Experiences and issues for environmental engineering sensor network deployments

Sensor network research is a large and growing area of academic effort, examining technological and deployment issues in the area of environmental monitoring. These technologies are used by environmental engineers and scientists to monitor a multiplicity of environments and services, and, specific to this paper, energy and water supplied to the built environment. Although the technology is developed by Computer Science specialists, the use and deployment is traditionally performed by environmental engineers. This paper examines deployment from the perspectives of environmental engineers and scientists and asks what computer scientists can do to improve the process. The paper uses a case study to demonstrate the agile operation of WSNs within the Cloud Computing infrastructure, and thus the demand-driven, collaboration-intense paradigm of Digital Ecosystems in Complex Environments

Experiences and issues for environmental science sensor network deployments

Sensor network research is a large and growing area of academic effort, examining technological and deployment issues in the area of environmental monitoring. These technologies are used by environmental engineers and scientists to monitor a multiplicity of environments and services, and, specific to this paper, energy and water supplied to the built environment. Although the technology is developed by Computer Science specialists, the use and deployment is traditionally performed by environmental engineers. This paper examines deployment from the perspectives of environmental engineers and scientists and asks what computer scientists can do to improve the process. The paper uses a case study to demonstrate the agile operation of WSNs within the Cloud Computing infrastructure, and thus the demand-driven, collaboration-intense paradigm of Digital Ecosystems in Complex Environments

Porting LooCI Components into Zigduino

AbstractLoosely-coupled Component Infrastructure (LooCI) is a middleware for building distributed component-based WSN applications. LooCI cleanly separates distributed concerns from component implementation, supports application-level interoperability between heterogeneous WSN platforms, and provides compatibility testing of bindings at runtime.In this paper, we describe our approach to porting LooCI/Contiki from the Raven platform to the Zigduino platform, which is an Arduino-compatible microcontroller environmentthat integrates an 802.15.4 radio on the board

Migrating software to mobile technology: a model driven engineering approach

Nowadays, organizations are facing the problematic of having to modernize or replace their legacy software. This software has involved the investment of money, time and other resources through the ages and there is a high risk in replacing it. The purpose of reengineering is to adapt software in a disciplined way in order to improve its quality in aspects such as operability, functionality or performance. The focus of reengineering is on improving an existing system with a higher return on investment than would be achieved by developing a new system. In the context of reengineering, the term legacy was associated with software that survived several generations of developers, administrators and users. The entry into the market of new technologies or paradigms is increasingly occurring and, motivates the growing demand for the adaptation of systems developed more recently. Mobile Computing is crucial to harvesting the potential of these new paradigms. Smartphones are the most used computing platform worldwide. They come with a variety of sensors (GPS, accelerometer, digital compass, microphone and camera) enabling a wide range of applications in Pervasive Computing, Cloud Computing and Internet of Things (IoT)

A New Technology of Smart Shopping Cart using RFID and ZIGBEE

Now a days it is common to see people getting enthusiast in online shopping through e-commerce websites but still the shopping centers are popular. We come across many types of carts used for shopping in malls and shopping centers. We are proposing smart shopping cart which uses the RFID and ZIGBEE technology to identify the products details and sends the data wirelessly to the receiver. We propose to have facility to browse the available products list on-screen in the display connected to the microcontroller which is situated in smart cart. The cart is interacting with the Main Server and it will have the facility to generate the bill for all the products added into the cart. The proposed system will be helpful for avoiding queues in shopping malls for billing. With the proposed design conventional queue system for billing generation and hence the shopping becomes easy and enjoyable

Things as a service: Service model for IoT

Leveraging the benefits of service computing technologies for Internet of Things (IoT) can help in rapid system development, composition and deployment. But due to the massive scale, computational and communication constraints, existing software service models cannot be directly applied for IoT based systems. Service discovery and composition mechanism need to be decentralized unlike majority of other service models. Moreover, IoT services' interfaces require to be light weight and able to expose the device profile for seamless discovery onto the IoT based system infrastructure. In addition to this, the 'things' data should be associated with its present context. To address these issues, this paper proposes a formal model for IoT services. The service model includes the physical property of 'things' and exposes it to the user. It also associates the context with the 'things' output, which in turn helps in extracting relevant information from the 'things' data. To evaluate our IoT service model, a weather monitoring system and its associated services are implemented using node.js [31]. The service data is mapped to SSN ontology for generating context-rich RDF data. This way, the proposed IoT service model can expose the device profile to the user and incorporate relevant context information with the things data

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Cloud-based data-intensive framework towards fault diagnosis in large-scale petrochemical plants

Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE

Performance Models for Frost Prediction in Public Cloud Infrastructures

Sensor Clouds have opened new opportunities for agricultural monitoring. These infrastructures use Wireless Sensor Networks (WSNs) to collect data on-field and Cloud Computing services to store and process them. Among other applications of Sensor Clouds, frost prevention is of special interest among grapevine producers in the Province of Mendoza - Argentina, since frost is one of the main causes of economic loss in the province. Currently, there is a wide offer of public cloud services that can be used in order to process data collected by Sensor Clouds. Therefore, there is a need for tools to determine which instance is the most appropriate in terms of execution time and economic costs for running frost prediction applications in an isolated or cluster way. In this paper, we develop models to estimate the performance of different Amazon EC2 instances for processing frosts prediction applications. Finally, we obtain results that show which is the best instance for processing these applications