The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms and stables for horse training. The study proposes a new classification approach of IoT in agriculture based on several factors and introduces performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The study utilizes COOJA, a realistic WSN simulator, to model and simulate the performance of the 6LowPAN and Routing protocol for low-power and lossy networks (RPL) in the two farming scenarios. The simulation settings for both fixed and mobile nodes are shared, with the main difference being node mobility. The study characterizes different aspects of the performance requirements in the two farming scenarios by comparing the average power consumption, radio duty cycle, and sensor network graph connectivity degrees. A new approach is proposed to model and simulate moving animals within the COOJA simulator, adopting the random waypoint model (RWP) to represent horse movements. The results show the advantages of using the RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. The proposed framework is experimentally validated and tested through simulation, demonstrating the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. The results have several practical implications for precision agriculture by providing an efficient monitoring and management solution for agricultural and livestock farms. Overall, this study provides a comprehensive evaluation of the performance scalability of WSNs in the agriculture sector, offering a new classification approach and performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The results demonstrate the suitability of the proposed framework for precision agriculture, providing efficient communication performance and low latency.

This paper presents a technical method to monitor the water distribution pipelines against leakage and to control the pump when the water level decreases in the tank. Water leakage is the most popular cause of water wasted in the domestic water distribution systems. Nowadays most people have their smartphone nearby them; therefore, adding an interface on the smartphone to control an automated system is a big plus. Energy saving is a benefit of the Optimization Water Leakage Detection (OWLD) system. It enables us to save energy, time and cost by having smart leakage detection in pipelines, measuring the water level in the tank and controlling the pump when the water level is low. This paper focuses mainly on two parts: The first part is an alarm based on Global System for Mobile technology (GSM) to send a Short Message Service (SMS) to the owner. This is made up of the following components: sensors, GSM Module, Arduino and relays to control the device. The second is the controlling part; it uses android application mobile to control the pump. The proposed system can effectively improve the efficiency of operation, reduce delay time and cost of maintenance pipelines after leakage detection.

No abstract

No abstract

Abstract. Although speed bumps are used to force drivers reduce car speed for avoiding accidents, these bumps may cause car crash or accident when drivers do not notice them. Studies have proposed different methods to detect bumps and alert drivers. However, these methods have limitations and require modifications to enable accurate detection. Also these methods did not propose speed reduction approaches. Therefore, in this research, we propose a method that utilizes smartphone microelectronic mechanical technology for speed bump detection. The system uses the gravity sensor to detect the vertical vibration of cars passes over bumps and the GPS to determine the position of the bump. To give accurate detection results, data are collected from crowd, stored and processed on the cloud. The system also contains a speed reduction unit which is attached to the brake pedal and reduces the speed if a bump is detected. A small scale experiment showed that the system detected the position and the height of bumps with a very small error. The system also reduced the speed of cars at the moment they hit the bumps to a point that does not cause any harm to cars or passengers.