In order to maintain productivity and alertness, individuals must be thermally comfortable in the space they occupy (whether it is a cubicle, a room, a car, etc.). However, it is often difficult to non-intrusively assess an occupants thermal comfort and hence most HVAC engineers adopt fixed temperature settings to err on the safe side. These set temperatures can be too hot or too cold for individuals wearing different clothing, and as a result lead to feelings of discomfort as well as wastage of energy. To address these challenges, we develop SiCILIA, a platform that extracts physical and personal variables of an occupants thermal environment to infer the amount of clothing insulation without human intervention. The proposed inference algorithm builds upon theories of body heat transfer, and is corroborated by empirical data. Experimental results show that the algorithm is capable of accurately predicting an occupants thermal insulation with a mean prediction error of approximately 0.2.
Delay- and Disruption- Tolerant Networks (DTNs) refer to a range of networks with link intermittency which is mainly driven by mobility, predictable or unpredictable network environmental conditions. Examples of DTNs include interplanetary networks, battlefield networks, smart highways, remote environmental and animal-movement outposts. Predicting an end-to-end delay in networks with disruptive links is more complicated than predicting the delay in connected networks. Disruptive patterns and underlying routing algorithms play a major role in an end-to-end delay modeling. In this paper, we introduce a new model that can be used to estimate the end-to-end delay in networks with intermittent links. The model incorporates the two non-deterministic delay distribution namely link intermittency and tandem queuing delay distributions. The model is based on an open queuing system with exponentially distributed link intermittency. With a probabilistic connectivity matrix, the model gives a close approximation of the average end-to-end delay and the jitter in closed forms. While the model is derived theoretically by a sound queuing model, simulation results on various networks and under different traffic conditions confirm the accuracy of the model within the conventional bounds of statistical significance.
Wireless Body Area Networks (WBANs) are seen as the enabling technology for developing new generations of medical applications, such as remote health monitoring. As such it is expected that WBANs will predominantly transport mission-critical and delay sensitive data. A key strategy towards building a reliable WBAN is to ensure such networks are highly immune to interference. To achieve this, new and intelligent wireless spectrum allocation strategies are required not only to avoid interference, but also to make bestuse of the limited available spectrum. This paper presents a new spectrum allocation scheme referred to as Smart Channel Assignment (SCA), which maximizes the resource usage and transmission speed by deploying a partially-orthogonal channel assignment scheme between coexisting WBANs as well as offering a convenient trade-off between spectral reuse efficiency, transmission rate and outage. Detailed analytical and experimental studies verify that the proposed SCA strategy is robust to variations in channel conditions, increase in sensor node-density within each WBAN and an increase in number of coexisting WBANs.
Monitoring phenomena and environments is an emergent and required field in our today systems and applications. Hence, wireless sensor networks have attracted considerable attention from the research community as an efficient way to explore various kinds of environments. Sensor networks applications can be useful in different domains. One of the major constraint in such networks is the energy consumption which increases when data transmission increases. Consequently, efficient data transmission is one of the most significant criteria in WSNs that can conserve energy of sensors. In this paper, we propose an efficient data transmission protocol which consists in two phases of data aggregation. Our proposed protocol searches, in the first phase, similarities between measures collected by each sensor. In the second phase, it uses distance-based functions to find similarity between sets of collected data. The main goal of these phases is to reduce the data transmitted from both sensors and cluster-heads in a clustering-based scheme network. To evaluate the performance of our protocol, experiments on real sensor data have been conducted. Compared to existing techniques, results show that our protocol can be effectively used to reduce data transmission and increase network lifetime, while still keeping data integrity of the collected data.
Smartphones have become the most pervasive device in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, including accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.
It has been a consensus that a certain relationship exists between personal emotions and usage pattern of smartphone. Based on users emotion and personality, more and more applications are developed to provide intelligent automation services on smartphone, such as music recommendation, stranger introduction in SNS. Most of existing work studies this relationship by learning large amounts of samples, which are manually labeled and collected from smartphone users. The manual labeling process, however, is very time-consuming and labor-intensive. To address this issue, we propose iSelf, a system which provides a general service of automatic detecting users emotions in cold-start conditions with smartphone. With the technology of transfer learning, iSelf achieves high accuracy given only a few labeled samples. We also embed a hybrid public/personal inference engine and validation system into iSelf, to make it maintain update continuously. Through extensive experiments in real traces, the inferring accuracy is tested above 74% and can be improved increasingly through validation and update. The API interface has been open online for other developers.
Indoor emergency response situations, such as urban fire, are characterized by dangerous constantly-changing operating environments with little access to situational information for first responders. In-situ information about the conditions, such as the extent and evolution of an indoor fire, can augment rescue efforts and reduce risk to emergency personnel. Static sensor networks that are pre-deployed or manually deployed have been proposed, but are less practical due to need for large infrastructure, lack of adaptivity and limited coverage. Controlled-mobility in sensor networks, i.e. the capability of nodes to move as per network needs can provide the desired autonomy to overcome these limitations. In this paper, we present SensorFly, a controlled-mobile aerial sensor network platform for indoor emergency response application. The miniature, low-cost sensor platform has capabilities to self deploy, achieve 3-D sensing, and adapt to node and network disruptions in harsh environments. We describe hardware design trade-offs, the software architecture, and the implementation that enables limited-capability nodes to collectively achieve application goals. Through the indoor fire monitoring application scenario we validate that the platform can achieve coverage and sensing accuracy that matches or exceeds static sensor networks and provide higher adaptability and autonomy.