LoRa Meets Artificial Intelligence to Optimize Indoor Networks for Static EDs
Corresponding Author
Malak Abid Ali Khan
Information System and Security Countermeasures Experimental Center, School of Information and Electronics, Beijing Institute of Technology, Beijing, China
Correspondence:
Malak Abid Ali Khan ([email protected])
Senlin Luo ([email protected])
Search for more papers by this authorCorresponding Author
Senlin Luo
Information System and Security Countermeasures Experimental Center, School of Information and Electronics, Beijing Institute of Technology, Beijing, China
Correspondence:
Malak Abid Ali Khan ([email protected])
Senlin Luo ([email protected])
Search for more papers by this authorHongbin Ma
National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, China
Search for more papers by this authorAmjad Iqbal
Department of System and Computer Engineering, Carleton University, Ottawa, Canada
Search for more papers by this authorCorresponding Author
Malak Abid Ali Khan
Information System and Security Countermeasures Experimental Center, School of Information and Electronics, Beijing Institute of Technology, Beijing, China
Correspondence:
Malak Abid Ali Khan ([email protected])
Senlin Luo ([email protected])
Search for more papers by this authorCorresponding Author
Senlin Luo
Information System and Security Countermeasures Experimental Center, School of Information and Electronics, Beijing Institute of Technology, Beijing, China
Correspondence:
Malak Abid Ali Khan ([email protected])
Senlin Luo ([email protected])
Search for more papers by this authorHongbin Ma
National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing, China
Search for more papers by this authorAmjad Iqbal
Department of System and Computer Engineering, Carleton University, Ottawa, Canada
Search for more papers by this authorABSTRACT
The architectural design of the Indoor Internet of Things (IIoT) network targeting static end devices (EDs) and gateways (GWs) has been innovatively formulated in this paper, integrating LoRa technology to mitigate losses and ensure seamless information reception through meticulous ED allocation. The arrangement of simultaneously transmitted data within the network server (NS) employs a deep neural network (DNN) with distributed machine learning (DML) to adjust transmission parameters, ensuring frequent uninterrupted bidirectional communication. This augmentation is obtained by strategically deploying EDs within distinct clusters determined by K-means and density-based spatial clustering with noise (DBSCAN), thus optimizing spreading factor (SF) and data rate (DR) allocation to prevent data congestion and improve signal-to-interference noise ratio (SINR). The proposed hybrid model (DR|SF) for pure and slotted ALOHA amplifies the network's performance metrics for indoor scenarios. A unified framework utilizing a one-slope model estimates path losses (PL) while exploring various bandwidths (BW), bidirectional interrogations, and duty cycles (DC) to lower the saturation and prolong the active lifespan of the EDs. The results manifest a packet rejection rate (PRR) of 0% for the DBSCAN, contrasting a 4.7% estimate for the K-means. The network saturation is minimized to 9.5% and 10.1%, correspondingly, significantly increasing the efficiency of slotted ALOHA (91%) and pure ALOHA (90.6%), thereby prolonging the longevity of EDs.
Conflicts of Interest
The authors declare no conflicts of interest.
Open Research
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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