Anchor nodes placement for effective passive localization

Background: Wireless Sensor Networks (WSNs) is considered one of the key research area in the recent. Various applications of WSNs need geographic location of the sensor nodes. Objective: Localization in WSNs plays an important role because without knowledge of sensor nodes location the information is useless. Finding the accurate location is very crucial in Wireless Sensor Networks. The efficiency of any localization approach is decided on the basis of accuracy and localization error. In range-free localization approaches, the location of unknown nodes are computed by collecting the information such as minimum hop count, hop size information from neighbors nodes. Methods: Although various studied have been done for computing the location of nodes but still, it is an enduring research area. To mitigate the problems of existing algorithms, a range-free Improved Weighted Novel DV-Hop localization algorithm is proposed. Main motive of the proposed study is to reduced localization error with least energy consumption. Firstly, the location information of anchor nodes is broadcasted upto M hop to decrease the energy consumption. Further, a weight factor and correction factor are introduced which refine the hop size of anchor nodes. Results: The refined hop size is further utilized for localization to reduces localization error significantly. The simulation results of the proposed algorithm are compared with other existing algorithms for evaluating the effectiveness and the performance. The simulated results are evaluated in terms localization error and computational cost by considering different parameters such as node density, percentage of anchor nodes, transmission range, effect of sensing field and effect of M on localization error. Further statistical analysis is performed on simulated results to prove the validation of proposed algorithm. A paired T-test is applied on localization error and localization time. The results of T-test depicts that the proposed algorithm significantly improves the localization accuracy with least energy consumption as compared to other existing algorithms like DV-Hop, IWCDV-Hop, and IDV-Hop. Conclusion: From the simulated results, it is concluded that the proposed algorithm offers 36% accurate localization than traditional DV-Hop and 21 % than IDV-Hop and 13% than IWCDV-Hop.

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Application of intelligent orienteering based on Internet of things

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01814-1 ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Donghui Zhang ◽

Ruijie Liu

Keyword(s):

Internet Of Things ◽

Network Architecture ◽

Emergency Services ◽

Simulation Experiment ◽

Experimental Studies ◽

Signal Stability ◽

Anchor Nodes ◽

Set Up ◽

The Relationship ◽

The Internet Of Things

Abstract Orienteering has gradually changed from a professional sport to a civilian sport. Especially in recent years, orienteering has been widely popularized. Many colleges and universities in China have also set up this course. With the improvement of people’s living conditions, orienteering has really become a leisure sport in modern people’s life. The reduced difficulty of sports enables more people to participate, but it also exposes a series of problems. As the existing positioning technology is relatively backward, the progress in personnel tracking, emergency services, and other aspects is slow. To solve these problems, a new intelligent orienteering application system is developed based on the Internet of things. ZigBee network architecture is adopted in the system. ZigBee is the mainstream scheme in the current wireless sensor network technology, which has many advantages such as convenient carrying, low power consumption, and signal stability. Due to the complex communication environment in mobile signal, the collected information is processed by signal amplification and signal anti-interference technology. By adding anti-interference devices, video isolators and other devices, the signal is guaranteed to the maximum extent. In order to verify the actual effect of this system, through a number of experimental studies including the relationship between error and traffic radius and the relationship between coverage and the number of anchor nodes, the data shows that the scheme studied in this paper has a greater improvement in comprehensive performance than the traditional scheme, significantly improving the accuracy and coverage. Especially the coverage is close to 100% in the simulation experiment. This research has achieved good results and can be widely used in orienteering training and competition.

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Indoor Passive Localization with Channel State Information Using A Single Access Point

IEEE Sensors Journal ◽

10.1109/jsen.2021.3081563 ◽

2021 ◽

pp. 1-1

Author(s):

Xiangyu Liu ◽

Yong Wang ◽

Mu Zhou ◽

Wei Nie ◽

Xiaolong Yang

Keyword(s):

Channel State Information ◽

Access Point ◽

Channel State ◽

State Information ◽

Single Access ◽

Passive Localization

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Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

Sensors ◽

10.3390/s21113936 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3936

Author(s):

Yannis Spyridis ◽

Thomas Lagkas ◽

Panagiotis Sarigiannidis ◽

Vasileios Argyriou ◽

Antonios Sarigiannidis ◽

...

Keyword(s):

Deep Learning ◽

Heuristic Method ◽

Sensor Nodes ◽

Convolutional Network ◽

Received Signal Strength Indicator ◽

Anchor Nodes ◽

Time Required ◽

Rf Signal ◽

Deep Learning Model

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.

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