanism based on data popularity and the passive cache update mechanism based on dummy locations, are designed to protect location privacy and improve the cache hit rate. The performance analysis and simulation results show that the proposed vehicle trajectory privacy preservation method can resist the long-term statistical attack (LSA) and location correlation attack (LCA) from inferring the vehicle trajectory at the LBS server and protect vehicle trajectory privacy effectively. In addition, the proposed cache update mechanisms achieve a high cache hit rate.

By enabling name-based routing and ubiquitous in-network caching, Named Data Networking (NDN) is a promising network architecture for sixth generation (6G) edge network infrastructures. However, the performance of content retrieval largely depends on the selected caching strategy, which is implemented in a distributed fashion by each NDN node. Previous research showed the effectiveness of caching decisions based on content popularity and network topology information. This paper presents a new distributed caching strategy for NDN edge networks based on a metric called popularity-aware closeness (PaC), which measures the proximity of the potential cacher to the majority of requesters of a certain content. After identifying the most popular contents, the strategy caches them in the available edge nodes that guarantee the higher PaC. Achieved simulation results show that the proposed strategy outperforms other benchmark schemes, in terms of reduced content retrieval delay and exchanged data traffic.

The transmission of a large amount of video and picture content brings more challenges to wireless communication networks. Unmanned aerial vehicle (UAV)-aided small cells with active content caching deployed on cellular networks are recognized as a promising way to alleviate wireless backhaul and support flexible coverage. However, a UAV cannot operate for a long time due to limited battery life, and its caching capacity is also limited. For this, a multi-UAV content-caching strategy and cooperative, complementary content transmission among UAVs are jointly studied in this paper. Firstly, a user-clustering-based caching strategy is designed, where user clustering is based on user similarity, concurrently taking into consideration similarities in content preference and location. Then, cooperative, complementary content transmission between multiple UAVs is modeled as a coalition formation game (CFG) to maximize the utility of the whole network. Finally, the effectiveness of the proposed algorithms is demonstrated through numerical simulations.