With the introduction of the Internet of Things (IoT) and its various applications, the need for more advanced wireless sensor networks is felt more than ever. Various applications of the Internet of Things introduce different needs to the field of wireless sensor networks. As one of these needs the elimination of energy constraints, frequency constraints, the reduction of interference generated by different networks in an environment for each other and the real-time transmission of data transmitted on the network, can be mentioned. The energy harvesting and cognitive radio technologies, as two suitable solutions for eliminating energy constraints and lack of frequency resources, have attracted many researchers. As a solution to the interference problem created by the various networks in the environment, multi-hop networks can be proposed, which is studied in this thesis, too. Also, backscatter communication technology can be used as a way to reduce the data transmission time, due to its passive nature which causes very low energy consumption. In this thesis, the HBCP algorithm is presented as a new algorithm for multi-hop networks. The proposed algorithm being able to overcome the above limitations as well as it improves network performance compared to similar algorithms. The HBCP algorithm has two proposed phases: SiHaB and HyBaC. In the SiHaB phase, each secondary user transmits some part of its information using the Ambient Backscatter Communication method to the next user and harvests energy, simultaneously. This feature causes delay reduction in sending information in sensitive-time IoT applications. In the HyBaC phase, each secondary user can decide which communication method to use for sending information, the conventional method or the Ambient Backscatter method, depending on the circumstances. To traarent how the secondary users function, a new structure is proposed that is consistent with the proposed HBCP algorithm. The structure uses two antennas, resulting in the simultaneous energy harvesting and data transmission in the SiHaB phase, and simultaneously energy harvesting and data reception in both the SiHaB and HyBaC phases. In order to obtain optimal resource allocation for the proposed HBCP algorithm, an optimization problem has been formulated and solved. From solving such an optimization problem, optimal times and powers will be obtained in the close form. Numerical implementation of the proposed HBCP algorithm in the MATLAB software presents the better performances of the proposed algorithm compered with similar algorithms, i.e., JOTPA and AB algorithms. Key Words: 1-Internet of Things 2-Wireless Sensor Networks 3-Cognitive Radio 4-Energy Har- vesting 5-Ambient Backscatter