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In this paper, we study the air quality monitoring and improvement system based on wireless sensor and actuator network using LoRa communication. The proposed system is divided into two parts, indoor cluster and outdoor cluster, managed by a Dragino LoRa gateway. Each indoor sensor node can receive information about the temperature, humidity, air quality, dust concentration in the air and transmit them to the gateway. The outdoor sensor nodes have the same functionality, add the ability to use solar power, and are waterproof. The full-duplex relay LoRa modules which are embedded FreeRTOS are arranged to forward information from the nodes they manage to the gateway via uplink LoRa. The gateway collects and processes all of the system information and makes decisions to control the actuator to improve the air quality through the downlink LoRa. We build data management and analysis online software based on The Things Network and TagoIO platform. The system can operate with a coverage of 8.5 km, where optimal distances are established between sensor nodes and relay nodes and between relay nodes and gateways at 4.5 km and 4 km, respectively. Experimental results observed that the packet loss rate in real-time is less than 0.1% prove the effectiveness of the proposed system.

Recent years have witnessed the process of computing gradually move to the edge network, where it is close to the physical data source, to serve applications that require large computations with very little latency. However, the terminal wireless devices’ limited computing and energy resources pose obstacles to the practical implementation of these applications. Mobile Edge Computing (MEC) based non-orthogonal multiple access (NOMA) technology is solving this problem well thanks to its ability to serve many users with high data rates and spectrum utilization efficiency. This study investigates the performance and optimization of MEC surveillance systems using NOMA. Specifically, two camera units (CUs) perform the monitoring task to be accomplished by the MEC access point (AP) through Rayleigh fading wireless links. We then proposed the four-phase protocol for this system. Accordingly, we derive the closed-form exact expressions of the successful computation probability (SCP), and study the impact of the network parameters on the system performance. Furthermore, we propose and compare three meta-heuristic-based algorithms, namely MSCP-GA, MSCP-PSO, and MSCP-HGAPSO, to find the optimal parameters set to help the proposed system achieve the maximum SCP. The results show that the proposed algorithms can significantly improve the system's performance by 40% higher than when the optimal algorithm is not used. Insights on the pros and cons of different algorithms are also discussed in this study. Finally, we use the Monte-Carlo simulation to verify the correctness of this study.

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