The focus of surveillance missions is to acquire and verify information about enemy capabilities and positions of hostile targets. Such missions often involve a high element of risk for human personnel and require a high degree of stealthiness. Hence, the ability to deploy unmanned surveillance missions, by using wireless sensor networks, is of great practical importance for the military. Because of the energy constraints of sensor devices, such systems necessitate an energy-aware design to ensure the longevity of surveillance missions. Solutions proposed recently for this type of system show promising results through simulations. However, the simplified assumptions they make about the system in the simulator often do not hold well in practice and energy consumption is narrowly accounted for within a single protocol. In this paper, we describe the design and implementation of a running system for energy-efficient surveillance. The system allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energyefficient and stealthy manner. We can trade off energyawareness and surveillance performance by adaptively adjusting the sensitivity of the system. We evaluate the performance on a network of 70 MICA2 motes equipped with dual-axis magnetometers. Our results show that our surveillance strategy is adaptable and achieves a significant extension of network lifetime. Finally, we share lessons learned in building such a complete running system. * This work was supported by the DAPRPA IXO offices under the NEST project (grant number F336615-01-C-1905). Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.  Categories and Subject Descriptors C.2.1 [Computer Communication Networks]: Network Architecture and Design Design, Performance, Experimentation, Measurement Sensor networks, Energy conservation, Tracking, Wireless One of the key advantages of wireless sensor networks (WSN) is their ability to bridge the gap between the physical and logical worlds, by gathering certain useful information from the physical world and communicating that information to more powerful logical devices that can process it. If the ability of the WSN is suitably harnessed, it is envisioned that WSNs can reduce or eliminate the need for human involvement in information gathering in certain civilian and military applications. In the near future, sensor devices will be produced in large quantities at a very low cost and densely deployed to improve robustness and reliability. They can be miniaturized into a cubic millimeter package (e.g., smart dust [16] ) in order to be stealthy in a hostile environment. Cost and size considerations imply that the resources available to individual nodes are severely limited. We believe, however ,that limited processor bandwidth and memory are temporary constraints in sensor networks. They will disappear with fast developing fabrication techniques. The energy constraints on the other hand are more fundamental. According to R.A. Powers [20] , battery capacity only doubles in 35 years. Energy constraints are unlikely to be solved in the near future with the slow progress in battery capacity and energy scavenging. Moreover, the untended nature of sensor nodes and the hazardous sensing environment preclude manual battery replacement. For these reasons, energy awareness becomes the key research challenge for sensor network protocol design. Several researchers have addressed energy conservation recently. Most of them 270
Real time experiments on Mica2 motes showed that the false alarm rates of the decisions taken based on the data from a single sensor can be as high as 60 percent REF .
Energy-efficient surveillance system using wireless sensor networks
MobiSys '04
Computer Science