BACKGROUND
[0001] There is a growing concern that a radiological dispersal device ("dirty bomb") could
be used by terrorist particularly in places with a high density of people or in areas
of high value commercial or government properties and facilities. Since such a device
would be small enough to be man or vehicle portable, the best probability to detect
and interdict such a device is to widely distribute a network of spectroscopic radiation
sensors that are mobile, man portable, work without operator intervention and are
connected to central command and control sensor and optionally to one another. This
provides the most general and dynamic scheme to monitor and map a large, uncontrolled
area potentially full of people. An additional important benefit of such a system
is that it can detect radioactive sources that, although not intended as terrorist
threats, still pose public safety problems. For example, there have been incidents
where untended industrial and medical sources have been released without proper safeguards
into public areas.
[0002] Any local measurement of radiation has contributions from Naturally Occurring Radioactive
Materials (NORM) in that locale and possible non-NORM sources ranging from medical
isotopes (from patients for example which just underwent a stress test), industrial
isotopes not properly secured or being used for nefarious purposes and Special Nuclear
Materials (SNM) that can be used in a nuclear device (or a nuclear device already
assembled). Detection algorithms must estimate the contributions of both NORM backgrounds
and the presence of possible non-NORM radioactive sources.
[0003] The detection of natural and manmade sources using distributed radiation sensors
over large geographic areas poses unique and complex networking and computational
problems. It has been demonstrated that fusing spectral data from sensors that are
in proximity offers higher sensitivity and low false alarm rates, for example, as
described in
PCT/US2014/012330, published as
WO 2014/0133687, and U.S. Pat. App.
US2017/0003404. The spatial scale over which spectral data fusion is useful is determined by intrinsic
sensor properties such as the absolute efficiency, and energy resolution of the sensor.
External properties of importance are the energy, and strength of the radioactive
source and any shielding materials that are between the source and sensor. For example,
spatial scales of order few times 10 m are obtained for handheld spectroscopic radiation
sensors. Thus in a large system of tens of thousands of sensors in a city there may
be a large number of clusters of sensors in which real time sharing (of order seconds)
of spectral data through a server or system of servers is helpful to reap the full
benefits of spectral data fusion. Efficient and responsive network systems can generate
a dynamic set of clusters that allow full data sharing for optimal detection performance.
For sensor nodes that are isolated (i.e., nodes that do not benefit from real time
data sharing), data transmission will consist of node position, health, background
spectra measurements and any detection alarms. The network SmartServer can dynamically
determine if open bandwidth is present so that even isolated nodes can dynamically
transmit spectral data for background mapping. Optionally, if the Server determines
that open bandwidth is not present the node can locally log data and upload to the
system when bandwidth is available and/or there is a local "base" connection to the
server. It is also desirable for two way communication between command and control
provided through the SmartServer. Usually the required bandwidth for such communication
consists of passing alarms, texts, etc. and is typically small as compared to that
required for data fusion.
[0004] As discussed above, a high spatial resolution statistically significant map of the
NORM background is crucial for high sensitivity searches for non-NORM radiation sources.
NORM background can vary significantly over small spatial scales particularly in urban
environmental with a large variety of construction materials are present. Therefore,
fine scale measurements of the background are highly desirable to ensure high sensitivity
to the presence of non-NORM sources of radiation while retaining a low rate of false
alarms. A distributed network of radiation sensors with spectroscopic capabilities
is an effective method for obtaining large area coverage of NORM background. Particularly
if the sensors are mobile, they can differentiate isotopes of interest and can do
this without operator intervention or supervision. Therefore, optimal network architecture
must be able to collect, store and distribute this constantly updated background map.
In a large system, a SmartServer can improve performance by allocating the available
resources between the search for non-NORM sources of radiation and the collection
and distribution of background data.
[0006] Chinese patent application
CN103546966 relates to a node positioning method of a wireless sensor based on a field environment.
The node positioning method comprises sensing the neighborhood relationship among
the nodes to obtain an index for measuring the distance, the neighborhood relationship
comprises the number of the neighborhood nodes of each node and the absolute distance
relationship between the nodes and the index is the common neighborhood distance;
the estimation position of the unknown node is determined according to the index;
the position of the unknown node is corrected according to the neighborhood relationship
and the estimation position.
[0007] PCT patent application no.
WO2004051868 describes a system to detect, classify, and locate sources of RF activity. The system
comprises one or more sensors positioned at various locations in a region where activity
in a shared radio frequency band is occurring and a server coupled to the sensors.
Each sensor monitors communication traffic, such as IEEE WLAN traffic, as well as
classifies non-WLAN signals occurring in the frequency band. The server receives data
from each of the plurality of sensors and executes functions to process the data supplied
by the plurality of sensors.
[0008] US patent application US2014099882 describes a communication system comprising one or more self-powered satellite units
each providing signal information to at least one command console through a segmented
cable assembly system in operable communication with a central station that receives
signal information from the at least one command console and relays signal information
back to the command console wirelessly and via the segmented cable assembly system.
SUMMARY
[0009] A large system of networked sensors requires the management and distribution of sensor
data via an intelligent server (SmartServer). In this network of sensors, one or more
SmartServers use the underlying processes of source detection to optimize network
data flow.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010]
FIG. 1 schematically shows one possible arrangement of elements in a system with dynamic
node grouping.
FIG. 2 shows schematically a series of sensor nodes spread out around an intersection
of streets.
FIG. 3 shows the same set of sensor nodes as FIG. 2, now divided into four groups,
shown schematically with dashed lines.
FIG. 4 shows the same set of sensor nodes as FIGS. 2 and 3 in their groups, but now
adds a source moving along one of the streets toward the intersection.
DETAILED DESCRIPTION
[0011] The invention is defined by the appended claims. The presence of a source can be
detected using a network of sensors. The network of sensors includes mobile and fixed
sensors. In order to patrol an area, mobile sensors are carried by a group of people
walking or mounted on vehicles. Mobile sensors are connected to a network through
a wired or wireless connection. The people that would patrol an area using mobile
sensors include, but not limited to, security personnel and first responders. Fixed
sensors are situated in a permanent location, where constant monitoring is preferred
or required. Locations that would use a fixed sensor include, but are not limited
to, security checkpoints, major intersections, exits or entrances of buildings, or
other significant, high importance locations. Fixed sensors are connected to a network
through a wired or wireless connection. The detector may be a radiation detector,
a chemical detector, or any other suitable detector for the source in question. In
normal operation each networked sensor collects data that may be transmitted to a
central node (for processing and archiving) or other mobile sensor nodes in the network,
and the sensor nodes may in turn receive such signals, either from other sensor nodes
or from a central node, with the result that data from the network as a whole is collectively
processed to determine if, when, and where a source is present.
[0012] As explained in
WO 2014/0133687, sources can be detected in real time by analysis of data collected by a network
of sensors. Computation and detection can be carried out in a single central computer,
or on a plurality of distributed computers, for example, in processors located at
each sensor node in the network. Computational detection can be carried out on a data
set obtained from any collection of sensors, for example, all sensors in the network,
or one or more subsets of all sensors in the network. Carrying out the computational
detection algorithms using data obtained from only a subset of the sensors can have
several advantages, as explained below, depending on how the subset is defined.
[0013] FIG. 1 schematically shows one possible arrangement of elements in a system with
dynamic node grouping. Data is stored in a database 1. Servers 2, 3, 4, communicate
with the database 1. Each server is associated with a group 5, 6, 7 of nodes, e.g.,
5a-5d, etc., in the network. The nodes are dynamically grouped according to the conditions
of the system. As shown, each server has an equal number of nodes, but this need not
be true generally; because the node grouping is dynamically responsive to system conditions,
the number of nodes in each grouping will vary from group to group and over time.
In this case, the nodes 5a-5d each carries a sensor for detecting a material of interest,
e.g., a radiation or chemical sensor, and may also include elements capable of determining
the sensor's position, velocity, orientation and acceleration. Each sensor node 5a-5d
in the group 5 communicates with its designated server 2. The server 2 can carry out
multiple functions. The server 2 can operate on the data reported from the nodes 5a-5d
to determine whether a source has been detected, for example, by using the methods
described in
WO 2014/0133687. Alternatively, the detection process can be carried out on other computers, such
as central processing node, or alternatively on computers on board each sensor node,
while the servers 2, 3, 4 focus on the optimal assignment of nodes to groups. The
server 2 can communicate with the other servers 3, 4 to determine whether any of the
sensor nodes 5a-5d should be moved to another node group 6, 7. For example, the server
2 can compare the locations of the various nodes to determine whether one node has
moved from a geographic area associated with a first group into a geographic area
associated with a second group. A server can communicate with other servers to determine
whether a source has been detected in a node group, in which case the server might
move nodes into or out of the group in which a detection has been made. A server might
measure the amount of network traffic amongst the nodes in its group and, by comparing
to the traffic in other groups, determine that system performance could be improved
or optimized if a node were to be moved from one group to another.
[0014] As shown in FIG. 1, all the servers can be physically distinct, networked computers
with their own dedicated processors. Alternatively, a system capable of all the same
functionality could be set up with a single server assigning nodes into subgroups.
In that case, a single server would collect the necessary data from all nodes on the
network, analyze the relevant network and system parameters such as network traffic,
geography, and presence or absence of detections, and determine whether the current
grouping or an alternative grouping is preferred.
[0015] Node groupings can be optimized to various effects. For example, in a system where
each node communicates with every other node in the group, the quantity of network
traffic in a single group will be of order of the square of the number of nodes in
the group. By dividing the nodes into N groups, the total traffic on the system will
be reduced by roughly a factor of N compared to the case where all nodes are in a
single system-wide group. Node groupings can also be based simply on geography. Each
sensor node will have a certain sensitivity, and correspondingly a certain geographic
range over which it is likely to be sensitive to the presence of a source. Sensor
nodes may be grouped to make sure that the ranges of the nodes in a given group overlap
sufficiently to provide full coverage of a certain geographic area. This may change
over time, as sensor nodes may be moved, as with nodes that are attached to patrol
personnel.
[0016] It may also be advantageous to group nodes geographically once a source has been
detected. For example, if a moving source has been detected, the system could dynamically
group nodes so as to follow the motion of the source, always having a higher concentration
of nodes near where the source is expected to be based on its last estimated velocity.
Similarly, in the case of a tentative detection, the system may allocate more nodes
to a group near the tentative detection to follow-up.
[0017] The system may also expand the geographical size of a group in response to a detection
or tentative detection. For example, if each group is initially designated to cover
an predetermined area, say 10,000 square meters, and a detection or tentative detection
is made in one such grouping, it may be helpful to merge adjacent groups into the
group where a detection was made in order to pursue that source and increase confidence
that the source will not move undetected out of its current group.
[0018] Similarly, the system may take into account
a priori knowledge of the geography to group nodes intelligently. For example, the system
may group nodes, not simply by geographic proximity to each other, but rather by proximity
to existing landmarks, such as streets. Rather than a grouping defined by a single
radius, the system could group nodes along a single street, or around an intersection.
See discussion of FIGS. 2-4 below.
[0019] Grouping sensor nodes is also an indirect way of intelligently allocating computing
power. In any detection algorithm, such as the particle filtering algorithm described
in
WO 2014/0133687, analysis of more data requires more computing power. But more data does not necessarily
increase useful sensitivity of the detection algorithm. For example, if two groups
of sensors are geographically segregated to the point where their ranges do not significantly
overlap, it may be more computationally efficient to treat the two groups totally
separately. Data from the first group will not typically help the system detect a
source in the geographic area of the second group, and vice versa. So there is no
benefit to including all the data in a single detection calculation, only computational
cost. Particle filtering algorithms are particularly well-suited to use with this
sort of dynamic grouping. Each particle represents a possible state in phase space
including information like source location, velocity and strength, and potentially
sensor properties as well. Since every group in which a particle filtering algorithm
is being run will use the same type of particles, those particles can easily be passed
from one group to another. This can be useful, for example, in tracing the movement
of a source from one geographic area to another.
[0020] Typically the initial assignment of sensor nodes to groups will be based on
a priori knowledge of the geographic area in which the system is operating. Groups may be
centered on a fixed position of interest, for example a secured entrance/exit from
a building. The fixed position may be focused on one or more stationary sensor nodes.
A group may be limited to sensors carried by personnel that are all part of a single
unit, for example a particular group or squad of first responders, or a security team.
[0021] Another advantage of the present systems and methods is scalability. Unlike a networked
system of nodes with full bi-directional communication between all nodes, in which
the volume of communication traffic (as well as corresponding computational problems)
is of order N
2, dividing the nodes up into groups allows an operator of the system to increase system
traffic and computation roughly proportional to the number of nodes. An operator that
wants to add another group of nodes to cover an additional geographic area need only
add the additional number of nodes and a proportional increase in number or power
of underlying servers. There is no need to scale the resources as N
2.
[0022] FIG. 2 shows schematically a series of sensor nodes
11-25 spread out around an intersection of streets. A stationary node
11 is centered on the intersection, for example, a sensor installed on a traffic light.
The other nodes
12-25 are schematically shown as smaller circles to indicate that they are mobile nodes,
perhaps attached to a person or to a vehicle. FIG. 3 shows the same set of nodes divided
into four groups,
26-29, shown schematically with dashed lines. Each group covers a single street leading
upto the intersection, but all groups include the stationary node
11 at the intersection. In this particular case, the groupings are defined by geography,
but not simply by the range of the detectors. Such
a priori knowledge of geography can be input into the system in the form of a map or series
of maps and combined with location information from the various sensor nodes, for
example, GPS data.
[0023] FIG. 4 shows the same sensor nodes in their groups, but now adds a source
30 that is detected, either confidently or tentatively, in group
28, the source moving along one of the streets toward the intersection. In such a case,
the system could make an algorithmic determination to expand group
28 to include, for example, nodes
18, 23, and
12, thereby allowing group
28 to continue accurately tracking the source
30 into and perhaps through the intersection. Or the system could combine all four groups
26-29 into a single group temporarily in order to determine which street the source
30 follows out of the intersection. Once the source
30 has left the intersection, where the node groups have a natural geographic overlap,
the system could divide the nodes back out into their original groupings as the system
continues to detect the source
30 moving along one of the streets.
[0024] In a situation such as the one described in FIG. 4, the system can also send feedback
and instructions to the individual sensor nodes. For example, if the source is stopped
in a particular location near the intersection, the system can instruct those carrying
particular nodes to gather around the source to improve detection confidence, or to
spread out in order to better capture future movements of the source.
[0025] In all the cases discussed herein, nodes can be grouped according to algorithms,
based on
a priori knowledge of the environment, based on specific instructions from human operators
perhaps intervening in algorithmic decision-making, and any combinations thereof.
[0026] Methods of detecting a source can employ a plurality of nodes, each node including
a sensor capable of collecting data and a transmitter configured to transmit at least
sensor data collected by the sensor and location data representing at least the location
of the node, a network capable of allowing transmission of data between and among
the plurality of nodes and between the plurality of nodes and at least one computer,
and a first computer having an input configured to receive data transmitted by nodes
through the network, a memory configured to collect data transmitted by the nodes
through the network, a processor configured to combine sensor and location data and
compare the combined data to a predetermined detection criterion to determine whether
a source is detected, and an output. Such methods can include collecting, in the first
computer, location data transmitted through the network from each node to the first
computer, the data being associated with a predetermined time, for each node, associating
the node at the predetermined time with at least one of a plurality of node groups
based at least in part on at least one of (a) a position of the node, (b) a velocity
of the node, and (c) a measure of traffic on the network, for each node group, determining
whether a source has been detected by the node group by combining, in the first computer,
sensor data and location data collected from the nodes in the group and comparing,
in the first computer, the combined sensor data and location data to at least one
predetermined detection criterion, and signaling, with the output, at least whether
a source has been detected.
[0027] Such methods can be iteratively repeated at each of a plurality of predetermined
times. The sensors can be, for example, radiation or chemical sensors. Each node can
be associated at the predetermined time with at least one of a plurality of node groups
based, for example, solely on a position of the node, solely on a velocity of the
node, or solely on a measure of traffic on the network, based on any combination of
position, velocity, and/or network traffic. Associating each node at the predetermined
time with at least one of a plurality of node groups can be carried out by the first
computer, or by a second computer that is not the first computer, or by a plurality
of computers none of which is the first computer.
1. A method of detecting a source (30) using:
a plurality of nodes (5a-d, 11-25), each node including a sensor capable of collecting
data, wherein the sensors are radiation or chemical sensors, and a transmitter configured
to transmit at least sensor data collected by the sensor and location data representing
at least the location of the node;
a network capable of allowing transmission of data between and among the plurality
of nodes and between the plurality of nodes and at least one computer; and
a first computer (2) having an input configured to receive data transmitted by nodes
through the network, a memory configured to collect data transmitted by the nodes
through the network, a processor configured to combine sensor and location data and
compare the combined data to a predetermined detection criterion to determine whether
a source is detected, and an output;
the method comprising:
collecting, in the first computer (2), location data transmitted through the network
from each node to the first computer, the data being associated with a predetermined
time;
for each node, associating the node at the predetermined time with at least one of
a plurality of node groups based at least in part on at least one of (a) a velocity
of the node, and (b) a measure of traffic on the network;
for each node group, determining whether a source has been detected by the node group
by combining, in the first computer (2), sensor data and location data collected from
the nodes in the group and comparing, in the first computer (2), the combined sensor
data and location data to at least one predetermined detection criterion; and
signaling, with the output, at least whether a source (30) has been detected.
2. A method of detecting a source (30) comprising iteratively repeating the method of
claim 1 at each of a plurality of predetermined times.
3. The method of any of claims 1-2 wherein each node (5a-d, 11-25) is associated at the
predetermined time with at least one of a plurality of node groups based solely on
a velocity of the node.
4. The method of any of claims 1-2 wherein each node (5a-d, 11-25) is associated at the
predetermined time with at least one of a plurality of node groups based solely on
a measure of traffic on the network.
5. The method of any of claims 1-2 wherein each node (5a-d, 11-25) is associated at the
predetermined time with at least one of a plurality of node groups based also on a
position of the node.
6. The method of any of claims 1-5 wherein associating each node (5a-d, 11-25) at the
predetermined time with at least one of a plurality of node groups is carried out
by a second computer (3, 4) that is not the first computer (2).
7. The method of any of claims 1-5 wherein associating each node (5a-d, 11-25) at the
predetermined time with at least one of a plurality of node groups is carried out
by a plurality of secondary computers (3, 4) none of which is the first computer ().
8. The method of any of claims 1-5 wherein associating each node (5a-d, 11-25) at the
predetermined time with at least one of a plurality of node groups is carried out
by the first computer (2).
9. A system of detecting a source comprising:
a first computer (2) having an input configured to receive data, a memory, a processor,
and an output;
wherein the system is configured to:
collect, in the first computer (2), data transmitted through a network from each node
of a plurality of nodes (5a-d, 11-25), each node including a sensor capable of collecting
data, wherein the sensors are radiation or chemical sensors, the data comprising at
least sensor data collected by the sensor and location data representing at least
the location of the node, the data being associated with a predetermined time;
for each node (5a-d, 11-25), associate the node at the predetermined time with at
least one of a plurality of node groups based at least in part on at least one of
(a) a velocity of the node, and (b) a measure of traffic on the network;
for each node group, determine whether a source (30) has been detected by the node
group by combining, in the first computer (2), sensor data and location data collected
from the nodes in the group and comparing, in the first computer (2), the combined
sensor data and location data to at least one predetermined detection criterion; and
signal, with the output, at least whether a source (30) has been detected.
10. The system of claim 9 further configured to associate each node (5a-d, 11-25) at the
predetermined time with at least one of a plurality of node groups based solely on
a velocity of the node.
11. The system of claim 9 further configured to associate each node (5a-d, 11-25) at the
predetermined time with at least one of a plurality of node groups based solely on
a measure of traffic on the network.
12. The system of claim 9 further configured to associate each node (5a-d, 11-25) at the
predetermined time with at least one of a plurality of node groups based also on a
position of the node.
13. The system of any of claims 9 to 12 further comprising at least one secondary computer
(3, 4), wherein the at least one secondary computer (3, 4) is configured to associate
each node (5a-d, 11-25) at the predetermined time with at least one of a plurality
of node groups.
1. Verfahren zur Erkennung einer Quelle (30) mittels
einer Mehrzahl von Knoten (5a-d, 11-25), wobei jeder Knoten einen Sensor, der zum
Sammeln von Daten imstande ist, wobei die Sensoren Strahlungs- und chemische Sensoren
sind, und einen Sender umfasst, der so ausgelegt ist, dass er wenigstens vom Sensor
gesammelte Sensordaten und Standortdaten sendet, die wenigstens den Standort des Knotens
darstellen;
eines Netzwerks, das zum Zulassen von Übertragung von Daten zwischen und unter der
Mehrzahl von Knoten und zwischen der Mehrzahl von Knoten und mindestens einem Computer
imstande ist; und
eines ersten Computers (2) mit einem Eingang, der zum Empfangen von Daten ausgelegt
ist, die von Knoten durch das Netzwerk gesendet werden, einem Speicher, der zum Sammeln
von Daten ausgelegt ist, die von den Knoten durch das Netzwerk gesendet werden, einem
Prozessor, der so ausgelegt ist, dass er Sensor- und Standortdaten kombiniert und
die kombinierten Daten mit einem vorgegebenen Erkennungskriterium vergleicht, um zu
bestimmen, ob eine Quelle erkannt wird, und einem Ausgang;
wobei das Verfahren umfasst:
Sammeln von Standortdaten im ersten Computer (2), die von jedem Knoten durch das Netzwerk
an den ersten Computer gesendet werden, wobei die Daten mit einer vorgegebenen Zeit
assoziiert sind;
Assoziieren für jeden Knoten des Knotens zu der vorgegebenen Zeit mit mindestens einer
von einer Mehrzahl von Knotengruppen wenigstens zum Teil basierend auf mindestens
einem von (a) einer Geschwindigkeit des Knotens und (b) einem Verkehrsausmaß im Netzwerk;
Bestimmen für jede Knotengruppe, ob eine Quelle erkannt wurde, durch die Knotengruppe
durch Kombinieren von Sensordaten und Standortdaten, die von den Knoten in der Gruppe
gesammelt werden, im ersten Computer (2) und Vergleichen der kombinierten Sensordaten
und Standortdaten im ersten Computer (2) mit mindestens einem vorgegebenen Erkennungskriterium;
und
Signalisieren mit dem Ausgang wenigstens, ob eine Quelle (30) erkannt wurde.
2. Verfahren zur Erkennung einer Quelle (30), umfassend ein iteratives Wiederholen des
Verfahrens nach Anspruch 1 zu jeder einer Mehrzahl von vorgegebenen Zeiten.
3. Verfahren nach einem der Ansprüche 1 bis 2, wobei jeder Knoten (5a-d, 11-25) zur vorgegebenen
Zeit ausschließlich basierend auf einer Geschwindigkeit des Knotens mit mindestens
einer von einer Mehrzahl von Knotengruppen assoziiert wird.
4. Verfahren nach einem der Ansprüche 1 bis 2, wobei jeder Knoten (5a-d, 11-25) zur vorgegebenen
Zeit ausschließlich basierend auf einem Verkehrsausmaß im Netzwerk mit mindestens
einer von einer Mehrzahl von Knotengruppen assoziiert wird.
5. Verfahren nach einem der Ansprüche 1 bis 2, wobei jeder Knoten (5a-d, 11-25) zur vorgegebenen
Zeit auch basierend auf einer Position des Knotens mit mindestens einer von einer
Mehrzahl von Knotengruppen assoziiert wird.
6. Verfahren nach einem der Ansprüche 1 bis 5, wobei das Assoziieren jedes Knotens (5a-d,
11-25) zur vorgegebenen Zeit mit mindestens einer von einer Mehrzahl von Knotengruppen
von einem zweiten Computer (3, 4) durchgeführt wird, der nicht der erste Computer
(2) ist.
7. Verfahren nach einem der Ansprüche 1 bis 5, wobei das Assoziieren jedes Knotens (5a-d,
11-25) zur vorgegebenen Zeit mit mindestens einer von einer Mehrzahl von Knotengruppen
von einer Mehrzahl von sekundären Computern (3, 4) durchgeführt wird, von welchen
keiner der erste Computer () ist.
8. Verfahren nach einem der Ansprüche 1 bis 5, wobei das Assoziieren jedes Knotens (5a-d,
11-25) zur vorgegebenen Zeit mit mindestens einer von einer Mehrzahl von Knotengruppen
vom ersten Computer (2) durchgeführt wird.
9. System zum Erkennen einer Quelle, umfassend:
einen ersten Computer (2) mit einem Eingang, der zum Empfangen von Daten ausgelegt
ist, einem Speicher, einem Prozessor und einem Ausgang;
wobei das System ausgelegt ist zum:
Sammeln von Daten im ersten Computer (2), die von jedem Knoten einer Mehrzahl von
Knoten (5a-d, 11-25) durch ein Netzwerk gesendet werden, wobei jeder Knoten einen
Sensor umfasst, der zum Sammeln von Daten imstande ist, wobei die Sensoren Strahlungs-
oder chemische Sensoren sind, die Daten wenigstens vom Sensor gesammelte Sensordaten
und Standortdaten umfassen, die wenigstens den Standort des Knotens darstellen, wobei
die Daten mit einer vorgegebenen Zeit assoziiert sind;
Assoziieren für jeden Knoten (5a-d, 11-25) des Knotens zu der vorgegebenen Zeit mit
mindestens einer von einer Mehrzahl von Knotengruppen wenigstens zum Teil basierend
auf mindestens einem von (a) einer Geschwindigkeit des Knotens und (b) einem Verkehrsausmaß
im Netzwerk;
Bestimmen für jede Knotengruppe, ob eine Quelle (30) erkannt wurde, durch die Knotengruppe
durch Kombinieren von Sensordaten und Standortdaten, die von den Knoten in der Gruppe
gesammelt werden, im ersten Computer (2) und Vergleichen der kombinierten Sensordaten
und Standortdaten im ersten Computer (2) mit mindestens einem vorgegebenen Erkennungskriterium;
und
Signalisieren mit dem Ausgang wenigstens, ob eine Quelle (30) erkannt wurde.
10. System nach Anspruch 9, ferner ausgelegt zum Assoziieren jedes Knotens (5a-d, 11-25)
zur vorgegebenen Zeit ausschließlich basierend auf einer Geschwindigkeit des Knotens
mit mindestens einer von einer Mehrzahl von Knotengruppen.
11. System nach Anspruch 9, ferner ausgelegt zum Assoziieren jedes Knotens (5a-d, 11-25)
zur vorgegebenen Zeit ausschließlich basierend auf einem Verkehrsausmaß im Netzwerk
mit mindestens einer von einer Mehrzahl von Knotengruppen.
12. System nach Anspruch 9, ferner ausgelegt zum Assoziieren jedes Knotens (5a-d, 11-25)
zur vorgegebenen Zeit auch basierend auf einer Position des Knotens mit mindestens
einer von einer Mehrzahl von Knotengruppen.
13. System nach einem der Ansprüche 9 bis 12, ferner umfassend mindestens einen sekundären
Computer (3, 4), wobei der mindestens eine sekundäre Computer (3, 4) so ausgelegt
ist, dass er jeden Knoten (5a-d, 11-25) zur vorgegebenen Zeit mit mindestens einer
von einer Mehrzahl von Knotengruppen assoziiert.
1. Procédé de détection d'une source (30) au moyen :
d'une pluralité de nœuds (5a-d, 11-25), chaque nœud comportant un capteur pouvant
collecter des données, les capteurs étant des capteurs de rayonnement ou chimiques,
et un émetteur configuré pour transmettre au moins des données de capteur collectées
par le capteur et des données d'emplacement représentant au moins l'emplacement du
nœud ;
d'un réseau pouvant autoriser la transmission de données entre et parmi la pluralité
de nœuds et entre la pluralité de nœuds et au moins un ordinateur ; et
d'un premier ordinateur (2) ayant une entrée configurée pour recevoir des données
transmises par des nœuds à travers le réseau, une mémoire configurée pour collecter
des données transmises par les nœuds à travers le réseau, un processeur configuré
pour combiner des données de capteur et d'emplacement et comparer les données combinées
à un critère de détection prédéterminé pour déterminer si une source est détectée,
et une sortie ;
le procédé comprenant :
la collecte, dans le premier ordinateur (2), de données d'emplacement transmises à
travers le réseau depuis chaque nœud au premier ordinateur, les données étant associées
à un moment prédéterminé ;
pour chaque nœud, l'association du nœud au moment prédéterminé avec au moins un d'une
pluralité de groupes de nœuds au moins en partie sur la base d'au moins un élément
parmi (a) une vitesse du nœud, et (b) une mesure de trafic sur le réseau ;
pour chaque groupe de nœuds, la détermination qu'une source a été détectée ou non
par le groupe de nœuds par combinaison, dans le premier ordinateur (2), de données
de capteur et de données d'emplacement collectées depuis les nœuds dans le groupe
et comparaison, dans le premier ordinateur (2), des données de capteur et données
d'emplacement combinées à au moins un critère de détection prédéterminé ; et
le signalement, avec la sortie, au moins qu'une source (30) a été détectée ou non.
2. Procédé de détection d'une source (30) comprenant la répétition itérative du procédé
de la revendication 1 à chacun d'une pluralité de moments prédéterminés.
3. Procédé de l'une quelconque des revendications 1 et 2 dans lequel chaque nœud (5a-d,
11-25) est associé au moment prédéterminé avec au moins un d'une pluralité de groupes
de nœuds uniquement sur la base d'une vitesse du nœud.
4. Procédé de l'une quelconque des revendications 1 et 2 dans lequel chaque nœud (5a-d,
11-25) est associé au moment prédéterminé avec au moins un d'une pluralité de groupes
de nœuds uniquement sur la base d'une mesure de trafic sur le réseau.
5. Procédé de l'une quelconque des revendications 1 et 2 dans lequel chaque nœud (5a-d,
11-25) est associé au moment prédéterminé avec au moins un d'une pluralité de groupes
de nœuds également sur la base d'une position du nœud.
6. Procédé de l'une quelconque des revendications 1 à 5 dans lequel l'association de
chaque nœud (5a-d, 11-25) au moment prédéterminé avec au moins un d'une pluralité
de groupes de nœuds est réalisée par un deuxième ordinateur (3, 4) qui n'est pas le
premier ordinateur (2).
7. Procédé de l'une quelconque des revendications 1 à 5 dans lequel l'association de
chaque nœud (5a-d, 11-25) au moment prédéterminé avec au moins un d'une pluralité
de groupes de nœuds est réalisée par une pluralité d'ordinateurs secondaires (3, 4)
dont aucun n'est le premier ordinateur ().
8. Procédé de l'une quelconque des revendications 1 à 5 dans lequel l'association de
chaque nœud (5a-d, 11-25) au moment prédéterminé avec au moins un d'une pluralité
de groupes de nœuds est réalisée par le premier ordinateur (2).
9. Système de détection d'une source comprenant :
un premier ordinateur (2) ayant une entrée configurée pour recevoir des données, une
mémoire, un processeur, et une sortie ;
le système étant configuré pour :
collecter, dans le premier ordinateur (2), des données transmises à travers un réseau
depuis chaque nœud d'une pluralité de nœuds (5a-d, 11-25), chaque nœud comportant
un capteur pouvant collecter des données, les capteurs étant des capteurs de rayonnement
ou chimiques, les données comprenant au moins des données de capteur collectées par
le capteur et des données d'emplacement représentant au moins l'emplacement du nœud,
les données étant associées à un moment prédéterminé ;
pour chaque nœud (5a-d, 11-25), associer le nœud au moment prédéterminé avec au moins
un d'une pluralité de groupes de nœuds au moins en partie sur la base d'au moins un
élément parmi (a) une vitesse du nœud, et (b) une mesure de trafic sur le réseau ;
pour chaque groupe de nœuds, déterminer si une source (30) a été détectée par le groupe
de nœuds en combinant, dans le premier ordinateur (2), des données de capteur et des
données d'emplacement collectées depuis les nœuds dans le groupe et en comparant,
dans le premier ordinateur (2), les données de capteur et données d'emplacement combinées
à au moins un critère de détection prédéterminé ; et
signaler, avec la sortie, au moins si une source (30) a été détectée.
10. Système de la revendication 9 également configuré pour associer chaque nœud (5a-d,
11-25) au moment prédéterminé avec au moins un d'une pluralité de groupes de nœuds
uniquement sur la base d'une vitesse du nœud.
11. Système de la revendication 9 également configuré pour associer chaque nœud (5a-d,
11-25) au moment prédéterminé avec au moins un d'une pluralité de groupes de nœuds
uniquement sur la base d'une mesure de trafic sur le réseau.
12. Système de la revendication 9 également configuré pour associer chaque nœud (5a-d,
11-25) au moment prédéterminé avec au moins un d'une pluralité de groupes de nœuds
également sur la base d'une position du nœud.
13. Système de l'une quelconque des revendications 9 à 12 comprenant en outre au moins
un ordinateur secondaire (3, 4), l'au moins un ordinateur secondaire (3, 4) étant
configuré pour associer chaque nœud (5a-d, 11-25) au moment prédéterminé avec au moins
un d'une pluralité de groupes de nœuds.