Practical server-side WiFi-based indoor localization: Addressing cardinality & outlier challenges for improved occupancy estimation

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Adaptive Transmission Power for Optimal Energy Reliable Multi-hop Wireless Communication

Abstract — We define a transmission power adaptation-based routing technique that finds optimal paths for minimum energy reliable data transfer in multi-hop wireless networks. This optimal choice of the transmission power depends on the link distance between the two nodes and the channel characteristics. Typical energy efficient routing techniques use a transmission power such that the received signal power at the destination minimally exceeds a desired threshold signal strength level. In this paper we argue that such a choice of the transmission power does not always lead to optimal energy routes, since it does not consider differences in the receiver noise levels. We first analyze the optimal transmission power choices for both the ideal case from an information-theoretic perspective, and for realistic modulation schemes. Subsequently we define our technique for transmission power adaptation that can be used in existin

Robust, fine-grained occupancy estimation via combined camera & WiFi indoor localization

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

LightSense: Exploiting smart bulbs for practical multimodal localization

Singapore National Research Foundatio

MASTAQ: A Middleware Architecture for Sensor Applications with Statistical Quality Constraints

We present the design goals and functional components of MASTAQ, a data management middleware for pervasive applications that utilize sensor data. MASTAQ allows applications to specify their quality-of information (QoI) preferences (in terms of statistical metrics over the data) independent of the underlying network topology. It then achieves energy efficiency by adaptively activating and querying only the subset of sensor nodes needed to meet the target QoI bounds. We also present a closed-loop feedback mechanism based on broadcasting of activation probabilities, which allows MASTAQ to activate the appropriate number of sensors without requiring any inter-sensor coordination or knowledge of the actual deployment.1