Power aware routing algorithms (PARA) in wireless mesh networks for emergency management

Al-Hadhrami, Tawfik; Saeed, Faisal; Olajide, Funminiyi

doi:10.1371/journal.pone.0204751

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…The DNN algorithm measures the weight, which can be intended as the function of mapping. The features of input rearrange as a vector that is specified in (7). Then it computes the weight based on the adaptive load balance factor.…”

Section: Dnn Based Relay Selectionmentioning

confidence: 99%

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Deep neural network with fuzzy algorithm to improve power and traffic-aware reliable reactive routing

Murugesan,

Kanapala,

Rajendran

et al. 2024

IJEECS

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<span>In wireless networks, link breaks, and restricted resources create fundamental challenges for maintaining network applications. Several wireless network routing techniques concentrate on power efficiency to expand the network lifetime, but the traffic and reliability parameters are not the primary concern. Though, these techniques are not capable of dealing with the wireless network. Hence, this paper proposes deep neural network (DNN) with a fuzzy algorithm to improve power and traffic-aware reliable reactive routing (PTAR) in wireless networks. The wireless network is formed by clustering by the node power and selects the cluster head (CH) based on a fuzzy algorithm. The wireless node power level, node buffer space, and node reliability to consider the input parameters of the fuzzy system. Then thefuzzy algorithm gives the output for CH round length. This selected CH improves the node reliability, power efficiency with minimized network congestion. Then we use a DNN algorithm to choose an optimal relay by applying an adaptive load balance factor in the network. DNN is a machine learning algorithm, and it provides high accuracy. From the simulation results, the PTAR approach improves the network performance, such as packet received ratio, delay, residual energy, and routing overhead.</span>

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Section: Dnn Based Relay Selectionmentioning

confidence: 99%

“…It enhances the routing life. However, this approach increases the network traffic and can not provide reliable routing [7]. To solve these issues, deep neural network (DNN) with a fuzzy algorithm to improve power and traffic-aware reliable reactive routing (PTAR) is proposed.…”

Section: Introductionmentioning

confidence: 99%

Deep neural network with fuzzy algorithm to improve power and traffic-aware reliable reactive routing

Murugesan,

Kanapala,

Rajendran

et al. 2024

IJEECS

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show abstract

“…In 2018, Tawfik Al-Hadhrami et al [7] analyzed the various routing selection schemes based on power and they proposed a power aware routing algorithm (PARA) for Wireless Mesh Networks. The proposed algorithm checks the power of next node and constructs optimal paths for sending packets.…”

Section: Review Of Literaturementioning

confidence: 99%

Quality of Service (Qos) Neighbor Routing In Grid and Triangular Mesh Topologies of Wireless Mesh Network

Rani¹,

Praba²

2019

IJITEE

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Quality of Service routing is constructing a route with enough resources for the QoS parameters. The routing protocols play a crucial part in providing quality service for Wireless Mesh Network (WMN). In this paper, the existing AODV protocol is enhanced to provide QoS in routing the packets as it may lead to node failure, congestion due to loss of energy and increased queue length. In the route construction process, initially the protocol focus on selecting a best forwarding node based on the node’s maximum net energy. Second, AODV is enhanced to construct a route by selecting a forwarding neighbor based on minimum queue length. Finally, the EEQ-AODV (Efficient Energy and Queue AODV) protocol selects the efficient path based on energy and queue length factors. The above mentioned tasks have been analyzed in Grid and Triangular Mesh topologies of Hybrid WMN architecture by considering the metrics Packet Delivery Ratio(PDR), dropped packets, average end-end delay, routing overhead and average throughput using NS-2. EEQ-AODV protocol is analyzed with the metrics energy consumed/packet and lifetime of the network

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“…Several key issues such as (1) knowledge discovery for rescue, (2) integrated asynchronous control of the large-scale emergency response systems (LSERS), and (3) prototyping platforms were given most priority to get sorted out using the EPN-based techniques. An poweraware routing algorithms (PARA) was recently proposed to support the emergency-victims by integrating an incident area network (IAN) with the wireless mesh networking (WMN) [52]. The study proved that PARA was capable to improve the existing network performance while minimizing the network-related overheads and retaining a high packet delivery ratio with a very low latency.…”

Section: Internet Of Thingsmentioning

confidence: 99%

Energy Packet Networks: an Annotated Bibliography

Ray

2019

SN COMPUT. SCI.

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Since the beginning of the age of the digital-revolution, computer and communication systems have used massive electrical power. As a result, the usage of electrical energy by the Information and Communication Technologies (ICT) has now become a major concern in the research community, so as to find new ways to minimize this overall power consumption. The Energy Packet Network (EPN) model was thus proposed to formulate and optimize the dynamic behavior of energy consumption by the ICT-based infrastructures. In this paper, we present an annotated bibliography of work that has been done regarding the EPN model, to provide a bird's eye view of this research. A taxonomic representation of EPNs is proposed covering wireless sensor networks, ad hoc networks, clouds, servers, and wired networks. The link between EPNs, Gelenbe Networks and Random Neural Networks is also discussed. The study concludes that the EPN model and paradigm offers a significant research and engineering design direction for the minimization and optimization of energy usage in the ICT ecosystem.

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Power aware routing algorithms (PARA) in wireless mesh networks for emergency management

Cited by 9 publications

References 25 publications

Deep neural network with fuzzy algorithm to improve power and traffic-aware reliable reactive routing

Deep neural network with fuzzy algorithm to improve power and traffic-aware reliable reactive routing

Quality of Service (Qos) Neighbor Routing In Grid and Triangular Mesh Topologies of Wireless Mesh Network

Energy Packet Networks: an Annotated Bibliography

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