Improved Indoor Geo-Localization System

2019 ◽  
pp. 183-206
Author(s):  
Abdaoui Noura ◽  
Ismahène Hadj Khalifa ◽  
Sami Faiz
Keyword(s):  
Sensor Networks ◽  
Recent Progress ◽  
Nearest Neighbor ◽  
Low Energy ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Smart Object ◽  
Low Energy Consumption ◽  
Localization Systems

In the concept of internet of things (IOT), physical position of smart object is very useful for relevant function over sensor networks. However, the invalid information of indoor geo-localization systems relative to these wireless sensor compromises the intelligence of IOT network. Therefore, this chapter produces the recent progress in the indoor geo-localization systems and the IOTs area. It defines the best indoor geo-localization technologies that meet their needs while respecting the constraints related to sensor networks. This framework combines between simplicity of Bluetooth low energy (BLE), popular wi-fi infrastructure, and the k-nearest neighbor (KNN) algorithm (in order to filter the initial fingerprint dataset). This new conception increases real-time detection accuracy and guarantees the low energy consumption.

scholarly journals Secure and Low Energy Consumption Range Query in Tiered Sensor Networks

2016 ◽  
Vol 12 (07) ◽  
pp. 4 ◽  
Author(s):  
Song Ling ◽  
Qi Dong Yang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Value Chain ◽  
Range Query ◽  
Privacy Preserving ◽  
Low Energy ◽  
Wireless Sensor ◽  
Simulation Experiments ◽  
Low Energy Consumption

For the requirement of low energy consumption and high privacy-preserving in wireless sensor networks of range query, we propose a low energy consumption secure and verifiable range query protocol called SPRQ.SPRQ uses a novel prime aggregation to protect the privacy of the query data; We further propose an idea of the and value chain whereby data items collected by each sensor will be linked with each other just like a chain.The Sink verifies the integrity of query results by checking whether the data chain of each sensor is complete or not. The results of simulation experiments prove that prime aggregation can effectively reduce the amount of increased data in the prefix encoding process,so,network energy consumption is lower compared with other secure range query protocols.

scholarly journals A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jeng-Shyang Pan ◽  
Fang Fan ◽  
Shu-Chuan Chu ◽  
Hui-Qi Zhao ◽  
Gao-Yuan Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Nearest Neighbor ◽  
Fog Computing ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
K Nearest Neighbor ◽  
Network Resources ◽  
Detection Model

The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.

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