A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Design and evaluation of a person-centric heart monitoring system over fog computing infrastructure

Heart disease and stroke are becoming the leading cause of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that helps physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECG have limited accuracy and rely on external resources to analyze the signal and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyze the signal and identify abnormal behavior. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud and (c) the overall power consumption. Based on this concept, the HEART platform is presented that combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate and instant monitoring of the heart. The performance of the system is evaluated concerning the accuracy of detecting abnormal events and the power consumption of the wearable device. Results indicate that a very high percentage of success can be achieved in terms of event detection ratio and the device being operative up to a several days without the need for a recharge