Current Issue : April-June Volume : 2024 Issue Number : 2 Articles : 5 Articles
Smart street LED lighting systems have received much attention driven by the need to save energy and the dramatic advances in the Internet of Things. This work proposes a new smart street lighting system that adaptively changes the street lights’ intensity based on traffic and weather conditions and provides a platform for monitoring road conditions and detecting lamp faults. The system transfers the data using the UDP protocol over NBIoT radio technology. It also maintains two-way communication between the luminaires and the central node. In order to ensure real-time response to traffic and avoid dimming delays, each light is locally controlled by a microcontroller based on the sensed traffic and weather data. The measurements of each luminaire are also sent to the central control node to locate lamp faults, detect emergency situations, and, if needed, broadcast on/off messages to the whole network’s luminaires. The system was implemented in a suburban street in Ras Al Khaimah. Evaluations proved that the system can locate and detect faulty lamps and vary the light intensity in real time based on traffic. It also resulted in energy savings of up to 55% compared to a normal LED street light network....
With emerging technologies like cloud computing and big data, managing traditional networks has become more demanding. Software-defined networking (SDN) promises faster implementation, flexibility, and simplified network management. However, due to SDN’s centralized nature, it encounters limitations. SDN controllers should have enough processing power to deal with a high amount of flow. In addition, a single point of failure may affect the network’s resiliency. For these issues, multi-instance implementation enables distributed control. However, this solution implies an intrinsic controller-to-controller synchronization channel. In this article, we propose different failure scenarios in both the data and control planes to provide network administrators with a clear view of the constraints of network reliability, load balancing, and scalability in SDN environments. The simulation results show that, regarding resiliency, SDN networks require half the time compared to traditional networks in order to recover from a link failure. Regarding load-balancing capabilities, load balancing is not guaranteed with the reactive forwarding approach (on-demand flow installation). Lastly, the SDN multi-instance solution impacts the network performance by between 1% and 21% compared to the single-instance case....
This article considers a wireless-powered communication network (WPCN) composed of a multiantenna hybrid access point (HAP) based on nonlinear energy harvesting (EH). To improve some distant WDs’ throughput performance, one of them is allowed to be selected as a cluster head (CH) to help transfer information from other cluster members (CMs). Nevertheless, the proposed clustering collaboration’s performance is essentially restricted by the CH’s energy-intensive consumption (EC), which requires to transfer every WDs’ information, covering its own. In order to figure out the question, the HAP’s energy beamforming (EB) capability with multiple antennas is utilized that can concentrate greater transmission power into the CH to equilibrate its EC to assist other WDs. To be specific, each WD’s throughput performance is firstly derived under the proposed approach. A high-efficiency optimization algorithm for addressing cooperative optimization problem is put forward. In addition, the simulations are carried out in the actual network environment, and the results demonstrate that our proposed clustering collaboration with multiple antennas can validly enhance the WPCN’s throughput fairness based on nonlinear EH....
Wireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis model was proposed. Firstly, a comprehensive analysis was conducted into the operative principles of wireless sensor systems, whereby fault-related features were then extrapolated. A Bayesian diagnostic model was constructed using the maximum likelihood method with sufficient sample features, and a joint tree model was introduced for node diagnosis. Due to the insufficient accuracy of Bayesian models in processing small sample data, a constrained maximum entropy method was proposed as the prediction module of the model. The use of small sample data to obtain the initial model parameters leads to improved performance and accuracy of the model. During parameter learning tests, the limited maximum entropy model outperformed the other two learning models on a smaller dataset of 35 with a distance value of 2.65. In node fault diagnosis, the diagnostic time of the three models was compared, and the average diagnostic time of the proposed diagnostic model was 41.2 seconds. In the node diagnosis accuracy test, the proposed model has the highest node fault diagnosis accuracy, with an average diagnosis accuracy of 0.946, which is superior to the other two models. In summary, the node fault diagnosis model based on Bayesian model proposed in this study has important research significance and practical application value in wireless sensor networks. By improving the reliability and maintenance efficiency of the network, this model provides strong support for the development and application of wireless sensor networks....
With the rapid development of the Internet of Things (IoT), improving the lifetime of nodes and networks has become increasingly important. Most existing medium access control protocols are based on scheduling the standby and active periods of nodes and do not consider the alarm state. This paper proposes a Q-learning and efficient low-quantity charge (QL-ELQC) method for the smoke alarm unit of a power system to reduce the average current and to improve the lifetime of the wireless sensor network (WSN) nodes. Quantity charge models were set up, and the QL-ELQC method is based on the duty cycle of the standby and active times for the nodes and considers the relationship between the sensor data condition and the RF module that can be activated and deactivated only at a certain time. The QL-ELQC method effectively overcomes the continuous state–action space limitation of Q-learning using the state classification method. The simulation results reveal that the proposed scheme significantly improves the latency and energy efficiency compared with the existing QL-Load scheme. Moreover, the experimental results are consistent with the theoretical results. The proposed QL-ELQC approach can be applied in various scenarios where batteries cannot be replaced or recharged under harsh environmental conditions....
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