Comparison of Results

We do state-of-the-art some adapting sampling algorithms for saving energy but evaluating all of them is extremely heavy. Hence, we compare our algorithm with DDASA [299], ASA [26] which are the most related to our approach. For transparency, the competitor results are derived from original paper.

In Table 8.1, we compare the NME of our algorithm with that of competitors in the same sample rate. The better algorithm has lower NME. As illustrated in Table 8.1, our proposal shows superiority over the competitors. To reduce original dataset to 297 and 421 samples, the NME values of our approach are about 4.96% and 4.07% in comparison with 9.99% and 8.43 % of DDASA, respectively. This means that the reconstructed data from our selected points is more likely to original data than others. Moreover, our approach is more consis-tent and robust than the competitor in high power saving scenarios. This is demonstrated by the minor change of NME when significantly reducing the number of samples. In more detail, our NME variance is around 0.79 compared with 13.6 of DDASA when decreasing the sample size from 1064 to 297.

In summary, comparing with other approaches, our algorithm has better performance demonstrated by lower and more consistent in MSE values.

DDASA DDASA DDASA ASA DDASA Fixed Rate

(t=0.03) (t=0.02) (t=0.015) (t=0.01) Sampling

Number of Samples 297 421 548 637 1064 2182

Competitor’s NME 9.99 % 8.43 % 5.31 % 5.52 % 1.62 % 0

OASA NME 4.96 % 4.07 % 4.37 % 3.86 % 2.84 % 0

Table 8.1 – OASA in comparison to the competitors.

8.5 Conclusion

In this work, we introduce an online adaptive sampling algorithm to estimate the opti-mal sampling frequency base on the changes in historical data. In addition, the proposed algorithm allows the end-users to control the saving energy levels. Hence, it has been demonstrated to reduce energy consumption in various use-cases while ensuring the ac-curacy of collected data. Our practical experiments have shown the proposed algorithm can reduce the number of acquired samples up to 4 times in comparison with a traditional fixed-rate approach at extremely low error around 4.37%. Consequently, the devices could significantly reduce the energy consumption in IoT devices.

9 Conclusions and Outlook

9.1 Conclusion

We have been witnessing the explosion of Internet of Things and its positive impacts on several real-life aspects. Based on the fact that IoT consists of small things, widely dis-tributed, and constrained capabilities in term of storage and computation capabilities. In addition, the raw data collected from IoT Things is massive, heterogeneous, and contains a vast amount of redundant information. For these reasons, several issues mainly related to interoperability and reliability in Internet of Things have arisen.

To mitigate these issues, this thesis proposed the solutions to enhance both the interoper-ability and reliinteroper-ability. Through this dissertation, the following objectives are achieved:

• Identify the current states and challenges of the cloud-based Internet of Things: In the scope of this thesis, we highlight the related work and issues related to interoper-ability, data reliinteroper-ability, and device reliability.

• Propose an IoT framework to interoperate IoT device connections using connectors:

The proposed solution represents a framework that simplifies establishing the con-nection process from heterogeneous IoT Things to cloud-based platforms by using connectors. In addition, our platform provides well-supplied web services based on a resource-oriented model. It allows the end-users to easily discover and perform management operations on the connectors. Another innovative aspect of our solu-tion is to facilitate and speed up the data acquisisolu-tion process from open data sharing web services like Accuweather, OpenSensorIO. The interoperability with other such implementations is preserved by using several ongoing IoT standardization methods.

• Introduce an IoT framework to maximize usable knowledge from IoT data using virtual sensors: The proposed framework simplifies creating and configuring virtual sensors with the programmable operators (rule, formula or function). To produce high-level information from collected data, these VSs are linked together to create a topology, namely logical data-flow (LDF). In addition, the LDF outcomes are formed under JSON-LD format, which is used to generate interpretable data across different IoT platforms. In this way, it significantly increases the interoperability of our solution.

A web-based virtual sensor editor is implemented on the top of the framework to facilitate the creation and configuration of LDF.

• Present a semantically Descriptive Language for Group of Things: We introduce a novel description language semantically presenting the group of Things, namely

Assets, in Massive IoT scenario. A WoT architecture is also presented to fully ex-ploit the benefits of the proposed language. Such combination is not only capable of presenting, accessing, and managing the Asset but also speeding up the IoT applica-tion development. The effectiveness of the designed soluapplica-tion is ensured by choosing and combining ongoing technologies and IoT frameworks that have been practically demonstrated in real use-cases.

• Propose an Active Learning method for errors and events detection in time series:

The proposed algorithm effectively detects both errors and events in a single algo-rithm powered by active learning while minimize user involvements. This is achieved by introducing a novel concept of nearest neighbor and integrating it into detecting and learning processes. Our experiments show that labeling a very limited number of data points significantly increases the error and event detection quality. In addition, this quality is controlled by end-users through the confidence weight of a classifica-tion model, which is then used as the terminaclassifica-tion condiclassifica-tion for the active learning procedure.

• Propose an Energy-Efficient Sampling Algorithm: The proposed algorithm minimizes energy consumption by estimating an optimal data collection frequency in real-time based on historical data. Given user’s saving desire, our algorithm minimizes the energy consumption of the IoT devices while ensuring the precision of collected data.

The practical experiments have shown that the proposed algorithm reduces the num-ber of samples up to four times in comparison to a traditional fixed-rate approach.

All proposed solutions have been implemented and operating in a cloud-based IoT platform of a start-up company. This platform aims to provide cloud-based services using Internet of Thing technologies to solve real-life problems. In order to handle the connection from heterogeneous IoT devices, our solution related to the interoperability at device communi-cation level is deployed at the communicommuni-cation layer of such platform. Then, they use the second solution about virtual sensors to maximize usable knowledge from sensed data. In addition, errors and events detection algorithm is implemented to ensure data quality. On the other hand, the energy efficient sampling algorithm is implemented on their IoT devices to reduce energy consumption, especially on constrained-resources devices.