This paper presents the design and implementation of a quadcopter capable of payload delivery. A quadcopter is a unique unmanned aerial vehicle which has the capability of vertical takeoff and landing. In this design, the quadcopter was controlled wirelessly from a ground control station using radio frequency. It was modeled mathematically considering its attitude and altitude, and a simulation carried out in MATLAB by designing a proportional Integral Derivative (PID) controller was applied to a mathematical model. The PID controller parameters were then applied to the real system. Finally, the output of the simulation and the prototype were compared both in the presence and absence of disturbances. The results showed that the quadcopter was stable and able to compensate for the external disturbances.

I. INTRODUCTIONRecently, the use of mobile wireless system became the most popular technology as a result of lots of services available in mobile cellular phone [1]. Some of these include voice call, video call, conference call, data based services and other multimedia applications. Despite the increase in number of base stations to augment high rate of subscribers, the quality of service delivery by the network providers still remains in poor state [2]. It is observed that the poor quality of services experienced could be due to several challenges experienced in the network channel [3]. On that note, it becomes paramount to identity these problems and propose solutions through research analysis.The communication channel experience Rayleigh fading as a result of obstruction to Line of Sight (LOS), resulted in reflection, absorption and scattering of transmitted signal [4], [5]. The knowledge of the propagation characteristic of a mobile radio channel is essential for designing any wireless communication system in a given region [6]. One of the most important problems of propagation environment is path loss [5]. Propagation path loss has great influence on the quality of service of a mobile communication system, the transmitted signal power get reduced with increased distance from base station to mobile station. Therefore, accurate estimation of propagation path loss is important factor for the good design and planning of mobile system [7]. The quality and coverage reliability of a wireless network design depends on the accuracy of the path loss model [4].There are many propagation path loss models proposed in previous works to predict coverage. These model are not sufficient for accurate analysis of the path loss on other terrain, the suitability of such models differs with different environment [8]. The objective of optimization is to make sure the network operates close to the original design in terms of handoff points, coverage, received signal strength and Quality of Service (QoS) [2], [3]. In order to overcome the problem of path loss, the parameters of certain models must be adjusted with reference to the targeted locations. This will enable the system achieve minimum error between Transmitted Signal Strength (TSS) Optimization is the process of obtaining the best solution to a system function efficiently as a result of the modification of existing models. The optimizations of these existing models have been carried out by many researchers in other regions, but some of them are based on Least Square (LS) method. Other optimization methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Bee Colony Algorithm (BCA), have not been commonly used relative to the reviewed literature. The particle swarm optimization uses the ideas of bird flocking and its survival of fitness by randomly chosen positions and velocities [2]. In this paper, we investigated the performance of some propagation models while appropriate modified model was developed for the terrain. PSO technique was further deploy...

Wireless Sensor Networks (WSNs) is an attractive solution for myriads of communication applications. The deployment of WSNs in restricted areas where battery replacement or recharging is difficult, demands that energy be sparingly utilized. In this study, a model of energy-efficient Wireless Sensor Networks (WSNs) for monitoring Aircraft Runway was developed to prolong the lifespan of the deployed network using a non-hierarchy and hierarchy based cluster configurations for two sampled Runways (R/W). A routing protocol which involved hierarchical data transfer along any shortest path to the Base Station (BS) was developed. The approach promotes rotation of Cluster Head (CH) during every transmission round based on residual energy of nodes and proximity to Base Station. Simulations were carried out for 5000 transmission rounds in MATLAB environment for the non-hierarchical and other levels of hierarchy; six clusters and eighteen clusters. Results of simulation revealed the lifespan of the networks as: 3375 and 3750 rounds for the non-hierarchical configurations, 3912 and 4660 rounds for six clusters hierarchical configurations, while in the eighteen clusters hierarchical configurations the network lasted for 4434 and above the estimated 5000 rounds. Thus, the best solution in terms of network lifespan of the proposed model was obtainable in the highest possible cluster formed.

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The introduction of 4G LTE communication technology was basically designed to meet the increasing demand by users for high-quality multimedia services, data communication speed and improved quality of service (QOS). It is pertinent to note that, with an ever-increasing subscriber base, it is essential to assess and analyze the network performance. To perform this task, there is a need to use the key performance indicators (KPI). This research study evaluates KPI's gathered from field measurements, using a statistical approach to establish the performance and determine the present condition of the quality of service offered by a 4G LTE network in Port Harcourt, Nigeria. In this study, a drive test approach was adopted to measure the KPI's and analysis was achieved with the use of TEMs Discovery software adopting a statistical approach. The result showed the value range of the measured KPI's were; RSSI (-90, -49.7dBm), RSRP (-117.7, -68.6 dBm), RSRQ (-14.2, -22.8dB) representing minimum and maximum values. The probability distribution of the various KPI's showed that the best signal ranges were distributed as 38.21%, 69.63% and 65.63% for RSSI, RSRP and RSRQ respectively. The KPI parameters were within the acceptable range, though require optimization to provide better service for a greater population.