Electronic rain meter for mobile sensor node using law of Torricelli

Rain in a city can cause material damage and risk for the population, hence the importance of implementing prevention and mitigation measures. These measures must be taken based on the analysis of the data collected by networks of environmental sensors. The rainfall-meter is one of the instruments used to measure rain, these are designed to operate at a fixed point. Coverage of the entire area of a city requires the installation of several of these elements. This paper shows the development of an electronic rain gauge that can operate in motion applying the principles of fluid dynamics. Two stages are proposed for its elaboration. The first step is the design, construction and testing of the sensor and transducer for the rain gauge. In the second step, the rain gauge communication is implemented. For this, the internet of things technology is incorporated, and the network is designed to provide mobility. The main result is a prototype mobile electronic rain gauge with a measurement error of 8.5%. Besides, mathematical model for the sensor, algorithm for the transducer, and communications architecture are obtained. It can be concluded that, rainfall can be monitoring in a city with few sensitive units in motion.

Indoor Location Tracking Using Time of Arrival Estimation Method

Background & Objective: Location-based services enable collection of location-oriented information which finds use in various fields. Methods: With its utility found in so many applications, various localization techniques are adopted to improve these services. One such property of a signal which is used for these estimations is known as ‘Time of Arrival’ property. The ‘Time of Arrival’ property of a signal is the time difference for a signal to go from the transmitter to the receiver. The most common application is to navigate through places, finding or tracking your personal belongings, targeted advertisements by knowing the nearby popular places and various other services like augmented reality gaming among others. Results & Conclusion: Through this paper, we demonstrate a method to track the location of a mobile sensor node using Trilateration algorithm with the help of Time of Arrival (ToA) property of signals. The time of arrival of packets at each node is recorded and data collected from the simulation of a wireless sensor network for this experiment is spread across various distributions to find the optimum statistical inference.

H-Best Particle Swarm Optimization Based Localization Algorithm for Wireless Sensor Network

Discussion of the work, which proposed the idea of virtual anchor nodes for the localization of the sensor nodes, with having the movement of single sensor node in the circular movement with being optimized by the HPSO. For the ranging the RSSI model has been proposed in the algorithm. As a reference node, single anchor node has been used for the localization of whole network. As of the random deployment of the sensor nodes (target nodes), when the target nodes fall under the range of the mobile anchor node, the Euclidean distance between the target node and mobile anchor node is being calculated. After the calculation of the Euclidean distance the two anchor nodes are being deployed with a difference of 600 angle. Using the directional information the projecting of virtual anchor nodes is done, to which the virtual anchor nodes helps in the calculation of the 2D coordinates. While the calculation the mobile sensor node follow ups the circular path. The mobile sensor node considering at a center of the area marks up distance of its maximum range, and with that distance as a radius its goes for other circular path movement if all sensor nodes don’t fell to its range. With its movement at constant velocity the algorithm runs again and again. The performance of the algorithm are done on the factors of the average localization error and convergence time. The problem as of the LoS, with the virtual anchor nodes have been minimized.

Sensing Coverage Algorithm of Sparse Mobile Sensor Node with Trade-Off between Packet Loss Rate and Transmission Delay

To solve the problem of sensing coverage of sparse wireless sensor networks, the movement of sensor nodes is considered and a sensing coverage algorithm of sparse mobile sensor node with trade-off between packet loss rate and transmission delay (SCA_SM) is proposed. Firstly, SCA_SM divides the monitoring area into several grids of same size and establishes a path planning model of multisensor nodes’ movement. Secondly, the social foraging behavior of Escherichia coli in bacterial foraging is used. A fitness function formula of sensor nodes’ moving paths is proposed. The optimal moving paths of all mobile sensor nodes which can cover the entire monitoring area are obtained through the operations of chemotaxis, replication, and migration. The simulation results show that SCA_SM can fully cover the monitoring area and reduce the packet loss rate and data transmission delay in the process of data transmission. Under certain conditions, SCA_SM is better than RAND_D, HILBERT, and TCM.