Fault-tolerant state estimation for stochastic systems over sensor networks with intermittent sensor faults

2022 ◽  
Vol 416 ◽  
pp. 126723
Author(s):  
Yichun Niu ◽  
Ming Gao ◽  
Li Sheng
Keyword(s):  
Sensor Networks ◽  
State Estimation ◽  
Stochastic Systems ◽  
Fault Tolerant ◽  
Sensor Faults ◽  
Tolerant State

scholarly journals Sensor selection schemes for fault tolerant state estimation via sensor trustworthiness

2018 ◽  
Vol 51 (24) ◽  
pp. 880-885
Author(s):  
Francesco Tedesco ◽  
Giuseppe Franzè ◽  
Alessandro Casavola
Keyword(s):  
State Estimation ◽  
Fault Tolerant ◽  
Sensor Selection ◽  
Tolerant State

Fault Tolerant Reliable Protocol (FTRP) Performance Evaluation in Wireless Sensor Networks: An Extensitive Study

2019 ◽  
pp. 1-36
Keyword(s):  
Wireless Sensor Networks ◽  
Fault Tolerance ◽  
Sensor Networks ◽  
Fault Tolerant ◽  
Cluster Head ◽  
Biological Data ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Good Performer ◽  
Wide Range

Fault Tolerant Reliable Protocol (FTRP) is proposed as a novel routing protocol designed for Wireless Sensor Networks (WSNs). FTRP offers fault tolerance reliability for packet exchange and support for dynamic network changes. The key concept used is the use of node logical clustering. The protocol delegates the routing ownership to the cluster heads where fault tolerance functionality is implemented. FTRP utilizes cluster head nodes along with cluster head groups to store packets in transient. In addition, FTRP utilizes broadcast, which reduces the message overhead as compared to classical flooding mechanisms. FTRP manipulates Time to Live values for the various routing messages to control message broadcast. FTRP utilizes jitter in messages transmission to reduce the effect of synchronized node states, which in turn reduces collisions. FTRP performance has been extensively through simulations against Ad-hoc On-demand Distance Vector (AODV) and Optimized Link State (OLSR) routing protocols. Packet Delivery Ratio (PDR), Aggregate Throughput and End-to-End delay (E-2-E) had been used as performance metrics. In terms of PDR and aggregate throughput, it is found that FTRP is an excellent performer in all mobility scenarios whether the network is sparse or dense. In stationary scenarios, FTRP performed well in sparse network; however, in dense network FTRP’s performance had degraded yet in an acceptable range. This degradation is attributed to synchronized nodes states. Reliably delivering a message comes to a cost, as in terms of E-2-E. results show that FTRP is considered a good performer in all mobility scenarios where the network is sparse. In sparse stationary scenario, FTRP is considered good performer, however in dense stationary scenarios FTRP’s E-2-E is not acceptable. There are times when receiving a network message is more important than other costs such as energy or delay. That makes FTRP suitable for wide range of WSNs applications, such as military applications by monitoring soldiers’ biological data and supplies while in battlefield and battle damage assessment. FTRP can also be used in health applications in addition to wide range of geo-fencing, environmental monitoring, resource monitoring, production lines monitoring, agriculture and animals tracking. FTRP should be avoided in dense stationary deployments such as, but not limited to, scenarios where high application response is critical and life endangering such as biohazards detection or within intensive care units.

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