Prolonging smart grid network lifetime through optimising number of sensor nodes and packet length

Elwekeil, Mohamed; Abdalzaher, Mohamed S.; Seddik, Karim G.

doi:10.1049/iet-com.2018.6272

Cited by 34 publications

(28 citation statements)

References 20 publications

(27 reference statements)

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“…On the physical layer, in the quest for more accurate wireless channel characteristics, most analytical models adopt empirical data-based channel models. [8][9][10][11][12][13][14][15][16][17][18][19] While the assumption of transmission features and corresponding energy dissipation characteristics differs a lot. The models in the literature 8,9 assume the transmission power to be fixed.…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by Kurt et al, 16 many analytical models of physical layer start to take both successful probability and energy dissipation of two-way handshaking mechanism into consideration. [17][18][19] Similarly, in this article, we model the physical layer according to the characteristics of the MICAz mote and take both modulation way and handshake mechanism into consideration.…”

Section: Related Workmentioning

confidence: 99%

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Modeling and parameter analysis of IEEE 802.15.4-based networks and the metering application

Wang

Yang

Han

et al. 2020

International Journal of Distributed Sensor Networks

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As the support of wireless sensor networks expands to various application scenarios, the communication environments and the performance requirements of different application scenarios vary a lot. To cope with different communication environments and performance requirements, both data transmission ability and medium access ability are equivalently important. In this article, a joint analytical model is proposed to fully and precisely estimate networks’ communication performance, energy efficiency, and scalability. In the proposed model, both the physical layer’s and medium access control layer’s key parameters are taken into consideration. By comparing with OPNET-based simulation model, the rationality of the proposed analytical model is first validated under a wide range of network scenarios. Then, a series of simulations under general network scenarios and metering network scenarios are conducted. With these simulations, the performance of adjusting both layers’ parameters in improving communication performance and energy efficiency was proved superior to single-layer’s parameter optimizations. Finally, by comparing the available range of different key parameters’ optimal value under different network scenarios, the maximum backoff numbers and the minimum backoff exponent are considered to be the most effective parameters for metering network optimization.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Modeling and parameter analysis of IEEE 802.15.4-based networks and the metering application

Wang

Yang

Han

et al. 2020

International Journal of Distributed Sensor Networks

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“…An interesting survey of different strategies for energy-saving in ZigBee WSNs in the context of IoT is shown in [14]. In general, the analyzed proposals are in three main lines: (a) management of channel and timing (physical level) [15][16][17]; (b) modification of the Medium Access Control (MAC) standard (MAC level) [18,19]; and (c) control of different parameters to reduce consumption [20][21][22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

“…That proposal did not modify the standard. Time Division Multiple Access is used in [23] to optimize the number of active nodes minimizing message retransmissions. Like the last works, our approach uses a timer to activate the EDs for transmitting data messages to the RD at which they are associated.…”

Section: Related Workmentioning

confidence: 99%

Extending the Battery Life of the ZigBee Routers and Coordinator by Modifying Their Mode of Operation

Marrero

Suárez

Macías

et al. 2019

Sensors

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Wireless sensor networks proliferate more and more in all social scopes and sectors. Such networks are implemented in smart homes, smart cities, security systems, medical resources, agriculture, automotive industry, etc. Communication devices and sensors of such networks are powered with batteries: the enlarging of battery life is a hot research topic. We focus on wireless sensor networks based on ZigBee technology. While sleep standard operation mode is defined for end devices, it is not the case for the rest of devices (routers and Coordinator), which usually always remain in active mode. We designed a formal optimization model for maximizing the enlarging of the battery life of routers and Coordinator, allowing us to delimit practical successful conditions. It was successfully tested with a standard ZigBee datasheet comprising technical data for sensors, routers, and coordinators. It was tested in a practical wireless sensor network assembly with XBee S2C devices. We derived, from the previous model, a novel but simple protocol of communication among routers and coordinators. It was tested in different use cases. We showed that when end devices generate traffic at regular intervals, the enlarging of the battery life of routers and Coordinator was possible only under certain use cases.Sensors 2020, 20, 30 2 of 22 named end devices (EDs), with different sleep modes. The EDs are programmed to realize a prefixed sampling period, from a few seconds to days or more, and they remain idle until the next sampling period. Conversely, all other devices, like Router Devices (RD) and the Coordinator (C), remain active always. That is, they never are in sleep mode.In our previous work [11][12][13], we proposed a sleep mode of operation for WiFi Access Points (AP) for regular emitted traffic from WiFi terminals obtaining a considerable amount of energy-saving. Despite WSN and WiFi networks having different characteristics, operations, and functions, in this paper, we apply those previous ideas to include the sleep mode of operation in RDs and C when EDs emit traffic periodically. At the beginning of each period of time, the RDs and C enter active mode and find out the WSN spanning tree using any routing algorithm. The leaf nodes of that tree are the EDs, the intermediary nodes are the RDs, and the root is the C. Each RD manages one branch of the tree and can estimate when it will receive data frames from its children nodes (EDs or others RDs in the tree). After that, the RDs will send data frames to the upper-level RD or to the C (at level 0 of the tree). Once received and after sending the data frames, the RDs or the C can pass to sleep mode until the next beginning of the period of time. The enlarging of their battery life corresponds to the amount of time the RDs and C will be in sleep mode. The main objective of this paper is to show, under previous assumptions, if it is possible to obtain a significate enlarging of the battery of ZigBee RDs and C considering commercial devices. Up to our knowledge, we are the first to c...

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“…The core mission of WSN is detecting information of interest, one of the common characteristics is the demand for the node number [22][23][24][25], we can respond correctly only number of the nodes are known accurately, it is also a premise of estimating their directions of arrival, positions, powers, and other parameters, actually we can determine this information according to their signals acquiring by some array sensors. In practical applications, there are variety of disturbances, and complex noise, meanwhile, sampling times is always limited by the hardware and harsh environments, then the node number are often calculated inexactly, leading to the deviation of other estimations.…”

Section: Introductionmentioning

confidence: 99%

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Zhen

Zhang

2020

IEEE Access

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Knowing number of nodes is the precondition of their locating and estimation of other parameters in wireless sensor network, actually it can be evaluated according to the signal received by the sensor array. The popular algorithms are only appropriate for the uncorrelated signals and circumstance of Gaussian white noise, the estimation precision will deteriorate at some harsh environments. Hence a new algorithm for determining coherent node number based on multiple feature extraction in wireless sensor network is presented in this paper. First, perform spatial difference smoothing to the array data. Then the eigenvalues and eigenvectors can be acquired by eigen-decomposition, consequently multiple features which are used for training are constructed. Finally, back propagation neural network and particle swarm optimization are exploited for calculating node number. Simulation results demonstrate that the algorithms has good performances under the background of colored noise and small samples.

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Prolonging smart grid network lifetime through optimising number of sensor nodes and packet length

Cited by 34 publications

References 20 publications

Modeling and parameter analysis of IEEE 802.15.4-based networks and the metering application

Modeling and parameter analysis of IEEE 802.15.4-based networks and the metering application

Extending the Battery Life of the ZigBee Routers and Coordinator by Modifying Their Mode of Operation

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

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