Programming Internet of Things (IoT) applications is challenging because developers have to be knowledgeable in various technical domains, from low-power networking, over embedded operating systems, to distributed alg...
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Programming Internet of Things (IoT) applications is challenging because developers have to be knowledgeable in various technical domains, from low-power networking, over embedded operating systems, to distributed algorithms. Hence, it will be challenging to find enough experts to provide software for the vast number of expected devices, which must also be scalable and particularly safe due to the connection to the physical world. To remedy this situation, we propose an architecture that provides Web-like scripting for low-end devices through Cloud-based application servers and a consistent, RESTful programming model. Our novel runtime container Actinium (Ac) exposes scripts, their configuration, and their lifecycle management through a fully RESTful programming interface using the Constrained Application Protocol (CoAP). We endow the JavaScript language with an API for direct interaction with mote-class IoT devices, the CoapRequest object, and means to export script data as Web resources. With Actinium, applications can be created by simply mashing up resources provided by CoAP servers on devices, other scripts, and classic Web services. We also discuss security considerations and show the suitability of this architecture in terms of performance with our publicly available implementation.
We all are facing one or the other problems in respect to health at present life. As the age increases our body strength decreases gradually, so in order to monitor disabled or aged people there should be an aid or a ...
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ISBN:
(数字)9781538624401
ISBN:
(纸本)9781538624418
We all are facing one or the other problems in respect to health at present life. As the age increases our body strength decreases gradually, so in order to monitor disabled or aged people there should be an aid or a person to take care of them always. To overcome this issue, we have built a modelwhere if any emergency arises we can get a message and also an image of the disabled by doing some simple motion or gesture. Therefore, we can monitor from faraway places *** gesture recognition is a process, by which the gesture made by user is used to convey the information. Moving the sensor in various way,passes on various messages. The movement of a hand in three opposite ways are distinguished by accelerometer and data will be transmitted to the controller. The received data is then compared with the loaded value.
We develop a practical, distributed algorithm to detect events, identify measurement errors, and infer missing readings in ecological applications of wireless sensor networks. To address issues of non-stationarity in ...
ISBN:
(纸本)9783540730897
We develop a practical, distributed algorithm to detect events, identify measurement errors, and infer missing readings in ecological applications of wireless sensor networks. To address issues of non-stationarity in environmental data streams, each sensor-processor learns statistical distributions of differences between its readings and those of its neighbors, as well as between its current and previous measurements. Scalar physical quantities such as air temperature, soil moisture, and light flux naturally display a large degree of spatiotemporal coherence, which gives a spectrum of fluctuations between adjacent or consecutive measurements with small variances. This feature permits stable estimation over a small state space. The resulting probability distributions of differences, estimated online in real time, are then used in statistical significance tests to identify rare events. Utilizing the spatio-temporal distributed nature of the measurements across the network, these events are classified as single mode failures - usually corresponding to measurement errors at a single sensor - or common mode events. The event structure also allows the network to automatically attribute potential measurement errors to specific sensors and to correct them in real time via a combination of current measurements at neighboring nodes and the statistics of differences between them. Compared to methods that use Bayesian classification of raw data streams at each sensor, this algorithm is more storage-efficient, learns faster, and is more robust in the face of non-stationary phenomena. Field results from a wireless sensor network (sensor Web) deployed at Sevilleta National Wildlife Refuge are presented.
CORIE is a pilot environmental observation and forecasting system (EOFS) for the Columbia River. The goal of CORIE is to characterize and predict complex circulation and mixing processes in a system encompassing the l...
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ISBN:
(纸本)9783540730897
CORIE is a pilot environmental observation and forecasting system (EOFS) for the Columbia River. The goal of CORIE is to characterize and predict complex circulation and mixing processes in a system encompassing the lower river, the estuary, and the near-ocean using a multi-scale data assimilation *** challenge for scientists is to maintain the accuracy of their modeling system while minimizing resource usage. In this paper, we first propose a metric for characterizing the error in the CORIE data assimilation model and study the impact of the number of sensors on the error reduction. Second, we propose a genetic algorithm to compute the optimal configuration of sensors that reduces the number of sensors to the minimum required while maintaining a similar level of error in the data assimilation model. We verify the results of our algorithm with 30 runs of the data assimilation model. Each run uses data collected and estimated over a two-day period. We can reduce the sensing resource usage by 26.5% while achieving comparable error in data assimilation. As a result, we can potentially save 40 thousand dollars in initial expenses and 10 thousand dollars in maintenance expense per *** algorithm can be used to guide operation of the existing observation network, as well as to guide deployment of future sensor stations. The novelty of our approach is that our problem formulation of network configuration is influenced by the data assimilation framework which is more meaningful to domain scientists, rather than using abstract sensing models.
We take an algorithmic approach to a well-known communication channel problem and develop several algorithms for solving it. Specifically, we develop power control algorithms for sensor networks with collaborative rel...
ISBN:
(纸本)9783540730897
We take an algorithmic approach to a well-known communication channel problem and develop several algorithms for solving it. Specifically, we develop power control algorithms for sensor networks with collaborative relaying under bandwidth constraints, via quantization of finite rate (bandwidth limited) feedback channels. We first consider the power allocation problem under collaborative relaying where the tradeoff between minimizing ones own energy expenditure and the energy for relaying is considered under the constraints of packet outage probability and bandwidth constrained (finite rate) feedback. Then we develop bandwidth constrained quantization algorithms (due to the finite rate feedback) that seek the optimal way of quantizing channel quality and power values in order to minimize the total average transmission power and satisfy the given probability of outage. We develop two kinds of quantization protocols and associated quantization algorithms. For separate source-relay quantization, we reduce the problem to the well-known k-median problem [1] on line graphs and show a a simple O((KJ)2N) polynomial time algorithm, where log2 KJ is the quantization bandwidth and N is the size of the discretized parameter space. For joint quantization, we first develop a simple 2-factor approximation of complexity O(KJN + N logN). Then, for Ɛ > 0, we develop a fully polynomial approximation scheme (FPAS) that approximates the optimal quantization cost to within an 1+Ɛ-factor. The running time of the FPAS is polynomial in 1/Ɛ, size of the input N and also ln F, where F is the maximum available transmit power.
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