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.
Efficient medium access control (MAC) is desirable for underwater sensor networks (UWSNs). However, designing an efficient underwater MAC protocol is challenging due to the long propagation delay of the underwater acoustic channel and the spatial-temporal uncertainty. In this paper, we propose a novel Traffic-Adaptive Receiver-Synchronized underwater MAC protocol, TARS, a stochastic light-weight channel access scheme that addresses the spatial-temporal uncertainty for maximizing the network throughput. We adjust the packet transmission time (phase) in a slot, determined by the sender-receiver distance, to align packet receptions for collision reduction. Both the sound propagation speed variation and the node mobility are considered in setting the optimal transmission phase and the slot size. We employ a queue-aware utility-optimization framework to determine the optimal transmission strategies dynamically, taking into account both the packet interference and the data queue status. Extensive simulation results show that compared to the existing representative underwater MAC protocols, TARS achieves better performance with higher network throughput and lower packet delay (e.g., about 13%~146% higher in throughput and 13%~21% lower in delay than others in a mobile ad hoc network), as well as robustness under network mobility. Thus our protocol is very suitable for mobile and traffic-varying UWSNs.
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.
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.
Real-time wireless networks have been recently adopted for industrial applications and cyber-physical systems. However, the underlying scheduling algorithms of these solutions cannot support real-time communication with mobile entities. This paper presents REWIMO, which guarantees both real-time and reliable communication with mobile nodes, irrespective to their mobility pattern. REWIMO has a two-tier architecture composed of (i) infrastructure nodes and (ii) mobile nodes that associate with infrastructure nodes as they move. REWIMO employs an on-join bandwidth reservation approach and benefits from a set of techniques to efficiently reserve bandwidth for each mobile node at the time of its admission and over its potential communication paths. To ensure mobile nodes associate with infrastructure nodes over high-quality links, REWIMO uses the two-phase scheduling technique that coordinates neighbor discovery with data transmission. To mitigate the overhead of handling network dynamics, REWIMO employs an additive scheduling algorithm that is capable of additive bandwidth reservation without modifying existing schedules. Compared to the algorithms used by static real-time wireless networks, the techniques and algorithms employed by REWIMO result in a significant increase in the number of mobile nodes, enhanced reliability, and considerably faster handling of network dynamics.
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.