Performance Assessment of tree topology sensor network based on scheduling algorithm for overseeing high-rise building structural health information

Haque, M. E.; Zain, Muhammad Fauzi Mohd.; Jamil, Maslina

doi:10.1016/j.ijleo.2015.04.023

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…This paper considers a FiWi network [46] in two stages: The first stage describes a wireless hybrid network [47,48] that articulates cellular technology and Wi-Fi to transport the information from the SMs to the BSs passing by a node of transition UDAP capable of supporting both technologies. This will ensure efficient and effective two-way communications within standardized parameters for the communication [46,[49][50][51]. In the second stage, data is merged in the BSs using optical fiber.…”

Section: Fiwi Network Architecturementioning

confidence: 99%

Optimal Deployment of FiWi Networks using Heuristic Method for Integration Microgrids with Smart Metering

Inga¹,

Campaña²,

Hincapié³

et al. 2018

Preprint

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The unpredictable increase in electrical demand affects the quality of theenergy throughout the network. A solution to the problem is the increase of distributedgeneration units which burn fossil fuels.While this is an immediate solution to theproblem the ecosystem gets affected by the emission of CO2.A promising solutionis the integration of Distributed Renewable Energy Sources (DRES) to the conventionalelectrical system, thus, introducing the concept of smart microgrids (SMG) that requirea safe, reliable and technically planned two-way communication system. This documentpresents a heuristic based on planning capable of providing a bidirectional communicationnear optimal route map, following the structure of an hybrid Fiber-Wireless (FiWi) with thepurpose of obtaining information of electrical parameters that help us to manage the useof energy by integrating conventional electrical system to SMG. A FiWi network is basedon the integration of wireless access and optical networks. This integration increases thecoverage and reliability at a lower cost. The optimization model is based on clusteringtechniques, through the construction of balanced conglomerates. The method is used forthe development of the clusters along with the Nearest-Neighbor Spanning Tree Algorithm(N-NST). Additionally, Optimal Delay Balancing (ODB) model will be used to minimizethe end to end delay of each grouping. In addition, the heuristic observes real designparameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes arebuilt following the minimum shorter path. Therefore, this paper presents a heuristic able to plan the deployment of smart meters (SMs) through a tree-like hierarchical topology for theintegration of SMG at the lowest cost.

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Section: Fiwi Network Architecturementioning

confidence: 99%

Optimal Deployment of FiWi Networks using Heuristic Method for Integration Microgrids with Smart Metering

Inga¹,

Campaña²,

Hincapié³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…This paper considers a FiWi network [ 47 ] in two stages: The first stage describes a wireless hybrid network [ 48 , 49 ] that articulates cellular technology and WiFi to transport the information from the SMs to the BSs passing by a node of transition UDAP capable of supporting both technologies. This will ensure efficient and effective two-way communications within standardized parameters for the communication [ 47 , 50 , 51 , 52 ]. In the second stage, data are merged in the BSs using optical fiber.…”

Section: Introductionmentioning

confidence: 99%

Optimal Deployment of FiWi Networks Using Heuristic Method for Integration Microgrids with Smart Metering

Inga

Campaña

Hincapié

et al. 2018

Sensors

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The unpredictable increase in electrical demand affects the quality of the energy throughout the network. A solution to the problem is the increase of distributed generation units, which burn fossil fuels. While this is an immediate solution to the problem, the ecosystem is affected by the emission of CO2. A promising solution is the integration of Distributed Renewable Energy Sources (DRES) with the conventional electrical system, thus introducing the concept of Smart Microgrids (SMG). These SMGs require a safe, reliable and technically planned two-way communication system. This paper presents a heuristic based on planning capable of providing a bidirectional communication that is near optimal. The model follows the structure of a hybrid Fiber-Wireless (FiWi) network with the purpose of obtaining information of electrical parameters that help us to manage the use of energy by integrating conventional electrical system with SMG. The optimization model is based on clustering techniques, through the construction of balanced conglomerates. The method is used for the development of the clusters along with the Nearest-Neighbor Spanning Tree algorithm (N-NST). Additionally, the Optimal Delay Balancing (ODB) model will be used to minimize the end to end delay of each grouping. In addition, the heuristic observes real design parameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes are built following the shortest path. Therefore, this paper presents a heuristic able to plan the deployment of Smart Meters (SMs) through a tree-like hierarchical topology for the integration of SMG at the lowest cost.

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“…Therefore, it is a complex and inefficient approach to managing several nodes by only one centralized controller with limited memory and battery power. Moreover, these techniques cannot efficiently adapt to the dynamic wireless environment [9,10]. The progressive nature and exploration in WSNs have experienced unprecedented development and is considered to be the most preferable medium for communication [11,12].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sleep Efficient Hybrid Medium Access Control algorithm for next-generation wireless sensor networks

Bakhsh

AlGhamdi

Altalhi

et al. 2017

J Wireless Com Network

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The scheduling algorithm is a fundamental design problem to allocate resources amongst different entities in distributive wireless sensor networks (WSNs). These sensor nodes have limited power and non-replenishable energy resources. In WSNs, the duty cycling mechanism is commonly used to save energy due to idle listening. On the other hand, a fixed duty cycling mechanism increases transmission latency in WSNs. Therefore, in order to ensure the prolonged network-life of WSNs, the medium access control (MAC) protocol should be tackled in an efficient manner to improve energy efficiency by minimizing idle listening, maximizing sleep duration, and eliminating data collision. This paper proposes a practical Adaptive Sleep Efficient Hybrid Medium Access Control (AEH-MAC) algorithm in which the key idea is to dynamically adjust nodes' sleep time to improve the scheduling in WSNs. The AEH-MAC allows nodes to adjust sleep time dynamically according to the traffic load and coordinate wakeup time with neighbour nodes. A series of short taken packets are transmitted to wake the receiver, and a prediction field is introduced in the ACK packets (GRANT/RELEASE) to reduce the waiting time of the source node. In the proposed algorithm, each node maps a conflict-free time slot for itself up to two-hop neighbouring nodes. The simulation results show that the AEH-MAC algorithm achieves high performance in terms of runtime, number of rounds, energy consumption, and slot reservation.

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Performance Assessment of tree topology sensor network based on scheduling algorithm for overseeing high-rise building structural health information

Cited by 9 publications

References 11 publications

Optimal Deployment of FiWi Networks using Heuristic Method for Integration Microgrids with Smart Metering

Optimal Deployment of FiWi Networks using Heuristic Method for Integration Microgrids with Smart Metering

Optimal Deployment of FiWi Networks Using Heuristic Method for Integration Microgrids with Smart Metering

Adaptive Sleep Efficient Hybrid Medium Access Control algorithm for next-generation wireless sensor networks

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