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The present application scenarios of the Internet of Things (IoT) often require the equipment to be adaptable, the resource allocation to be efficient, and the signal monitoring and transmission to be effective. However, the existing algorithms cannot solve the problem of system capacity reduction caused by the mutual interference between regions in data rates. Aiming at effectively improving the performance of the IoT monitoring system and ensuring the fairness of each monitoring terminal, this paper attempts to explore interference suppression and resource allocation strategies based on IoT monitoring. First, the paper established an IoT monitoring network model, and elaborated on interference suppression strategies for inter-layer interferences of “Macro Base Station (BS) – Micro Cells” and “Micro BS – Macro Cells” and for intra-layer interference that include the interference between local monitoring networks and interference between terminals in local area networks; then, the paper proposed a sub-carrier resource allocation scheme for IoT monitoring system with multiple inputs and outputs and a water-filling strategy of system channel power; at last, experimental results verified the effectiveness of the proposed interference suppression and resource allocation algorithm.

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In this paper, an offloading algorithm based on Markov Decision Process (MDP) is proposed to solve the multi-objective offloading decision problem in Mobile Edge Computing (MEC) system. The feature of the algorithm is that MDP is used to make offloading decision. The number of tasks in the task queue, the number of accessible edge clouds and Signal-Noise-Ratio (SNR) of the wireless channel are taken into account in the state space of the MDP model. The offloading delay and energy consumption are considered to define the value function of the MDP model, i.e. the objective function. To maximize the value function, Value Iteration Algorithm is used to obtain the optimal offloading policy. According to the policy, tasks of mobile terminals (MTs) are offloaded to the edge cloud or central cloud, or executed locally. The simulation results show that the proposed algorithm can effectively reduce the offloading delay and energy consumption.