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Environment Situation Awareness for Day3:Scenario1. The values presented in Table 2 express the degrees of beliefs DoB corresponding to a 24 hours time steps for one of the sensors sensor one starting from midnight t 0 to eleven in the night t Similar probability patterns were found for the other sensors. They have not been reported for space limitiation.

The experimental results depicted by the related figures reveal four situation recognitions:. Battery life awareness revealed by Figure 13 a with probabilities as degrees of beliefs DoB corresponding to the time steps, e. Signal strength awareness revealed by Figure 13 b with probabilities as degrees of beliefs DoB corresponding to the time steps, e. Light intensity awareness revealed by Figure 14 a with probabilities as degrees of beliefs DoB corresponding to the time steps, e. Temperature awareness revealed by Figure 14 a with probabilities as degrees of beliefs DoB corresponding to the time steps, e.

We note that the high degrees of beliefs show reliability on the environmental situation awareness. The combination of these figures and the associated probabilities provides answers to the probabilistic questions raised in the following subsections and evidences that will guide the trend analysis on the USN system considered in terms of questions and answers. The functionalities of the system reveals the situational results and provides questions or thinking approach to be answered by the users towards a precise guide to correct decision making processes.

Figure 13 a reveals the results obtained by a user when raising the following Probabilistic queries concerning the battery level of the sensors:. Pr Value t? A1: The figure reveals that the battery voltages of the 3 sensors generally have similar behaviour but operate at varied power levels.

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It is observed that the power of sensor one operates uniformly at 8. It uniformly remains at this level for the rest of its operation. One can see the other sensors operating similarly in the same way. A2: Since most of the 3 sensors start dropping or change states at 7am, one could think that the power of the batteries are suspiciously affected by external interferences, environmental noise or high volume of workload.

However, this seems to be a normal battery discharge pattern for the batteries since they were designed to be depleted after three days before recharge. The figure reveals also that sensor one is depleted quicker than the other two sensors. This is because this sensor was placed as a longer distance from the base station and configured to operate with higher output power in order to reach the base station.

A3: If environmental noise is a significant factor that may affect the power of the batteries, protective layers may be used as lagging to prevent or minimise the noise.


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Figure 13 b reveals the results obtained by a user when raising the following Probabilistic queries concerning the received signal strength RSSI of the sensors:. Observe the RSSI of sensors 2 and 3 having close values 76 and 80 respectively, which remain uniformly constant over time. This is in agreement with what was revealed by answer A1 concerning sensor one being far from the base station as compared to the other two sensors. A3: It is suspected that any possible actions taken on the sensors batteries will have effects on the RSSI levels of operations.

Figure 14 a reveals the results obtained by a user when raising the following Probabilistic queries concerning the light intensity revealed by the sensors:. A1: The signals of light sent by the sensors to the gateway have similar patterns as one can see that they are greatly based on day and night. Between am, the intensity of light increases and they start dropping around pm. A2: The longer day and shorter night of summer periods are reflected in the situational patterns revealed. A3: To send high light signals in the night may require the provision of artificially generated light in the sensors environment.

A4: If the next day is still summer time, then similar situational patterns are likely revealed. Figure 14 b reveals the results obtained by a user when raising the following Probabilistic queries concerning the temperature revealed by the sensors:. A1: Most of the temperature signals sent by the 3 sensors are above the level They have very few irregular drops. A2: The temperature is as high as that because summer is associated with hot environment. The irregular drops in temperature might result from the Cape strong winds. A3: If the experiment is repeated in winter, then the temperature falls and the irregularities may be controlled.

A4: If the experiment is repeated in summer, similar patterns are likely to be revealed because summer has fixed properties. Building upon a common vision of pervasive computing, this paper presents a network management system for ubiquitous sensor networks and proposes a framework using the ESA technology to achieve situation recognition. Using an outdoor environment monitoring in the city of Cape Town, we illustrate the use of the proposed technology in terms of sensor system operating conditions and environmental situation awareness.

There is room to extend the situation recognition framework proposed in this paper to achieve future situation awareness when predicting future situations from current datasets. The scalability of the ESA technology when dealing with the massive datasets collected from sensor readings is another issue that needs to be addressed for the wide deployment of this technology in ubiquitous sensor networks.

It is another avenue for future research work. National Center for Biotechnology Information , U. Journal List Sensors Basel v. Sensors Basel.

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Published online Dec 3. Antoine B. Author information Article notes Copyright and License information Disclaimer.


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Abstract Achieving situation recognition in ubiquitous sensor networks USNs is an important issue that has been poorly addressed by both the research and practitioner communities. Keywords: wireless sensor networks, energy efficiency, situation awareness, situation recognition, probabilistic model.

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Introduction The combination of wireless sensors with the RFID technology [ 1 , 2 ] is emerging as an important segment of the first mile connectivity of a next generation ubiquitous Internet where the information will be accessed not only anywhere and anytime but also by anyone and using anything. The Ubiquitous Sensor Network System As illustrated by Figure 1 a from [ 1 ], different layers are used by a Ubiquitous Sensor Network USN to provide different services to different types of applications in a multi-technology, multi-devices and multi-protocol platform.

1. Introduction

Open in a separate window. Figure 1. The Situation Recognition System Building upon the layered approach depicted by Figure 1 a , we adopted a situation recognition system SRS depicted by Figure 1 b where software and hardware components are used to deliver different services to the different layers of the system.


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Figure 2. Figure 3. An Application to Outdoor Environment Monitoring This section describes the experimental environment used to evaluate the performance of the proposed ESA framework and discusses the experimental results obtained in an outdoor setting when using a star-based single-hop wireless sensor network WSN.

While the former is expressed by the evolution of environment parameters such as light and temperature, the latter is based on the following sensor system performance parameters: Received Signal Strength Indicator RSSI. Overview of Experiments Two experiments were performed to evaluate the effect of distance on the performance of the motes.

Figure 4. Table 1. Experimental Setting. Mote Distance from base station Height above roof base station - 0. Applying a Deterministic Model Using a deterministic model, we evaluated the performance of our sensor testbed by running the experiments at full and reduced power until the batteries in all the motes died usually around 72 hours.

Figure 5. Figure 6. Figure 7.

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Figure 8. Experiment 2: Distance Trial at Reduced Power Experiment 2 is a repeat of Experiment 1, with the motes now transmitting at the reduced power level of -4 dBm as compared to 0 dBm in Experiment 1. Figure 9. Figure Applying the Probabilistic Model As part of its functionalities, our ESA system was designed to guide sensor managers through correct and precise decision making processes using an interactive functionality of question and answer session.

Table 2. Time steps Experiment-1 Experiment-2 Experiment-3 Experiment-4 t 0 Experiment1: Sensor System Awareness The functionalities of the system reveals the situational results and provides questions or thinking approach to be answered by the users towards a precise guide to correct decision making processes. Figure 13 a reveals the results obtained by a user when raising the following Probabilistic queries concerning the battery level of the sensors: Pr Value t? Q1: What is Happening?

Q2: Why is it happening? Q3: What can we do about it? Q4: What will happen next? Figure 13 b reveals the results obtained by a user when raising the following Probabilistic queries concerning the received signal strength RSSI of the sensors: Pr Value t?

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A4: It is difficult to predict what will happen next in this case. Experiment2: Environment Situation Awareness Figure 14 a reveals the results obtained by a user when raising the following Probabilistic queries concerning the light intensity revealed by the sensors: Pr Value t? Figure 14 b reveals the results obtained by a user when raising the following Probabilistic queries concerning the temperature revealed by the sensors: Pr Value t? Conclusions and Future Work Building upon a common vision of pervasive computing, this paper presents a network management system for ubiquitous sensor networks and proposes a framework using the ESA technology to achieve situation recognition.

References 1. Wireless Sensor Networks: A Survey. Comput Netw. Autonomous insight Identify groups and relationships in data that were previously unknown. Autonomous insight Pattern analysis. Automotive Visit our automotive hub to learn more about solution areas and customers in this sector. Automotive hub. Energy Visit our energy hub to learn more about solution areas and customers in this sector.

Energy hub. Healthcare Visit our healthcare hub to learn more about solution areas and customers in this sector. Healthcare hub. Other industries We are working on specific content for many other industries. All industries. Learn more Engage a Bayes Server expert to learn more about services and solutions for your industry. Engage an expert. Technology to support Bayesian Belief Network construction and inference developed by. Bayesian Networks; Bayesian net; belief net; probability; expert system software; influence diagram; probabilistic modelling; simulation software; data mining; machine learning.

The demonstration version of Netica Application is full-featured but allows only limited model size. This version can be freely used, subject to agreement with the Norsys License Agreement for Netica Application. It has an intuitive and smooth user interface for drawing the networks, and the relationships between variables may be entered as individual probabilities, in the form of equations, or learned from data files Compiles belief Bayesian networks into a junction tree of cliques for fast probabilistic reasoning. Extensive on-screen help and a detailed printed manual.

Can test the performance of a network using a file of cases.

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Can find optimal decisions for sequential decision problems i. Can solve influence diagrams efficiently by using clique trees. Can learn probabilistic relations from data.