IEEE 802.15.4 Based Wireless Sensor Network Design for Smart Grid Communications

Lo, Chun; Ansari, Nirwan

doi:10.1016/b978-0-12-415844-3.00004-8

Cited by 7 publications

(7 citation statements)

References 50 publications

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“…in our case, SOM grid has K neurons, therefore, we have K initial clusters. In a series of the subsequent steps, the algorithm of a hierarchical agglomeration always recognizes a pair of clusters that have the smallest value of mutual distance using the distance (linkage) metric 𝔇 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 described in (6).…”

Section: Figure 6 Concept Of Hierarchical Agglomerative Clustering Algorithmmentioning

confidence: 99%

“…WSN was originally designed to have features of low data rate escorted with low complexity and energy consumption. These features enable WSN to be deployed at a large-scale as well as within a low implementation cost [6]. Wireless Sensor Networks can be defined as a system of nodes that collaboratively sense, monitor, capture, process, and control other input-output (in form of data or signals) of other systems or support dealings between computational systems, people and the surrounding neighbor.…”

Section: Introductionmentioning

confidence: 99%

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Applying of (SOM, HAC, and RBF) algorithms for data aggregation in wireless sensors networks

Abdalkafor

Aliesawi

2022

Bulletin EEI

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Wireless sensor network (WSN) is one of the most promising technologies due to its size, cost-effective nature, and its ability to easily deploy in the target environment, as well as for its entry into many sensitive applications. However, making the most of the potential of this network is very difficult due to many issues, including the data received from the sensor nodes contains a huge amount of data redundant that negatively affects the overall network performance. Recent years have witnessed an increasing interest in data aggregation technology intending to eliminate redundant data from neighboring sensor nodes before transferring to the base station, thus improve performance efficiency and increasing the wireless sensor networks lifespan. This paper focused on applying three intelligent algorithms (SOM, HAC, and RBF) and describing the impact of data aggregation strategy on WSNs through the results obtained. As well as, an accurate description of the literature that applied these algorithms. A Competitive classification accuracy has been achieved when the proposed work is implemented and tested via the intel berkeley research lab dataset.

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Section: Figure 6 Concept Of Hierarchical Agglomerative Clustering Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying of (SOM, HAC, and RBF) algorithms for data aggregation in wireless sensors networks

Abdalkafor

Aliesawi

2022

Bulletin EEI

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“…At this step, we admit that the energy consumed during the Superframe period E B I is suggested throw the expression (8):…”

Section: Parametric Computing: Beacon Order Superframe Ordermentioning

confidence: 99%

“…In its full version, the latter is dedicated to defining the network with respect to the low power consumption, range, as well as throughput. As part of the Wireless Personal Area Network (WPAN), the LR-WPAN appears to display various benefits, mainly, ease of installation, short-range operation, remarkably low costs, reliable information-transmission procedure, moderate energy consumption, along with a selection of noticeably flexible protocols [8]. Our paper is organised by the way that section 2 presents a general overview about the standard IEEE 802.15.4.…”

Section: Introductionmentioning

confidence: 99%

Network Lifetime Management in Wireless Sensor Networks

et al. 2018

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The energy consumption in wireless sensor networks stills a very interesting challenge in both industrial and research field. The IEEE 802.15.4 standard is an excellent result of the collaboration between these both fields in order to well managing the energy consumed in the node by controlling its duty cycle. It presents the most efficient standards for low energy consumption that is why it is considered as the main technology for the Internet of Thing. This paper consists mainly of validating through an experimental test bed, the two proposed methods. The first approach proposed is about computing the energy consumed by the node in its different transmission states with more efficiency. Although the second approach deals with computing, it is most convenable duty cycle basing on the remaining energy in the battery of the node which presents a very interesting issue for the energy consumption in wireless communication.

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“…These concentrators are data collectors, and are connected via high speed wired backhaul links to the control center. We adopt the IEEE 802.15.4g due to its common acceptance as an efficient technology to connect low-rate smart meters and sensors to the concentrators [15]. Nevertheless, the work in this paper can be extended to consider other wireless technologies later on.…”

Section: System Modelmentioning

confidence: 99%

Wireless Neighborhood Area Networks With QoS Support for Demand Response in Smart Grid

Kong

2016

IEEE Trans. Smart Grid

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In order to support various innovative demand response programs, smart grid needs a wireless communication network with quality-of-service (QoS) support. This paper studies the issue of providing QoS in terms of packet delay, packet error probability, and outage probability to a large number of sensors and smart meters in a neighborhood area network of a densely populated urban area. We assume the network is based on the IEEE 802.15.4g Standard. Given that bandwidth is limited, we propose to divide smart meters into groups and each group will take a turn to access a shared wireless channel in a time-division-multiplexing manner. Within each allocated time duration, a group of smart meters will compete for channel access using a simple slotted Aloha protocol. We have developed an analytical model to quantify the QoS metrics. Through the analytical model, we can determine the minimum concentrator density that is required to support a given smart meter density. We have verified the analytical model through simulations. The results show that we need less than ten concentrators per km 2 to support a node density of 500 units per km 2 , while making sure that packet delay does not exceed 1.0 s, packet error probability is below 0.005 and outage probability is lower than 0.01.Index Terms-Concentrator density, neighborhood area network (NAN), quality-of-service (QoS), sensor, smart grid, smart meter.

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IEEE 802.15.4 Based Wireless Sensor Network Design for Smart Grid Communications

Cited by 7 publications

References 50 publications

Applying of (SOM, HAC, and RBF) algorithms for data aggregation in wireless sensors networks

Applying of (SOM, HAC, and RBF) algorithms for data aggregation in wireless sensors networks

Network Lifetime Management in Wireless Sensor Networks

Wireless Neighborhood Area Networks With QoS Support for Demand Response in Smart Grid

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