Mobility-Aware Coded Probabilistic Caching Scheme for MEC-Enabled Small Cell Networks

With caching popular contents at the small-cell base stations (SBSs), cooperative edge caching has emerged as an effective approach to offload explosively increasing network traffic from a massive number of users in mobile edge networks (MENs). However, most previous works ignored impact of user mobility on caching strategy, thus having limited practical applications. How to improve the caching strategy by exploiting user mobility is still a challenging problem. Accordingly, in this paper, we propose a mobility-aware and cooperative coded caching (MoCoCoCa) for achieving traffic offloading in MENs, by considering user mobility and hard service deadline constraints. Specifically, we first develop the MoCoCoCa framework to satisfy users' requests locally and conduct the construction of access trajectory set via random walks on a Markov chain, which takes the randomness of contact into account. Then, based on user mobility predictions, we formulate the caching problem into a mixed integer nonlinear programming (MINLP) problem to minimize the load of core network, subject to finiteness of network resources (e.g., storage capacity, transmission rate) and non-uniformity of content popularity constraints. The objective function's submodular property is proved and a cooperative placement algorithm is given, which can achieve (1-1/e) -optimality. Furthermore, to adapt the given content placement configuration to the spatio-temporal dynamics of realtime request flow, the original placement problem is decomposed into some independent subproblems of content placement at individual SBSs. Each subproblem is converted into a submodular optimization problem, and a request-aware distributed replacement algorithm with linear computational complexity is proposed. Trace-based simulations and numerical results demonstrate that our proposed caching strategy can offload up to 69% network traffic than existing caching strategies.

To provide ubiquitous services and the same real-time services as those available on the wired networks for mobile users, this paper proposes an efficient and intelligent QoS-aware mobility scheme, coined dynamic point forwarding scheme under mobility scenario in future all-IP wireless networks. Considering dynamic channel characteristics and user mobility in wireless networks, the proposed scheme takes advantage of both the point forwarding scheme in mobility management and dynamic RSVP (DRSVP) in QoS guarantee. Moreover, in order to overcome deficiencies in conventional point forwarding scheme, we develop a new decision criterion for performing forwarding chain update, which achieves a better trade-off between network resource consumed by routing path and signaling load for forwarding chain updates. Based on the performance analyses, our proposed scheme can substantially reduce more resource reservation than the conventional pointer forwarding scheme, thus it can achieve efficient resource utilization and guarantee QoS requirements of multimedia applications in the future wireless all-IP networks.

Mobile edge computing (MEC) has emerged as a promising technique to address the challenge arising from the exponentially increasing data traffic. It not only supports mobile users to offload computations but also caches and delivers popular contents to mobile users. In this paper, we aim at designing novel content caching strategies in MEC networks to reduce access latency and improve energy efficiency. First, the distributed content delivery network based on MECs is developed to support users' requests locally. Moreover, based on users' distribution characteristics and MECs' location, a grouping-based and hierarchical collaborative caching strategy is proposed. Simulation results prove that our caching strategy is more efficient than alternative benchmark strategies in terms of average access latency, total energy consumption and content diversity.

This paper will discuss the distributed caching strategies in clustered heterogeneous cellular networks. Basically, there are two obstacles to accomplish the caching policy in the wireless access networks, i.e., the limited storage capacity of small cells and the too small coverage area of each base station to imply content aggregation effect. Traditionally, the existing caching strategies only concern the local requests within one base station coverage, which neglects the clustered characteristics of network infrastructure and user traffic demand. In this paper, we consider a two-tier clustered cellular networks, within which small cells can exchange content to fulfill each user request. Intuitively, this cooperation between base stations increases the service capacity of clustered cellular networks as testified in our numerical simulation.

Due to signal fading and user mobility in wireless networks, quality-of-service provisioning for wireless services has become more challenging. As a promising technique, cooperative communications make use of the broadcasting nature of the wireless medium to facilitate data transmission and, thereby, reduce energy consumption. However, in many studies on wireless cooperative diversity, it is often assumed that the number of relays or the relay distribution intensity is known a priori. In this paper, we relax this assumption and propose an algorithm to estimate the relay intensity for a backoff-based cooperative scheme, where the relays are distributed as a homogeneous Poisson point process. It is proved that the algorithm can converge to an optimal solution with the minimum estimation error. Based on the estimated relay intensity, we further investigate a distributed energy saving strategy, which selectively turns off some relays to reduce energy consumption while maintaining the required transmission success probability. The performance of the proposed cooperative scheme is analytically evaluated with respect to the collision probability. The numerical and simulation results demonstrate the high accuracy and efficiency of the intensity estimation algorithm and validate the theoretical analysis. Moreover, the proposed cooperative scheme exhibits significant energy savings and satisfactory transmission performance, which offers a good match to accommodate green communications in wireless networks.

Caching at the user devices is a key technology to alleviate backhaul load and improve users' quality of experience in mobile edge networks. However, due to the concern of privacy and limited battery life, users are not willing to share their resources. In this study, the authors jointly design the caching strategy and incentive scheme to encourage the users to share their storage resources and improve the social welfare of the cellular network. First, the user equipments (UEs) are classified into different types according to their preference to share cache resources. Then, based on contract theory, an optimisation problem with necessary constraints is formulated aiming at maximising the base station (BS)'s utility. The reward the BS pays for a UE is based on the total amount of contents it shares with other UEs, which is related to the caching policy of UEs. A heuristic caching strategy is proposed and the optimal contract is obtained. Finally, the system performance of the proposed solution is analysed, simulation results show that the incentive scheme can improve the willingness of users to share contents, and the proposed caching strategy contributes more to the social welfare compared with two baseline caching schemes: fair caching and random caching.

Recent advances in big data processing and mobile edge computing (MEC) have demonstrated the feasibility of further performance improvement on wireless caching via exploiting contextual information, like mobility-aware caching. However, most related works either focus on the utilization of single contextual information or ignore the impact of social network on reshaping a user's behavior when integrating various context data. In this work, a context-aware caching scheme with social behavior that integrates various contextual information is proposed for MEC-enabled small cell networks (SCNs), with a novel model provided to characterize how social networks influence and reshape behaviors. Based on a multi-armed bandit algorithm, a user's behavior patterns such as its mobility pattern, preference towards different files and the amount of traffic it may consume in each time slot are learned under the impact of social relationships. Numerical results and comparisons show that the proposed caching scheme outperforms normal context-aware caching schemes by more than 30% in terms of backhaul data offloading ratio.

Emerging 5G cellular networks are expected to face a dramatic increase in the volume of mobile traffic and IoT user requests due to the massive growth in mobile devices and the emergence of new compute-intensive applications. Running high-intensive compute applications on resource-constrained mobile devices has recently become a major concern, given the constraints of finite computation and limited storage capacities. Mobile Edge Computing (MEC) has recently become the key technology to overcome these issues by providing cloud computing capabilities and placing IT infrastructures at the mobile network edge. In this survey, we present a list of relevant research papers for the MEC infrastructure implementation phases, including (1) MEC infrastructure designing and dimensioning, (2) MEC infrastructure virtualization using Network Function Virtualization (NFV) concept, and the use of virtualized service placement and auto-scaling methods to deploy an agile system framework, (3) MEC resource management frameworks, and (4) approaches used to optimize the MEC resources on the physical infrastructure. The main focus of this survey is to determine the required aspects to implement an auto-scaled and proactive MEC-NFV infrastructure to support a dynamic and heterogenous mobile users’ demand at mobile network operators.

Caching at base stations (BSs) has been proposed as a promising technology to address the challenges of the increasing traffic. In this article, cache-enabled heterogeneous networks (HetNets) considering the limited backhaul is analyzed with the aid of the stochastic geometry approach. A hybrid caching policy, in which the most popular contents are cached in the macro BSs tier with the deterministic caching strategy and the less popular contents are cached in the helpers tier with the probabilistic caching strategy, is proposed. Correspondingly, the content-centric association strategy is designed based on the comprehensive state of the access link, the cache and the backhaul link. By utilizing the hybrid caching policy, analytical expression of successful content delivery probability (SCDP) is derived. The simulation results show that the proposed caching policy outperforms the most popular caching policy in HetNets with the limited backhaul. SCDP increases with the increasing backhaul capacity until converging, whose convergence rate increases with the increment of cache capacity.

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

As one of essential techniques to support the multimedia transmissions over the fifth generation (5G) mobile wireless networks, the statistical quality of service (QoS) provisioning has been proved to be effective in statistically guaranteeing time sensitive video transmission over time-varying wireless channels. But, how to efficiently design and implement statistical QoS provisioning enabled wireless network remains as the open challenging problem. To overcome these challenges, in this paper we propose the hierarchical-caching architecture to efficiently implement the statistical QoS guarantees in 5G wireless networks, which cooperatively caches the popular multimedia data contents at different wireless network caching tiers, efficiently trading the widely available storage resources for precious wireless bandwidth resources. However, the hierarchical caching scheme imposes the multi-hop wireless link statistical delay-bounded QoS provisioning problem when the requested multimedia data contents are fetched across tiers. In particular, we develop two hierarchical caching schemes to adaptively cache the delay bounded multimedia data contents: (1) caching in mobile users, which yields the single-hop wireless transmission and (2) caching in routers, which yields the tandem two-hop wireless transmission. To maximize the average effective capacity when cached data contents dynamically switch between these two schemes, we apply the Markov chain model to derive the optimal departure rates for data contents leaving cacheable mobile users and routers, respectively. U sing these two departure rates, we develop the optimal caching schemes in terms of the probability distribution of how long to cache the data contents and the probability of where to cache the data contents. Finally, we evaluate and validate these two optimized caching schemes through numerical analyses.

This paper presents a caching algorithm that offers better reconstructed data quality to the requesters than a probabilistic caching scheme while maintaining comparable network performance. It decides whether an incoming data packet must be cached based on the dynamic caching probability, which is adjusted according to the priorities of content carried by the data packet, the uncertainty of content popularities, and the records of cache events in the router. The adaptation of caching probability depends on the priorities of content, the multiplication factor adaptation, and the addition factor adaptation. The multiplication factor adaptation is computed from an instantaneous cache-hit ratio, whereas the addition factor adaptation relies on a multiplication factor, popularities of requested contents, a cache-hit ratio, and a cache-miss ratio. We evaluate the performance of the caching algorithm by comparing it with previous caching schemes in network simulation. The simulation results indicate that our proposed caching algorithm surpasses previous schemes in terms of data quality and is comparable in terms of network performance.

Content Centric Mobile Ad-hoc NETwork (CCMANET) applies the advantages of Content Centric networking (CCN) into Mobile Ad Hoc Network(MANET) to overcome the drawbacks of low efficiency and unstable in transmission. Caching scheme is one of the key components of CCMANET. However, the caching scheme in CCMANET has not been well explored. In this paper, a novel cooperative caching scheme based on generalized dominating set and local content popularity for CCMANET is proposed. First, a virtual backbone in CCMANET is constructed by generalized dominating set to make the arbitrary topology become two-level hierarchy and a collaborative cache placement scheme between the two hierarchies is designed. Second, we propose a method of computing the local content popularity and a cache replacement scheme based on the local content popularity. Simulation results show that the proposed caching scheme can effectively reduces both path stretch value and server load, and improves the hit ratio compared with the existing caching schemes.

TCP is a protocol which performs well with networks that are physically linked and hosts are stationary. To establish a TCP connection, one must associate an IP address with a port number for both sender and receiver. However, in wireless environments mobile hosts are subject to change their attachment point during communication, this process is called handoff. Thus, in mobile environments the TCP operation is more complicated, because the mobile moves from one cell to another and it changes of network’s domain, then it acquires a new temporary IP address. Hence, the TCP packet loss is increased. Mobile IP was developed to manage the user mobility in wireless networks. However, this protocol is not adapted to support TCP session throughout the handover process. In this study, we have modeled the proposed architecture which ensures the continuity of TCP connection when a mobile executes a handover. We have evaluated the performance of the architecture by an analytical model.

This work considers necessary conditions for privacy guarantees under active fingerprinting attacks in power-law bipartite networks. The scenario arises naturally in social network analysis, tracking user mobility in wireless networks, and forensics applications, among others. A stochastic growing network generation model — called the popularity-based model — is investigated, where the bipartite network is generated iteratively, and in each iteration vertices attract new edges based on their assigned popularity values. It is shown that using the appropriate choice of initial popularity values, the node degree distribution follows a power-law distribution with arbitrary parameter α > 2, i.e. fraction of nodes with degree d is proportional to d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−α</sup> . An active fingerprinting deanonymization attack strategy called the augmented information threshold attack strategy (A-ITS) is proposed which uses the attacker’s knowledge of the node degree distribution along with the concept of information values for deanonymization. Sufficient conditions for the success of the A-ITS, based on network parameters, are derived. It is shown through simulations that the proposed attack significantly outperforms the state-of-the-art attack strategies.