Emergency management systems using mobile cloud computing: A survey

Among today's rapidly evolving technologies, artificial intelligence plays a significant role in making decisions by a system without any human intervention. An Emergency Management System (EMS) is a decision support system where emergencies such as tsunami, landslide, fire, cyclone, and electrical short circuits can be prevented with prior detection and can be addressed immediately in an efficient way after the emergency occurred. Automation of EMS can avoid or manage multiple emergencies, which alternatively can save lives, economy, and environment. Quality demand response with fast data transfer, error minimized computation, and effective resource utilization is very much essential while developing the EMSs. This will provide a bridge between the technology and emergency responders. Resource-limited smart devices can be made rich in computational behavior by outsourcing their requirements such as storage, virtual servers, and web services using Mobile Cloud Computing (MCC). In this work, we have done a detailed survey on both MCC applications and EMS applications proposed in the literature, and we have also identified the design challenges handled in both MCC and EMS applications. We have presented the design challenges and possible solutions for development of EMS using MCC. We propose an architecture for building an automated EMS using MCC. Finally, we conclude the paper with specific future directions.     

测量激光二极管条微沟道封装热沉的热阻尼系数

重庆师范学院应用物理研究室已经用自己的工艺制造出高功率激光二极管线阵的微沟道冷却封装组件。通过泵压一流量 ,耗散热一温升 ,激光峰位波长—压强差倒数等曲线的测量可推算出的该器件热阻系数随水力学功变化的公式。     

A Survey of Mobile Cloud Computing

Mobile Cloud Computing (MCC) is emerging as one of the most important branches of cloud computing. In this paper, MCC is defined as cloud computing extended by mobility, and a new ad-hoc infrastructure based on mobile devices. It provides mobile users with data storage and processing services on a cloud computing platform. Because mobile cloud computing is still in its infancy, we aim to clarify confusion that has arisen from different views. Existing works are reviewed, and an overview of recent advances in mobile cloud computing is provided. We investigate representative infrastructures of mobile cloud computing and analyze key components. Moreover, emerging MCC models and services are discussed, and challenging issues are identified that will need to be addressed in future work.     

A novel cost optimization method for mobile cloud computing by dynamic processing of demands based on their power consumption

Mobile cloud computing (MCC) is an emerging technology that is introduced to combat the existing limitations in mobile computing such as constrained energy and storage. MCC enables mobile users to perform their tasks in the operator cloud and benefit from the offered services. On the other hand, operators are required to decrease their costs to stay in the competitive market. In this paper, we propose a method to reduce the cost of power consumption and increase the profit of 4G/5G network operators delivering MCC services. We propose an online method that is based on dynamic processing of mobile users’ demands based on their power consumption in the cloud, called Dyn-PDPC. In this algorithm, the power consumption of demands is estimated based on event counters, and demands are classified and processed accordingly. Unlike the offline methods, the proposed online method can be implemented with the existing information and there is no need for prior knowledge. We also present an extended version of Dyn-SP algorithm, in which we had an unrealistic assumption about the energy consumption of demands. In Dyn-PDPC, by using control parameters, when the electricity price is low, demands with high power consumption are processed, and then the low power-consumption demands are processed. Similarly, when the electricity price is high, demands with low power consumption are processed at first. Simulation results demonstrate that the proposed algorithm has more accuracy, and more reduction in long-term cost compared to other online methods in MCC networks.     

VCMIA: A Novel Architecture for Integrating Vehicular Cyber-Physical Systems and Mobile Cloud Computing

The advances in wireless communication technologies, vehicular networks and cloud computing boost a growing interest in the design, development and deployment of Vehicular Cyber-Physical Systems (VCPS) for some emerging applications, which leads to an increasing demand on connecting Mobile Cloud Computing (MCC) users to VCPS for accessing the richer applications and services. In this paper, we first identify the key requirements of designing an efficient and flexible architecture for integrating MCC and VCPS. Based on the requirements, we design a VCPS and MCC Integration Architecture (VCMIA), which provides mobile services for potential users such as drivers and passengers to access mobile traffic cloud. Then, we ana- lyze two crucial cloud-supported components: GIS with traffic-aware capability and cloud-supported dynamic vehicle routing. Finally, we select Vehicle Maintenance Services (VMS) as an application scenario to carry out the validation. The proposed VCMIA can provide the flexibility for enabling diverse applications.     

Collaborative research as a function of proximity, industry, andcompany: a case study of an R&D consortium

Collaborative work among researchers at US Universities, industry, and federal laboratories is increasingly advocated in the globally competitive marketplace. Reliable and meaningful measures of such collaboration have been sparse. This study uses a bibliographic search of published, co-authored research papers to analyze collaboration patterns among researchers at one of the nation's oldest, largest, and most complex R&D consortia, the Microelectronics and Computer Technology Corporation (MCC) and its shareholders, other industry participants, universities, and federal laboratories. The data indicate that, in terms of a defined population of published research articles, MCC researchers have collaborated more frequently with academic than with corporate researchers and more often with nonshareholder than shareholder organizations. MCC is located in Austin, Texas. Geographic proximity has played a role in the consortium's collaboration with universities only for the local research university, The University of Texas at Austin. Proximity has not been an important factor in corporate collaboration, with the consortium. Among MCC shareholders, collaboration has varied by type of industry, while universities have played an important linking role between MCC and industry. R&D expenditures per employee for member companies are inversely related to the frequency of co-authorship of articles with MCC researchers. Collaboration of MCC shareholders with international researchers has been fairly frequent, given that MCC is closed to foreign membership     

MCC8: Throughput enhancement of EB direct writer

To obtain higher throughput of more than 5 wafer per hour (WPH), authors propose a high throughput 50 kV e-beam direct writer MCC8 that has eight column. MCC8 has multi column cell (MCC), character projection (CP) with design for e-beam (DFEB) and high current density technologies. Proof-of-concept evaluations for combination between MCC and CP have finished in the MASK-D2I project of ASET, including mix-and-match overlay results of better than 5 nm. By the combination of these proven technologies, MCC8 realizes a throughput of 5 WPH. Furthermore, over 30 WPH can be realized by clustering six MCC8s together.     

A Distributed and Elastic Application Processing Model for Mobile Cloud Computing

The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources and powerful computational cloud is tapped for enabling intensive applications on SMDs. In Mobile Cloud Computing (MCC), application processing services of computational clouds are leveraged for alleviating resource limitations in SMDs. The particular deficiency of distributed architecture and runtime partitioning of the elastic mobile application are the challenging aspects of current offloading models. To address these issues of traditional models for computational offloading in MCC, this paper proposes a novel distributed and elastic applications processing (DEAP) model for intensive applications in MCC. We present an analytical model to evaluate the proposed DEAP model, and test a prototype application in the real MCC environment to demonstrate the usefulness of DEAP model. Computational offloading using the DEAP model minimizes resources utilization on SMD in the distributed processing of intensive mobile applications. Evaluation indicates a reduction of 74.6% in the overhead of runtime application partitioning and a 66.6% reduction in the CPU utilization for the execution of the application on SMD.

     

Computing Coastal Ocean Surface Currents From Infrared and Ocean Color Satellite Imagery

Many previous studies have demonstrated the viability of estimating advective ocean surface currents from sequential infrared satellite imagery using the maximum cross-correlation (MCC) technique when applied to 1.1-km-resolution Advanced Very High Resolution Radiometer (AVHRR) thermal infrared imagery. Applied only to infrared imagery, cloud cover and undesirable viewing conditions (gaps in satellite data and edge-of-scan distortions) limit the spatial and temporal coverage of the resulting velocity fields. In addition, MCC currents are limited to those represented by the displacements of thermal surface patterns, and hence, isothermal flow is not detected by the MCC method. The possibility of supplementing MCC currents derived from thermal AVHRR imagery was examined, with currents calculated from 1.1-km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color imagery, which often have spatial patterns complementary to the thermal infrared patterns. Statistical comparisons are carried out between yearlong collections of thermal and ocean color derived MCC velocities for the central California Current. It is found that the image surface patterns and resulting MCC velocities complement one another to reduce the effects of poor viewing conditions and isothermal flow. The two velocity products are found to agree quite well with a mean correlation of 0.74, a mean rms difference of 7.4 cm/s, and a mean bias less than 2 cm/s which is considerably smaller than the established absolute error of the MCC method. Merging the thermal and ocean color MCC velocity fields increases the spatial coverage by approximately 25% for this specific case study     

Toward Advanced Mobile Cloud Computing for the Internet of Things: Current Issues and Future Direction

Cloud computing is the coming new era of information processing and has proved its benefits in high scalability and functional diversity. However, almost all cloud-computing architectures including SaaS, PaaS, and IaaS are vulnerable to serious security issues. Similarly, Mobile Cloud Computing (MCC) is vital to overcoming mobile limited storage and computing capabilities. MCC authentication and authorization issues must be provided on two levels: login password control and the environment from where the cloud is accessed. MCC has overcome the barrier of limited storage by providing remote storage but requires a strict security system that is responsible for retrievability, integrity, and seamless storage access. Elasticity and connectivity are also of major concern in MCC because delays and jitters cause degradation in the user experience. Cloud-computing architecture creates more challenges in maintaining security because of the liberty of users to choose any MCC architecture. Thus in this paper we discuss current cloud computing issues and future directions.     

Cuckoo: flexible compute‐intensive task offloading in mobile cloud computing

Mobile cloud computing (MCC) is an emerging technology to facilitate complex application execution on mobile devices. Mobile users are motivated to implement various tasks using their mobile devices for great flexibility and portability. However, such advantages are challenged by the limited battery life of mobile devices. This paper presents Cuckoo, a scheme of flexible compute‐intensive task offloading in MCC for energy saving. Cuckoo seeks to balance the key design goals: maximize energy saving (technical feasibility) and minimize the impact on user experience with limited cost for offloading (realistic feasibility). Specifically, using a combination of static analysis and dynamic profiling, compute‐intensive tasks are fine‐grained marked from mobile application codes offline. According to the network transmission technologies supported in mobile devices and the runtime network conditions, adopting “task‐bundled” strategy online offloads these tasks to MCC. In the task‐hosted stage, we propose a skyline‐based online resource scheduling strategy to satisfy the realistic feasibility of MCC. In addition, we adopt resource reservation to reduce the extra energy consumption caused by the task multi‐offloading phenomenon. Further, we evaluate the performance of Cuckoo using real‐life data sets on our MCC testbed. Our extensive experiments demonstrate that Cuckoo is able to balance energy consumption and execution performance. Copyright © 2016 John Wiley & Sons, Ltd.     

最大互相关熵多凸组合自适应滤波算法

基于最大互相关熵准则(MCC)的自适应滤波算法在非高斯噪声环境下具有强鲁棒性,得到了广泛应用.然而,传统MCC滤波算法在选择参数时依然受到收敛速度与稳态精度之间固有矛盾的困扰.为解决这一问题,该文提出一类多凸组合MCC算法,能够充分发挥不同参数组合下滤波算法的性能优势,从而获得更好的信道跟踪能力.理论分析得出了所提算法...     

A survey of mobile cloud computing: architecture,applications, and approaches

Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life, storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing. This paper gives a survey of MCC, which helps general readers have an overview of the MCC including the definition, architecture, and applications. The issues, existing solutions, and approaches are presented. In addition, the future research directions of MCC are discussed. Copyright © 2011 John Wiley & Sons, Ltd.     

A New Fault-Information Model for Adaptive & Minimal Routing in 3-D Meshes

In this paper, we rewrite the minimal-connected-component (MCC) model in 2-D meshes in a fully-distributed manner without using global information so that not only can the existence of a Manhattan-distance-path be ensured at the source, but also such a path can be formed by routing-decisions made at intermediate nodes along the path. We propose the MCC model in 3-D meshes, and extend the corresponding routing in 2-D meshes to 3-D meshes. We consider the positions of source & destination when the new faulty components are constructed. Specifically, all faulty nodes will be contained in some disjoint fault-components, and a healthy node will be included in a faulty component only if using it in the routing will definitely cause a non-minimal routing-path. A distributed process is provided to collect & distribute MCC information to a limited number of nodes along so-called boundaries. Moreover, a sufficient & necessary condition is provided for the existence of a Manhattan-distance-path in the presence of our faulty components. As a result, only the routing having a Manhattan-distance-path will be activated at the source, and its success can be guaranteed by using the information of boundary in routing-decisions at the intermediate nodes. The results of our Monte-Carlo-estimate show substantial improvement of the new fault-information model in the percentage of successful Manhattan-routing conducted in 3-D meshes.     

Efficient Computation Offloading Decision in Mobile Cloud Computing over 5G Network

Due to the significant advancement of Smartphone technology, the applications targeted for these devices are getting more and more complex and demanding of high power and resources. Mobile cloud computing (MCC) allows the Smart phones to perform these highly demanding tasks with the help of powerful cloud servers. However, to decide whether a given part of an application is cost-effective to execute in local mobile device or in the cloud server is a difficult problem in MCC. It is due to the trade-off between saving energy consumption while maintaining the strict latency requirements of applications. Currently, 5th generation mobile network (5G) is getting much attention, which can support increased network capacity, high data rate and low latency and can pave the way for solving the computation offloading problem in MCC. In this paper, we design an intelligent computation offloading system that takes tradeoff decisions for code offloading from a mobile device to cloud server over the 5G network. We develop a metric for tradeoff decision making that can maximize energy saving while maintain strict latency requirements of user applications in the 5G system. We evaluate the performances of the proposed system in a test-bed implementation, and the results show that it outperforms the state-of-the-art methods in terms of accuracy, computation and energy saving.     

网格中基于最小连接块的启发式容错路由算法

矩形无效块模型可以用来解决网格下的容错路由问题,最小连接块(MCC)模型是它的一个改良模型.本文在MCC基础上,建立MCC 重叠图,当发现不存在曼哈顿路径的时候,给出一套算法,来计算出一条避免无效块的尽可能短的路径.模拟试验表明,通过这种算法找到的路径,与最短路径相差很小.比起花费更多的时间去找寻最短路径,这种启发式容错算法无疑是更好的选择.     

Performance of MIMO-UWB for LOS and N-LOS Propagation

Mobile cloud computing (MCC) enables ubiquitous access to a diverse range of Internet multimedia services in a pay-as-you-go economic model. In an MCC environment with highly mobile users, the migration of service requests from one cloud server to another due to user movement may frequently occur. We note that when the load offered to the cloud server is increased beyond the capacity limit, particularly when migrated traffic due to user movement suddenly appears, the probability to disrupt existing services gets higher, consequently resulting in the degradation of user quality of experience (QoE). To keep the service disruption probability at an acceptable level so as to maintain a high user-perceived QoE for different classes of multimedia services, this paper proposes a QoE-aware service continuity strategy for the cloud server in an MCC environment. The strategy is based on the buffer-occupancy threshold policy that differentiates newly arriving service requests coming from the mobile users and offers effective protection for migrated service requests against traffic fluctuation in newly arriving service requests. With the proposed strategy, the cloud server can dynamically change the buffer thresholds for different classes of service requests based on the offered traffic load and the user mobility to improve resource utilization, and, most importantly, to keep the service disruption probability at an acceptable level. Besides, by taking the effect of migrated traffic into account, we develop an analytical model to study the performance of the cloud server using the proposed strategy. With the analytical model, we propose an iterative method to determine the optimal buffer thresholds that maximize resource utilization while keeping an acceptable user QoE for different classes of services.

     

Sampling the mesoscale ocean surface currents with various satellite altimeter configurations

Ten-day composites of maximum cross-correlation (MCC) ocean surface current vectors from 1-km spatial resolution Advanced Very High Resolution Radiometer (AVHRR) 11-/spl mu/m thermal infrared images are used to simulate the ocean surface current retrieval capabilities of three satellite altimeter configurations over a large California coastal region. Ground track positions of the nadir sampling TOPEX/Poseidon (TP; now the Jason-1) satellite altimeters are used to compute the cross-track velocity components from the corresponding optimally interpolated MCC vectors for a ten-day period. Next, the Jason-1-only and the TP plus Jason-1 "tandem mission" sampling are simulated as well as the combination of all available satellite altimeters including European Remote Sensing Satellite 2 and Geosat Follow-On. Finally, we simulate surface current retrievals from the proposed Wide Swath Ocean Altimeter (WSOA), which will have both along- and cross-track velocity components over a spatial swath. Comparisons of vector current fields, their differences, and wavenumber spectra from optimally interpolated maps of the "simulated altimetry velocities" with the corresponding MCC field indicates that (1) the combined coverage from Jason-1 plus TP as well as the combination of all available satellite altimeters results in a better representation of the currents than that of Jason-1 alone and (2) the retrieved currents from the WSOA provide an even greater improvement over the tandem mapping and multiple satellites. It will be possible in the future to regularly map the mesoscale surface currents of the ocean with wide-swath ocean altimeters.     

A bandwidth allocation and energy-optimal transmission rate scheduling scheme in multi-services wireless networks

The energy-saving of mobile devices during their application offloading process has always been the research hotspot in the field of mobile cloud computing (MCC). In this paper, we focus on the scenario where multiple mobile devices with MCC and non-MCC services coexist. A bandwidth allocation and the corresponding transmission rate scheduling schemes are proposed with the objectives of simultaneously maximizing the overall system throughput and minimizing the energy consumption of individual mobile device with MCC service. To allocate the bandwidth to all mobile devices, two different algorithms are proposed, i.e., 0–1 integer programming algorithm and Lagrange dual algorithm. The transmission rate scheduling scheme for mobile device with MCC service is presented based on reverse order iteration method. The numerical results suggest that energy consumed by individual mobile device with MCC service can be remarkably saved while the overall system throughput can also be maximized. Moreover, the results show that 0–1 integer programming algorithm can get greater system throughput but has higher computational complexity, which means the algorithm is more suitable for small-scale systems, whereas Lagrange dual algorithm can achieve a good balance between the performance and computational complexity.     

A multi‐channel cooperative clustering‐based MAC protocol for V2V communications

The Internet of vehicles (IoV) is an emerging networking technology, which can support information sharing and interactions among users, vehicles, and infrastructures. Various applications can be provided by IoVs, and they have very different quality‐of‐service (QoS) requirements. It is a great challenge to design an efficient MAC protocol to meet the different QoS demands of various applications in IoVs, because of unreliable links and high vehicle mobility. On the other hand, cooperative communication is effective in mitigating wireless channel impairments by utilizing the broadcast nature of wireless channels. In this paper, a multi‐channel cooperative clustering‐based MAC (MCC‐MAC) protocol, under the Dedicated Short Range Communication (DSRC) multi‐channel architecture, is presented to improve the transmission reliability of safety messages and provision QoS for different applications in IoVs. Further, we analyze the performance of MCC‐MAC, in terms of average transmission delay. In addition, extensive simulations with ns‐2 are conducted to demonstrate the performance of the proposed MCC‐MAC. Copyright © 2016 John Wiley & Sons, Ltd.