Energy Efficient Routing Protocols for Underwater Wireless Sensor Networks: A Review

Ahmed, Mukhtiar; Salleh, Mazleena; Channa, Muhammad Ibrahim; Rohani, Mohd Foad

doi:10.12928/telkomnika.v15i1.4706

Cited by 9 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…it is expressed as the linear combination error rate of nodes in the next step. Therefore, for all 1 ≤ k ≤ L and 1 ≤ i ≤ #(k), we can find 𝜕𝐸𝑝 𝜕O 𝑘 𝑖 𝑝 , using mathematical equations ( 8) and (9). Thus, we have α as a parameter of the adaptive network: (10) where: S= shows the set of nodes whose output depends on α.…”

Section: Adaptive Neuro Fuzzy Inference System (Anfis) Algorithmmentioning

confidence: 99%

“…This network contains interconnected sensor nodes that exchange sensed data using wireless technologies such as LoRa [2], ZigBee [3], and many others [4]. Using its characteristics solution which is node to node communication (adhoc network) it has low cost for deploying it [5], and it can be used everywhere from mountain [6], forest [7], cities [8], even in underwater [9] application. Even though WSN has many advantages, this network also has many disadvantages such as limited energy [10] and data transmission problem such as interference [11], delay [12], packet loss [13], and others [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Self-Organized Wireless Sensor Network (SOWSN) for Dense Jungle Applications

Hakim,

Habaebi,

Islam

et al. 2023

IEEE Access

View full text Add to dashboard Cite

To facilitate wireless sensor networks deployment in dense jungle environments, the challenges of unreliable wireless communication links used for routing data between nodes and the gateway, and the limited battery energy available from the nodes must be overcome. In this paper, we introduce the Self-Organized Wireless Sensor Network (SOWSN) to overcome these challenges. To develop the traits needed for such SOWSN nodes, three types of computational intelligence mechanisms have been featured in the design. The first feature is the introduction of Multi Criteria Decision Making (MCDM) algorithm with simple Additive Weight (SAW) function for clustering the SOWSN nodes. The second feature is the introduction of the fuzzy logic ANFIS-optimized Near Ground Propagation Model to predict the wireless transmission link quality and power transfer between transmitters. The third feature is the introduction of the (Levenberg Marquardt artificial neural network (LM-ANN) for Adaptive Dynamic Power Control to further optimize the transmitter power levels, radio modulation, Spreading Factor configurations, and settings of the employed SOWSN LoRaWAN nodes based on predicted wireless transmission link quality parameters. The introduced features were extensively evaluated and analyzed using simulation and empirical measurements. Using clustering, near-ground propagation, and adaptive transmission power control features, a robust wireless data transmission system was built while simultaneously providing power conservation in SOWSN operation. The payload loss can be improved using SAW clustering from 1275 bytes to 5100 bytes. The result of power conservation can be seen from the reduction of transmission power in SOWSN nodes with the increase of transmission time (TOA) as its side effect. With the original power transmission at 20 dBm, original TOA time at 96.832 milliseconds for all nodes, and SNR 3 as input, transmission power was reduced to 12.76 dBm and the TOA increased to 346.78 milliseconds for all nodes.

show abstract

Section: Adaptive Neuro Fuzzy Inference System (Anfis) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Organized Wireless Sensor Network (SOWSN) for Dense Jungle Applications

Hakim,

Habaebi,

Islam

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…WSN can be used for environmental, seismic, navigation, forest monitor etc. [2]. Node strategically placed in the desired region and tasked with monitor, detect, provide information and communicate [3].…”

Section: Introductionmentioning

confidence: 99%

Reference broadcast synchronization and time division multiple access implementation on WSN

2019

View full text Add to dashboard Cite

Various kinds of technology have been developed to assist obtain information. One of them is a Wireless Sensor Network (WSN). WSN is a wireless network consisting of multiple nodes connected wirelessly. WSN nodes on a device have small resources in the form of batteries. The main problem which owned by WSN was in the data collection process possible collisions data, there are nodes that transmit data at the same time. Time Division Multiple Access (TDMA) was able to provide data on the delivery schedule of each node. So no nodes that transmit data at the same time. But in order to apply the system each node should have equal time. One method that able to provide equalization time was Reference Broadcast Synchronization (RBS). This method synchronizes multiple nodes that have different local time (on the receiver) with the help of node that provides synchronization marks (beacons). Hence this each node was able to transmit data in accordance with the TDMA method that has been implemented. In addition time synchronization performed using RBS give equal time with accuracy up to microseconds. That case certainly makes the WSN node able to provide accurate information to guarantee the absence of errors due to data collisions. This research succesfully sending data delivery schedule by time slots that provided by RBS and time synchronization by TDMA average time delay 2285.9 microseconds.

show abstract

“…Three dimensional deployment of sensor nodes is not an easy task due to the underwater pressure and environmental conditions [9]- [11]. However the behavior of sensor node in underwater environment is also uncontrollable due to water pressure and water current [12], [13]. Another issue for deployment of sensor node is localization.…”

Section: Introductionmentioning

confidence: 99%

RMEER: Reliable Multi-path Energy Efficient Routing Protocol for Underwater Wireless Sensor Network

Ahmed

Salleh

Channa

et al. 2018

IJECE

Self Cite

View full text Add to dashboard Cite

Underwater Wireless Sensor Networks (UWSNs) is interesting area for researchers.To extract the information from seabed to water surface the the majority numbers of routing protocols has been introduced. The design of routing protocols faces many challenges like deployment of sensor nodes, controlling of node mobility, development of efficient route for data forwarding, prolong the battery power of the sensor nodes, and removal of void nodes from active data forwarding paths. This research article focuses the design of the Reliable Multipath Energy Efficient Routing (RMEER) which develops the efficient route between sensor nodes, and prolongs the battery life of the nodes. RMEER is a scalable and robust protocol which utilizes the powerful fixed courier nodes in order to enhance the network throughput, data delivery ratio, network lifetime and reduces the end-to-end delay. RMEER is also an energy efficient routing protocol for saving the energy level of the nodes. We have used the NS2.30 simulator with AquaSim package for performance analysis of RMEER.We observed that the simulation performance of RMEER is better than D-DBR protocol.

show abstract

Energy Efficient Routing Protocols for Underwater Wireless Sensor Networks: A Review

Abstract: The

Cited by 9 publications

References 8 publications

Self-Organized Wireless Sensor Network (SOWSN) for Dense Jungle Applications

Self-Organized Wireless Sensor Network (SOWSN) for Dense Jungle Applications

Reference broadcast synchronization and time division multiple access implementation on WSN

RMEER: Reliable Multi-path Energy Efficient Routing Protocol for Underwater Wireless Sensor Network

Contact Info

Product

Resources

About