LWSN Research
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Welcome to the Laboratory for Wireless Systems and Networks (LWSN) at Western Michigan University! |
Green Electronics: Energy Harvesting for Sustainable Wireless Sensor Networks
Going green means different things to different people, but it generally means reducing pollution; conserving resources and ecosystems; being energy efficient; and reducing climate change. As wireless sensor networks are to be widely deployed in the future, we look into sustainable engineering solutions that incorporate environmental and social constraints as well as economic considerations into engineering decisions. Micro-scale energy harvesting, along with ultra-low-power technology, offers virtually perpetual wireless sensor networks and little or no adverse environmental effects. Micro-energy harvesting systems can scavenge milliwatts from solar, radio, vibrational, thermal and biological sources. The current research starts with wireless networks with energy harvested from radio emission. We have developed analytical models of UHF voltage multipliers under ultra-low-power operating conditions. The models are verified through HSPICE simulations, and are used for design and analysis of radio-triggered wake up circuits (Figure). With an incident 2.4-GHz signal of less than -30 dBm power, the circuits can produce a voltage above 1 V within tens of microsecond. This dc output switches an inverter of 200 M Ohm load and wakes up the rest of the circuits on a wireless node.
Energy-Efficient Wireless Information Network
Network information theory involves efficient and reliable communication in multi-terminal settings. This field has recently attracted renewed attention due to fast-growing applications such as the Internet, wireless cellular and local-area-network data services, ad hoc networks and sensor networks. This research focuses on the balance of energy consumption and communication performance of wireless information network. In distributed ad hoc networks, untethered nodes depend on small batteries for continuous sensing, processing, and communication. Traditionally, most power is consumed by radio transmission. However, for short-range and low-data-rate multihop ad hoc and sensor networks, the energy consumed by circuit signal processing is comparable to that consumed by radio transmission. There is an interesting question that needs to be addressed: How to distribute the individually constrained node energy between radio transmission and transceiver signal processing, so that the maximum network throughput can be achieved? The circuit energy consumption needs to be incorporated in network design to minimize the total energy consumption. The research considers a tradeoff between energy used in a network and the performance of a network taking into account a plethora of factors that affect performance from the physical layer up to the network layer.
Cooperative Localization and Tracking in Ad Hoc Networks
As wireless networks evolve over time, there is an increasing interest in combining location awareness with communications. The future of wireless networks will not follow the traditional base-station to mobile model, but will be peer-to-peer. Without fixed infrastructure or centralized administration, the knowledge of node positions plays an important role in network implementation. Location awareness can aid network routing and provide information to improve network performance and efficiency. Location awareness can be exploited for design of cognitive wireless systems and ad hoc networks, and it is an enabling technology for the seamless integration of cyber and physical systems. The node location information alongside data collection and distribution can enable many location-aware applications in networked sensing and control for manufacturing, person and asset tracking, healthcare, smart homes, transportation and security, search and rescue, and in future pervasive sensing, computation, communication, and actuation. This research develops cooperative localization and tracking in wireless ad hoc networks. Cooperation among network nodes can be exploited to improve positioning and tracking reliability.
Vehicular Ad Hoc Network Augmenting GPS Systems
Smart vehicle involves a variety of technologies ranging from basic automotive engineering to vehicle navigation. Global Positioning Systems (GPS) have been widely applied for identifying vehicle positions. However, large positioning errors may occur when the system is used in urban canyon environments, where tall buildings occlude satellite signals as well as reflecting them. There have been some efforts to improve the positioning accuracy: (1) using inertial navigation system (INS) to fill the gaps when GPS signals are temporarily interrupted; (2) using terrestrial television broad-casting or cellular signals for positioning in downtown areas; and (3) reducing the required minimum number of "visible" satellites by modeling the vehicle path in downtown areas and adding this information as a constraint. These possible solutions have their limitations due to cost and other implementation issues. Future smart vehicle systems rely on vehicle-to-vehicle and vehicle-to-roadside-infrastructure communications. The vehicular ad hoc network (VANET) consists of the vehicles as mobile nodes and the roadside elements as anchor/reference nodes. This applied research aims at the development of a VANET that can be incorporated with the GPS to provide reliable navigation services for vehicles in urban canyons. Using pair-wise measurements among neighboring vehicles, the VANET system will generate relative position information of multiple vehicles present in the urban area. This relative map will be incorporated with accurate GPS information provided by, for example, GPS receivers on vehicles at the edge of the downtown area who have good GPS reception, or a few GPS repeaters that are installed on buildings at major intersections of the downtown area (Figure). The hardware modules are to be tested with specifications of dedicated short-range communications for intelligent transportation systems.
There are other cases where GPS is intentionally or unintentionally denied. Conventionally, the INS of vehicles can be calibrated by the GPS to correct integration drift. In GPS-denied environment, alternative means is required for the INS to maintain accurate tracking. When a fleet of unmanned aerial vehicles (UAVs) fly in proximity within GPS-denied environment, the INS drifts could cause serious navigation problems even collisions. As multiple UAVs form an airborne wireless ad hoc network, vehicle relative positions and velocities can be estimated for collective INS calibration. This technique can also be employed in cases when GPS receivers are not available onboard every single UAV due to cost or damage during mission. Fast algorithm is instrumental to adapt to the real-time process for high dynamic vehicles. In addition, for a highly dynamic VANET such as that consists of UAVs, mobility-aware opportunistic routing can be adopted for reliable and effective network operation. A fleet of autonomous underwater vehicles (AUVs) can be another example, where positioning and tracking of each vehicle can be achieved with an underwater wireless network using acoustic signals.
Wireless Sensor Networks for Disaster Response
Disasters such as earthquakes, storms, floods, fires and terrorist attacks can be of large scale. Situational awareness in a disaster is critical to effective response. As normal organized community support is possibly damaged or destroyed, a wireless ad hoc sensor network can be solution to quick deployment and easy installation. For examples, wireless sensor nodes can be dropped by airplanes to form a networked coverage over an area devastated by a major earthquake (Figure). Various types of life sensors collaborate inter- and intra-node to detect people trapped beneath the rubble. Such a network should be able to support disaster response with distributed collaborative sensing, accurate localization, and topology-aware routing using a multichannel protocol. The information needs to be delivered timely to the disaster responders and rescue teams. Sensing suites use collaborative and distributed mechanisms to optimize data collection and minimize total energy use. For disaster response, multi-target classification, effective data fusion, and prompt and accurate decision making are challenges. This applied research implements decentralized classification in cluster-based wireless sensor networks with non-identically distributed observations. Limited network energy and computational/communication constraints will be considered as design variables. Moreover, the measurement signal strength and the communication signal-to-noise ratio determine the weight for each local decision in the decision fusion.
Communications and Signal Processing
Network Theory and Applications