Implementation of ICT and Wireless Sensor Networks for Earthquake Alert and Disaster Management in Earthquake Prone Areas

Rahman, Munib ur; Rahman, Soliha; Mansoor, Seema; Deep, Vikas; Aashkaar, M.

doi:10.1016/j.procs.2016.05.184

Cited by 60 publications

(26 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The gateway node is assumed as most powerful actor node in the subnet. For detection of an earthquake, three types of sensors are employed (Rahman et al 2016), i.e., animal, water pressure and radon sensors. Animal sensors are deployed on bodies of animals to measure their body temperature and detect their behavior.…”

Section: Problem Statement and System Modelmentioning

confidence: 99%

“…The analysis indicated that the change trend of postseismic vegetation conditions was grouped into three classes namely recovering, fluctuating, and deteriorating. Internet of Things in terms of web enabling framework and message queue telemetry transport techniques are used to focus earthquakes analysis taking seismic data for an Early Warning System capable of anticipating up to 12 s (Rahman et al 2016). An analysis of pictures through computer vision is made as application in the area of smart cities and smart homes using the idea of Internet of Things to detect the people (García et al 2017).…”

Section: Related Workmentioning

confidence: 99%

“…An early earthquake prediction system based on WSNs is designed using information and communication technologies for disseminating earthquake information (Rahman et al 2016). Unlike this work, our work uses WSANs to predict earthquakes and minimize damages caused by earthquakes.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Formal model of earthquake disaster mitigation and management system

Zafar

Afzaal

2017

Complex Adapt Syst Model

View full text Add to dashboard Cite

Background Wireless sensor and actor networks (WSANs) consist of sensors and actors connected through wireless medium. In the network, sensors are used to sense complex events in an environment and actors take intelligent decisions and perform actions as required. Sensors are cheap devices which have low power batteries, slow processing capabilities and short communication range as compared to actors which are more expensive and powerful in terms of resources. It is noted that actors are capable of sensing the environment but sensors do not have ability to perform any action (Akyildiz and Kasimoglu 2004). As an example from military defence system, sensors detect enemy troops and report to actors which destroy the troops by coordinating with each other. For an effective monitoring and decision making, there must be a continuous sensor-sensor, sensor-actor and actor-actor communication to perform an appropriate action whenever

show abstract

Section: Problem Statement and System Modelmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Formal model of earthquake disaster mitigation and management system

Zafar

Afzaal

2017

Complex Adapt Syst Model

View full text Add to dashboard Cite

show abstract

“…Wireless Sensor Network (WSN) is a multi-hop selforganizing network system formed by wireless communication. The sensor node cooperatively senses, collects, and processes information of the perceived object in the network coverage area and sends it to the observer [1,2].Wireless sensor networks are widely used in many important fields such as forest fire detection, animal tracking, military area monitoring, early earthquake detection, and border monitoring due to their low cost, low power consumption and multi-function [3,4,5,6,7].But as the size of the network increases, it becomes increasingly difficult and unrealistic to periodically replace batteries for all nodes, and limited battery energy will eventually lead to limited network life. In order to solve the problem of limited life in wireless sensor networks, many scholars at home and abroad have conducted a lot of research, and the solutions can be divided into three categories: node energy saving, natural energy collection and wireless charging.…”

Section: Introductionmentioning

confidence: 99%

Optimization Strategy of Wireless Charger Node Deployment Based on Improved Cuckoo Search Algorithm

Wang

wang

Zhu

et al. 2020

Preprint

View full text Add to dashboard Cite

In wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an Improved Cuckoo Search (ICS) algorithm is proposed. This algorithm is based on the traditional Cuckoo Search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird ’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

show abstract

“…Therefore, WSNs should be able to adapt dynamically with the charged environment. Recently, WSNs play important role in various necessaries of human such as flood monitoring [14], weather monitoring, earthquake detection [22], tracking [19], volcanic eruption, military necessity [10], healthcare observation [3] agriculture automation [26], and manufacturing automation [17].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs

Misbahuddin

Ratna

Sari

2018

INT J COMPUT COMMUN

View full text Add to dashboard Cite

In multi-hop routing, cluster heads close to the base station functionaries as intermediate nodes for father cluster heads to relay the data packet from regular nodes to base station. The cluster heads that act as relays will experience energy depletion quicker that causes hot spot problem. This paper proposes a dynamic multihop routing algorithm named Data Similarity Aware for Dynamic Multi-hop Routing Protocol (DSA-DMRP) to improve the network lifetime, and satisfy the requirement of multi-hop routing protocol for the dynamic node clustering that consider the data similarity of adjacent nodes. The DSA-DMRP uses fuzzy aggregation technique to measure their data similarity degree in order to partition the network into unequal size clusters. In this mechanism, each node can recognize and note its similar neighbor nodes. Next, K-hop Clustering Algorithm (KHOPCA) that is modified by adding a priority factor that considers residual energy and distance to the base station is used to select cluster heads and create the best routes for intra-cluster and inter-cluster transmission. The DSA-DMRP was compared against the KHOPCA to justify the performance. Simulation results show that, the DSA DMRP can improve the network lifetime longer than the KHOPCA and can satisfy the requirement of the dynamic multi-hop routing protocol.

show abstract

Implementation of ICT and Wireless Sensor Networks for Earthquake Alert and Disaster Management in Earthquake Prone Areas

Cited by 60 publications

References 4 publications

Formal model of earthquake disaster mitigation and management system

Formal model of earthquake disaster mitigation and management system

Optimization Strategy of Wireless Charger Node Deployment Based on Improved Cuckoo Search Algorithm

Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs

Contact Info

Product

Resources

About