WSN nodes power consumption using multihop routing protocol for illegal cutting forest

Mutiara, Giva Andriana; Suryana, Nanna; Othman, Mohd Fairuz Iskandar

doi:10.12928/telkomnika.v18i3.14844

Cited by 7 publications

(7 citation statements)

References 27 publications

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“…We chose a jungle site for our real-time measurement because the WSN was usually used in those environments, one of example WSN used in jungle such as illegal logging monitoring [25], Forest Fire monitoring [26], and others. Overgrown jungle vegetation covers a substantial portion of the landscape.…”

Section: B Field Measurement Jungle Environmentmentioning

confidence: 99%

“…The criteria for this would be the quantity of node follower with weight 0.1, the connection with other cluster head or gateway with weight 1, and minimum RSSI level at -100 dBm would be weight 1. Hence the SAW Cluster Routing Model can be seen on equation 25: (25) where: 𝑟 𝑖𝑗 = performance value of alternative A, i=1,2,….,m, and j =1,2,….,n. A 𝑖 = Selection Node output value.…”

Section: B Saw Cluster Routing Modelmentioning

confidence: 99%

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

Hakim,

Habaebi,

Islam

et al. 2023

IEEE Access

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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.

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Section: B Field Measurement Jungle Environmentmentioning

confidence: 99%

Section: B Saw Cluster Routing Modelmentioning

confidence: 99%

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

Hakim,

Habaebi,

Islam

et al. 2023

IEEE Access

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“…Routers can divide traffic well and have multiple paths in multipath routing, improving network performance, security, and reliability because multipath routing makes full use of network resources [4]. However, the application of multipath routing in IoT has a problem that requires significant computing resources [5], [6]. Given the problem of limited resource computing on IoT, it is necessary to have an integration process with Software Defined Network (SDN) architecture, commonly referred to as SD-IoT, as a potentially feasible solution to strengthen management and control capabilities on IoT networks.…”

Section: Introductionmentioning

confidence: 99%

Multipath Routing Implementation in SD-IoT Network Using OpenFlow-based Routing Metrics

Atthariq,

Hidayat,

Sadida

et al. 2023

JOIV : Int. J. Inform. Visualization

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The implementation growth of the Internet of Things (IoT) may increase the complexity of the data transmission process between smart devices. The route generation process between available nodes on the network will burden the intermediary node. One of the possible solutions for resolving the problem is the integration of Software Defined Networks and IoT (SD-IoT) to provide network automation and management. The separation of networking control and data forwarding functions may provide a multipath delivery path between each node in the IoT environment. In addition, the controller can directly extract the resource usage of the intermediary devices, which can be utilized as the routing metric variable in order to maintain the resource utilization on the intermediary devices. Instead of using traditional routing, this paper aims to develop multipath routing based on Deep First Search (DFS) and Dijkstra algorithms for acquiring an efficient path using OpenFlow-based routing metrics. The traffic monitoring module delivered the metrics extraction process, which obtained the variables using Port and Aggregate Flow Statistic features. The metrics calculation aimed to provide the multipath, which was constructed based on switches resource usage. Each selected path was chosen based on the smallest cost and probability provided by the group table feature in OpenFlow. The results showed that the Dijkstra algorithm could create the multipath more swiftly than DFS with a time difference of 0.6 s. The Quality of Service (QoS) results also indicated that the proposed routing metric variables could maintain the transmission process efficiently.

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“…In recent years, Wireless Sensor Networks (WSN) [3] have been playing a pivotal role in monitoring indoor and outdoor areas, providing interesting solutions for different scenarios [4]. Concerning WSN technologies and protocols, besides low-power short-range networks based on Zigbee and Bluetooth [5], Long Range (i.e., LoRa) [6] solutions have been introduced to achieve Low-Power Wide-Area Networks (LPWANs). LoRa offers low-power, low-cost, long-range, and long-term functionality with no maintenance, thus providing wide coverage and low battery consumption [7].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Illegal Tree Cutting through Ultra-Low-Power Smart IoT Devices

Andreadis

Giambene

Zambon

2021

Sensors

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Forests play a fundamental role in preserving the environment and fighting global warming. Unfortunately, they are continuously reduced by human interventions such as deforestation, fires, etc. This paper proposes and evaluates a framework for automatically detecting illegal tree-cutting activity in forests through audio event classification. We envisage ultra-low-power tiny devices, embedding edge-computing microcontrollers and long-range wireless communication to cover vast areas in the forest. To reduce the energy footprint and resource consumption for effective and pervasive detection of illegal tree cutting, an efficient and accurate audio classification solution based on convolutional neural networks is proposed, designed specifically for resource-constrained wireless edge devices. With respect to previous works, the proposed system allows for recognizing a wider range of threats related to deforestation through a distributed and pervasive edge-computing technique. Different pre-processing techniques have been evaluated, focusing on a trade-off between classification accuracy with respect to computational resources, memory, and energy footprint. Furthermore, experimental long-range communication tests have been conducted in real environments. Data obtained from the experimental results show that the proposed solution can detect and notify tree-cutting events for efficient and cost-effective forest monitoring through smart IoT, with an accuracy of 85%.

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WSN nodes power consumption using multihop routing protocol for illegal cutting forest

Cited by 7 publications

References 27 publications

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

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

Multipath Routing Implementation in SD-IoT Network Using OpenFlow-based Routing Metrics

Monitoring Illegal Tree Cutting through Ultra-Low-Power Smart IoT Devices

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