J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 Journal of the Nigerian Society of Physical Sciences Original Research Investigating the Performance of Point to Multipoint Microwave Connectivity across Undulating Landscape during Rainfall W. Nwankwoa, K. E. Ukhureborb,∗ aSoftware Engineering/Cyber Physical Lab, Department of Mathematics and Computer Science, Edo University Iyamho, P.M.B. 04 Auchi, Edo State, Nigeria bClimatic/Environmental/Telecommunication Physics Unit, Department of Physics, Edo University Iyamho, P.M.B. 04 Auchi, Edo State, Nigeria Abstract One of the most debated issues surrounding wireless connectivity is performance especially under different topographic and climatic scenarios. Performance has a direct relationship with throughput measured in terms of how well a given wireless connectivity provides consistent services over a given period compared to the wired alternative. Research has shown that wireless connectivity is constrained by significant physical components such as topography, weather conditions, propagation frequency, and distance. It is commonplace to see notable vendors of wireless network products make claims as to how their technologies are designed to remedy any signal degradation that may arise from the aforementioned physical elements. This paper is aimed at evaluating the performance of a point to multipoint connectivity using Ubiquiti’s 5.8 GHz Point to Multipoint Base Stations deployed within a landscape marked by series of undulating highlands and lowlands. In this experiment, a base station node is established with connectivity to two other nodes of same specifications with one node as the destination radio whereas the other acts as the control which is located on a table land. The nodes were separated by triangular distances of 3 km and network connectivity was maintained over thirty days during periods of rainfall. Packets sent and received across each node was carefully recorded. The results from the analysis showed that packet losses to and from the control node was significantly lower than that of the other node under same weather conditions. Keywords: Landscape, Microwave, Nodes, Performance, Signal Article History : Received: 10 October 2019 Received in revised form: 27 October 2019 Accepted for publication: 29 October 2019 Published: 14 October 2019 c©2019 Journal of the Nigerian Society of Physical Sciences. All rights reserved. Communicated by: O. J. Abimbola 1. Introduction The technological breakthrough and development experi- enced in the field of wireless communication would not have been a success if references are not made to early scientists who did a lot of research in the field of wireless communica- tions. Several inventions, theories and principles have been es- tablished in order to generate background foundation towards enormous advancement in the field of wireless communication [1]. ∗Corresponding author tel. no: +2348035383194 Email address: ukhurebor.kingsley@edouniversity.edu.ng (K. E. Ukhurebor ) These technologies continued to develop for over a century as people began to unravel the intricacies of telecommunica- tions [1, 2, 3]. The development from 1G to 4G and now into LTE and beyond to 5G is a milestone. The dramatic develop- ment was not just experienced at an instance, but was seen as an improvement over an existing technology, a breakthrough in the field, and a transition from one to another with marked elemental upgrades. The evolution of mobile communication cannot be well understood if proper description of mobile com- munications, is not reviewed in respect of the overall technol- ogy, speed, frequency and system in numeric generations such as 3G, 4G or 5G [1]. Each generation has unique technologies 103 Nwankwo and Ukhurebor / J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 104 that define it, and explains the differences throughout the evo- lution of mobile communications and what we can expect from the future generations of these technologies. Wireless networks have some constraints to a wider option in enterprise network design and implementation. Such con- straints include: attenuation, latency, interference, distortion of signals, as well as the effect of weather conditions specifically rainfall on performance and signal propagation [4-18]. But with consistent advancement in technology, some mech- anisms have been integrated into high-end radios to help miti- gate some of these constraints. However, there are still some debatable issues as to the performance of wireless networks un- der extreme weather conditions and unusual landscapes [10, 11, 12]. The aim of this study is to investigate the performance of a 5 GHz point-to-point and point-to-multipoint radios across un- dulating landscape during rainfall. We intend to achieve this aim by understudying the geography of the study area, which according to previous studies [1-12], have remarkable topo- graphic characteristics with emphasis on the distribution of weather conditions and signal propagation. We would attempt to deter- mine the propagation and monitor the behaviour of radio sig- nals within the 5 GHz frequency band from one point to another having regard to the undulating lowlands and highlands during rainfall. Appropriate comparison on performance of same ra- dio specifications under normal weather conditions on lowlands would also be considered in this study. 2. Review of Related Works Indisputably, movement of vegetation structures introduces an adverse environment for high frequency radio wave propa- gation [10]. Hashim et al., [19], examines a series of vegetation scatter- ing measurement campaigns during various wind conditions. Their measurements were divided into controlled and outdoor environments. The controlled environment measurements were conducted in an anechoic chamber at 0.9,2.0,12.0 and 17.0 GHz., while the outdoor measurements were carried out at 1.8 GHz, as well as recording of a transmitted signal originating from an existing digital cellular system (DCS-1800) base station. Their results were presented in terms of first-order and second-order statistics. From their analysis, the received signal behaviour was highly wind dependent, especially when the environment is changing from calm to a windy condition. The signal fast- fading was found to be Rician distributed and an empirical model of the k-factor variation over wind speed were also presented in their study. In the study carried out by Perras et al., [20], they com- pared the various temporal characteristics of radio channels for a broad range of frequencies, including 2.45,5.25,29.0 and 60.0 GHz, in various vegetation and weather conditions. A considerable number of data points, were in excess of 1.9 billion at 500 samples per second (equivalent to 45 days) was collected and analysed for three particular types of channels (foliated decid- uous trees, non-foliated deciduous trees and coniferous trees). The radio channels were statistically analysed and the result- ing probability density functions (PDFs) and cumulated density functions CDFs) were compared with existing models. Fur- thermore, wind speeds and rain precipitation were correlated with the power samples, this was done in order to consider RF propagation dependencies. Second order statistics was de- rived including level crossing rate (LCR) and average facie du- ration (AFD). The power profile was analysed for spectral com- ponents. The frequency characteristics of the RF propagation channel were also evaluated. They also presented the channel- specific RF propagation attributes. Pellet et al., [21], used a fixed terrestrial broadband wire- less system such as Multipoint Microwave Distribution Sys- tem (MMDS) which is a cost-effective solution for cable cov- erage to the immediate surrounding rural area. The wireless system was operated with the existing cable headend and the same subscriber-end cable modem. The system works well with clear line-of-sight transmit/receive antennas. However, in near- line-of-sight transmission where a few foliated trees block the line-of-sight, signal distortion were experienced, especially un- der conditions of high wind. They argued, that the motion of the trees was responsible for the huge and rapid signal fading. Their measurements were taken on fixed wireless paths blocked with a few trees in the vicinity of the receive antenna. Fading characteristics of a 6 MHz channel centred at about 2.6 GHz were provided. The fades were mostly flat across the band but with some frequency selective fading. Fading rates under windy conditions ranged from 0.5 to 2.0 f ades/second. The slope of the fades occasionally reached 50 dB/second. Dal Bello et al., [22], carried out their studies on propaga- tion in an urban forested park area, their aim was to investigate the statistical nature of the time fading for frequencies ranging from 0.9 GHz to 1.8 GHz, as well as to examine the range de- pendence and the base station height gain. They used a received signal of around 30 s intervals for a stationary mobile to design the distribution functions for the fading. According to them, the distribution could be approximated by a Rician distribution, whose K-factor was found to depend on transmitter height. Cuiñas et al., [23], reported that the presence of vegetation in the radio channel could affect the coverage areas of cellular mobile phone systems. They argued that the various compo- nents of a tree have influence in the performance of the radio system. Although, the trunk is commonly in a fixed and stable location, the leaves could be in continuous movement as a result wind. Accordingly, the time variation which can be correlated with the wind speed and direction, could strongly modify the at- tenuation and scattering effects of the trees on the radio channel performance. Their study presents the results (both long-term and short-term) of a measurement campaign of scattering and attenuation effects of isolated trees, under the action of artificial wind of different controlled speeds and directions. They show that the median effect of the presence of a tree is the induction of higher attenuation in the shadow areas and a new distribu- tion of the scattering pattern all around the specimen. But, the wind on the leaves forces their movement and an increment in the time variation around the median received power values. Zennaro et al., [24], revisited the issue of link quality in 104 Nwankwo and Ukhurebor / J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 105 Wireless Sensor Networks (WSN). They studied the temporal and energy characteristics of a 24 GHz sensor network in an outdoor environment using different values of output power and sampling period. They analysed battery behaviour in motes placed at different distances and reported that farther motes have a shorter battery life. They suggested that when deployed in the real world, the sampling periods of sensor networks be adjusted according to distance to normalize battery lifetime and a more accurate energy-aware routing protocol be developed. According to Zhao et al., [25], wireless sensor networks promise fine-grain monitoring in a wide variety of environments. Many of these environments (like indoor environments or habi- tats) can be harsh for wireless communication. From a network- ing perspective, the fundamental aspect of wireless communi- cation is the packet delivery performance (the spatio-temporal characteristics of packet loss and its environmental dependence). These factors would severely have impact on the performance of data acquisition from these networks. Their study was cen- tred on a systematic medium-scale (up to sixty nodes) mea- surement of packet delivery in three different environments (an indoor office building, a habitat with moderate foliage and an open parking lot). Their results would have interesting impli- cations for the design and evaluation of routing and medium- access protocols for sensor networks. Puccinelli and haenggi [26], reported that multipath fading severely contributes to the unreliability of wireless links, caus- ing fairly huge deviations from link quality predictions based on path loss models. Accordingly, its impact on wireless sensor networks is considerable. Although, analytical models provide a probabilistic description, multipath fading is a deterministic phenomenon. Moreover, in the case of static nodes, fading is time-invariant. They illustrated its spatial nature with experi- mental evidence obtained using lower-end sensing node hard- ware. They also show the limitations of the supposed immunity of wideband radios to multipath fading in indoor deployments. The works of Ukhurebor et al., [27] and Ukhurebor and Umukoro [28] dealt extensively on the effects of some essential meteorological variables such as temperature, relative humidity and mean sea level pressure on the Ultra High Frequency (UHF) and Very High Frequency (VHF) radio signals. They stated that the radio signals from both the UHF and VHF television sta- tions were directly proportional to the temperature, inversely proportional to the relative humidity and no defined pattern of proportionality with the mean sea level pressure. They argued that, the radio signals from the UHF television station were seen to be mostly affected by these weather variables. These effects according to them were more pronounced during the months with high relative humidity compared with the months with lower relative humidity. According to [4]-[12], signal attenuation caused by rainfall, is a major challenge to microwave satellite communication es- pecially at frequencies above 10 GHz. In several occasions, they cause signals unavailability. Rainfall attenuation predic- tions have become one of the vital considerations while setting up a satellite communication link [4] - [12]. In their respec- tive studies, rainfall attenuation models, cumulative distribution curves and other analytical tools for successful prediction of rain attenuation were presented. Basically, this study focussed on comprehensive evaluation of the performance and operation of 5 GHz microwave radios during propagation and transmission of signals across undulat- ing land areas taking into consideration the effect of weather in such circumstances [29]. Apparently, the results obtained offered some additional and novel insight on the effects of to- pography on the propagation of signals under certain weather conditions even where high frequency radios are employed. 3. Materials and Methods The hierarchical approach to network design and imple- mentation was adopted in this study. The hierarchical approach stipulates that a network be structured in such a way as to cat- egorize similar functions into a layer and separate each layer from other layers, while ensuring communication among the different layers [4] - [12]. Each layer focuses on specific func- tions. The advantages of the hierarchical approach are notable, which include: i. Enables the network planner or designer to identify and make proper choice of the right equipment for the design and implementation of each layer. ii. Ensures proper evaluation of features that makes up the layers. iii. Suitability for network designs of varying sizes and re- quirements. In a hierarchical network the entire network is segmented into discrete layers. Each layer, or tier, provides specific func- tions that define its role within the overall network. This helps to optimize and select the right network hardware, software and features to perform specific roles in the network. Three main layers are recognized: Access, Distribution, and Core layers re- spectively. 3.1. Hardware i. Three units of Ubiquiti rocket M5 point to multi-point (PtmP) base stations. The specification of the radios is: 5−5.83 GHz spectrum, hi-power 2×2 MIMO TDMA AirMAX, Power- supply: 24 V , 1 A POE. ii. Router (Huawei 1200 series). iii. Switch (S5700 8-port). iv. Cat-6 shielded twisted pair cable (40 m crimped cables). v. Three computer systems with same specifications: HP Pro- book 6450b, intel R coreTM i5 CPU a©2.40 GHZ, running Microsoft Windows 10 professional operating system. vi. Infinix S4 Smart phone with Android 9.0, GPS coordinate and elevation apps respectively. 3.2. Software i. Wireshark 3.0.5(Network packet analyzer) ii. Enterprise Network Simulation Platform (eNSP) iii. Microsoft Visio 2010 iv. IBM SPSS v.24 v. Ubiquiti Unifi controller software 105 Nwankwo and Ukhurebor / J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 106 3.3. Study Area This study was conducted in Iyamho community, a small town in Etsako West Local Government of Edo State, Nigeria. Iyamho is host to the fast-growing model University, Edo Uni- versity Iyamho. It has approximately 5,000 inhabitants who are mostly rural dwellers. However, since the establishment of the University, the community is rapidly transforming to a beau- tiful semi-urban centre following the influx of workers, visi- tors and students. Consequent upon the foregoing, the town is witnessing an upsurge of modern communication infrastructure amidst other notable capital infrastructure such as police bar- racks, community health clinics and private-owned businesses. In the area of communication, there is relatively a heavy pres- ence of mobile network base stations which have given rise to increased growth in the use of the Internet. It is presumably estimated that 55 % of the population are active users of the in- ternet. Geographically, Iyamho is located on latitude 7.07 o N and longitude 6.27 o E with an elevation of about 188 m above sea level [16, 17, 18]. Like other tropical areas of Southern Nigeria, Iyamho enjoys two seasons often categorized as rainy and dry seasons. It enjoys a Savannah vegetation. According to Ukhurebor et al., [17], its topography is marked with undu- lating and table lands. Figure 1 shows the aerial view of the community. Figure 1: Aerial View of Iyamho, EtsakoWest of Edo State, Nigeria (Source: Google Earth). 3.4. Design of Experiment The experiment involved three locations which were marked as follows: Primary Station (the Base Station) is located at the Faculty of Science, Edo University Iyamho. The primary station has an elevation of about 45 m above sea level. Its location provided by coordinate 7.15174 o N and 6.30098 o E. The experiment is modelled as shown in Figure 2. Remote Station 1 (the Control Station) is located at the Administrative building in the main campus of Edo University Iyamho. The control station has an elevation of about 188 m above sea level. The Remote Station 2 (the Test station) was deployed in an undulating landscape, located in Iyamho town). The elevation is 88 m above the sea level. 3.5. Method of Data Collection Data collection was done using Wireshark 3.0.5 installed on all the participating computers. Connectivity between the base station and the two remote stations located 3 km away from the base station was continuously monitored. Packets from the two remote stations were captured simultaneously from April to September, 2019 over a period of 30 days during rainfall. The failure rates from the base station to the remote stations were recorded. The failure and success rates at which packets were received from both remote stations were recorded under the same weather conditions. 3.6. Network Modelling The following tools were used for Network modelling and simulation: Huawei enterprise network simulation platform (eNSP); Huawei AP6510DN series wireless 5 GHz radios, Huawei S700 series switch, and a HP Probook. Figure 3 shows the topol- ogy of the model. A class C network with network address of 192.168.1.0 and subnet mask 255.255.255.0 was used. The base station is connected to highly efficient switch, while the other remote stations were connected directly to a HP Notebook of same specifications. Figure 2: Diagrammatic Description of Experimental Design. The essence of using same computer specification across the three locations was to ensure that no extraneous factors were introduced into the network in respect of performance [29]. Presumably, the use of same computer specification with same configuration and applications installed which are operat- ing within same physical conditions would help ensure unifor- mity in performance. Following the modelling, a 64k ping flood was sent from the BASE to the remote stations (REMOTE 1 and REMOTE 2) for three consecutive hours. The same ping flood was sent from the two remote access points to the BASE over same period. Wireshark was used to capture the frequency of the packets sent and the packets received. 3.7. System Requirements and Physical Configuration The physical configuration involves coupling the Ubiquiti Rocket M5 radios together with the sector antennae which were positioned at the three locations. For the base station, a Huawei 106 Nwankwo and Ukhurebor / J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 107 Figure 3: Topology of the Network. S700 series switch was used to connect the HP notebook and the Access point. Figure 4-6 show the various configurations of the base station (BASE) and the remote stations (REMOTE 1 and REMOTE 2) respectively. Figure 4: Configuration of the Base Station Radio. 3.8. Data Capture with Wireshark As mentioned earlier, data capture on the physical network was done using wireshark installed on the three HP notebooks. Prior to capture on physical network, a simulation was done using eNSP and wireshark on one of the HP notebooks. The result of the simulation did not yield any departure as regards traffic sent to and packets received from the remote stations. In the physical network, 64k frames were flooded to the remote stations from the base station for over a period of 30 days rainy period. It should be noted that the period was only during the rainy cloudy weather. 4. Results and Discussion Table 1 to 6 show the statistics of the packets relayed and received by the base station from the remote stations. Note that the remote stations are tagged; REMOTE 1 and REMOTE 2. As previously stated, we used REMOTE 1 as a control station while REMOTE 2 was the test station. Figure 7 shows the comparative analysis of the packet losses to both Remote 1 and Remote 2. On the other hand, Figure 8 shows the relationship between the duration of rainfall and packet losses. The charts are created using data from Table 5. Figure 5: Configuration of the Control Radio (Remote 1). Figure 6: Configuration of the Test Radio (Remote 2). Figure 7: Comparative Analysis of Packet Losses from Base Station to Remote Stations. Figure 9 shows a stacked area chart. The area chart summa- rized the relationship between the lost frames at both Remote 1 and Remote 2 respectively. In this experiment, three radios were used. The base station was used as the reference point to coordinate the point to mul- tipoint connections. Two remote stations were employed with one as the control radio and the second as the test radio. From the charts in Figures 7-9, it is evident that regardless of the dura- tion of rainfall or cloudy weather conditions, data losses across the radio located on undulating paths are higher than that lo- cated on plane paths. It may be submitted that notwithstanding 107 Nwankwo and Ukhurebor / J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 108 Table 1: Traffic States on Base Station . Day Period of Rainfall (Minutes) Packets Sent from Base to REMOTE 1 Packets Received from REMOTE 1 1 15 7005 5600 2 55 3575000 3479154 3 23 112876 102876 4 18 926547 725467 5 75 2367234 1803467 6 10 2136 1159 7 33 82345 61234 8 46 234654 223456 9 32 436788 394659 10 16 64763 59874 11 42 2348765 1863534 12 26 104566 80001 13 31 934525 802211 14 22 932442 823426 15 39 120001 103736 16 4 2034 1108 17 18 789686 723456 18 34 100870 90711 19 53 2346000 2012664 20 8 1264 1023 21 24 109827 90736 22 24 101453 90534 23 21 204332 183454 24 32 2364758 2002374 25 13 23487 21763 26 23 102876 98234 27 10 1726 1407 28 22 841234 80023 29 13 2305 1600 30 11 1656 1432 108 Nwankwo and Ukhurebor / J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 109 Table 2: Traffic on Base Station Computer. Day Period of Rainfall (Minutes) Packets Sent from Base to REMOTE 2 Packets Received from REMOTE 2 1 15 7005 4600 2 55 3575000 3179152 3 23 112876 92176 4 18 926547 525167 5 75 2367234 1503467 6 10 2136 1102 7 33 82345 60024 8 46 234654 200856 9 32 436788 312259 10 16 64763 50174 11 42 2348765 1023434 12 26 104566 60020 13 31 934525 801241 14 22 932442 811606 15 39 120001 100123 16 4 2034 1003 17 18 789686 702356 18 34 100870 80034 19 53 2346000 2002344 20 8 1264 0865 21 24 109827 90502 22 24 101453 89534 23 21 204332 153454 24 32 2364758 2001234 25 13 23487 21456 26 23 102876 98234 27 10 1726 959 28 22 841234 80023 29 13 2305 1200 30 11 1656 1325 109 Nwankwo and Ukhurebor / J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 110 Table 3: Traffic from REMOTE 1 to Base Station. Day Rainfall Time (minutes) Packets Received by BASE Packets Sent to BASE Station 1 15 5523 5600 2 55 3455000 3479154 3 23 100876 102876 4 18 700547 725467 5 75 1727234 1803467 6 10 1140 1159 7 33 61200 61234 8 46 200654 223456 9 32 380788 394659 10 16 57763 59874 11 42 1858765 1863534 12 26 78956 80001 13 31 800525 802211 14 22 800442 823426 15 39 100001 103736 16 4 1100 1108 17 18 700686 723456 18 34 89487 90711 19 53 1986000 2012664 20 8 980 1023 21 24 889827 90736 22 24 90045 90534 23 21 180332 183454 24 32 1934758 2002374 25 13 19487 21763 26 23 92876 98234 27 10 1200 1407 28 22 789234 80023 29 13 1400 1600 30 11 1043 1432 110 Nwankwo and Ukhurebor / J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 111 Table 4: Traffic from REMOTE2 to BASE Station. Days Rainfall Time (minutes) Packets Received by BASE Station Packets Sent to BASE Station 1 15 4002 4600 2 55 3375000 3179152 3 23 75876 92176 4 18 486547 525167 5 75 1367234 1503467 6 10 950 1102 7 33 59345 60024 8 46 194654 200856 9 32 296788 312259 10 16 44763 50174 11 42 164876 1023434 12 26 50456 60020 13 31 724525 801241 14 22 782442 811606 15 39 90080 100123 16 4 950 1003 17 18 659686 702356 18 34 73387 80034 19 53 1896000 2002344 20 8 820 0865 21 24 88582 90502 22 24 80245 89534 23 21 162332 153454 24 32 1834758 2001234 25 13 18487 21456 26 23 92876 98234 27 10 800 959 28 22 72123 80023 29 13 1105 1200 30 11 1100 1325 111 Nwankwo and Ukhurebor / J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 112 Table 5: Statistics on Packets Losses to Remote Stations. Days Rainfall Time (minutes) Packet Loss to REMOTE 1 Packet Loss to REMOTE 2 1 15 1405 2405 2 55 95846 395848 3 23 10000 20700 4 18 201080 401380 5 75 563767 863767 6 10 977 1034 7 33 21111 22321 8 46 11189 33798 9 32 42129 124529 10 16 4889 14589 11 42 485231 1325331 12 26 24565 44546 13 31 132314 133284 14 22 109016 120836 15 39 16265 19878 16 4 926 1031 17 18 66230 87330 18 34 10159 20836 19 53 333336 343656 20 8 241 399 21 24 19091 19325 22 24 10919 11919 23 21 20878 50878 24 32 362384 363524 25 13 1724 2031 26 23 4642 4642 27 10 319 767 28 22 761211 761211 29 13 705 1105 30 11 224 331 112 Nwankwo and Ukhurebor / J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 113 Table 6: Statistics on Packet Delivery to Remote Stations. Days Rainfall Time (Minutes) Packet Sent Packet Received by Remote 1 Packet Received by Remote 2 1 15 7005 5600 4600 2 55 3575000 3479154 3179152 3 23 112876 102876 92176 4 18 926547 725467 525167 5 75 2367234 1803467 1503467 6 10 2136 1159 1102 7 33 82345 61234 60024 8 46 234654 223456 200856 9 32 436788 394659 312259 10 16 64763 59874 50174 11 42 2348765 1863534 1023434 12 26 104566 80001 60020 13 31 934525 802211 801241 14 22 932442 823426 811606 15 39 120001 103736 100123 16 4 2034 1108 1003 17 18 789686 723456 702356 18 34 100870 90711 80034 19 53 2346000 2012664 2002344 20 8 1264 1023 0865 21 24 109827 90736 90502 22 24 101453 90534 89534 23 21 204332 183454 153454 24 32 2364758 2002374 2001234 25 13 23487 21763 21456 26 23 102876 98234 88234 27 10 1726 1407 959 28 22 841234 80023 80023 29 13 2305 1600 1200 30 11 1656 1432 1325 113 Nwankwo and Ukhurebor / J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 114 Figure 8: Relationship between Duration of Rainfall and Packet Losses to Remote Stations. Figure 9: Stacked Area Chart Showing the relationship between Lost Frames from the two Remote Stations. the line of sight existing between two geographically located radios, the impact of rainfall is very likely to intensify when radios are placed on terrains with highly undulating surfaces. 5. Conclusion Following the data capture and subsequent statistical analy- sis, we have made the following conclusions: i. Signal strength and propagation of 5 GHz radios placed on undulating terrains are severely affected by deteriorating or adverse weather conditions. ii. Though the cause of the manifest deviation obtained are not very clear, it appears that signal losses are attributed to interference caused by rainfall owing to high relative hu- midity, amplified by possible bias introduced by the undu- lating terrains. iii. Future studies are to take into consideration more meteo- rological variables, over a long period of time in order to have more beneficial and comprehensive results. Acknowledgments We thank the referees for the positive enlightening com- ments and suggestions, which have greatly helped us in making improvements to this paper. References [1] G. R. Maurya, P. A. Kokate, S. K. Lokhande & J. A. Shrawankar J. A, “A Review on Investigation and Assessment of Path Loss Models in Urban and Rural Environment”, IOP Conf. Ser.: Mater. Sci. Eng. 225 (2017) 012219. [2] K. E. Ukhurebor, M. S. Maor & E. E. Aigbe “Mollification of WCDMA Interference on Uplink Channels in Cellular Network Using the Power Control Approach”, British Journal of Applied Science & Technology, 20 (2017) 1. [3] B. Y. Lawal, K. E. Ukhurebor, M. A. Adekoya & E. E. Aigbe “Quality of Service and Performance Analysis of A GSM Network in Eagle Square, Abuja and Its Environs, Nigeria”, International Journal of Scientific & Engineering Research, 7 (2016) 1992. [4] D. Y. Choi, J. Y. Pyun, S. K. Noh & S. W. Lee “Comparison of Measured Rain Attenuation in the 12.25 GHz Band with Prediction by the ITU- R Model”, International Journal of Antennas and Propagation, 415398 (2012) 1. [5] L. A. R Da Silva Mello, M. S. Pontes, R. M. de Souza & N. A. Pérez- Garcı́a “Prediction of Rain Attenuation in Terrestrial Links using Full Rainfall Rate Distribution”, Electronics Letters, 43 (2007) 1442. [6] J. W. F. Goddard “Propagation in Rain and Cloud Spatial Temporal Struc- tures of Rain. In: Propagation of Radio Waves”, (2nd ed) UK, IEE, Lon- don (2013). [7] J. S. Ojo, M. O. Ajewole & S. K. Sarkar “Rain Rate and Rain Attenu- ation Prediction for Satellite Communication in Ku and Ka Bands over Nigeria”, Progress in Electromagnetic Research B, 5 (2008) 207. [8] A. Y. Abdulrahman, T. A. Rahman, S. K. A. Rahim & M. R. U. Islam “A New Rain Attenuation Conversion Technique for Tropical Regions”, Progress in Electromagnetics Research B, 26 (2010) 53. [9] U. Siddique, L. Ahmad L & G. Raja “Microwave Attenuation and Pre- diction of Rain Outage for Wireless Networks in Pakistan’s Tropical Region”, International Journal of Microwave Science and Technology, 14927 (2011) 1. [10] Y. S. Meng & Y. H. Lee “Investigations of Foliage Effect on Modern Wire- less Communication Systems: A Review”, Progress in Electromagnetics Research, 105 (2010) 313. [11] G. N. Ezeh, N.S. Chukwuneke, N.C. Ogujiofor & U.H. Diala U.H “Ef- fects of Rain Attenuation on Satellite Communication Link.”, Advances in Science and Technology Research Journal, 8 (2014) 1. [12] M. C. Kestwal, S. Joshi & L. S. Garia “Prediction of Rain Attenuation and Impact of Rain in Wave Propagation at Microwave Frequency for Tropi- cal Region (Uttarakhand, India)”, International Journal of Microwave Sci- ence and Technology, 958498 (2014) 1. [13] K. E. Ukhurebor, S. O. Azi, I. C. Abiodun & E. Enoyoze “Approximation of the Dew Point Temperature Using a Cost-Effective Weather Monitoring System”, Physical Science International Journal, 14 (2017) 1. [14] K. E. Ukhurebor, T. B. Batubo, I. C. Abiodun & E. Enoyoze “The Influ- ence of Air Temperature on the Dew Point Temperature in Benin City, Nigeria”, Journal for Applied Science and Environmental Management, 21 (2017) 657. [15] K. E. Ukhurebor, I. C. Abiodun & F. Bakare “Relationship between Rel- ative Humidity and the Dew Point Temperature in Benin City, Nigeria”, Journal for Applied Science and Environmental Management, 21 (2017) 953. [16] K. E. Ukhurebor & S. O. Azi “Review of Methodology to Obtain Param- eters for Radio Wave Propagation at Low Altitudes from Meteorological Data: New Results for Auchi Area in Edo State, Nigeria”, Journal of King Saud University – Science, https://doi.org/10.1016/j.jksus.2018.03.001 (2018). [17] K. E. Ukhurebor, S. O. Azi, I. C. Abiodun & S. E. Ojiemudia “The In- fluence of Weather Variables on Atmospheric Refractivity over Auchi, South-South, Nigeria”, Journal for Applied Science and Environmental Management, 22 (2018) 471. [18] K. E. Ukhurebor & I. Odesanya “Relationship between Meteorological Variables and Effective Earth Radius Factor over Auchi, Edo State, South- South, Nigeria”, Covenant Journal of Physical & Life Sciences, 7 (2019) 1. [19] M. Hashim & S. Stavrou “Measurements and Modelling of Wind Influ- ence on Radiowave Propagation through Vegetation” IEEE Transactions on Wireless Communications, 5 (2006) 1055. 114 Nwankwo and Ukhurebor / J. Nig. Soc. Phys. Sci. 1 (2019) 103–115 115 [20] S. Perras & L. Bouchard “Fading Characteristics of RF Signals due to Foliage in Frequency Bands from 2 to 60 GHz”, The 5th International and Symposium Wireless Personal Multimedia Communications, (2002) 1. [21] E. R. Pelet, J. E. Salt & G. Wells “Effect of Wind on Foliage Obstructed Line-of-Sight Channel at 2.5 GHz”, IEEE Trans. Broadcast, 50 (2004) 224. [22] J. C. R. Dal Bello, L. S. Glaucio & L. B. Henry “Theoretical Analysis and Measurement Results of Vegetation Effects on Path Loss for Mo- bile Cellular Communication Systems”, IEEE Transactions on Vehicular Technology 49 (2000). [23] I. Cuin̄as, A.V. Alejos, M.G. Sánchez & R.F.S. Caldeirinha “Wind Effect on the Scattering from Vegetation at Cellular Phone Frequencies”, Pro- ceedings of the IEEE International Geoscience & Remote Sensing Sym- posium, IGARSS Barcelona, Spain (2007). [24] M. Zennaro, H. Ntareme & A. Bagula “Experimental Evaluation of Tem- poral and Energy Characteristics of an Outdoor Sensor Network”, Pro- ceedings of the International Conference on Mobile Technology, Appli- cations, and Systems, 99 (2008). [25] J. Zhao & R. Govindan “Understanding Packet Delivery Performance in Dense Wireless Sensor”, Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, Los Angeles, California, USA (2003). [26] D. Puccinelli & M. Haenggi “Multipath Fading in Wireless Sensor Net- works: Measurements and Interpretation”, Proceedings of the Interna- tional Conference on Wireless Communications and Mobile Computing, Vancouver, British Columbia, Canada (2006). [27] K. E. Ukhurebor K. E, S. A. Olayinka, W. Nwankwo & C. Alhasan “Eval- uation of the Effects of some Weather Variables on UHF and VHF Re- ceivers within Benin City, South-South Region of Nigeria”, Journal of Physics: IOP Conference Series. 1299 (2019) 012052. [28] K. E. Ukhurebor & O. J. Umukoro “Influence of Meteorological Vari- ables on UHF Radio Signal: Recent Findings for EBS, Benin City, South- South, Nigeria”, IOP Conference Series: Earth & Environment Science, 173 (2018) 012017. [29] ECC Report 192 “The Current Status of Dynamic Frequency Selection (DFS) In the 5 GHz Frequency Range”, (2014). 115