**6. Conclusions**

**a4** 110 **a5** 110

(C1) avg. = (*J* (a1, a2) + *J* (a1, a4) + *J* (a1, a5) + *J* (a2, a4) + *J* (a2,a5) + *J* (a4, a5))/

(C2) avg. = (*J* (a1, a2) + *J* (a1, a4) + *J* (a1, a5) + *J* (a2, a3) + *J* (a2, a4) + *J* (a2, a5) +

The host computer of the simulations runs under Linux Ubuntu 16.04 and has

The part data mapping using AI was the final part of a quite complete process, beginning with sensors placement followed by the data routing till their smooth handling where we used the business intelligence to create materialized views in a dynamic processing. A mathematic approach has been proposed to present the workload and the attributes used. Next, we introduced our proposed methodology, split into two algorithms. The first is a neural network used for learning using

an Intel(R) xenon(R) CPU E5-2640 v4 processor running 2.4 GHz � 2.4 GHz (2processors) with 128 GB of memory. All the algorithms were implemented in Python 3. After a learning period, the experimental results (**Figure 18**) show a

reasonable decreasing between the real solution and the prediction.

3 ∗ *w*<sup>1</sup> þ 3 ∗ *w*<sup>2</sup> þ 2 ∗ *w*<sup>3</sup> þ 2 ∗ *w*<sup>4</sup> þ 2 ∗ *w*<sup>5</sup> ¼ 496 (27)

*We calculate W* ¼ ð Þ *w*1, *w*2, *w*3, *w*4, *w*<sup>5</sup> *with the following equation*

• **At time t3** *Q* ¼ *Q*1, *Q*<sup>2</sup> f g , *Q*<sup>3</sup>

*J* (a3, a4) + *J* (a3, a5) + *J* (a4, a5))/9 = 6/9.

*V30 and V31 exist in final solution FS496*

=*J* (a2, a5) =2/3;

{a1, a2, a4, a5} ⇔V30; {a1, a2, a3, a4, a5} ⇔V31

6 = 1;

**5. Result**

**Figure 18.**

**120**

*Real solution vs. predicted solution.*

*J* (a3, a4) =*J* (a3, a5) =1/3; *J* (a1, a2) = *J* (a4, a5) =1 *J* (a1, a3) = *J* (a1, a4) = *J* (a1, a5) = *J* (a2, a3) = *J* (a2, a4)

*Wireless Sensor Networks - Design, Deployment and Applications*

Graph C1 = {a1, a2, a4, a5} where similarity = 1 Graph C2 = {a1, a2, a3, a4, a5} where similarity = 1

A wireless sensor network is a network of distributed autonomous devices that can cooperatively detect or monitor physical or environmental conditions. WSNs are used in many applications such as environmental monitoring, habitat monitoring, natural disaster prediction and detection, medical monitoring, and structural health monitoring, which we have encompassed in this work through the application event detection or fact detection. WSNs consist of a large number of small, inexpensive, disposable, autonomous sensor nodes that are typically deployed on an ad hoc basis over large geographic areas for remote operations. Sensor nodes are severely limited in terms of storage resources, computing capacity, communication bandwidth and power supply, which is why the decision was made to perform heavy computing outside the WSN network in computing centers. Indeed, the sensor nodes are grouped into clusters, and each cluster has a node that acts as the cluster head. All nodes transmit their sensor data to the cluster head, which in turn routes it to a specialized processing node(s) via multi-hop routing. In event detection applications, nodes detect the environment and immediately evaluate the data to determine its usefulness. If useful data (an event) is detected, the data is transmitted to the base station(s). Data traffic is difficult to predict events typically occur randomly and the resulting data traffic is sporadic. However, an amount of data must be exchanged for route management and network controls, even if no events are detected. A wireless sensor network, can, contrastingly, be considered as an intelligent, scalable system for monitoring and sensing the physical world's properties. In the last few years, the database research and development community claimed that if one considers a WSN as a database (which means that an essential feature of its smart design is the ability to perform explicit requests), this could lead to a considerable savings in the development of software engineering that runs a data acquisition program for the wireless sensor network, as well as a very favorable cost-effectiveness ratio due to the optimization of data queries. This work outlines a query computing model for the wireless sensor network that meets many of the requirements of considering the WSN as a database.

### **Acknowledgements**

This work is part of a Tunisian-South African cooperation scientific research project. In this context, we thank the Ministry of Higher Education and Scientific Research of Tunisia. Our partner in the project, Distinguished Professor Qing-Guo WANG (https://www.scopus.com/authid/detail.uri?authorId=7408170159) acknowledges the financial support of the National Research Foundation of South Africa (Grant Numbers: 113340, 120106), which partially funded his research on this project.

*Wireless Sensor Networks - Design, Deployment and Applications*

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