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Many wireless sensor network applications require sensor nodes to be deployed on the ground or other surfaces. However, there has been little effort to characterize the large-and small-scale path loss for surface-level radio communications. We present a comprehensive measurement of path loss and fading characteriztics for surface-level sensor nodes in the 400 MHz band in both flat and irregular outdoor terrain in an effort to improve the understanding of surface-level sensor network communications performance and to increase the accuracy of sensor network modeling and simulation. Based on our measurement results, we characterize the spatial small-scale area fading effects as a Rician distribution with a distance-dependent K-factor. We also propose a new semi-empirical path loss model for outdoor surface-level wireless sensor networks called the Surface-Level Irregular Terrain (SLIT) model. We verify our model by comparing measurement results with predicted values obtained from high-resolution digital elevation model (DEM) data and computer simulation for the 400 MHz and 2.4 GHz band. Finally, we discuss the impact of the SLIT model and demonstrate through simulation the effects when SLIT is used as the path loss model for existing sensor network protocols. General Terms: Performance, DesignAdditional Key Words and Phrases: Modeling of systems and physical environments, foundations of sensor networks, simulation tools and environments, surfacelevel communications ACM Reference Format: Chong, P. K. and Kim, D. 2013. Surface-level path loss modeling for sensor networks in flat and irregular terrain.

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In this paper, we present a real application system based on wireless sensor network (WSN) for fence surveillance which is implemented on our development platform for WSN, called ANTS (An evolvable Network of Tiny Sensors). Our system, called the WFS system, is expanded to connect and control a robot (UGV/UAV) and a camera sensor network for the purpose of fence surveillance. Two kinds of sensor nodes, ground nodes and fence nodes, are deployed and collaborative detection is performed and the result is reported to the base station (BS). The BS does not only give a control message to the camera to show the place where an event has occurred, but it also issue orders to the robots to extend the communication distance of the system, to approach and sense the object more precisely, or even to attack an enemy autonomously. This paper describes various techniques and know-how to fulfill a WSN-based integrated surveillance system. A new adaptive threshold algorithm to detect intruders is proposed and some sensing results in the real field of our system are shown. In conclusion, we show the high accuracy of the WFS system.

School zones are areas near schools that have lower speed limits and where illegally parked vehicles pose a threat to school children by obstructing them from the view of drivers. However, these laws are regularly flouted. Thus, we propose a novel wireless sensor network application called School zone Safety System (S3) to help regulate the speed limit and to prevent illegal parking in school zones. S3 detects illegally parked vehicles, and warns the driver and records the license plate number. To reduce the traveling speed of vehicles in a school zone, S3 measures the speed of vehicles and displays the speed to the driver via an LED display, and also captures the image of the speeding vehicle with a speed camera. We developed a state machine based vehicle detection algorithm for S3. From extensive experiments in our testbeds and data from a real school zone, it is shown that the system can detect all kinds of vehicles, and has an accuracy of over 95% for speed measurement. We modeled the battery life time of a sensor node and validated the model with a downscaled measurement; we estimate the battery life time to be over 2 years. We have deployed S3 in 15 school zones in 2007, and we have demonstrated the robustness of S3 by operating them for over 1 year.

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Current network simulators abstract out wireless propagation models due to the high computation requirements for realistic modeling. As such, there is still a large gap between the results obtained from simulators and real world scenario. In this paper, we present a framework for improved path loss simulation built on top of an existing network simulation software, NS-3. Different from the conventional disk model, the proposed simulation also considers the diffraction loss computed using Epstein and Peterson’s model through the use of actual terrain elevation data to give an accurate estimate of path loss between a transmitter and a receiver. The drawback of high computation requirements is relaxed by offloading the computationally intensive components onto an inexpensive off-the-shelf parallel coprocessor, which is a NVIDIA GPU. Experiments are performed using actual terrain elevation data provided from United States Geological Survey. As compared to the conventional CPU architecture, the experimental result shows that a speedup of 20x to 42x is achieved by exploiting the parallel processing of GPU to compute the path loss between two nodes using terrain elevation data. The result shows that the path losses between two nodes are greatly affected by the terrain profile between these two nodes. Besides this, the result also suggests that the common strategy to place the transmitter in the highest position may not always work.

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