No Arabic abstract
Mobile IPv6 will be an integral part of the next generation Internet protocol. The importance of mobility in the Internet gets keep on increasing. Current specification of Mobile IPv6 does not provide proper support for reliability in the mobile network and there are other problems associated with it. In this paper, we propose Virtual Private Network (VPN) based Home Agent Reliability Protocol (VHAHA) as a complete system architecture and extension to Mobile IPv6 that supports reliability and offers solutions to the security problems that are found in Mobile IP registration part. The key features of this protocol over other protocols are: better survivability, transparent failure detection and recovery, reduced complexity of the system and workload, secure data transfer and improved overall performance.
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.
Due to explosive growth of online video content in mobile wireless networks, in-network caching is becoming increasingly important to improve the end-user experience and reduce the Internet access cost for mobile network operators. However, caching is a difficult problem due to the very large number of online videos and video requests,limited capacity of caching nodes, and limited bandwidth of in-network links. Existing solutions that rely on static configurations and average request arrival rates are insufficient to handle dynamic request patterns effectively. In this paper, we propose a dynamic collaborative video caching framework to be deployed in mobile networks. We decompose the caching problem into a content placement subproblem and a source-selection subproblem. We then develop SRS (System capacity Reservation Strategy) to solve the content placement subproblem, and LinkShare, an adaptive traffic-aware algorithm to solve the source selection subproblem. Our framework supports congestion avoidance and allows merging multiple requests for the same video into one request. We carry extensive simulations to validate the proposed schemes. Simulation results show that our SRS algorithm achieves performance within 1-3% of the optimal values and LinkShare significantly outperforms existing solutions.
In sensor networks communication by broadcast methods involves many hazards, especially collision. Several MAC layer protocols have been proposed to resolve the problem of collision namely ARBP, where the best achieved success rate is 90%. We hereby propose a MAC protocol which achieves a greater success rate (Success rate is defined as the percentage of delivered packets at the source reaching the destination successfully) by reducing the number of collisions, but by trading off the average propagation delay of transmission. Our proposed protocols are also shown to be more energy efficient in terms of energy dissipation per message delivery, compared to the currently existing protocol.
We provide a novel solution for Resource Discovery (RD) in mobile device clouds consisting of selfish nodes. Mobile device clouds (MDCs) refer to cooperative arrangement of communication-capable devices formed with resource-sharing goal in mind. Our work is motivated by the observation that with ever-growing applications of MDCs, it is essential to quickly locate resources offered in such clouds, where the resources could be content, computing resources, or communication resources. The current approaches for RD can be categorized into two models: decentralized model, where RD is handled by each node individually; and centralized model, where RD is assisted by centralized entities like cellular network. However, we propose LORD, a Leader-based framewOrk for RD in MDCs which is not only self-organized and not prone to having a single point of failure like the centralized model, but also is able to balance the energy consumption among MDC participants better than the decentralized model. Moreover, we provide a credit-based incentive to motivate participation of selfish nodes in the leader selection process, and present the first energy-aware leader selection mechanism for credit-based models. The simulation results demonstrate that LORD balances energy consumption among nodes and prolongs overall network lifetime compared to decentralized model.
This letter proposes two novel proactive cooperative caching approaches using deep learning (DL) to predict users content demand in a mobile edge caching network. In the first approach, a (central) content server takes responsibilities to collect information from all mobile edge nodes (MENs) in the network and then performs our proposed deep learning (DL) algorithm to predict the content demand for the whole network. However, such a centralized approach may disclose the private information because MENs have to share their local users data with the content server. Thus, in the second approach, we propose a novel distributed deep learning (DDL) based framework. The DDL allows MENs in the network to collaborate and exchange information to reduce the error of content demand prediction without revealing the private information of mobile users. Through simulation results, we show that our proposed approaches can enhance the accuracy by reducing the root mean squared error (RMSE) up to 33.7% and reduce the service delay by 36.1% compared with other machine learning algorithms.