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1. 发明专利

AU2021102882A4 HYBRID SWARM OPTIMIZATION ALGORITHM FOR CLUSTERING AND ROUTING IN IOT NETWORKS 未知
公开(公告)号：AU2021102882A4
公开(公告)日：2021-07-15
申请号：AU2021102882
申请日：2021-05-27
申请人： A P JYOTHI DR , A SIVASANGARI DR , AINAPURE BHARATI DR , DIXIT ANITA DR , K N MADHUSUDHAN DR , KUMAR NEELAM SANJEEV DR , P AJITHA DR , S LALITHA MS , SANGEETHA J MARGARET MS , VELLADURAI M MR
发明人： AINAPURE BHARATI , DIXIT ANITA , A P JYOTHI , KUMAR NEELAM SANJEEV , S LALITHA , K N MADHUSUDHAN , A SIVASANGARI , P AJITHA , VELLADURAI M , SANGEETHA J MARGARET
IPC分类号： H04W40/32 , G05B13/02 , H04W84/18
摘要： HYBRID SWARM OPTIMIZATION ALGORITHM FOR CLUSTERING AND ROUTING IN IOT NETWORKS Abstract The number of IoT devices is rapidly increasing owing to innovative IoT technology. The WSN is a critical component of the Internet of Things. Supporting surveillance tasks in huge areas is extremely important. Due to the finite resources of WSN nodes, effective CH node selection, as well as node collaboration, is currently the most pressing issues. Complex node challenges can be transformed into human adaptability to the system using hybrid swarm optimization algorithms, which can then be used to iteratively make the best decision. The hybrid swarm optimization algorithm is thought to be capable of solving a wide variety of issues in WSN applications. The major concerns in WSN applications are overcome using hybrid swarm optimization algorithms including the CH selection problem. A swarm optimization algorithm is used for both clustering and routing in IoT networks. The clustering technique includes nonlinear programming whereas the routing technique includes linear programming. The proposed Hybrid Swarm Optimization algorithmic approach includes the association of GSO and PSO to enable effective clustering and routing in IoT networks. The proposed hybrid PSO-GSO method initially collects the information regarding the sensor nodes. The PSO-based K-means optimization includes the clustering and identification of other sensor nodes and cluster heads (CH). The sensor nodes send the acquired data to the CH, which then sends it to the base station via other CHs. After determining the cluster heads and sensor nodes, GSO is used to optimize them. The shortest route for CHs and sensor nodes moving towards the base station is identified using the GSO optimization algorithm. Routing is done based on the fitness value received from GSO. HYBRID SWARM OPTIMIZATION ALGORITHM FOR CLUSTERING AND ROUTING IN IOT NETWORKS Diagram IN loT NETWORKS, INFORMATION REGARDING WSNs IS PSO-BA SED K-MEA NS PREDICTING CLUSTER HEAD S (CHs) AND SENSOR HEADS IMPLEMENTING THE PSO OPTIMIZATION ROUTING USING THE BE ST FITNE SS VALUE EVALUATING THE PERFoRMANCE OF THE HYBRID SWARM OPTIMIZATION Figure 1: The proposed hybrid swarm optimization algorithmic approach.

2. 发明专利

AU2020101968A4 LOW LEVEL INSTRUMENTATION LAYER FOR COMPONENT-LEVEL DIAGNOSIS OF FAILURES IN INDUSTRIAL IOT SYSTEM 未知
公开(公告)号：AU2020101968A4
公开(公告)日：2020-10-01
申请号：AU2020101968
申请日：2020-08-25
申请人： AINAPURE BHARATI SANJAY DR , B KIRANMAI DR , BEJJAM JYOSHNA MS , DIXIT ANITA DR , R GEETHA MS , S CHIDAMBARANATHAN DR , S RAVI DR
发明人： S RAVI , S CHIDAMBARANATHAN , AINAPURE BHARATI SANJAY , BEJJAM JYOSHNA , B KIRANMAI , R GEETHA , DIXIT ANITA
IPC分类号： G11C16/02 , G06F11/00 , G11C29/42 , H04L1/00
摘要： LOW LEVEL INSTRUMENTATION LAYER FOR COMPONENT-LEVEL DIAGNOSIS OF FAILURES IN INDUSTRIAL IOT SYSTEM Abstract There has been an increased graph when it comes to system-level testing for radiation effects. This invention involves the development of different software and firmware that can be added to different layers of the application fixedly or dynamically at the time of compilation. They tend to overcome the limitations that are available when performing the system-level testing of the application. The invention improves the component-level detection of failures at system-level testing of the application. The proposed invention helps in improving the observability of the system by adding different Instrumentation Layers (ILs). The Instrumentation Layer (IL), which is used to improve observability at system-level testing for radiation effects, is developed using the Zynq7000 SoC as SoM. The system-level issues, failures, errors are observed using a case study, and correction code are developed. 11 P a g e LOW LEVEL INSTRUMENTATION LAYER FOR COMPONENT-LEVEL DIAGNOSIS OF FAILURES IN INDUSTRIAL IOT SYSTEM Diagram System CPU + Check AES Check FIR Check PID Check PWM Initialization Iitialization checksum checksum checksum checksum inr U o -n OCM (Shared memory) -- F H AES -Checksum Checksuml PlD Checksumr~ Sc alin Chec plksum Irrput FIR FIFO Output FIR FIFO Input PlWM FIFO Output PWVM FIFO FIR -PWM -i 1|P age

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