Government Transparency: Six Strategies for More Open and Participatory Government

Offers strategies for realizing Knight's 2009 call for e-government and openness using Web 2.0 and 3.0 technologies, including public-private partnerships to develop applications, flexible procurement procedures, and better community broadband access

The Feasibility of Wearables in an Enterprise Environment and Their Impact on IT Security

This paper is intended to explore the usability and feasibility of wearables in an enterprise environment and their impact on IT Security. In this day and age, with the advent of the Internet of Things, we must explore all the new technology emerging from the minds of the new inventors. This means exploring the use of wearables in regards to their benefits, limitations, and the new challenges they pose to securing computer networks in the Federal environment. We will explore the design of the wearables, the interfaces needed to connect them, and what it will take to connect personal devices in the Federal enterprise network environment. We will provide an overview of the wearable design, concerns of ensuring the confidentiality, integrity, and availability of information and the challenges faced by those doing so. We will also review the implications and limitations of the policies governing wearable technology and the physical efforts to enforce them

Mechanisms for improving information quality in smartphone crowdsensing systems

Given its potential for a large variety of real-life applications, smartphone crowdsensing has recently gained tremendous attention from the research community. Smartphone crowdsensing is a paradigm that allows ordinary citizens to participate in large-scale sensing surveys by using user-friendly applications installed in their smartphones. In this way, fine-grained sensing information is obtained from smartphone users without employing fixed and expensive infrastructure, and with negligible maintenance costs. Existing smartphone sensing systems depend completely on the participants\u27 willingness to submit up-to-date and accurate information regarding the events being monitored. Therefore, it becomes paramount to scalably and effectively determine, enforce, and optimize the information quality of the sensing reports submitted by the participants. To this end, mechanisms to improve information quality in smartphone crowdsensing systems were designed in this work. Firstly, the FIRST framework is presented, which is a reputation-based mechanism that leverages the concept of mobile trusted participants to determine and improve the information quality of collected data. Secondly, it is mathematically modeled and studied the problem of maximizing the likelihood of successful execution of sensing tasks when participants having uncertain mobility execute sensing tasks. Two incentive mechanisms based on game and auction theory are then proposed to efficiently and scalably solve such problem. Experimental results demonstrate that the mechanisms developed in this thesis outperform existing state of the art in improving information quality in smartphone crowdsensing systems --Abstract, page iii

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

Dynamic Resource Scheduling in Cloud Data Center

Cloud infrastructure provides a wide range of resources and services to companies and organizations, such as computation, storage, database, platforms, etc. These resources and services are used to power up and scale out tenants' workloads and meet their specified service level agreements (SLA). With the various kinds and characteristics of its workloads, an important problem for cloud provider is how to allocate it resource among the requests. An efficient resource scheduling scheme should be able to benefit both the cloud provider and also the cloud users. For the cloud provider, the goal of the scheduling algorithm is to improve the throughput and the job completion rate of the cloud data center under the stress condition or to use less physical machines to support all incoming jobs under the overprovisioning condition. For the cloud users, the goal of scheduling algorithm is to guarantee the SLAs and satisfy other job specified requirements. Furthermore, since in a cloud data center, jobs would arrive and leave very frequently, hence, it is critical to make the scheduling decision within a reasonable time. To improve the efficiency of the cloud provider, the scheduling algorithm needs to jointly reduce the inter-VM and intra-VM fragments, which means to consider the scheduling problem with regard to both the cloud provider and the users. This thesis address the cloud scheduling problem from both the cloud provider and the user side. Cloud data centers typically require tenants to specify the resource demands for the virtual machines (VMs) they create using a set of pre-defined, fixed configurations, to ease the resource allocation problem. However, this approach could lead to low resource utilization of cloud data centers as tenants are obligated to conservatively predict the maximum resource demand of their applications. In addition to that, users are at an inferior position of estimating the VM demands without knowing the multiplexing techniques of the cloud provider. Cloud provider, on the other hand, has a better knowledge at selecting the VM sets for the submitted applications. The scheduling problem is even severe for the mobile user who wants to use the cloud infrastructure to extend his/her computation and battery capacity, where the response and scheduling time is tight and the transmission channel between mobile users and cloudlet is highly variable. This thesis investigates into the resource scheduling problem for both wired and mobile users in the cloud environment. The proposed resource allocation problem is studied in the methodology of problem modeling, trace analysis, algorithm design and simulation approach. The first aspect this thesis addresses is the VM scheduling problem. Instead of the static VM scheduling, this thesis proposes a finer-grained dynamic resource allocation and scheduling algorithm that can substantially improve the utilization of the data center resources by increasing the number of jobs accommodated and correspondingly, the cloud data center provider's revenue. The second problem this thesis addresses is joint VM set selection and scheduling problem. The basic idea is that there may exist multiple VM sets that can support an application's resource demand, and by elaborately select an appropriate VM set, the utilization of the data center can be improved without violating the application's SLA. The third problem addressed by the thesis is the mobile cloud resource scheduling problem, where the key issue is to find the most energy and time efficient way of allocating components of the target application given the current network condition and cloud resource usage status. The main contribution of this thesis are the followings. For the dynamic real-time scheduling problem, a constraint programming solution is proposed to schedule the long jobs, and simple heuristics are used to quickly, yet quite accurately schedule the short jobs. Trace-driven simulations shows that the overall revenue for the cloud provider can be improved by 30\% over the traditional static VM resource allocation based on the coarse granularity specifications. For the joint VM selection and scheduling problem, this thesis proposes an optimal online VM set selection scheme that satisfies the user resource demand and minimizes the number of activated physical machines. Trace driven simulation shows around 18\% improvement of the overall utility of the provider compared to Bazaar-I approach and more than 25\% compared to best-fit and first-fit. For the mobile cloud scheduling problem, a reservation-based joint code partition and resource scheduling algorithm is proposed by conservatively estimating the minimal resource demand and a polynomial time code partition algorithm is proposed to obtain the corresponding partition

Obstacles of eHealth Capacity Building and Innovation Promotion Initiative in African Countries

eHealth applications and tools have the potential to improve coordination, knowledge, and information sharing between health professionals as well as continuity of care. One of the main obstacles hindering its full integration and use, particularly in the healthcare sector in developing and low and middle-income countries is the lack of qualified staff and healthcare personnel. To explore obstacles that hinder capacity and innovation promotion initiatives, a survey was conducted among BETTEReHEALTH partners. A questionnaire was used to collect quantitative data from 37 organizations. Although there are different buckets of capacity-building and innovation promotion activities going on, the findings showed very few targeting policymakers and eHealth specialists. The findings found that obstacles to capacity building and innovation promotion include lack of finance, poor infrastructure, poor leadership, and governance, and these obstacles are context or region specific. Findings from our study concur with those from previous research on the need to identify practical solutions and simple interventions to address eHealth obstacles to capacity building in developing countries. As measures to mitigate these obstacles, our study proposed the need for adequate policies, strong political commitment, the development of academic modules to be integrated into existing educational programs, and the creation of more in-country and on-site capacity-building activities. While this study contributes to the discourse on eHealth capacity-building and innovation promotion initiatives among healthcare and public health professionals, the study has a limitation as data was collected only from BETTEReHEALTH partners.publishedVersio