Path selection using handover in mobile networks with cloud-enabled small cells

With the ever increasing data and computational demands from mobile users, heterogenous wireless networks (HetNets) and mobile cloud computing (MCC) have been advocated as a promising solution to meet these demands. Insufficient bandwidth is one of the most important challenges being faced by a successful implementation of the MCC technology due to heavy data traffic. The MCC implementation on HetNet increases the bandwidth available to each base station (BS) by frequency reuse. In this paper, a novel data storage method for big data files is proposed, along with a data correction technique to deal with the issue of failure of a data chunk retrieval. The proposed algorithm exploits the multiple paths that are available between a user and the cloud storage system in a MCC-HetNet environment. Since the bottleneck in the MCC is the wireless link between the user equipment (UE) and the BS, we have implemented the algorithm on the wireless links between the UE and the BS. The simulated results show that the proposed method outperforms the conventional data storage method between the mobile device and the cloud system.

The highly computationally capable mobile devices and the continuously increasing demand for high data rates and mobility, which are required by several mobile network services, enabled the research on fifth-generation (5G) mobile networks that are expected to be deployed beyond the year 2020 in order to support services and applications with more than one thousand times of today’s network traffic. On the other hand, the huge and complex location-aware datasets exceed the capability of spatial computing technologies. In this direction, the mobile cloud computing (MCC) technology was introduced as the combination of cloud computing and mobile computing, enabling the end-users to access the cloud-supported services through mobile devices (e.g., smartphones, tablets, portable computers or wearable devices). The mobile applications exploit cloud technologies for data processing, storage and other intensive operations, as they are executed on resource providers external to the devices. This tutorial article is a comprehensive review of the current state-of-the-art and the latest developments on mobile cloud computing under the 5G era, which helps early-stage researchers to have an overview of the existing solutions, techniques and applications and investigate open research issues and future challenges in this domain.

Mobile cloud computing (MCC) is a technique or model, in which mobile applications are built, powered and hosted using cloud computing technology. It refers to the availability of Cloud Computing services in a mobile environment and it is the combination of the heterogeneous fields like mobile phone device, cloud computing and wireless networks. Of all that has been written about cloud computing, precious little attention has been paid to authentication in the cloud. In this paper we have designed a new effective security model for mobile cloud Database as a Service (DBaaS). A user can change his/her password, whenever demanded. Furthermore, security analysis realizes the feasibility of the proposed model for DBaaS and achieves efficiency. We also proposed an efficient authentication scheme to solve the authentication problem in MCC. The proposed solution which we have provided is based mainly on improved Needham-Schroeder’s protocol to prove the users’ identity to determine if this user is authorized or not. The results showed that this scheme is very strong and difficult to break it.

As mobile computing has been developed for decades, a new model for mobile computing, namely, mobile cloud computing, emerges resulting from the marriage of powerful yet affordable mobile devices and cloud computing. In this paper we survey existing mobile cloud computing applications, as well as speculate future generation mobile cloud computing applications. We provide insights for the enabling technologies and challenges that lie ahead for us to move forward from mobile computing to mobile cloud computing for building the next generation mobile cloud applications. For each of the challenges, we provide a survey of existing solutions, identify research gaps, and suggest future research areas.

We characterize a multi-tier wireless network consisting of multi-radio access technology (RAT) small cells, operating in both sub-6GHz and millimeter wave (mm-wave) bands, overlaid on top of traditional macro cells. To realistically characterize the user equipment (UE) performance, we model the position of the small cells along roads. First, we provide tractable, yet realistic models to characterize the mm-wave interference and the effect of vehicular blockages on the mm-wave signals. Then, we introduce an association policy, where an UE selects the serving tier using the powers measured on the sub-6GHz band, and then, using the biased power of the mm-wave band, selects the RAT. Based on this, we derive the signal to interference plus noise ratio (SINR) coverage probabilities of pedestrian UEs. We investigate the effect of the RAT selection bias on the vehicular blockage, SINR coverage, and rate coverage experienced by the UEs. Leveraging the results of our analysis, we propose to use a varied range of RAT selection biases to support the diverse applications of the fifth generation (5G) mobile networks. Accordingly, we provide a slice-aware RAT selection strategy to support three types of services, characterized by different requirements in terms of reliability, coverage, and data rates.

Mobile cloud computing (MCC) is the availability of cloud computing services in a mobile environment. By providing optimal services for mobile users, MCC incorporates the elements of mobile networks and cloud computing. In mobile cloud computing, all the data and complicated computing modules can be processed in clouds, and mobile devices do not need a powerful configuration like CPU speed, memory capacity, etc. However, the mobile devices are facing up with many struggles in their resources (e.g., battery life, storage, and bandwidth) and communications (e.g., privacy, mobility, and security). These challenges have a significant effect on the improvement of service qualities. In this paper, a literature survey on the MCC and security issues in MCC has presented.

Heterogeneous networks (HetNets) are expected to be a key feature of long-term evolution (LTE)-advanced networks and beyond and are essential for providing ubiquitous broadband user throughput. However, due to different coverage ranges of base stations (BSs) in HetNets, the handover performance of a user equipment (UE) may be significantly degraded, especially in scenarios where high-velocity UE traverse through small cells. In this article, we propose a context-aware mobility management (MM) procedure for small cell networks, which uses reinforcement learning techniques and inter-cell coordination for improving the handover and throughput performance of UE. In particular, the BSs jointly learn their long-term traffic loads and optimal cell range expansion and schedule their UE based on their velocities and historical data rates that are exchanged among the tiers. The proposed approach is shown not only to outperform the classical MM in terms of throughput but also to enable better fairness. Using the proposed learning-based MM approaches, the UE throughput is shown to improve by 80% on the average, while the handover failure probability is shown to reduce up to a factor of three.

In recent years, mobile networks have faced with the increase of traffic demand. By emerging mobile applications and cloud computing, Mobile Cloud Computing (MCC) has been introduced. In this research, we focus on the 4th and 5th generation of mobile networks. Data Centers (DCs) are connected to each other by high-speed links in order to minimize delay and energy consumption. By considering a model of the geographical distribution of DCs which uses a wideband optical network, renewable energy and sharing resources for new generations of mobile networks, the real effect of issues on the consumed energy, cost, and profit in the mobile cloud computing are investigated. We derived a penalty function for cost and then by using Lyapunov optimization theorem; we designed an algorithm to minimize the average cost of energy consumption based on the online information in MCC. The time average cost is at most O(1/V) above the optimum target, while the average queue size is O(V). The parameter V can be tuned to make the time average cost as close to (or below) the optimum as desired. We designed three scenarios and two classes of applications to set up our simulation environment. The provided results illustrate the efficiency of our proposed scheme and validate the mathematical model.

As the technology is developing with a huge rate, the functionality of smartphone is also getting higher. But the smartphones have some resource constraints like processing power, battery capacity, limited bandwidth for connecting to the Internet, etc. Therefore, to improve the performance of smartphone in terms of processing power, battery and memory, the technology namely, augmented execution is the best solution in the mobile cloud computing (MCC) scenario. Mobile cloud computing works as the combination of mobile computing and cloud computing. Augmented execution alleviates the problem of resource scarcity of smartphone. To get the benefits from the resource-abundant clouds, massive computation intensive tasks are partitioned and migrated to the cloud side for the execution. After executing the task at the cloud side, the results are sent back to the smartphone. This method is called as the computation offloading. The given survey paper focuses on the partitioning techniques in mobile cloud computing.

The effect of LH on the intracellular free Ca2+ concentration ([Ca2+]i) was investigated in highly purified small and large bovine luteal cell populations. Luteal cells were obtained from midcycle corpora lutea dispersed with collagenase and separated by flow cytometry into large and small cells. Resting levels of Ca2+ were higher (P less than 0.05) in the large than small cells [314 +/- 25 nM (n = 5) vs. 186 +/- 13 nM (mean +/- SE; n = 13) for large and small cells, respectively]. LH rapidly increased [Ca2+]i in both small and large cells loaded with the fluorescent Ca2+ probe fura-2. In the small cells, [Ca2+]i was immediately increased 2- to 6-fold (from 176 +/- 8 to 468 +/- 8 nM; n = 5) after adding LH. The LH induced [Ca2+]i rise occurred in two phases: an initial peak due to intracellular Ca2+ mobilization and a secondary rise due to Ca2+ influx from extracellular sources. Preincubation of the small cells with EGTA reduced the initial phase and abolished the secondary rise in [Ca2+]. Both forskolin and 8-bromo-cAMP increased [Ca2+]i in the small cells. In contrast, only a single phase of [Ca2+]i rise was observed in LH-treated large cells, and the response was 1.5- to 2-fold greater than the resting Ca2+ levels [314 +/- 25 vs. 435 +/- 60 nM (n = 4), for resting vs. LH-treated values, respectively]. The addition of both LH and prostaglandin F2 alpha (PGF2 alpha) to the large cells resulted in increases in [Ca2+]i that were greater than those induced by each hormone separately (2.0-fold for LH and 2.7-fold for PGF2 alpha vs. 7- to 9-fold in the presence of both hormones). These findings demonstrate that LH induces rapid increases in intracellular [Ca2+]i that differ in magnitude and profile between the small and large bovine luteal cells. Furthermore, LH and PGF2 alpha interacted to promote increases in [Ca2+]i in the large cells, that were higher than the sum of [Ca2+]i induced by each hormone separately.

Mobile cloud computing has become a research hotspot in the fields of cloud computing and mobile computing. At present, many researches on offloading computation to cloud resource and their related work have been done and some achievements have been obtained in the field of mobile cloud computing. However, some problems of the current researches have also been found such as the separation between computation and data, limited network resource, and lack of location-aware, etc. In this paper, we have presented a novel mobile cloud computing model and proposed a heuristic approach MCC-particle swarm optimization (PSO) of placement selection of mobile applications’ partitions for minimizing the overall processing time and energy saving. The algorithm of the proposed MCC-PSO approach includes two parts. One is that it combines the PSO idea with the simulated annealing (SA) idea to achieve an improved PSO-based approach with the better global search’s ability. The other one is that it uses the probability theory and mathematical statistics and once again utilizes the SA idea to deal with the data obtained from the improved PSO-based process to get the final solution. And thus the whole approach achieves a long-term optimization of mobile cloud computing. The experimental results demonstrate that MCC-PSO evidently reduces the overall processing time of mobile applications and energy consumption of mobile devices while better guaranteeing the performance of executing mobile applications. MCC-PSO better achieves the idea of mobile cloud computing and makes the mobile cloud computing model more high-effective and meaningful.

Ultra dense network of small cells (SCs) was introduced to support high data rate services and increase network capacity. Owing to user equipment (UE) mobility and limited coverage of SCs, the load across a SC network recurrently becomes unbalanced. Such unbalanced loads result in performance degradation of network in terms of throughput, inefficient utilization of network resources and link failure. This paper proposes and evaluates a novel load balancing methodology based on UE-SC association, which incorporated two main factors:(i)load of SCs in UE’s neighborhood (ii)the received Signal to Interference and Noise Ratio (SINR)/Channel Quality Indicator (CQI) and Quality of Service (QoS) requirements of user. The proposed algorithm ensures that the standard deviation of load in SCs in the neighborhood of a given UE remains minimum when the UE is associated to a potential SC. Consequently, achieving load balancing among SCs proactively while maintaining high SINR/CQI levels for UE, with each UE-SC pairing. System-level simulations show that the proposed algorithm provides a more balanced load across network, which is quantified by a smaller standard deviation of load across the SCs and higher network throughput when compared to other load balancing algorithms.

Following the growth of mobile device applications in the past years, mobile computing and cloud computing are suggesting a new computing paradigm. Demands for computing capability regarding potential technologies that are provided as mobile service are also growing. Meanwhile, necessity of Mobile cloud computing (MCC) is increasing due to the problems of mobile devices, such as resource poverty, battery constraint. However, clear definition is required as the concept of MCC is still ambiguous. Currently, it is necessary to classify the configuration methods of the resource-providing infrastructure environment of MCC. This paper provides a comprehensive overview of MCC and proposes a basic scheme. We suggest instance MCC (iMCC), which is a framework implementing offloading for the purpose of achieving efficient computing process and service provision. iMCC includes improved resource allocation method in MCC infrastructure as well as efficient offloading method. Finally, we summarize the conclusion and discuss future research.

This paper analyzes the problem of optimum user association and sum rate maximization for software defined radio access networks (SD-RANs) with access node diversity for fifth generation (5G) wireless networks. We consider four complementary types of access nodes namely, a massive multiple- input multiple-output (MIMO) base station (BS), MIMO BSs, small cells (SCs), and indoor and outdoor distributed antenna systems (DAS). The SDRAN user association problem is solved through a novel three-stage optimization scheme called the place coverage (TPC). TPC divides the user equipment (UEs) into two sets of indoor and outdoor UEs. Initially, TPC associates indoor UEs with the indoor DAS access nodes. Next, outdoor UEs are associated with the outdoor DAS access nodes, SCs, MIMO BSs, and the massive MIMO BS. Finally, the remaining resources of indoor and outdoor access nodes are used to serve the UEs that have not been served. The scope of TPC is multifold. First, TPC reflects the closest model to a real-world diverse 5G network. Secondly, servicing the indoor UEs with iDAS antennas results in lower power load per user for the access network resulting in better coverage, quality, and data speed. Thirdly, TPC provides reduced radiation levels for indoor UEs. Numerical results show that TPC provides significant sum rate and fairness gains over the received signals strength (RSS) based association schemes [1]. Moreover, TPC provides comparable performance levels as the optimum association scheme based on exhaustive search while bringing significant complexity reduction.

This paper presents a QoS handover scheme (QHS) with priorities for mobile communications on LTE small-cell networks. In a high-dense deployment of small cells, a mobile UE (User Equipment) may handover very frequently. In the proposed QHS, handover priority of a UE can be determined by its traffic type and required data rate. Specifically, QHS gives a UE with higher QoS requirement a higher priority to handover. Additionally, when QHS reveals that the target eNB (Enhanced Node Base-station) cannot bear the data rate of a higher-priority UE to handover, the QoS of lower-priority UEs is sacrificed by delaying or rejecting their handover. To validate the proposed QHS, we implement the associated modules on a Linux-based small cell platform. Traffic in the experiment consists of various types, such as video conferencing, video on demand, FTP, Web browsing, etc. Performance of the proposed QHS in terms of the occurrence of handover and average handover delay is evaluated. The experimental results have demonstrated that the proposed QHS can substantially improve the handover efficiency of multimedia traffic, as it is compared to a traditional scheme.