Kelvin Lopes Dias

Approaches to resource reservation for migrating real-time sessions in future mobile wireless networks

This paper presents two novel frameworks for session admission control and resource reservation in the context of next generation mobile and cellular networks. We also devised a special scheme that avoids per-user reservation signaling overhead in order to meet scalability requirements needed for next generation multi-access networks. The first proposal, Distributed Call Admission Control with Aggregate Resource Reservation (VR), uses mobility prediction based on mobile positioning system location information and takes into account the expected bandwidth to be used by calls handing off to and from neighboring cells within a configurable estimation time window. In conjunction, a novel concept called virtual reservation has been devised to prevent per-user reservation. Our second proposal, Local Call Admission Control and Time Series-based Resource Reservation, takes into account the expected bandwidth to be used by calls handed off from neighboring cells based only on local information stored into the current cell a user is seeking admission to. To this end, we suggest the use of two time series-based models for predicting handoff load: the Trigg and Leach (TL), which is an adaptive exponential smoothing technique, and Autoregressive Integrated Moving Average (ARIMA) that uses the Box and Jenkins methodology. It is worth to emphasize that the use of bandwidth prediction based on ARIMA technique still exist for wireless networks. The novelty of our approach is to build an adaptive framework based on ARIMA technique that takes into account the measured handoff dropping probability in order to tuning the prediction time window size so increasing the prediction accuracy. The proposed schemes are compared through simulations with the fixed guard channel (GC) and other optimized dynamic reservation-based proposals present in the literature. The results show that our schemes outperform many others and that the simpler local proposal based on TL can grant nearly similar levels of handoff dropping probability as compared to those from more the complex distributed approach.

Interoperability issues on heterogeneous wireless communication for smart cities

Smart cities have become a reality around the world. They rely on wireless communication technologies, and they have provided many benefits to society, such as monitoring road traffic in real-time, giving continuous healthcare assistance to residents and managing the environment. This article revisits key interoperability questions in heterogeneous wireless networks for smart cities, and outlines a simple, modular architecture to deal with these complex issues. The architecture is composed by sensing, access network, Internet/cloud and application layers. Different features provided by the architecture, such as interoperability among technologies, low cost, reliability and security, have been evaluated through experiments and simulations under different scenarios. The QoS support and the seamless connectivity between pairs of heterogeneous technologies are proposed through a policy-based management (PBM) framework and MIH (Media Independent Handover). Moreover, an 802.11 mesh backbone composed of different types of mesh routers has been deployed for interconnecting the sensors and actuators to the Internet. Key results from experiments in the backbone are examined. They compare: (i) the performance of a single-path routing protocol (OLSR) with a multipath one (MP-OLSR); (ii) the monitoring delays from the proposed low cost sunspot/mesh and arduino/mesh gateways; and (iii) the authentication mechanisms employed. Significant results from simulations allow the analysis of the reliability on vehicular/mesh networks under jamming attacks by applying the OLSR and MP-OLSR routing protocols. Finally, this article provides an overview of open research questions.

Predictive call admission control for all-IP wireless and mobile networks

This paper proposes a novel call admission control (CAC) scheme for wireless and mobile networks. Our proposal avoids per-user reservation signaling overhead and takes into account the expected bandwidth to be used by calls handed off from neighboring cells based only on local information stored into the current cell where user is seeking admission. To this end, we propose the use of two time series-based models for predicting handoff load: the Trigg and Leach (TL), which is an adaptive exponential smoothing technique, and ARIMA (Autoregressive Integrated Moving Average) that uses the Box & Jenkins methodology. These methods are executed locally by each base station or access router and forecast how much bandwidth should be reserved on a periodic time window basis. The two prediction methods are compared through simulations in terms of new call blocking probability and handoff dropping probability. Despite the TL method simplicity, it can achieve similar levels of call blocking probability and handoff dropping probability than those of the computational demanding ARIMA models. In addition, depending on the schemes settings, the prediction methods can grant an upper bound on handoff dropping probability even under very high load scenarios.

Supporting mobility-aware computational offloading in mobile cloud environment

Abstract Mobile Cloud Computing (MCC) enables resource-constrained smartphones to run high processing and storage intensive applications through public clouds or cloudlet code offloading. However, handover support in the MCC context has not been thoroughly explored, since most works do not address the devices mobility during the offloading operation. This paper presents the Mobile Offloading System (MOSys), which supports seamless offloading operations during user mobility between wireless networks. To this end, MOSys benefits from the software-defined networking (SDN) paradigm for mobility management and remote caching techniques to reduce the offloading response time. Also, middleware is responsible for ensuring code and data offloading, profiling service, cloud discovery and application deployment. To evaluate the proposed system and to analyze mobility impact on the offloading performance, several experiments were conducted using different smartphones categories and benchmark applications, on three scenarios. Our results have shown that the MOSys’ mobility management application is energy efficient, especially considering the low-cost smartphone category, while remote caching proved to be an attractive alternative for reducing the offloading response time.

A performance evaluation of Software Defined Networking load balancers implementations

Traditional network load balancers have been developed and deployed by combining software and hardware into network appliances. This approach makes network load balancers inflexible (limited number schedule disciplines implemented), expensive and hard to perform network management tasks. Software Defined Networking (SDN) is emerging as new network paradigm that separates software and hardware roles in devices (vertical integration), enables programmability of networks, generalizes network devices and functions, and centralizes network management. Those ideas can be apply to development of new load balancers to overlap the disadvantages of traditional load balancers. Thus, this work evaluated the performance of SDN load balancers implementations with different schedule disciplines (Random, Round-Robin and CPU Usage), and the reactive and active SDN load balancers. Through careful performance evaluation based on collected data and the reviewed literature, we concluded that SDN load balancers implementations are very effective, viable, flexible and inexpensive solutions to distributed the network load.

Three-layered prioritized cognitive radio networks with channel aggregation and fragmentation techniques

Dynamic spectrum allocation techniques are usually based on the assumption of a homogeneous cognitive radio network. In this work we describe a Continuous time Markov chain model that allows different types of bandwidth requirements and priority traffic. We have improved the lowest priority users performance using channel aggregation and fragmentation, which are mechanisms envisioned for the LTE-A/4G standard. The evaluation consists on analytical and simulation results that have shown that pure channel aggregation performs similarly to the combined use of aggregation and fragmentation while both outperformed the fixed bandwidth approach.

A context-sensitive offloading system using machine-learning classification algorithms for mobile cloud environment

Abstract Computational offloading in Mobile Cloud Computing (MCC) has attracted attention due to benefits in energy saving and improved mobile application performance. Nevertheless, this technique underperforms if the offloading decision ignores contextual information. While recent studies have highlighted the use of contextual information to improve the computational offloading decision, there still remain challenges regarding the dynamic nature of the MCC environment. Most solutions design a single reasoner for the offloading decision and do not know how accurate and precise this technique is, so that when applied in real-world environments it can contribute to inaccurate decisions and consequently the low performance of the overall system. Thus, this paper proposes a Context-Sensitive Offloading System (CSOS) that takes advantage of the main machine-learning reasoning techniques and robust profiling system to provide offloading decisions with high levels of accuracy. We first evaluate the main classification algorithms under our database and the results show that JRIP and J48 classifiers achieves 95% accuracy. Secondly, we develop and evaluate our system under controlled and real scenarios, where context information changes from one experiment to another. Under these conditions, CSOS makes correct decisions as well as ensuring performance gains and energy efficiency.

A systematic mapping study on mobility mechanisms for cloud service provisioning in mobile cloud ecosystems

Abstract Hand-over is a critical issue with regard to Mobile Cloud Computing (MCC) infrastructure, notably as it impacts applications’ quality of service due to hardware constraints of the device, which can result in low connectivity in a wireless environment. Over the last couple of years, several research efforts have been targeted at supporting mobility mechanisms development and adapting existing technologies for the mobile cloud environment. The goal of this paper is to use a Systematic Mapping Study (SMS) method for identifying and analyzing mobility mechanisms in the MCC context. Our review comprised the following contributions: (i) characterizing existing research by considering six facets: overall purpose, network technology, decision parameter, mobile application, cloud model, and mobility strategy; (ii) producing a spreadsheet with information about each paper in the screening and extraction processes; and (iii) systematically mapping the solutions to determine research gaps and trends.

A flexible-bandwidth model with channel reservation and channel aggregation for three-layered Cognitive Radio Networks

Abstract The Fifth Generation (5G) of wireless communication systems is expected to meet a large demand for mobile traffic and provide higher data rates, supporting bandwidth-hungry applications. In this respect, Cognitive Radio and Channel Aggregation (CA) are envisioned as key 5G enablers providing additional spectrum resources through the Dynamic Spectrum Access, and higher data rates through multiple contiguous or non-contiguous spectrum aggregation. Moreover, since 5G networks should comprise heterogeneous applications that may have different Quality of Service (QoS), Quality of Experience (QoE) and security requirements, multiple service class support becomes a must, and thus multiple priorities have been assigned for different flow types in current wireless standards. Previous works have studied Cognitive Radio Networks (CRN) as homogeneous two-priority queueing systems, composed of primary (PUs) and secondary (SUs) users, however, those are usually not capable of analyzing SUs with different QoS requirements. In addition, most authors are concerned about proving the efficiency of QoS provisioning approaches such as channel reservation or channel aggregation, frequently using separate models in unloaded scenarios. This paper proposes and analyzes an M/M/N/N three-layered system in which the unlicensed traffic is detached in two priority classes (i.e., high and low), encompassing all possible bandwidth arrangements, a multi-level reservation feature and multiple aggregation strategies. Previous works on CA have shown that, regardless the network state, this technique should always boost the overall performance, differently from the reservation process that presents high inefficiency in overloaded networks. For this reason, CA was enabled to mitigate the reservations drawbacks while scaling the benefits of both techniques, in a single model.

Secondary Virtual Network Mapping onto Cognitive Radio Substrate: A Collision Probability Analysis

This letter proposes a wireless virtualization mapping process based on cognitive radio (CR). Primary virtual networks (PVNs) and secondary virtual networks (SVNs) are mapped onto the same CR substrate. The SVN mapping is an NP-hard problem in which both SVN demands and PVN activities must be considered, in order to ensure reduced interference level to the PVN. The interactions between PVNs and SVNs are modeled by an M/M/N/N queue with priority and preemptive service. In addition, a collision probability formulation is proposed, validated, and analyzed in order to assess the SVN mapping behavior under different primary and secondary loads.