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As wireless sensor networks (WSNs) must operate for long periods on a limited power budget, estimating the energy cost of software operations is critical. Contemporary reconfiguration approaches for WSN allow for software evolution at various granularities; from reflashing of a complete software image, through replacement of complete applications, to the reconfiguration of individual software components. This paper contributes a generic model for measuring and modeling the energy cost of reconfiguration in WSN. We validate that this model is accurate in the face of different hardware platforms, software stacks, and software encapsulation approaches. We have embedded this model in the loosely coupled component infrastructure middleware, resulting in the first energy aware reconfigurable component model for sensor networks. We evaluate our approach using two real-world WSN applications and demonstrate that our model predicts the energy cost of reconfiguration with 93% accuracy. Using this model, we demonstrate that selecting the most appropriate software modularization approach is key to minimizing energy consumption.

© 2014, Australian Computer Society, Inc.The quality and diversity of available data sources has a large impact on the potential for sensor networks to support rich applications. The high cost and narrow focus of new sensor network deployments has led to a search for diverse, global data sources to support more varied sensor network applications. Social networks are culturally and geographically diverse, and consist of large amounts of rich data from users. This provides a unique opportunity for existing social networks to be leveraged as data sources. Using social media as a data source poses significant challenges. These include the large volume of available data, the associated difficulty in isolating relevant data sources and the lack of a universal data format for social networks. Integrating social and other data sources for use in sensor networking applications requires a cohesive framework, including data sourcing, collection, cleaning, integration, aggregation and querying techniques. While similar frameworks exist, they require long-term collection of all social media data for aggregation, requiring large infrastructure outlays. This paper presents a novel framework which is able to source social data, integrate it into a common format and perform querying operations without the high level of resource requirements of existing solutions. Framework components are fully extensible, allowing for the addition of new data sources as well as the extension of query functionality to support sensor networking applications. This framework provides a consistent, reliable querying interface to existing social media assets for use in sensor networking applications and experiments - without the cost or complexity of establishing new sensor network deployments.

Wireless Sensor Networks (WSNs) are subject to high levels of dynamism arising from changing environmental conditions and application requirements. Reconfiguration allows software functionality to be optimized for current environmental conditions and supports software evolution to meet variable application requirements. Contemporary software modularization approaches for WSNs allow for software evolution at various granularities; from monolithic re-flashing of OS and application functionality, through replacement of complete applications, to the reconfiguration of individual software components. As the nodes that compose a WSN must typically operate for long periods on a single battery charge, estimating the energy cost of software evolution is critical. This paper contributes a generic model for calculating the energy cost of the reconfiguration in WSN. We have embedded this model in the LooCI middleware, resulting in the first energy aware reconfigurable component model for sensor networks. We evaluate our approach using two real-world WSN applications and find that (i.) our model accurately predicts the energy cost of reconfiguration and (ii.) component-based reconfiguration has a high initial cost, but provides energy savings during software evolution.

Physiological monitoring can be useful in a number of scenarios to evaluate or diagnose the status of individuals or groups, for health or mental reasons. The devices used to collect this data have become increasingly portable, but deriving useful metrics from such data can often take significant processing - a commodity not always available in mobile environments. This paper presents and evaluates a system designed to easily process physiological signals in mobile environments by utilising commonly-available smartphone hardware to collaboratively transform collected data.

Peer-to-peer networks have gained a significant amount of popularity since their inception, being used in a wide variety of contexts; both legal and illegal. The demand for monitoring techniques able to analyse performance, identify copyright infringers and detect the presence of peer-to-peer traffic on networks has similarly risen. Along with them ethical issues have surfaced, awareness of which is essential when planning research requiring peer-to-peer monitoring. This paper discusses the collection of data through the monitoring of peer-to-peer networks and identifies areas of particular ethical concern in its use.

Physiological monitoring has been used in a wide range of scenarios to assist in disease diagnosis, athlete monitoring and other activities. There are also many opportunities in analysing aggregate data from groups of people rather than individuals such as public event monitoring or athletic team performance optimisation. Numerous difficulties exist pertaining to this, particularly concerning how to process and transform the resulting physiological data in real-time when many devices are producing data. This paper proposes a system that is designed to monitor, analyse and report physiological data in real-time by leveraging mobile devices as distributed processors.