Device-centric sensing: An alternative to data-centric approaches

© 2007-2012 IEEE. When pieces of information originate from the physical world through the sensing infrastructure, there is a pressing need to cope with the overhead and inherent limitations lying in merely shifting huge amounts of aggregated data across the net. In this scenario, a key point is the minimization of wasted bandwidth to accommodate for ever-growing demands of sensing data. For effective treatment of sensing data, BigData principles and approaches should be adopted, particularly the one by which computing has to be brought as near as possible to data. In this paper, we propose a new approach to deal with sensing data inspired by this principle, injecting intelligence on the device instead of just using it as source of data, thus reversing the trend from the current data-centric paradigm toward a device-centric one. This way, we shift the focus from the application level onto the infrastructure one, adopting a Cloud-oriented approach to abstract and virtualize sensor-hosting boards ready to be reconfigured with custom logic, such as MapReduce, by providing resources on demand, as a service. Theoretical, design, and technical aspects have been addressed in this paper through the evaluation of a device-centric sensing infrastructure-as-a-service (IaaS) stack implementation. In particular, a prototype for mobiles is described, getting into platform-dependent details where needed. The facilities so far implemented under the Android platform have been put under preliminary testing through a mobile application

An application adaptation layer for wireless sensor networks

In wireless sensor networks, poor performance or unexpected behavior may be experienced for several reasons, such as trivial deterioration of sensing hardware, unsatisfactory implementation of application logic, or mutated network conditions. This leads to the necessity of changing the application behavior after the network has been deployed. Such flexibility is still an open issue as it can be achieved either at the expense of significant energy consumption or through software complexity. This paper describes an approach to adapt the behavior of running applications by intercepting the calls made to the operating system services and changing their effects at run-time. Customization is obtained through small fragments of interpreted bytecode, called adaptlets, injected into the network by the base station. Differently from other approaches, where the entire application is interpreted, adaptlets are tied only to specific services, while the bulk of the application is still written in native code. This makes our system able to preserve the compactness and efficiency of native code and to have little impact on the overall application performance. Also, applications must not be rewritten because the operating system interfaces are unaffected. The adaptation layer has been implemented in the context of TinyOS using an instruction set inspired to the Java bytecode. Examples that illustrate the programming of the adaptation layer are presented together with their experimental validatio