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Artificial intelligence can be used to better monitor Maine's forests, UMaine study finds

Monitoring and measuring forest ecosystems is a complex challenge because of an existing combination of softwares, collection systems and computing environments that require increasing amounts of energy to power. The University of Maine’s Wireless. ....

United States , University Of Maine , Aaron Weiskittel , Ali Abedi , Research On Sustainable Forests , University Of Maine Wireless Sensor Networks Wise , Springer International Journal Of Wireless Information Networks , National Science Foundation , University Of Vermont , University Of New Hampshire , Wireless Sensor Networks , New Hampshire , National Science , Sustainable Forests , International Journal , Wireless Information , Artificial Intelligence Can Be Used To Better Monitor Maines Forests , Maine Study Finds ,

Artificial intelligence can be used to better monitor Maine's forests, UMaine study finds

Artificial intelligence can be used to better monitor Maine's forests, UMaine study finds
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United States , University Of Maine , Aaron Weiskittel , Ali Abedi , Research On Sustainable Forests , University Of Maine Wireless Sensor Networks Wise , Springer International Journal Of Wireless Information Networks , National Science Foundation , University Of Vermont , University Of New Hampshire , Wireless Sensor Networks , New Hampshire , National Science , Sustainable Forests , International Journal , Wireless Information ,

"Feasibility Analysis of Data Transmission in Partially Damaged IoT Net" by Wei Wei, Jun Shen et al.

Nowadays, vehicle-oriented Internet of Things (IoT) is a new generation of IoT networks in which sensors are deployed on electronic hardware modules of vehicles. A secure and feasible IoT-assisted vehicle environment should include a robust data transmission mechanism for transferring and collecting data packets from both onboard and roadside sensors, resulting in the accurate delivery of packages without delay. When designing such Internet of Vehicles (IoV) networks, the vulnerability of the network should be considered to facilitate data transmission in the remaining network under the condition that some nodes (e.g., vehicles) and channels are damaged due to the dynamic environmental factors and unpredicted failures at various nodes. Fractional Critical Deleted Graph (FCDG), which is used in graph theory, can act as Fractional Factor (FF) in the IoV networks to maintain the IoT network stable and provide reliable network connectivity when a part of data transmission network is damage ....

Critical Deleted Graph , Fractional Factor , Data Communication , Ractional Critical Deleted Graph Fcdg , Ractional Factor Ff , Graph Theory , Intelligent Transportation System , Internet Of Vehicles , Public Transportation , Reliability Theory , Wireless Sensor Networks ,

"Energy Efficient Virtual Network Embedding in Virtualized Wireless Sen" by Vahid Maleki Raee, Amin Ebrahimzadeh et al.

In traditional Wireless Sensor Networks (WSNs) the applications are embedded in sensor nodes, making them application-oriented, domain-specific devices. Virtualization is a promising approach to allow several applications to use the physical resources of the same deployed WSN. Given that WSN sensor nodes hold limited available energy, efficient allocation and utilization of resources become important. In this paper, we study the problem of virtual network embedding in virtualized WSNs in a static setting. Our objective is to minimize the overall energy consumption by embedding the application virtual network requests onto the physical WSN substrate network. We formulate the problem as an Integer Linear Programming (ILP), accounting for latency as the service-level agreement (SLA). We then propose our so-called E2NE heuristic to achieve a near-optimal solution in a computationally efficient manner. Our results show that embedding virtual sensor networks in a virtualized WSN leads to mor ....

Wireless Sensor Networks Wsns , Wireless Sensor Networks , Integer Linear Programming , Internet Of Things , Resource Allocation , Irtual Network Embedding , Wireless Sensor Networks ,

Artificial intelligence can be used to better monitor Maine's forests

Artificial intelligence can be used to better monitor Maine's forests
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United States , University Of Maine , Aaron Weiskittel , Ali Abedi , Research On Sustainable Forests , University Of Maine Wireless Sensor Networks Wise , Springer International Journal Of Wireless Information Networks , National Science Foundation , University Of Vermont , University Of New Hampshire , Wireless Sensor Networks , New Hampshire , National Science , Sustainable Forests , International Journal , Wireless Information ,