Towards distributed architecture for collaborative cloud services in community networks

Internet and communication technologies have lowered the costs for communities to collaborate, leading to new services like user-generated content and social computing, and through collaboration, collectively built infrastructures like community networks have also emerged. Community networks get formed when individuals and local organisations from a geographic area team up to create and run a community-owned IP network to satisfy the community’s demand for ICT, such as facilitating Internet access and providing services of local interest. The consolidation of today’s cloud technologies offers now the possibility of collectively built community clouds, building upon user-generated content and user-provided networks towards an ecosystem of cloud services. To address the limitation and enhance utility of community networks, we propose a collaborative distributed architecture for building a community cloud system that employs resources contributed by the members of the community network for provisioning infrastructure and software services. Such architecture needs to be tailored to the specific social, economic and technical characteristics of the community networks for community clouds to be successful and sustainable. By real deployments of clouds in community networks and evaluation of application performance, we show that community clouds are feasible. Our result may encourage collaborative innovative cloud-based services made possible with the resources of a community.Peer ReviewedPostprint (author’s final draft

Managing big data experiments on smartphones

The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones

Practical service placement approach for microservices architecture

Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. To reduce the complexity of service deployment, community micro-clouds have recently emerged as a promising enabler for the delivery of cloud services to community users. By putting services closer to consumers, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of the services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, it requires of aPeer ReviewedPostprint (author's final draft

Monitoring wild animal communities with arrays of motion sensitive camera traps

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location -specific information on movement and behavior. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans

Scenarios for Educational and Game Activities using Internet of Things Data

Raising awareness among young people and changing their behavior and habits concerning energy usage and the environment is key to achieving a sustainable planet. The goal to address the global climate problem requires informing the population on their roles in mitigation actions and adaptation of sustainable behaviors. Addressing climate change and achieve ambitious energy and climate targets requires a change in citizen behavior and consumption practices. IoT sensing and related scenario and practices, which address school children via discovery, gamification, and educational activities, are examined in this paper. Use of seawater sensors in STEM education, that has not previously been addressed, is included in these educational scenaria

Prototyping Incentive-based Resource Assignment for Clouds in Community Networks

Wireless community networks are a successful example of a collective where communities operate ICT infrastructure and provide IP connectivity based on the principle of reciprocal resource sharing of network bandwidth. This sharing, however, has not extended to computing and storage resources, resulting in very few applications and services which are currently deployed within community networks. Cloud computing, as in today's Internet, has made it common to consume resources provided by public clouds providers, but such cloud infrastructures have not materialized within community networks. We analyse in this paper socio-technical characteristics of community networks in order to derive scenarios for community clouds. Based on an architecture for such a community cloud, we implement a prototype for the incentive-driven resource assignment component, deploy it in a testbed of community network nodes, and evaluate its behaviour experimentally. Our evaluation gives insight into how the deployed prototype components regulate the consumption of cloud resources taking into account the users' contributions, and how this regulation affects the system usage. Our results suggest a further integration of this regulation component into current cloud management platforms in order to open them up for the operation of an ecosystem of community cloud

Gossip-based service monitoring platform for wireless edge cloud computing

Edge cloud computing proposes to support shared services, by using the infrastructure at the network's edge. An important problem is the monitoring and management of services across the edge environment. Therefore, dissemination and gathering of data is not straightforward, differing from the classic cloud infrastructure. In this paper, we consider the environment of community networks for edge cloud computing, in which the monitoring of cloud services is required. We propose a monitoring platform to collect near real-time data about the services offered in the community network using a gossip-enabled network. We analyze and apply this gossip-enabled network to perform service discovery and information sharing, enabling data dissemination among the community. We implemented our solution as a prototype and used it for collecting service monitoring data from the real operational community network cloud, as a feasible deployment of our solution. By means of emulation and simulation we analyze in different scenarios, the behavior of the gossip overlay solution, and obtain average results regarding information propagation and consistency needs, i.e. in high latency situations, data convergence occurs within minutes.Peer ReviewedPostprint (author's final draft

FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities

Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena