In the next generation network, the physical things will enable to exchange the information among them. Internet of Things (IoT) is an emerging technology that provides facility to connect physical things with the digital world and able to exchange the information. Mobile ad-hoc networks (MANET) is consistently self-designing, framework less system of smart devices associated with each other remotely. Every smart device is enabled to change their locations using the mobility feature of MANET. These devices are also able to act as a bridge to exchange information between devices. MANET in the IoT becomes more attractive with its important approach in the communication among smart objects because MANET has a special feature that can create a network by own self or can connect with another huge network. In this research, the authors propose a solution that describes the convergence of MANET in the IoT. The results found in this paper have been tested and implemented using different seniors. 

This study investigates the relationship between coverage area and propagation profile. Most studies on propagation profile do not link the profile with the coverage area of the transmitter and vice versa. Using both coverage area and propagation profile to describe the variations of electric field intensity of propagating wave makes a vivid description of the transmitter performance. This study creates propagation profile from the contour of coverage area of Benue Radio transmitter. Data for the study were collected during a measurement campaign carried out around the transmitter. The data were interpolated to produce a contour of the coverage area. A cross sectional profile elicits from the contour of coverage area at equally spaced eight different vertical plains provides the propagation profiles of the transmitter. This was achieved by transforming the coordinates of the contour of coverage area into distance thus producing a two dimensional plain from a three dimensional contour. Values of coordinates and electric field intensity were taken from some points on the contour and were compared with the equivalent positions on the propagation profile yields the same value of electric field intensity and equivalent position in distance. Similar procedure was carried out on the propagation profile provides the same value of electric field intensity and equivalent positions in terms of coordinates. The study concludes that propagation profiles are cross sectional parts of coverage area.

Interference and Mobility management are the main parameters which affect the signal capacity and reduce the transmission efficiency for D2D communication in LTE-A networks. When the users are in the same spectrum, due to the coexistence of D2D pairs and cellular users interference management becomes a critical issue. In this paper, we introduce an interference management algorithm that maximizes the performance of D2D communication in LTE-A networks for both uplink and downlink transmissions. Optimal routing selection techniques reduce the total path distance which helps to minimize the interference and improve the overall network capacity. Mobility management is also a challenging issue in IP-mobile networks. When two DUEs change their locations from one BS to the other, handover delay calculation becomes very important to maintain the communication without interruption. In this paper we show power control techniques which helps to reduce the effect of interference and also we addressed a method for the lower delay under complex mobility issues with uninterrupted D2D communication.

Spectrum sensing is of paramount importance in the Cognitive Radio Network (CRN) due to massive spread of wireless services. However, spectrum sensing in CRN is affected by multipath effects that make detection difficult. Square- Law Combining (SLC) technique, which is one of the methods previously used to address this problem, is associated with hardware complexity that results in long processing time. Hence, this paper aim to modify SLC technique for primary user detection in the CRN. The modified model consists of three Secondary User (SU) antennas which receive the faded signals through the Rayleigh fading channel. The received signals are combined using Switch Combiner (SC) at Radio Frequency (RF) stage. The selected signal passes through only one Energy Detector (ED) before making decision. The modified model is incorporated into simulation model which consists of Primary User (PU) transmitter that processes the randomly generated data through some signal processing techniques for transmission to the SU receiver. Probability of False Alarm (PFA) expression is derived for the modified Square-Law Combiner (mSLC) to set the thresholds at 6.64 and 9.14 for PFA of 0.01 and 0.02, respectively. The modified model is evaluated using Probability of Missing (PM), Probability of Detection (PD) and Processing Time (PT) to determine the performance. The results of the mSLC show that at SNR of 4 dB and PFA of 0.01, the values obtained for PD, PM, PT are 0.6575, 0.3530, 5.5540 s, respectively, as against the conventional SLC of 0.4000, 0.600, 6.2055 s, respectively. At SNR of 4 dB and PFA of 0.02, the values obtained for the mSLC are 0.7600, 0.3457, 6.1945 s for PD, PM and PT, respectively, as against 0.4000, 0.6000, 7.2197 s for conventional SLC. The results show that mSLC gives lower PM, higher PD and lower PT values when compared with conventional SLC.