Air compressors are widely used in industrial fields. Compressed air systems aggregate air flows and then supply them to places of demand. These huge systems consume a significant amount of energy and generate heat internally. Machine components in compressed air systems are vulnerable to heat, and, in particular, a radiator to cool the heat of the overall air compressor is the core component. Dirty radiators increase energy consumption due to anomalous cooling. To reduce the energy consumption of air compressors, this mechanism emphasizes a machine learning-based radiator fault detection, using features such as RPM, motor power, outlet pressure, air flow, water pump power, and outlet temperature with slight true fault labels. Moreover, the proposed system adds an LSTM-based motor power prediction model to point out the initial judgment of radiator fault possibility. Via the rigorous analysis and the comparison among machine learning models, this meticulous approach improves the performance of radiator fault prediction up to 93.0%, and decreases the mean power consumption of the air compressor around 2.24%.

In a tactical mobile ad-hoc network (MANET), unmanned vehicles are deployed for surveillance and reconnaissance. They send multimedia data to a center node in real time. In this letter, we propose a centralized TDMA slot and power scheduling schemes which maximize energy efficiency (EE) considering Quality-of-Service (QoS) for the tactical MANET. We formulate this problem as a non-concave ratio optimization, and propose the optimal slot allocation and power control algorithms based on the Dinkelbach method and the concave-convex procedure. The performance of the proposed algorithm is verified by numerical results, which show a fairly good energy efficiency.

With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid.

On a future tactical-battle field network, combat radio nodes will be deployed for various operations, forming a mobile ad-hoc network (MANET). However, because of the nodes’ mobility, a single group might be divided into several small groups with fewer nodes. Conversely, several small groups might be merged into one group. In such an environment, an unmanned aerial vehicle (UAV) will provide an effective way to improve network coverage and connectivity among the small groups. However, some issues should be considered for the optimal deployment of the UAV. One issue is to find the proper position of the UAV, which enhances the connectivity among the groups, because a tactical network places a high priority on network survivability rather than throughput maximization. We also need to exploit real topographic information to obtain more accurate connectivity information among nodes. Second, an efficient resource allocation scheme for reliable communications through the UAV should be taken into account. Since most of the links between the UAV and the ground nodes are line-of-sight (LoS), due to the good quality of these links, the traffic via the UAV will be heavy in spite of the limited data slot resources. Moreover, the traffic flows of the network require diverse quality-of-service (QoS), and different priorities should be imposed upon the nodes depending on the involved tactical operations. In this paper, we formulate a UAV positioning problem maximizing group connectivity (GC) and a data slot-allocation problem maximizing GC utility. Subsequently, we propose an optimal positioning and slot allocation algorithm, which satisfies QoS requirement and supports the predetermined priorities of the nodes and the groups. Numerical analysis verifies that the proposed scheme is effective in UAV positioning and slot allocation, outperforming conventional methods.

IEEE 802.11e MAC (medium access control) is one of the IEEE 802.11 WLAN (wireless local area network) standards define for QoS (quality of service) support on the wireless communications. The 802.11e MAC is based on both center controlled channel access and contention-based channel access mechanisms. EDCA (enhanced distributed coordination function) is contention-based channel access mechanism which provides differentiated channel access method for frames that have different priorities. In this paper, we analyze and compare both theoretical throughput value of EDCA MAC and throughput value of FPGA (field-programmable gate array) implemented EDCA MAC platform at MAC layer. The QAP (QoS access point) is provided with EDCA function and the station is provided with DCF (distributed coordination function). The results on throughput evaluation of the FPGA implemented EDCA MAC Platform shows 92. 6 ~ 94.3% as much as theoretical throughputs of the EDCA MAC Simulation. The parameters evaluated for the throughput measurement are 802.11a PHY, offered load, access category, frame size and number of stations. The results show various throughput values in accordance to the measurement parameter changes.