scholarly journals A Robot for Combat and Calamities with Encrypted WSN

2021 ◽  
Author(s):  
S Abiraj ◽  
D Eeswar Samhithan ◽  
B Sanjai Kumar ◽  
Y Venkatesh
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Networks ◽  
Sensor Networks ◽  
Real Time ◽  
Search And Rescue ◽  
Wireless Sensor ◽  
Natural Conditions ◽  
Sensor Temperature ◽  
The Military ◽  
Manual Mode

The needs of search and rescue teams in numerous ways reflect the requirements of the military. They both work in perilous situations, they need to discover ways to gather data while keeping personnel out of hurt, and they are both seeking out for individuals. So, this Robot Commands in manual mode using a Smartphone, Wireless cam for Real-time Broadcast, PIR and Metal detection sensors, GPS & GSM modules, ultrasonic sensor, Gas & Fire sensor, temperature and humidity sensor, Acid gun, RFID and Relay switch, In this wireless sensor networks (WSN) is an Encrypted Network and infrastructure-less wireless networks to Screening physical or natural conditions, such as vibrations, pressures,movement, or toxins and to agreeably transmit their information retrieved by using IOT Server.

Real-Time Routing Based on On-Demand Multi-Hop Lookahead in Wireless Sensor Networks

2011 ◽  
Vol E94-B (2) ◽  
pp. 569-572
Author(s):  
Soochang PARK ◽  
Euisin LEE ◽  
Juhyun JUNG ◽  
Sang-Ha KIM
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
Wireless Sensor ◽  
On Demand

SPREAD - A Routing Protocol to Meet End-to-end Adjusted Deadline in Real Time Wireless Sensor Networks

2020 ◽  
Vol 10 (1) ◽  
pp. 63-78
Author(s):  
Neetika Jain ◽  
Sangeeta Mittal
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
Routing Protocol ◽  
Resource Constraints ◽  
Wireless Sensor ◽  
Network Simulator ◽  
Packet Delivery ◽  
High Data ◽  
Hard Real Time

Background: Real Time Wireless Sensor Networks (RT-WSN) have hard real time packet delivery requirements. Due to resource constraints of sensors, these networks need to trade-off energy and latency. Objective: In this paper, a routing protocol for RT-WSN named “SPREAD” has been proposed. The underlying idea is to reserve laxity by assuming tighter packet deadline than actual. This reserved laxity is used when no deadline-meeting next hop is available. Objective: As a result, if due to repeated transmissions, energy of nodes on shortest path is drained out, then time is still left to route the packet dynamically through other path without missing the deadline. Results: Congestion scenarios have been addressed by dynamically assessing 1-hop delays and avoiding traffic on congested paths. Conclusion: Through extensive simulations in Network Simulator NS2, it has been observed that SPREAD algorithm not only significantly reduces miss ratio as compared to other similar protocols but also keeps energy consumption under control. It also shows more resilience towards high data rate and tight deadlines than existing popular protocols.

The Debris Flow Disaster Faster-Than-Early Forecast System (DFS) with Multi-Sensors Integrated Wireless Sensor Networks

2013 ◽  
Vol 347-350 ◽  
pp. 975-979
Author(s):  
Rong Zhao ◽  
Cai Hong Li ◽  
Yun Jian Tan ◽  
Jun Shi ◽  
Fu Qiang Mu ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Debris Flow ◽  
Real Time ◽  
Debris Flows ◽  
Base Station ◽  
Base Stations ◽  
Wireless Sensor ◽  
Forecast System ◽  
Debris Flow Disaster

This paper presents a Debris Flow Disaster Faster-than-early Forecast System (DFS) with wireless sensor networks. Debris flows carrying saturated solid materials in water flowing downslope often cause severe damage to the lives and properties in their path. Faster-than-early or faster-than-real-time forecasts are imperative to save lives and reduce damage. This paper presents a novel multi-sensor networks for monitoring debris flows. The main idea is to let these sensors drift with the debris flow, to collect flow information as they move along, and to transmit the collected data to base stations in real time. The Raw data are sent to the cloud processing center from the base station. And the processed data and the video of the debris flow are display on the remote PC. The design of the system address many challenging issues, including cost, deployment efforts, and fast reaction.

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