Abstract:
A method for detecting significant events using neuronal sensor networks is provided. The method includes monitoring an environment surrounding a plurality of sensors for the presence of an event, when one or more events are detected, integrating the detected events over space and time using one or more collectors responsive to the plurality of sensors, determining when the one or more events are significant events and identifying and tracking significant events using processors responsive to the one or more collectors.

Description:
TECHNICAL FIELD 
   The present invention relates generally to the field of data communications, and in particular, to systems and methods of wide area sensor networks. 
   BACKGROUND 
   The use of sensor networks to detect, identify and track moving targets, particularly vehicles, is one that has been increasingly developed. Moving targets such as vehicles are often easy to identify due to the large seismic, magnetic or acoustic signals presented to the sensors that can easily distinguish them from the background noise. The effective range for sensors targeting moving vehicles as a result can be very large and thus only a few sensors are needed to cover a wide area. However, moving targets such as humans, horses or deer or the like provide signals that are often very small and difficult to distinguish from the surrounding background noise. Therefore, the effective range for sensors targeting humans, horses and deer can be very small. 
   Providing wide area sensor networks targeted for humans, horses and deer is currently problematic. To track targets that present small signals to the sensors requires a dense deployment of sensors to cover a wide area due to the limited range of each sensor. Each sensor must be small for reasons of cost and ease of deployment, and in some cases, the sensors need to be hidden from the targets. However, limiting the size of the sensors requires that the sensors provide the necessary communications to a processor unit at a low bandwidth and using a low amount of power. 
   For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the area of sensor networks for a low cost method of sensing moving targets such as humans, horses and deer or the like over a wide area using limited power and bandwidth. 
   SUMMARY 
   A system for detecting events using neuronal sensor networks is provided. The system includes a plurality of sensors that produce event detection signals when an event is detected and exceeds a minimum event threshold level, one or more collectors adapted to receive one or more of the event detection signals and produce threshold detection signals and one or more processors, adapted to receive threshold detection signals from the one or more collectors. The event detection signals use a simple communication protocol. 
   A method for detecting significant events using neuronal sensor networks is provided. The method includes monitoring an environment surrounding a plurality of sensors for the presence of an event, when one or more events are detected, integrating the detected events over space and time using one or more collectors responsive to the plurality of sensors, determining when the one or more events are significant events and identifying and tracking significant events using processors responsive to the one or more collectors. 
   A method for detecting events using neuronal sensors is provided. The method includes monitoring the surrounding environment for an event, determining whether the strength of the event exceeds the minimum event threshold level and transmitting an event detection signal to a collector when the strength of the event exceeds the minimum event threshold level. 
   A system for detecting events using neuronal sensor networks is provided. The system includes a plurality of sensors that produce and receive event detection signals when an event is detected, one or more collectors wherein each collector receives event detection signals from an associated subset of the plurality of sensors and produce threshold detection signals and one or more processors adapted to receive threshold detection signals from the one or more collectors. The event detection signals use a simple communication protocol. 

   
     DRAWINGS 
       FIG. 1  is a diagram of one embodiment of a wide area sensor network  100  in accordance with the teachings of the present invention. 
       FIG. 2  is a flow chart that illustrates one embodiment of a method  200  for sensor event detection, according to the teachings of the present invention. 
       FIG. 3  is a diagram of another embodiment of a wide area sensor network  300  in accordance to the teachings of the present invention. 
       FIG. 4  is a flow chart that illustrates another embodiment of a method  400  for sensor event detection, according to the teachings of the present invention. 
       FIG. 5  is a flow chart that illustrates one embodiment of a method  500  for collector threshold detection according to the teachings of the present invention 
   

   DETAILED DESCRIPTION 
   In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific illustrative embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be utilized and that logical, mechanical and electrical changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense. 
   Embodiments of the present invention provide systems and methods of wide area sensor networks. One example of an area to be covered is an area extending along a section of a trail or road, with the covered area extending several tens of meters to each side of the trail or road and extending several hundred meters along the road. Another example is the area surrounding an intersection of two roads or trails. In one or more embodiments, the present invention through the use of simple sensors provides low cost systems and methods for detecting moving targets such as humans, horses and deer over a wide area using limited power and bandwidth. The neuronal sensor network method allows a large number of simple sensors, densely deployed over a large area, to act as one sensor. Communication requirements for this method are minimized to simple ultra short-range detection transmissions. Also the signal processing occurs as a result of the method of communication, allowing basic integration over space and time. 
   In one embodiment, the sensors emit a signal only when it is stepped upon, using the energy provided by the stepping action (via piezoelectricity, for example). The detection range of this sensor is extremely short (the size of the foot), the signal it emits is simple (“ouch”), and it requires no energy source. With a sufficient density of these sensors, and appropriate collectors, the field of sensors could easily track a human across an area, and separate the passage of a human from the passage of a four-legged animal. 
     FIG. 1  is a diagram of one embodiment of a wide area sensor network  100  in accordance with the teachings of the present invention. Network  100  comprises a plurality of sensors  110 - 1  to  110 -N. Sensors  110 - 1  to  110 -N each comprise a simple ultra-short range transmitter  160  coupled to a controller  170 . Also, each sensor of sensors  110 - 1  to  110 -N is adapted to produce an event detection signal  115 - 1  to  115 -N. 
   Network  100  also comprises one or more collectors  120 - 1  to  120 -K that gather event detection signals  115 - 1  to  115 -N from adjacent sensors that form an associated subset of sensors. Each collector of collectors  120 - 1  to  120 -K is adapted to produce a threshold detection signal  125 - 1  to  125 -K Lastly, network  100  also comprises a processor  130  that receives threshold detection signals  125 - 1  to  125 -K from collectors  120 - 1  to  120 -K. It will be appreciated by those skilled in the art, with the benefit of the present description, that the system can include one or more processors  130 . However the description has been simplified to better understand the present invention. Also shown in  FIG. 1  is an event  150  that triggers each sensor  110 - 2 ,  110 - 3  and  110 - 8  of sensors  110 - 1  to  110 -N to send an event detection signal  115 - 2 ,  115 - 3  and  115 - 8 , respectively, to controller  120 - 1 . It will also be appreciated by those skilled in the art, that sensors  110 - 1  to  110 -N can use a variety of sensing methods including seismic, magnetic, acoustic, and the like methods to detect an event such as event  150 . 
   Network  100  allows a large number of sensors deployed in a wide area the ability to work as one sensor. In operation, sensors  110 - 1  to  110 -N are scattered over a wide area with collectors  120 - 1  to  120 -K in close proximity to each of its associated subset of neuronal sensor network sensors. Sensors  110 - 1  to  110 -N monitor the surrounding area for any event that crosses a set minimum threshold level. To this end, sensors  110 - 1  to  110 -N have two basic functions. The first function is that sensors  110 - 1  to  110 -N have threshold detection capability. Threshold detection capability requires the sensor  110 - 1  to  110 -N to identify when an event passes the minimum sensor threshold and to determine how far above the minimum sensor threshold the event exceeds. The second function of sensors  110 - 1  to  110 -N is to send an event detection signal  115 - 1  to  115 -N to a nearby collector  120 - 1  to  120 -K when an event occurs. These event detection signals  115 - 1  to  115 -N provide the nearby collector  120 - 1  to  120 -K with the location of the event and the strength of the event above the sensor threshold level. 
   The implementation of collectors  120 - 1  to  120 -K in close proximity to its associated subset of sensors allows sensors  110 - 1  to  110 -N to be very basic, low cost devices. Sensors  110 - 1  to  110 -N are only required to detect events using controller  160  and transmit ultra short-range event detection signals  115 - 1  to  115 -N using transmitter  170  to a nearby collector  120 - 1  to  120 -K. Also, the transmitted event detection signals  115 - 1  to  115 -N only require a simple communication protocol. An example of one such communications protocol might be the transmission of a number of ultra-short pulses, with the number of pulses proportional to the strength of the detected event, in a manner analogous to the way a sensory cell transmits signals in a neuron. Another example of a simple communications protocol is to have each sensor simply transmit the event detection signal, relying on the use of short messages and short transmission ranges to avoid collisions between transmissions from different sensors. Thus, in one embodiment, sensors  110 - 1  to  110 -N are very small and run on ultra low power. In some embodiments, sensors  110 - 1  to  110 -N obtain, from its environment, sufficient power, such as solar power, to run without a battery. In addition to solar power, other possible methods of harvesting energy include thermal energy, barometric pressure changes, wind, or mechanical energy. 
   Network  100  provides an effective method for sensing moving targets such as humans, horses, deer and the like. As shown in  FIG. 1 , when event  150  is detected by one or more of sensors  110 - 2 ,  110 - 3  and  110 - 8 , sensors  110 - 2 ,  110 - 3  and  110 - 8  immediately transmit event detection signals  115 - 2 ,  115 - 3  and  115 - 8 , respectively to collector  120 - 1 . As a result, collector  120 - 1  gathers event detection signals  115 - 2 ,  115 - 3  and  115 - 8  from sensors  110 - 2 ,  110 - 3  and  110 - 8  and transmits a threshold detection signal  125 - 1  to processor  130  indicating a significant event in the collector  120 - 1  area has occurred. Processor  130  uses threshold detection signal  125 - 1  to identify and track event  150 . 
     FIG. 2  is a flow chart that illustrates one embodiment of a method  200  for sensor event detection, according to the teachings of the present invention. Method  200  begins at block  210 , where a sensor is continuously monitoring its surrounding area. At block  220  the sensor determines whether an event is detected. If no event is detected the sensor continues to monitor the surrounding area. However, if the sensor does detect an event, method  200  goes to block  230 . At block  230  the sensor determines if the strength of the event surpasses the minimum threshold level. If the event does not surpass the minimum threshold level, method  200  goes back to block  210 , where the sensor resumes monitoring the surround area for another event. If, however, the strength of the event does surpass the minimum threshold level, method  200  moves to block  240 . At block  240  the sensor sends an event detection signal to a nearby collector. Once the sensor sends the event detection signal to the nearby collector, method  200  goes back to block  210 , where the sensor resumes monitoring the surrounding area for an event. 
     FIG. 3  is a diagram of another embodiment of a wide area sensor network  300  in accordance to the teachings of the present invention. Network  300  comprises a plurality of sensors  310 - 1  to  310 -T. Sensors  310 - 1  to  310 -T each comprise a simple ultra-short range transmitter  360  coupled to a controller  370  and a receiver  380  also coupled to controller  370 . Also, each sensor of sensors  310 - 1  to  310 -T is adapted to produce an event detection signal  315 - 1  to  315 -T. 
   Network  300  also comprises one or more collectors  320 - 1  to  320 -P that gather event detection signals  315 - 1  to  315 -T from adjacent neuronal sensor network sensors that form an associated subset of sensors. Each collector of collectors  320 - 1  to  320 -P is adapted to produce a threshold detection signal  325 - 1  to  325 -P. Lastly, network  300  also comprises a processor  330  that receives threshold detection signals  325 - 1  to  325 -P from collectors  320 - 1  to  320 -P. It will be appreciated by those skilled in the art, with the benefit of the present description, that network  300  can include one or more processors  330 . However the description has been simplified to better understand the present invention. Also shown in  FIG. 3  is an event  350  that triggers sensors  310 - 2 ,  310 - 3  and  310 - 8  of sensors  310 - 1  to  310 -T to each send an event detection signal  315 - 2 ,  315 - 3  and  315 - 8  to controller  320 - 1 . It will also be appreciated by those skilled in the art, that sensors  310 - 1  to  310 -T can use a variety of sensing methods including seismic, magnetic, acoustic sensing methods and the like to detect an event such as event  350 . 
   Network  300  allows a large number of sensors deployed in a wide area the ability to work as one sensor. In operation, sensors  310 - 1  to  310 -T are scattered over a wide area with collectors  320 - 1  to  320 -P in close proximity to each sensor  310 . Sensors  310 - 1  to  310 -T monitor the surrounding area for any event that crosses a set minimum threshold level. To this end, sensors  310 - 1  to  310 -T have three basic functions. The first function is that sensors  310 - 1  to  310 -T have threshold detection capability. Threshold detection capability requires the sensor  310  to identify when an event passes the minimum sensor threshold and to determine how far above the sensor threshold the event exceeds. The second function of sensors  310 - 1  to  310 -T is to send an event detection signal  315 - 1  to  315 -T to a nearby collector of collectors  320 - 1  to  320 -P as well as to nearby sensors of sensors  310 - 1  to  310 -T when an event occurs. Lastly, sensors  310 - 1  to  310 -T must have the ability to receive nearby event detection signals of event detection signals  315 - 1  to  315 -T from nearby sensors of sensors  310 - 1  to  310 -T. These event detection signals  315 - 1  to  315 -T provide the collectors  320 - 1  to  320 -P and nearby sensors of sensors  310 - 1  to  310 -T with the location of the event and the strength of the event above the sensor threshold level. 
   The implementation of collectors  320 - 1  to  320 -P in close proximity to each of its associated subset of sensors allows sensors  310 - 1  to  310 -T to be very basic, low cost devices. As described above, sensors  310 - 1  to  310 -T have only three tasks. First, sensors  310 - 1  to  310 -T are required to detect events using controller  360 . Sensors  310 - 1  to  310 -T also transmit ultra short-range event detection signals  115 - 1  to  115 -T using transmitter  370  to a nearby collector of collectors  320 - 1  to  320 -P as well as to nearby sensors of sensors  310 - 1  to  310 -T. Also, the transmitted event detection signals  115 - 1  to  115 -T only require a simple communication protocol. Lastly, sensors  310 - 1  to  310 -T receive ultra short-range transmissions using receiver  380  from nearby sensors of sensors  310 - 1  to  310 -T. Thus, sensors  310 - 1  to  310 -T can be very small and run on ultra low power. In some embodiments sensors  310 - 1  to  310 -T can obtain from its environment sufficient power, such as solar power, to run without a battery. 
   Network  300  provides an effective method for sensing moving targets such as humans, horses and deer. As shown in  FIG. 3 , when event  350  is monitored by sensors  310 - 2 ,  310 - 3  and  310 - 8  of sensors  310 - 1  to  310 -T, sensors  310 - 2 ,  310 - 3  and  310 - 8  immediately transmit event detection signals  315 - 2 ,  315 - 3  and  315 - 8  to collector  320 - 1  as well as to nearby sensors of sensors  310 - 1  to  310 -T. As a result, collector  320 - 1  gathers event detection signals  315 - 2 ,  315 - 3  and  315 - 8  from sensors  310 - 2 ,  310 - 3  and  310 - 8  and transmits a threshold detection signal  325 - 1  to processor  330  indicating a significant event in the collector  320 - 1  area has occurred. Processor  330  can then use threshold detection signal  325 - 1  of threshold detection signals  325 - 1  to  325 -P to identify and track event  350 . 
     FIG. 4  is a flow chart that illustrates another embodiment of a method  400  for sensor event detection, according to the teachings of the present invention. Method  400  begins at block  410 , where a sensor is continuously monitoring its surrounding area for an event. At block  420  the sensor determines whether an event is detected. If no event is detected the method goes back to block  410  and the sensor continues to monitor the surrounding area. However, if the sensor does detect an event, method  400  goes to block  430 . At block  430  the sensor determines if the strength of the event surpasses the minimum threshold level. If the event does not surpass the minimum threshold level, method  400  moves to block  450 . If, however, the strength of the event does surpass the minimum threshold level, method  400  moves to block  440 . 
   At block  440  the sensor sends an event detection signal to the nearby collector and to other nearby sensors. When nearby sensors receive an event detection signal the nearby sensors will lower their minimum threshold level and continue to monitor its surroundings for an event. By lowering the minimum threshold level of nearby sensors when an event is detected allows the nearby collector to determine whether the event detected by the original sensor is a legitimate event or a random error. The method then moves on to block  450 . 
   At block  450  the sensor checks to see if it has received any event detection signals from nearby sensors. If the sensor does not receive an event detection signal from a nearby sensor, method  400  goes back to block  410 , where the sensor resumes monitoring the surrounding area for an event. If the sensor receives an event detection signal from a nearby sensor, method  400  goes to block  460 . At block  460  the sensor will lower the minimum threshold level for a set amount of time (depending on the likely speed of the target and the sensor modality), after which method  400  goes back to block  410 , where the sensor resumes monitoring the surrounding area for an event. 
     FIG. 5  is a flow chart that illustrates one embodiment of a method  500  for collector threshold detection according to the teachings of the present invention. Method  500  begins at block  510 , where a collector waits for event detection signals from nearby sensors. At block  520 , method  500  checks to see if the collector has received an event-detection signal from a nearby sensor. If the collector has not received an event-detection signal from a nearby sensor, method  500  goes back to block  510 , where the collector continues to wait for an event detection signal from nearby sensors. If the collector has received an event detection signal from a nearby sensor, method  500  goes to block  530 . 
   At block  530  the collector integrates the event detection signal over space and time with any other event detection signals received by the collector and produces a stored summation. Method  500  then proceeds to block  540 . At block  540  the collector determines whether the stored summation exceeds a predetermined collector threshold level. If the stored summation does not exceed the collector threshold level, method  500  goes back to block  510 , where the collector continues to wait for event detection signals from nearby sensors. If the stored summation does exceed the collector threshold level, method  500  goes to block  550 . At block  550  the collector transmits a threshold detection signal to a processor to determine whether a target, such as a human, horse or deer was detected. Method  500  then returns to block  510 , where the collector continues to wait for event detection signals from nearby sensors.