A method for routing and data aggregating in cluster‐based wireless sensor networks

Sharifi, Sedigheh Sadat; Barati, Hamid

doi:10.1002/dac.4754

Cited by 39 publications

(17 citation statements)

References 23 publications

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“…WSNs energy and power modeling also one of the important aspects of deployment of sensors in applications. A method of hierarchical routing and data aggregation for WSNs is proposed in Reference 19. A rendezvous region is constructed and backbone nodes are selected and finally a tree is formed within backbone‐tree nodes.…”

Section: Related Workmentioning

confidence: 99%

Similarity‐based clustering and data aggregation with independent component analysis in wireless sensor networks

Shahina

Kumar

2022

Trans Emerging Tel Tech

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Wireless sensor networks are consisted of group of sensor nodes, which are scattered in inaccessible areas with limited resources. Data aggregation and clustering methods minimize sensor network's energy usage by reducing the quantity of data transmission. Machine learning techniques like reinforcement learning, neural networks, and swarm intelligence significantly cut down the transmission of data and make use of network's distributive features. Quantity of aggregated data can be reduced if the measured data are similar. Based on this concept, a similarity based clustering method is proposed in which sensor node with similar data are grouped as a cluster for data aggregation. Then an algorithm is proposed that aggregates the data using independent component analysis which is performed on cluster head sensor nodes. Data aggregation process is executed on clusters which are having higher data similarity. Implementation shows that proposed method is performing better in terms of computational overhead, aggregation ratio, and energy consumption.

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Section: Related Workmentioning

confidence: 99%

Similarity‐based clustering and data aggregation with independent component analysis in wireless sensor networks

Shahina

Kumar

2022

Trans Emerging Tel Tech

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“…3,4 Wireless sensor network (WSN) is a microelectrical mechanical system that plays a vital role in collecting the data from the nearby surrounding. [5][6][7][8][9][10][11] In order to monitor and control the smart grid resources, the WSN is used in smart grid power distribution. From conventional distribution systems, the smart power distribution grids are famed for their interactive characteristics, self-sufficiency, self-healing, and reliability.…”

Section: Introductionmentioning

confidence: 99%

“…Wireless sensor network (WSN) is a microelectrical mechanical system that plays a vital role in collecting the data from the nearby surrounding 5–11 . In order to monitor and control the smart grid resources, the WSN is used in smart grid power distribution.…”

Section: Introductionmentioning

confidence: 99%

Proportional‐integral‐derivative controller‐based self‐healing of distribution system using wireless sensor networks in smart grid

Cheena

Amgoth

Shankar

2022

Int J Communication

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Summary The grid is a platform of distributing the power to the consumers; if an automatic controlling and monitoring are connected with the grid, it referred to as smart grid (SG). Self‐healing is the approach of restoring the unaffected areas without affecting the system performance. The term restoration is if a fault occurs on a distribution system, it must be isolated; after that, it can be reconfigured to restore the unaffected areas. The distribution system cannot work as efficient without the self‐healing approach because it will affect the unaffected areas without any proper reason. This paper proposed a multiagent system with controlled voltage (MAS‐CV) along with self‐healing of wireless sensor network (WSN) with proportional integral derivative (PID) controller in smart grid. At initial, the magnetic flux sensors are placed in the pole transformer for detection of flux leakage. The sensed data are transferred to the distribution grid, where the multiagent system (MAS) is established to carrying out the load restoration process (i.e., self‐healing). Connection agents (CA), monitor agent (MA), main feeder agent (MFA), backup feeder agent (BFA), supporting feeder agent (SFA), and local agent (LA) are the agents presented in the paper. Each agent is communicating via agent communication language (ACL) for the restoration process; after all the loads are restored, the voltage drop of the system is controlled by the proportional integral derivative controller. The simulation results of the proposed method show that the restoration of loads is improved and number of switching is reduced with improved data transmission. Thus, the cost of the proposed method is reduced by the reduced switches. At last, the controlled power from the distribution grid is send to the consumer.

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“…In fact, they are employed in inaccessible areas, where recharging resources is impossible 7,8 . In WSNs, the data packet reaches the base station through multi‐hop paths 9 . Applying a proper routing method in wireless network is very imprortant.…”

Section: Introductionmentioning

confidence: 99%

HDRM: A hole detection and recovery method in wireless sensor network

Papi

Barati

2022

Int J Communication

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Summary Wireless sensor networks (WSNs) consist of many sensor nodes, gathering data from the environment and sending it to base station (BS). One of the major challenges in WSNs is coverage maintenance. Since batteries provide sensor nodes' energy, battery depletion leads to the death of sensor nodes; when several nodes are dead, connectivity is lost, and network coverage fails. This paper proposed a method consisting of two phases to provide and maintain coverage in WSNs. In the first phase, hole detection is performed through a simple distributed method with low overhead. Nodes explore whether there are holes around them or not. In the second phase, coverage holes are covered by mobile nodes. When the number of holes exceeds mobile nodes, the holes with the highest priority and the shortest distance to mobile nodes will be selected. The performance of the proposed method (HDRM) was compared with Dezfuli et al. method, PEAS‐LI, CACA, Dezfuli et al. method, and Verma et al. method in terms of average coverage percentage, the average number of alive nodes, energy consumption, and network lifetime. The results of the simulation reveal that the proposed method leads to a decrease in the energy consumption of network, an increase in network lifetime, and improved coverage in the network.

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A method for routing and data aggregating in cluster‐based wireless sensor networks

Cited by 39 publications

References 23 publications

Similarity‐based clustering and data aggregation with independent component analysis in wireless sensor networks

Similarity‐based clustering and data aggregation with independent component analysis in wireless sensor networks

Proportional‐integral‐derivative controller‐based self‐healing of distribution system using wireless sensor networks in smart grid

HDRM: A hole detection and recovery method in wireless sensor network

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