Smartphones and a growing number of wearable devices are equipped with powerful sensors. This has led to increased interest in developing applications using sensor feeds from such devices to offer services across a wide variety of domains including healthcare, entertainment, environmental monitoring and transportation. However, most of these applications require continuous sensing, which places a heavy demand on the typically limited battery power of devices. This paper presents ShareSens, our approach to opportunistically merge sensing requirements of independent applications. We achieve this using sensing schedulers for sensors, which determine the lowest sensing rate which would satisfy all requests, and then use custom filters to send out only the required data to each application. Applications can request fixed sampling rates or ranges of rates, creating the opportunity for sending them higher rates than minimally required-“for free.” Sensing requests made through our ShareSens API are forwarded to the relevant schedulers, which determine the optimum sensing rates to satisfy all requests, and set up filters to deliver required feeds to the applications. The design and implementation of ShareSens is presented, along with results from our experimental work on the power savings that can be achieved by using it.

Although there are models and prototype implementations for controlling resource use in Actor systems, they are difficult to implement for production implementations of Actors such as Akka. This is because the messaging and scheduling infrastructures of runtime systems are increasingly complex and significantly different from one system to another. This paper presents our efforts in implementing resource control support for Actor systems implemented using the Akka library. Particularly, given the lack of support in Akka for direct scheduling of actors, we compare two different ways of approximating actor-level control support. The first implementation expects messages to actors to provide estimates of resources likely to be consumed for processing them; these estimates are then relied upon to make scheduling decisions. In the second implementation, resource use of scheduled actors is tracked, and compared against allocations to decide when they should be scheduled next. We present experimental results on the performance cost of these resource control mechanisms, as well as their impact on resource utilization.

The growing ubiquity of personal connected devices -- each with a number of sensors -- has created the opportunity for a wide range of crowd-sourced services. Although the programming required for offering new services can be significant if done from scratch, we identify core communication mechanis