Energy Efficient Opportunistic Sensing Management in Fog Cloud Environment

With so many people now wearing mobile devices with sensors (such as smartphones), utilizing the immense capabilities of these business mobility goods has become a prospective skill to significant behavioural and ecological sensors. A potential challenge for pervasive context assessment is opportunistic sensing, has been effectively used to a wide range of applications. The sensor cloud combines cloud technology with a wireless sensor, resulting in a scalable and cost-effective computing platform for real-time applications. Because the sensor's battery power is limited and the data centre’s servers consume a significant amount of energy to supply storage, a sensor cloud must be energy efficient. This study provides a Fog-based semantic for enabling these kinds of technologies quickly and successfully. The suggested structure is comprised of fundamental algorithms to help set up and coordinate the fog sensing jobs. It creates effective multihop routes for coordinating relevant devices and transporting acquired sensory data to fog sinks. It was claimed that energy-efficient sensor cloud approaches were categorized into different groups and that each technology was examined using numerous characteristics. The outcomes of a series of thorough test simulation in NS3 to define the practicality of the created console, as well as the proportion of each parameter utilized for each technology, are computed.

Opportunistic weather sensors: an Amsterdam case study of private weather stations, commercial microwave links and smartphones

<p>Several opportunistic sensors (private weather stations, commercial microwave links and smartphones) are employed to obtain weather information and successfully monitor urban weather events. The ongoing urbanisation and climate change urges further understanding and monitoring of weather in cities. Two case studies during a 17-day period over the Amsterdam metropolitan area, the Netherlands, are used to illustrate the potential and limitations of hydrometeorological monitoring using non-traditional and opportunistic sensors. We employ three types of opportunistic sensing networks to monitor six important environmental variables: (1) air temperature estimates from smartphone batteries and personal weather stations; (2) rainfall from commercial microwave links and personal weather stations; (3) solar radiation from smartphones; (4) wind speed from personal weather stations; (5) air pressure from smartphones and personal weather stations; (6) humidity from personal weather stations. These observations are compared to dedicated, traditional observations where possible, although such networks are typically sparse in urban areas. First we show that the passage of a front can be successfully monitored using data from several types of non-traditional sensors in a complementary fashion. Also we demonstrate the added value of opportunistic measurements in quantifying the Urban Heat Island (UHI) effect during a hot episode. The UHI can be clearly determined from personal weather stations, though UHI values tend to be high compared to records from a traditional network. Overall, this study illustrates the enormous potential for hydrometeorological monitoring in urban areas using non-traditional and opportunistic sensing networks.</p>