Wireless underground sensor networks (WUSNs) have recently been investigated for a wide range of applications. One challenge in wireless underground communications is its high bit error rate, especially at long distances. In WUSNs, the communication quality is substantially affected by the environment, especially the soil moisture. Thus, the underground channel quality can be effectively estimated based on local soil moisture readings. By utilizing the local soil moisture values to estimate the channel quality, adaptive error control mechanisms can be implemented for underground nodes. In this paper, two error control mechanisms, adaptive-rate forward error control and adaptive transmit power control, are considered for WUSNs. The results indicate that compared to ARQ, adaptive FEC code can increase the ranges of soil moisture values within which the network is reliable by reducing the bit error rate. In addition, adaptive transmit power control can improve energy efficiency when a wide range of transmit power levels is available. Our evaluations show that to achieve 60m communication distance in practical soil settings, the output power of the transmitter should to be adjusted in a range of 0dBm to 25dBm to improve the energy efficiency of the underground nodes.

Wireless underground sensor networks (WUSNs) consist of sensors that are buried in and communicate through soil. The channel quality of WUSNs is strongly impacted by environmental parameters such soil moisture. Thus, the communication range of the nodes and the network connectivity vary over time. To address the challenges in underground communication, above ground nodes are deployed to maintain connectivity. In this paper, the connectivity of WUSNs under varying environmental conditions is captured by modeling the cluster size distribution under sub-critical conditions and through a novel aboveground communication coverage model for underground clusters. The resulting connectivity model is utilized to analyze two communication schemes: transmit power control and environment-aware routing, which maintain connectivity while reducing energy consumption. It is shown that transmit power control can maintain network connectivity under all soil moisture values at the cost of energy consumption. Utilizing relays based on soil moisture levels can decrease this energy consumption. A composite of both approaches is also considered to analyze the tradeoff between connectivity and energy consumption.

In this demo, a novel underground communication system and an online underground sensor network testbed is demonstrated. The underground communication system, developed in the Cyber-physical Networking (CPN) Laboratory at the University of Nebraska-Lincoln, includes an underground antenna that is tailored to mitigate the adverse effects of soil on underground communication. An online connection is established with the CPN underground sensor network testbed that is located at Clay Center, Nebraska. The underground sensor network testbed consists of a network of underground communication systems equipped with soil moisture sensors and a mobile data harvesting unit equipped with cellular communication capabilities. Real-time soil moisture data delivery from Nebraska to Korea is demonstrated.

Wireless Underground Sensor Networks (WUSNs) are an emerging type of wireless sensor networks (WSNs), where sensor nodes are located under the ground and communicate through soil. The major challenge in the development of efficient communication protocols for WUSNs is the characterization of the underground channel. So far, none of the existing models fully capture all the components of electromagnetic signal propagation in the soil medium. In this paper, three major components that influence underground communication are identified: direct, reflected, and lateral waves, where the latter has not been analyzed for WUSNs so far. Accordingly, a closed- form three-wave (3W) channel model is developed based on EM propagation principles of signals through soil. The 3W channel model is shown to agree well with both underground testbed experiments and EM analysis based on Maxwell's equations, which cannot be represented in closed-form.

Wireless embedded smart cameras provide flexibility in camera deployment in terms of the locations and number of the cameras. However, these battery-powered embedded vision sensors have very limited energy, memory, and processing power. Energy consumption and latency are two major concerns in wireless embedded camera networks. In multi-camera tracking applications, the amount of data exchanged between cameras has an effect on the tracking accuracy, the energy consumption of the camera nodes and the latency. In this paper, we provide a detailed quantitative analysis of the accuracy-latency-energy tradeoff for overlapping and non-overlapping camera setups when different-sized data packets are transferred in a wireless manner. The experiments have been performed with an actual wireless embedded smart camera network employing CITRIC motes, and performing tracking of objects.

In Wireless multimedia sensor networks (WMSNs), two graphs, communication network graph and vision graph, can be established. The camera nodes connected in the vision graph share overlapped field of views (FOVs) and they depend on the densely deployed relay nodes in the communication network graph to communicate with each other. Given a uniformly deployed camera sensor network with relay nodes, the problem is to find the number of hops for the vision-graph- neighbor-searching messages to construct the vision graph in an energy efficient way. In this paper, mathematical models are developed to analyze the FOV overlap of the camera nodes and the multi-hop communications in two dimensional topologies, which are utilized to analyze the optimal hop number. In addition, simulations are conducted to verify our models.

Energy and bandwidth are limited resources in wireless sensor networks, and communication consumes significant amount of energy. When wireless vision sensors are used to capture and transfer image and video data, the problems of limited energy and bandwidth become even more pronounced. Thus, message traffic should be decreased to reduce the communication cost. In many applications, the interest is to detect composite and semantically higher-level events based on information from multiple sensors. Rather than sending all the information to the sinks and performing composite event detection at the sinks or control-center, it is much more efficient to push the detection of semantically high-level events within the network, and perform composite event detection in a peer-to-peer and energy-efficient manner across embedded smart cameras. In this paper, three different operation scenarios are analyzed for a wireless vision sensor network. A detailed quantitative comparison of these operation scenarios are presented in terms of energy consumption and latency. This quantitative analysis provides the motivation for, and emphasizes (1) the importance of performing high-level local processing and decision making at the embedded sensor level and (2) need for peer-to-peer communication solutions for wireless multimedia sensor networks.