Proximity as a Service via Cellular Network-Assisted Mobile Device-to-Device

PhD ThesisThe research progress of communication has brought a lot of novel technologies to meet the multi-dimensional demands such as pervasive connection, low delay and high bandwidth. Device-to-Device (D2D) communication is a way to no longer treat the User Equipment (UEs) as a terminal, but rather as a part of the network for service provisioning. This thesis decouples UEs into service providers (helpers) and service requesters. By collaboration among proximal devices, with the coordination of cellular networks, some local tasks can be achieved, such as coverage extension, computation o oading, mobile crowdsourcing and mobile crowdsensing. This thesis proposes a generic framework Proximity as a Service (PaaS) for increasing the coverage with demands of service continuity. As one of the use cases, the optimal helper selection algorithm of PaaS for increasing the service coverage with demands of service continuity is called ContAct based Proximity (CAP). Mainly, fruitful contact information (e.g., contact duration, frequency, and interval) is captured, and is used to handle ubiquitous proximal services through the optimal selection of helpers. The nature of PaaS is evaluated under the Helsinki city scenario, with movement model of Points Of Interest (POI) and with critical factors in uencing the service demands (e.g., success ratio, disruption duration and frequency). Simulation results show the advantage of CAP, in both success ratio and continuity of the service (outputs). Based on this perspective, metrics such as service success ratio and continuity as a service evaluation of the PaaS are evaluated using the statistical theory of the Design Of Experiments (DOE). DOE is used as there are many dimensions to the state space (access tolerance, selected helper number, helper access limit, and transmit range) that can in uence the results. A key contribution of this work is that it brings rigorous statistical experiment design methods into the research into mobile computing. Results further reveal the influence of four factors (inputs), e.g., service tolerance, number of helpers allocated, the number of concurrent devices supported by each helper and transmit range. Based on this perspective, metrics such as service success ratio and continuity are evaluated using DOE. The results show that transmit range is the most dominant factor. The number of selected helpers is the second most dominant factor. Since di erent factors have di erent regression levels, a uni ed 4 level full factorial experiment and a cubic multiple regression analysis have been carried out. All the interactions and the corresponding coe cients have been found. This work is the rst one to evaluate LTE-Direct and WiFi-Direct in an opportunistic proximity service. The contribution of the results for industry is to guide how many users need to cooperate to enable mobile computing and for academia. This reveals the facts that: 1, in some cases, the improvement of spectrum e ciency brought by D2D is not important; 2, nodal density and the resources used in D2D air-interfaces are important in the eld of mobile computing. This work built a methodology to study the D2D networks with a di erent perspective (PaaS)

Stochastic Game based Cooperative Alternating Q-Learning Caching in Dynamic D2D Networks

Edge caching has become an effective solution to cope with the challenges brought by the massive content delivery in cellular networks. In device-to-device (D2D) enabled caching cellular networks with time-varying content popularity distribution and user terminal (UT) location, we model these dynamic networks as a stochastic game to design a cooperative cache placement policy. The cache placement reward of each UT is defined as the caching incentive minus the transmission power cost for content caching and sharing. We consider the long-term cache placement reward of all UTs in this stochastic game. In an effort to solve the stochastic game problem, we propose a multi-agent cooperative alternating Q-learning (CAQL) based cache placement algorithm. The caching control unit is defined to execute the proposed CAQL, in which, the cache placement policy of each UT is alternatively updated according to the stable policy of other UTs during the learning process, until the stable cache placement policy of all the UTs in the cell is obtained. We discuss the convergence and complexity of CAQL, which obtains the stable cache placement policy with low space complexity. Simulation results show that the proposed algorithm can effectively reduce the backhaul load and the average content access delay in dynamic networks

Qos Provisioning for Energy Efficiency in Mobile Ad-Hoc Network

In mobile ad-hoc networks Quality of Service QoS of a multicast routing protocol is one of the most key performance metrics Slotconditions and network topology frequently change Topology dynamic and in order to achieve a certain level of QoS complexalgorithms and protocols are needed Network graph conditionsare neglected during the design of aexisting multicast protocol However vulnerability against network graph errors can severely affect theperformance of a multicast protocol To address this here the author proposesanenergy efficient network graph pre-processing approach to enable traffic engineering and enhance the performance of energy efficiency in terms of network efficiency by QoSprovisioning to cater the multicast routing issue in MANETS In this approach prioritized admission control PAC scheme is implemented to improvise D2D Device to Device communications into cellular network to overcome the limitations of MANET

Seed selection for data offloading based on social and interest graphs

Copyright © 2018 Tech Science Press The explosive growth of mobile data demand is becoming an increasing burden on current cellular network. To address this issue, we propose a solution of opportunistic data offloading for alleviating overloaded cellular traffic. The principle behind it is to select a few important users as seeds for data sharing. The three critical steps are detailed as follows. We first explore individual interests of users by the construction of user profiles, on which an interest graph is built by Gaussian graphical modeling. We then apply the extreme value theory to threshold the encounter duration of user pairs. So, a contact graph is generated to indicate the social relationships of users. Moreover, a contact-interest graph is developed on the basis of the social ties and individual interests of users. Corresponding on different graphs, three strategies are finally proposed for seed selection in an aim to maximize overloaded cellular data. We evaluate the performance of our algorithms by the trace data of real-word mobility. It demonstrates the effectiveness of the strategy of taking social relationships and individual interests into account

Mobile data and computation offloading in mobile cloud computing

Le trafic mobile augmente considérablement en raison de la popularité des appareils mobiles et des applications mobiles. Le déchargement de données mobiles est une solution permettant de réduire la congestion du réseau cellulaire. Le déchargement de calcul mobile peut déplacer les tâches de calcul d'appareils mobiles vers le cloud. Dans cette thèse, nous étudions d'abord le problème du déchargement de données mobiles dans l'architecture du cloud computing mobile. Afin de minimiser les coûts de transmission des données, nous formulons le processus de déchargement des données sous la forme d'un processus de décision de Markov à horizon fini. Nous proposons deux algorithmes de déchargement des données pour un coût minimal. Ensuite, nous considérons un marché sur lequel un opérateur de réseau mobile peut vendre de la bande passante à des utilisateurs mobiles. Nous formulons ce problème sous la forme d'une enchère comportant plusieurs éléments afin de maximiser les bénéfices de l'opérateur de réseau mobile. Nous proposons un algorithme d'optimisation robuste et deux algorithmes itératifs pour résoudre ce problème. Enfin, nous nous concentrons sur les problèmes d'équilibrage de charge afin de minimiser la latence du déchargement des calculs. Nous formulons ce problème comme un jeu de population. Nous proposons deux algorithmes d'équilibrage de la charge de travail basés sur la dynamique évolutive et des protocoles de révision. Les résultats de la simulation montrent l'efficacité et la robustesse des méthodes proposées.Global mobile traffic is increasing dramatically due to the popularity of smart mobile devices and data hungry mobile applications. Mobile data offloading is considered as a promising solution to alleviate congestion in cellular network. Mobile computation offloading can move computation intensive tasks and large data storage from mobile devices to cloud. In this thesis, we first study mobile data offloading problem under the architecture of mobile cloud computing. In order to minimize the overall cost for data delivery, we formulate the data offloading process, as a finite horizon Markov decision process, and we propose two data offloading algorithms to achieve minimal communication cost. Then, we consider a mobile data offloading market where mobile network operator can sell bandwidth to mobile users. We formulate this problem as a multi-item auction in order to maximize the profit of mobile network operator. We propose one robust optimization algorithm and two iterative algorithms to solve this problem. Finally, we investigate computation offloading problem in mobile edge computing. We focus on workload balancing problems to minimize the transmission latency and computation latency of computation offloading. We formulate this problem as a population game, in order to analyze the aggregate offloading decisions, and we propose two workload balancing algorithms based on evolutionary dynamics and revision protocols. Simulation results show the efficiency and robustness of our proposed methods