iMouse Full Seminar Report and PPT (Integrated Mobile Surveillance and Wireless Sensor System)
Incorporating the environment-sensing capability of wireless sensor networks into video-based surveillance systems can provide advanced services at a lower cost than traditional surveillance systems. The integrated mobile surveillance and wireless sensor system (iMouse) uses static and mobile wireless sensors to detect and then analyze unusual events in the environment.
Wireless sensor networks (WSN) provide an inexpensive and convenient way to monitor physical environments. Integrating the context-aware capability of WSN into surveillance systems is an attractive direction. An integrated mobile surveillance and wireless sensor (iMouse) system, consists of a large number of inexpensive static sensors and a small number of more expensive mobile sensors. The former, is to monitor the environment, while the latter can move to certain locations and takes more advanced actions. The iMouse system is a mobile, context-aware surveillance system.
The remarkable advances of micro sensing micro electromechanical systems (MEMS) and wireless communication technologies have promoted the development of wireless sensor networks. A WSN consists of many sensor nodes densely deployed in a field, each able to collect environmental information and together able to support multihop ad-hoc routing. WSNs provide an inexpensive and convenient way to monitor physical environments. With their environment-sensing capability, WSNs can enrich human life in applications such as healthcare, building monitoring, and home security.
A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. The development of wireless sensor networks was originally motivated by military applications such as battlefield surveillance. However, wireless sensor networks are now used in many civilian application areas, including environment and habitat monitoring, healthcare applications, home automation, and traffic control.
The applications for WSNs are many and varied. They are used in commercial and industrial applications to monitor data that would be difficult or expensive to monitor using wired sensors. They could be deployed in wilderness areas, where they would remain for many years (monitoring some environmental variables) without the need to recharge/replace their power supplies. They could form a perimeter about a property and monitor the progression of intruders (passing information from one node to the next).
Typical applications of WSNs include monitoring, tracking, and controlling. Some of the specific applications are habitat monitoring, object tracking, nuclear reactor controlling, fire detection, traffic monitoring, etc. In a typical application, a WSN is scattered in a region where it is meant to collect data through its sensor nodes.
A sensor node, also known as a mote, is a node in a wireless sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network. The main components of a sensor node are microcontroller, transceiver, external memory, power source and one or more sensors.
Microcontroller: Microcontroller performs tasks, processes data and controls the functionality of other components in the sensor node.
Tranceiver: The functionality of both transmitter and receiver are combined into a single device know as transceivers are used in sensor nodes. Transceivers lack unique identifier. The operational states are Transmit, Receive, Idle and Sleep.
External Memory: From an energy perspective, the most relevant kinds of memory are on-chip memory of a microcontroller and FLASH memory – off-chip RAM is rarely if ever used. Flash memories are used due to its cost and storage capacity. Memory requirements are very much application dependent.
Power Source: Power consumption in the sensor node is for the Sensing, Communication and Data Processing. More energy is required for data communication in sensor node. Energy expenditure is less for sensing and data processing. Power is stored either in Batteries or Capacitors. Batteries are the main source of power supply for sensor nodes. Current sensors are developed which are able to renew their energy from solar or vibration energy. Two major power saving policies used are Dynamic Power Management (DPM) and Dynamic Voltage Scaling (DVS). DPM takes care of shutting down parts of sensor node which are not currently used or active. DVS scheme varies the power levels depending on the non-deterministic workload. By varying the voltage along with the frequency, it is possible to obtain quadratic reduction in power consumption.
Sensors: Sensors are hardware devices that produce measurable response to a change in a physical condition like temperature and pressure. Sensors sense or measure physical data of the area to be monitored. The continual analog signal sensed by the sensors is digitized by Analog-to-Digital converter and sent to controllers for further processing. Characteristics and requirements of Sensor node should be small size, consume extremely low energy, operate in high volumetric densities, be autonomous and operate unattended, and be adaptive to the environment. As wireless sensor nodes are micro-electronic sensor device, can only be equipped with a limited power source of less than 0.5Ah and 1.2 V. Each sensor node has a certain area of coverage for which it can reliably and accurately report the particular quantity that it is observing.
Traditional surveillance systems typically collect a large volume of videos from wallboard cameras, which require huge computation or manpower to analyze. Integrating WSNs’ sensing capability into these systems can reduce such overhead while providing more advanced, context-rich services. For example, in a security application, when the system detects an intruder, it can conduct in-depth analyses to identify the possible source.
Integrated mobile surveillance and wireless sensor system (iMouse) consists of numerous static wireless sensors and several more powerful mobile sensors. The benefits of iMouse include the following:
- It provides online real-time monitoring. For example, when the system is capturing events, the static sensors can immediately inform users where the events are occurring, and the mobile sensors can later provide detailed images of these events.
- It’s event-driven, in the sense that only when an event occurs is a mobile sensor dispatched to capture images of that event. Thus, iMouse can avoid recording unnecessary images when nothing happens.
- The more expensive mobile sensors are dispatched to the event locations. They don’t need to cover the whole sensing field, so only a small number of them are required.
- It’s both modular and scalable. Adding more sophisticated devices to the mobile sensors can strengthen their sensing capability without substituting existing static sensors.
Because mobile sensors run on batteries, extending their lifetime is an important issue. So a dispatch problem is proposed that addresses how to schedule mobile sensors to visit emergency sites to conserve their energy as much as possible. If the number of emergency sites is no larger than the number of mobile sensors, the problem can be transformed to a maximum matching problem in a bipartite graph; otherwise, the emergency sites are grouped in to clusters so that one mobile sensor can efficiently visit each cluster.
2. RELATED WORK IN WIRELESS SURVEILLANCE
Traditional visual surveillance systems continuously videotape scenes to capture transient or suspicious objects. Such systems typically need to automatically interpret the scenes and understand or predict actions of observed objects from the acquired videos. For example, a video-based surveillance network in which an 802.11 WLAN card transmits the information that each video camera captures.
Researchers in robotics have also discussed the surveillance issue. Robots or cameras installed on walls identify obstacles or humans in the environment. These systems guide robots around these obstacles. Such systems normally must extract meaningful information from massive visual data, which requires significant computation or manpower.
Some researchers use static WSNs for object tracking. These systems assume that objects can emit signals that sensors can track. However, results reported from a WSN are typically brief and lack in-depth information. Edoardo Ardizzone and his colleagues propose a video-based surveillance system for capturing intrusions by merging WSNs and video processing techniques. The system complements data from WSNs with videos to capture the possible scenes with intruders. However, cameras in this system lack mobility, so they can only monitor some locations.
Researchers have also proposed mobilizers to move sensors to enhance coverage of the sensing field and to strengthen the network connectivity. The integration of WSNs with surveillance systems has not well addressed, which led to propose the iMouse system
The proposed iMouse integrates WSN technologies into surveillance technologies to support intelligent mobile surveillance services. On one hand, these mobile sensors can help improve the weakness of traditional WSNs that they only provide rough environmental information of the sensing field. By including mobile cameras, we can obtain much richer context information to conduct more in-depth analysis. On the other hand, surveillance can be done in an event-driven manner. Thus, the weakness of traditional surveillance systems can be greatly improved because only critical context information is retrieved and proactively sent to users.
The prototyped iMouse system can be improved/extended in several ways. First, the way to navigate mobile sensors can be further improved. For example, localization schemes can be integrated to guide mobile sensors instead of using color tapes. Second, the coordination among mobile sensors, especially when they are on-the-road, can be exploited. Third, how to utilize mobile sensors to improve the network topology deserves further investigation.
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