Nowadays, many communities are emerging towards smart environments, requiring the communication and collaboration of diverse Internet-of-Things (IoT) devices. A smart environment exploits the use of IoT technology to share and process data among such devices for a better living. However, this comes with additional costs, such as the exponential growth of IoT devices, the heterogeneity of IoT use cases, and the new complex features encountered by IoT data, which complicate their processing and analysis using the traditional techniques. This causes a dramatic performance degradation of the used processing resources, which directly affects the overall efficiency and performance of IoT-based systems. Although different studies have presented resource utilization approaches for IoT systems, but they were not evaluated from different resource utilization perspectives. Besides, no efforts have been directed to investigate their effectiveness to process the unprecedented IoT data features that inevitably impact the accuracy and efficiency of resource utilization. In this paper, the Triple Phases Resource Utilized Data Fusion (TPRUDF) framework is proposed as the first IoT-based cost-aware resource utilization using data fusion. It exclusively considers different IoT data features by employing three phases of data fusion: (1) data in – data out, (2) data in – feature out, and (3) feature in – decision out. TPRUDF fuses the raw IoT data by maintaining the complex IoT data features, independent of the IoT domain or the computing model, using the spatiotemporal data fusion (STDF) IoT-based data fusion approach. TPRUDF then fuses the uncorrelated data features via the Principal Component Analysis. Finally, it employs two different resource utilization techniques: (1) Genetic Algorithms and (2) Particle Swarm Optimization, fusing their results using the voting logic fusion technique. A public edge-computing simulator is used to evaluate TPRUDF via three real-world smart cities datasets. The experimental results of the proposed TPRUDF framework indicate that it: (1) achieves an average accuracy level of resource utilization equal to 91%, (2) increases the resource utilization throughput by an average of 40% and eventually minimizes the processing delay, (3) boosts the resource utilization availability by 60%, and (4) decreases the energy consumption by 35%. 

An IoT-based resource utilization framework using data fusion for smart environments

Data aggregation is an energy-saving technology in wireless sensor networks (WSNs). Because of the high density of nodes in sensor networks, many nodes discover the same data (in many cases with a large quantity), leading to a lot of energy consumption and may be packets loss. These challenges can be resolved by using a data collection policy when routing packets from the source nodes to the base station (BS). Researchers are still struggling to choose an effective and appropriate data collection method from the current WSN literature. In this paper, we propose an energy-aware Huffman coding-based LEACH protocol for WSN (HC-LEACH). The experiment results show the effectiveness of the proposed scheme in enhancing energy consumption by approximately 38% compared to LEACH.

HC-LEACH: Huffman Coding-based energy-efficient LEACH protocol for WSN

https://hal.archives-ouvertes.fr/hal-02949522/document

Design of a multi-agent system for exploiting the communicating concrete in a SHM/BIM context

In 2010, the Research Center for Automatic Control (CRAN) proposed the concept of “communicating materials”, which led to this current project. Indeed, 3 French laboratories (the CRAN, LAAS and LIB) associated with one company (360SmartConnect/FINAO SAS) are working in the framework of the McBIM project to design a “communicating concrete”, made of concrete equipped with embedded low-energy wireless micro-sensors. This paper briefly describes the context of the McBIM project with the main research goal and objectives and then focus on the results in the different parts of the project regarding the different research issues, explored by the project. 

Materials communicating with the BIM: results of the McBIM project

Photovoltaic power generation has been extensively utilized for power supply in remote areas. Due to the effect of weather and environmental conditions, its power generation experiences fluctuations. This paper proposes a dedicated WSN-based digital twin design for photovoltaic power generation. In this design, WSN is used to regularly collect and transmit monitoring data. The cloud platform and neural network are then integrated to displays equipment's status and predict power generation. Experimental results demonstrate that this digital twin design can effectively display the real-time operating status and environmental information. Using the LSTM-CNN algorithm achieve an accurate power prediction, with the RMSE and MAE values of 0.436 and 0.209 respectively. The real-time status monitoring and power prediction of photovoltaic equipment can not only provide a basis for fault diagnosis, but also optimize power grid dispatch in remote areas.

An Intelligent WSN-Based Digital Twin Design for Photovoltaic Power Generation Forecast

Wireless sensor networks consist of different sensor nodes, able to monitor inaccessible environments. Our objective is to improve the lifetime of specific WSNs in the context of communicating material. And we apply it to smart concrete design. The McBIM project, its objectives and constraints are introduced with some existing solutions for data collection. Protocols including aggregation features have been preferred, in order to reduce the number of messages and optimize network lifetime. Three main methods in data aggregation are presented with their main advantages and drawbacks: chain-based, cluster-based and tree-based methods. Furthermore, a comparison of the different associated algorithms is proposed based on required performance metrics, related to our context of application. Finally, some perspectives for the future work are presented.

/pdf/defining-the-communication-architecture-for-data-aggregation-2fddxb45w6.pdf

Defining the Communication Architecture for Data Aggregation in Wireless Sensor Networks: Application to Communicating Concrete Design

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Defining the Communication Architecture for Data Aggregation in Wireless Sensor Networks: Application to Communicating Concrete Design