Abstract-Modern day wireless networks have tremendously evolved driven by a sharp increase in user demands, continuously requesting more data and services. This puts significant strain on infrastructure based macro cellular networks due to the inefficiency in handling these traffic demands, cost effectively. A viable solution is the use of unmanned aerial vehicles (UAVs) as intermediate aerial nodes between the macro and small cell tiers for improving coverage and boosting capacity. This letter investigates the problem of user demand based UAV assignment over geographical areas subject to high traffic demands. A neural based cost function approach is formulated in which UAVs are matched to a particular geographical area. It is shown that leveraging multiple UAVs not only provides long range connectivity but also better load balancing and traffic offload. Simulation study demonstrate that the proposed approach yields significant improvements in terms of 5th percentile spectral efficiency up to 38% and reduced delays up to 37.5% compared to a ground-based network baseline without UAVs.Index Terms-UAVs, 5G, interference coordination, quadcopter, unmanned aerial base stations.
Detrital-zircon fi ssion-track (FT) ages from the Lower Cenozoic Sub-Himalayanforeland basin refl ect the progressive effects of crustal thickening and exhumation on the Himalayan source rocks as a consequence of the India-Asia collision. The oldest stratum, the transgressive marine Paleocene-Eocene Subathu Formation (57-41.5 Ma) contains ca. 50 Ma detrital-zircon P1 peak, which was derived from the Indus Tsangpo Suture Zone and the Ladakh Batholith of the Asian plate. A dominant 302.4 ± 21.9 Ma peak with a few 520 Ma grains in this formation has been derived by erosion of the zircon partialannealing zone (ZPAZ) of 240-180 °C. As the fi rst imprint of the collision, this zone affected the Himalayan Proterozoic basement and its Tethyan sedimentary cover.Since the detritus in the Subathu has been derived both from the Indian and Asian plates, the possible suturing of these plates took place during the Subathu sedimentation. A sudden change in the provenance is recorded in the detrital-zircon FT cooling ages in the OligoMiocene Dagshai and Kasauli Formations, which have dominant 30 and 25 Ma P1 peaks, respectively. We interpret a distinct unconformity spanning ~10 m.y. between the Subathu and Dagshai Formations. Since ca. 30 Ma, molassic sedimentation coincides with shifting of the source rocks to the Himalayan metamorphic belt. This belt has sequentially undergone three distinct cooling and exhumation pulses after the ultrahigh-pressure-high-pressure (UHP-HP) metamorphism (53-50 Ma) in the extreme north and two widespread M1 and M2 metamorphisms (40-30 and 25-15 Ma) in the middle parts. These events appear to be largely responsible for the deposition of the ca. 30 Ma zircon Himalayan peak and ca. 25 and 15 Ma young Himalayan peaks, respectively; the latter appears within the Lower Siwalik Subgroup (13-11 Ma). During the Lower Siwalik deposition, pre-Himalayan peaks gradually decrease with the intensifi cation of the Himalayan events in source rocks. In spite of uninterrupted fl uvial sedimentation in the Dagshai-Kasauli-Lower Siwalik sequences since 30 Ma, breaks of ~5-7 m.y. in the zircon FT ages reveal pulsative cooling and exhumation in the well-identifi ed source areas. Although cooling and exhumation of the Himalayan source rocks remained almost uniform during the Eocene, source heterogeneity is refl ected in fl uvial sedimentation since 37 Ma from Pakistan to Nepal in response to the India-Asia collision.
With the requirement of better connectivity and enhanced coverage, collaborative networks are gaining a lot of popularity these days. One of such collaborative networks is formed between the wireless sensor networks (WSNs) and the unmanned aerial vehicles (UAVs). WSNs comprise static nodes arranged in a flat grid topology which may be randomly deployed or by some particular distribution. With the sensors operating on batteries, WSNs face a crucial issue of energy depletion during network operations. Integration of WSNs with UAVs can provide a solution to this excessive utilization of energy resources. UAVs provide a maneuvering support by playing a pivotal role of a manager node in these networks. However, integration of these networks demands an improved approach for data dissemination for effective utilization of network resources. For this, we propose a new data dissemination approach, which utilizes the attraction properties of fire fly optimization algorithm to provide energy efficient relaying. The proposed approach provides continuous connectivity, better lifetime, and improved coverage in the UAV coordinated WSNs. The performance of the proposed model is presented in terms of significant gains attained for parameters, namely, throughput, coverage, mean hops, lifetime, and delays, in comparison with the EEGA, ERIDSR, and I-ERIDSR approaches.
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