GB2476511A

GB2476511A - Determining the geometry of a network of nodes using a mass spring model

Info

Publication number: GB2476511A
Application number: GB0922619A
Authority: GB
Inventors: Timothy Bauge
Original assignee: Thales Holdings UK PLC
Current assignee: Thales Holdings UK PLC
Priority date: 2009-12-23
Filing date: 2009-12-23
Publication date: 2011-06-29
Also published as: GB0922619D0

Abstract

A method and system for determining the geometry of a plurality of nodes using a mass spring model. The method comprises: estimating a position of each node; determining a range or distance between each pair of neighbouring nodes; providing a confidence level for the estimated position of each node; setting a respective estimated position adjustment factor for each node; setting a spring length between each pair of neighbouring nodes; and determining the geometry of the group of nodes using the mass spring model. The nodes may contain sensors, and may also have a transceiver and a processor, the transceivers being used to establish the ranges between nodes.

Description

Method and system for determining the geometry of a plurality of nodes

Field of the Invention

The invention relates to a method and system for determining the geometry of a plurality of nodes using their inter-node range measurements.

Background of the Invention

Sensor node networks can be viewed as a group of devices, each with a limited computing and communication capability, located over a predefined area. Dependent on the application the network of devices can collect data associated with a wide variety of physical or environmental conditions. For example, typically the sensors can monitor sound, pressure, temperature, vibration, motion or pollutants, at different locations over a given area. Sensor networks can find applications in healthcare, home automation, traffic control and security. In addition to monitoring the above parameters the network can integrate the collected data and/or answer queries related to the data.

Each sensor device in the network defines a node of the network, and each sensor device typically has a radio transceiver, a small microprocessor and an energy source such as a battery. The exact location of the sensor node in the area to be monitored is often critical information and this is added to the metadata of the sensing information transmitted by the node to a gateway node of the network.

Methods of detecting the location of a node vary from manually establishing look up tables, which are established during deployment or through a post deployment survey, to fully automated and dynamic localisation of nodes, which involves deploying and letting the devices work out where they are. Localisation information may take a variety of forms, which will be appropriate to different scenarios: (x, y, z) information may be required in some cases, while semantic localisation (e,g. "first-floor, main bedroom ceiling") may be required in others.

Accordingly, as envisaged, localisation in sensor networks cannot be achieved through a "one size fits all" solution. For example, a network of sensors attached to a person are strategically deployed around the external area of that person's body and remain in a fixed relative position to one another. Predetermined semantic locations typically dictate the deployment of each sensor, for example any ECG sensors are normally required to be deployed on the person's chest. The position of the body area sensor network in a global frame of reference is determined by the position of one of its devices.

As a gateway node has more resources than the sensor devices that form the other nodes, and has a wider area network connectivity, it may be localised either through GPS or UWB indoors for example, or by cellular network triangulation. A large-scale environmental sensor field on the other hand has very different localisation constraints.

The large number of devices precludes strategic positioning, and the restricted resources on the devices cannot support external localisation such as GPS. Sensor network specific solutions must therefore be tailored to specific requirements of the scenario.

Techniques for determining localisation in sensor networks have been proposed. One such method is based on a mass spring model that can be used to determine the geometry of a group of sensor devices using their inter-device range measurements.

The sensor devices can be considered as free moving masses, and the inter-device ranges can be considered as springs between the vertices. The nominal length of the spring is the measured range between the devices, and this data can be applied to an estimated position algorithm so designed to minimise the sum of all forces applied to the springs. If a global minimum is achieved, the resulting geometry provides an estimation of the physical geometry. Such an approach is described in a paper by N. Priyantha, H. Balakrishnan, E. Demaine, S. Teller entitled <<Anchor-Free Distributed Localization in Sensor Networks." Tech Report #892, MIT Lab for Computer Science, April 2003, the whole contents of which are incorporated herein by reference.

Such an algorithm can be run in a distributed fashion, which is particularly appropriate in the context of sensor networks. This is an iterative and concurrent method, in which each node independently calculates the forces which are applied by the springs attached to it. The resultant force indicates the direction in which the particular node's estimated position should be shifted. The distance that it should move is proportional to the force, the actual proportion being an algorithm parameter known as "an estimated position adjustment factor. The new estimated position of each node is shared amongst its neighbours (one hop neighbourhood only), ready for the next iteration. The algorithm stops when the resulting forces on the nodes become less than a given threshold. This process is also described in detail in the following paragraphs with reference to figure 1.

Referring to figure 1, the process 100 commences with an initialisation process including setting an initial estimated position of each node in the sensor network (step 102). The initial estimated position of each node can be set based on statistics obtained from previous computations. It is further noted that the initial estimated position of at least one of the node could also be set by means of a positioning device such as a GPS receiver or a manual configuration.

In step 104, a range between each pair of neighbouring nodes is determined.

Generally, two range measurements are taken for each pair of neighbouring nodes and a single value is subsequently obtained (for example by computing the mean between the two range measurements) to provide a determined range between the pair of nodes.

In step 106, a signal is broadcast from each node to its "one hop" neighbour. Such a signal contains information such as the determined range value and/or its initial estimated position.

A mass spring model computation is applied in step 108 to determine the forces which are applied to each node in order to derive a resultant force for each respective node.

The estimated position of each node is then updated accordingly (step 110) by moving the node in the direction of the resultant force. The distance that is moved is proportional to the resultant force, the actual proportion being an algorithm parameter known as "an estimated position adjustment factor".

In step 112, it is determined whether the resultant force associated with the forces applied to the local node is below a predetermined threshold level. In other words, it determines whether the local minimum is achieved. If the associated force is above a predetermined threshold level, steps 102 to 110 of the process will be repeated.

Otherwise, the current estimated position of the node is considered as the determined position of the node as the output of the algorithm, and it will be decided whether to stop the process completely or to switch to low iteration rate (steps 114, 116) to enter a position maintenance phase.

The known mass spring optimisation algorithm has a number of prerequisites, and a major caveat * A coordinate system must be defined in which to describe the node's positions, and initial positions must be defined for each node prior to performing the optimisation.

* The caveat in the mass spring optimisation is the possibility of a folded geometry as a result of the algorithm (usually associated with a local minimum of the optimisation process).

Summary of the Invention

The present invention strives to provide an improved method of determining the geometry of a group of nodes, more particularly, but not exclusively, sensor nodes used in a wireless sensor network (WSN).

According to one aspect of the present invention, there is provided a method of determining the geometry of a group of nodes using a mass spring model, the method comprising: estimating a position of each node; determining a range between each pair of neighbouring nodes; providing a confidence level for the estimated position of each node; setting a respective estimated position adjustment factor for each node; setting a spring length between each pair of neighbouring nodes; and determining the geometry of said group of nodes.

In one embodiment the confidence level is used for setting the estimated position adjustment factor, the estimated position adjustment factor value being a function of the confidence level value, and the determined range is used for setting the spring length, the spring length value being a function of the determined range. The functional relationships may be linear, and in one embodiment the confidence level measurement is used as the value for the estimated position adjustment factor value, and the determined range is used as the value for the spring length.

The determined range may be an average measured range based on at least two measurements of range between each pair of neighbouring nodes.

In one embodiment an initial confidence level value may be determined based on the manner in which the estimated position is initially set. For example, the confidence level value is set to "low" if the initial estimated position is set based on statistics, and is set to "high" if the estimated position is set by means of a GPS receiver or other manual configuration.

In an embodiment the method comprises the step of providing information from each node to its "one hop" neighbouring node. The information including the determined range between said each node and a neighbouring node, said estimated position, and/or said position confidence level.

In one embodiment of the present invention, the method determines forces applied to each node and calculates a resultant force of said node based on said determined forces.

Accordingly, the method calculates and allocates different estimated position adjustment factor values to each node in the model, so that the estimated position adjustment factor of each node is a function of the confidence level attached to the node's estimated position.

In another embodiment, the confidence level may be modified based on two major factors, namely: (1) the resultant force is below a predetermined threshold level and a global minimum is achieved -in this case the confidence value increases, or (2) external factors such as when the node is been physically moved, for example, if there is an indication that the node has been moved, the confidence level will be reduced accordingly.

In another embodiment the method comprises iterating the steps of any one of claims 1 to 10 if said resultant force is above a predetermined threshold.

In a further embodiment the method comprises adapting an iteration rate for iterating the steps of any one of claims I to 10.

The method may further comprise providing an output once the global minimum is achieved. The output may include the estimated position of the node and the corresponding confidence value of the estimated position.

In another aspect of the invention there is provided a system for determining the geometry of a group of nodes in a network using a mass spring model, the system comprising a plurality of nodes, each node having a transceiver and a processor adapted to determine a range between itself and a plurality of neighbouring nodes, said system adapted to estimate a position of each node and to provide a confidence level for the estimated position of each node; and a spring length for each pair of neighbouring nodes for determining the geometry of the group of nodes using the mass spring model.

In a further aspect of the invention there is provided a node for use in a node network, said node having a transceiver and a processor adapted to estimate its position, determine a range between itself and neighbouring nodes in said node network, a spring length between itself and each neighbouring node and a confidence level for the estimated position for itself.

In a yet further aspect of the invention there is provided a sensor node network wherein each node has a transceiver and a processor adapted to estimate its position, determine a range between itself and neighbouring nodes in said node network, a spring length between itself and each neighbouring node and a confidence level for the estimated position for itself.

In different embodiments this localised position data may be retained locally by the neighbouring nodes without further communication, or transmitted to other nodes within the network, for example by way of metadata. The position of each node and/or localised geometry of the neighbouring set of nodes can also be transmitted to a central monitoring unit either directly from each node or indirectly via other nodes, for example via a gateway node. The gateway node or nodes, in view of their increased processing power, may themselves comprise the monitoring unit for determining the geometry of the whole node network.

It will be appreciated from the above that the geometry of a node network employs inter-node distance measurements. The node network may be divided into localised groups of neighbouring nodes, each node adapted to determine the geometry of its own group of neighbouring nodes using a mass spring model. A single range is calculated from at least two distance measurements made between each pair of neighbouring nodes (an example might be to use the mean of the measurements) and a position of each node is estimated and a respective confidence level is provided for the estimated position. Each mean range determines a respective spring length between neighbouring nodes, and the confidence level determines an estimated position adjustment factor value for each node. Inserting these values into the spring model algorithm provides the location coordinates of the node relative to its neighbouring nodes, the geometry of the neighbouring group of nodes and ultimately this data can be used to provide the whole geometry of the node network.

Brief description of the Drawings

The present invention will be described in greater detail with reference to the accompanying figures, in which: Figure 1 is a flowchart il'ustrating the steps of determining the geometry of a group of nodes in a network using a mass spring model according to the prior art; Figure 2 is a flowchart illustrating the steps of determining the geometry of a group of nodes in a network using a mass spring model according to an embodiment of the invention; and Figure 3 is a schematic representation of measured ranges between three neighbouring nodes in accordance with an embodiment of the present invention.

Detailed Description

In the following description, specific implementations of the invention are described, It will be appreciated by the reader that these are provided by way of example only, and are not intended to provide restriction or limitation on the scope of the invention which is defined in the appended claims.

The embodiments of the invention make use of a localisation approach. Localisation approaches typically require a combination of the following three components: * A mode, defining which physical stimulus is being measured and processed to derive positions.

* A metric, defining which measurement is applied to the particular mode * An algorithm which combines the measurements into positions.

The embodiments of the invention define a localisation approach which is innovative in the third component, by proposing an enhanced positioning algorithm to integrate nodes with different levels of confidence in their initial position into an optimal set of positions.

The method according to an embodiment of the present invention will now be described with reference to figure 2.

Similarly, this method, which can be carried out concurrently in each node in the network, commences with an initialisation process in step 202. The initialisation process (step 202) includes setting an initial estimated position of each node in the sensor network. The estimated position of each node can be set at random, based on network topology or statistics obtained from previous computation. Alternatively, the initial estimated position of at least one node could also be set close to its actual position by means of a GPS receiver or a manual configuration by an operator. The initialisation process, step 202, further includes determining an initial position confidence level based on the manner in which the initial estimated position is set. The confidence level is set to "low" if the estimated position is set based on statistics, and is set to "high" if the estimated position is set by means of a GPS receiver.

In step 204, a range between each pair of neighbouring nodes is determined.

Generally, two range measurements are carried out for each pair of neighbouring nodes and a single value is computed (for example by taking the mean between the two range measurements) to provide the determined range between the pair of nodes.

An example of determining the range between each pair of nodes will be discussed in detail in due course with reference to figure 3.

The position confidence level may be modified or updated based on two major factors, namely: (1) when a global minimum has been achieved, for example if the resultant force (see step 212) is below a predetermined threshold level, the confidence value will be increased, or (2) external factors such as when the node is been physically moved would cause the confidence level to reduce. A node may detect that it has been physically moved through hardware support, for example by means of an accelerometer integrated with the node, or when there is a significant change in the measured range between itself and its neighbouring nodes.

The position confidence level of a node may be broadcast to its "one hop" neighbours together with its estimated position in an iteration of the algorithm. This allows deadlock situations to be resolved, in the case where one or more nodes are mistakenly *confident in their estimated position leading to all the springs in a neighbourhood to be experiencing tension while all nodes refuse to move.

In step 208, a signal is broadcast from each node to its one hop neighbour. The signal contains information including the determined range between itself and a neighbouring node, the estimated position, and/or the position confidence value.

A mass spring model computation is applied in step 210 to determine the forces which are applied to each node in order to derive a resultant force. The estimated position of each node is updated accordingly (step 212) by moving the node in the direction of the resultant force. The distance in which the node moves is proportional to the estimated position adjustment factor which is a function of the position confidence value.

Therefore, if the confidence value is high, the node will be resistant to change its current estimated position. Conversely, if the confidence value is low, the node will be keen to change its current estimated position. Therefore, the confidence level determines the willingness of the node to change its current estimated position.

In step 214, it is determined whether the resultant force associated with the forces applied to each node is below a predetermined threshold level. If the associated force is above the predetermined threshold level, steps 204 to 214 of the process will be repeated. Otherwise, the current estimated position of the node is considered as the determined position of the node as the output of the algorithm, and it will be decided whether to stop the process completely or to switch to low iteration rate (steps 216 to 218) to enter a position maintenance phase.

Figure 3 illustrates three nodes 1, 2 and 3 arranged in a triangular configuration so that each node has two neighbouring nodes. This arrangement of nodes is shown for simplicity of illustration only for in practice any node will have more neighbouring nodes, typically four or more neighbouring nodes if located within the node network away from the boundary.

Two range measurements are shown in figure 3 for each pair of neighbouring nodes.

A range r 1,2, from node 1 to node 2 as determined by node 1; and a range r 2,1 from node 2 to node I as determined by node 2. It should be noted that these measured ranges are unlikely to be the same, and in practice many more measurements of the ranges between the two nodes 1 and 2 are likely to be taken. These need to be computed into a single value (an example may be to take the mean) to give the determined range between the two nodes. In a similar fashion the average is taken of a series of measurements of the ranges r 2,3 and r 3,2, and r 1,3 and r 3,1 for each pair of neighbouring nodes 2, 3 and 1, 3 respectively to determine the calculated ranges there between.

Once these ranges have been collected (regardless of the technology used to calculate them), an algorithm is required in order to determine the position of the sensor nodes.

One example makes use of ranges provided by different ranging technologies simultaneously. These ranges will typically have widely varying accuracy, depending on which technology has been used to determine them. For example, the ranges between two neighbouring nodes may be determined by monitoring the period between a transmitted signal being sent by one of the neighbouring nodes and the receipt of a reply signal received from the other neighbouring node. Another ranging technology monitors the signal strength of received signals to determine the range, the weaker the signal the greater the range. In some cases, a subset of the ranges in the network may be measured manually and have very high accuracy (for example, the distance between boundary nodes may be a known value, based on the deployment site).

Use is made of a concurrent and anchor free algorithm, based on a mass spring model, To guard against common sources of distortion in the position calculation, the approach is both topology-fold resistant, and optimised to ensure that if a small subset of nodes have accurate initial estimated positions through configuration of additional technology, the topology fold resistance is enhanced.

Before any notion of position can be defined, a coordinate system must be established.

In the absence of any external infrastructure to provide such a coordinate system, the network of sensor nodes must establish its own. In order to have a consistent reference system throughout the network, this process should be performed network-wide. This has the major benefit of not requiring the distribution of range measurements throughout the whole network as the subsequent phases of the algorithm only require ranges to be shared amongst neighbours. A central node and three axes are selected in an iterative process by maximising the hop count between the elected nodes which define the reference system. The aim is to get an origin which is "in the middle" of the network, and to define the axes by using suitably spaced out nodes, called reference nodes, on the periphery of the network.

Once a coordinate system has been defined, the question of initial positions for the nodes is then addressed. The nodes can be placed either at the origin or in random positions. In a technical article by A. Howard, M. Mataric, G. Sukhatme. entitled "Relaxation on a mesh: A formalism for generalized localization." In Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS 2001) Wailea, Hawaii, Oct 2001, the whole contents of which is enclosed herewith by reference, it is mentioned that the mass-spring approach can converge to a local minimum, if nodes start with random coordinates assignment. In N. Priyantha et a!. it is observed in simulations that local optima in spring-based optimisation is most often characterised by sections of the graph folding over, with respect to the true configuration. It is therefore important to use a different approach to selecting initial positions, in order to minimise the chance of folded topologies occurring. One approach bases the initial positions on the communication topology graph. The underlying principle is that given the maximum communication range R, the topology graph can be drawn and vertex positions assigned based on the assumption that two nodes, which can communicate, can be no more than R metres away. Although this approach does not provide a real estimate of position or geometry, it does provide a stretched graph, as the graph's edges are based on the maximum communication range and therefore is very likely to be an overestimate.

One approach of the present invention is that an operator can set the initial positions of a small subset of nodes (possibly a number of nodes at the edge of the physical topology) by means of a GPS to provide said small subset of nodes with a good initial estimated position.

This approach sets the initial positions for all the nodes in the network, ready for the mass spring optimisation phase, in such a way to minimise the possibility of the algorithm settling on a local minimum.

In the approach described above, the mass spring optimisation uses the measured range between two nodes as the nominal length of the spring, and allocates an identical estimated position adjustment factor to all nodes, and identical spring constants to all springs. This assumes that all nodes have the same estimated position confidence. Two situations may invalidate this assumption: (1) An operator manually configures a number of nodes in the network to provide these nodes with accurate estimated positions, while the remaining nodes have a high degree of uncertainty with regard to their estimated position.

(2) Following convergence of the localisation process, nodes may be moved.

This may be detected by means of hardware support such as accelerometers, or software mechanisms such as neighbour set and range analysis. Consequently, a node which have been moved will have a lower confidence level in its estimated position than the unmoved nodes.

In these real-life situations, the assumption that all nodes should be treated equally (i.e. having the same willingness to change their estimated position to converge the localisation algorithm) is flawed. The present invention introduces an optimisation to the default mass spring model, by introducing mechanisms to evaluate a node's confidence in its estimated position. Once this evaluation has been performed, the estimated position adjustment factor values of the nodes are adjusted so that each estimated position adjustment factor value has an influence inversely proportional to the estimated position confidence of the node. The mass spring optimisation phase is then performed as described above.

The estimated position adjustment factor being a function of the confidence level value which is used in the setting phase of the localisation process is one of the novel enhancements introduced to improve the mass-spring optimisation phase and allow the whole localisation scheme to be more resilient against folds if a small number of nodes are preconfigured with more accurate estimated positions, and to converge faster when a small number of nodes are moved within a localised network.

In one embodiment the confidence level is used for setting the estimated position adjustment factor, the estimated position adjustment factor value being a function of the confidence level value, and the determined range is used for setting the spring length, the spring length value being a function of the determined range.

In an embodiment a fully distributed algorithm can be run requiring only local interactions between nodes, especially appropriate to WSNs. The methods dynamically assign the estimated position adjustment factor values in the model to optimise the position calculation process. If a node is confident in its estimated position, it sets its estimated position adjustment factor value to be low in the localisation model. This causes it to be resistant to change its estimated position during the algorithm iterations.

If it has low confidence in its estimated position, it sets its estimated position adjustment factor value to be high in the localisation model. This causes it to be keen to change its estimated position during the algorithm iterations.

A node's confidence in its estimated position may come from a convergence of the positioning algorithm and/or acquiring a position from an external system, GPS, manual

entry for example.

A node may reduce its confidence in its estimated position if a significant proportion of the measured ranges to its neighbour change and/or positioning algorithm has not converged (bootstrapping).

While the foregoing specific description of an embodiment of the invention has been provided for the benefit of the skilled reader, it will be understood that it should not be read as mandating any restriction on the scope of the invention. The invention should be considered as characterised by the claims appended hereto, as interpreted with reference to, but not bound by, the supporting description.

Claims

CLAIMS1. A method of determining the geometry of a plurality of nodes using a mass spring model, the method comprising estimating a position of each node; determining a range between each pair of neighbouring nodes; providing a confidence level for the estimated position of each node; setting a respective estimated position adjustment factor for each node; setting a spring length between each pair of neighbouring nodes; and determining the geometry of the group of nodes using the mass spring model.
2. A method according to claim 1, wherein the determined confidence level is used for setting the estimated position adjustment factor, the estimated position adjustment factor being a function of the confidence level value.
3. A method according to claim 2, wherein the functional relationship is linear.
4. A method according to any one of the preceding claims, wherein the determined range is used as the value for the spring length.
5. A method according to any one of the preceding claims, wherein the determined range is an average measured range based on at least two range measurements between each pair of neighbouring nodes.
6. A method according to any one the preceding claims, wherein the position of each node is estimated based on statistics and/or means of a positioning device or a manual operation.
7. A method according claim 6, further comprising the step of determining an initial confidence value based the manner in which the position of each node is estimated.
8. A method according to any one of the preceding claims, further comprising providing information from each node to at least one "one hop" neighbouring node.
9. A method according to claim 8, wherein said information include the determined range between said each node and a neighbouring node, said estimated position, and/or said confidence level.
10. A method according to any one of the preceding claims, further comprising the step of determining forces applied to each node, and calculating a resultant force of said node based on said determined forces.
11. A method according to claim 10, further comprising providing an output if said resultant force is below a predetermined threshold level.
12. A method according to claim 11, wherein said output includes the estimated position of said node and the corresponding confidence value of said estimated position.
13. A method according to claim 10, further comprises iterating the steps of any one of claims 1 to 10 if said resultant force is above a predetermined value.
14. A method according to claim 13, further comprises adapting an iteration rate for iterating the steps of any one of claims 1 to 10.
15. A system for determining the geometry of a plurality of nodes in a network using a mass spring model, the system comprising a plurality of nodes, each node having a transceiver and a processor adapted to determine a range between itself and a plurality of neighbouring nodes, said system adapted to estimate a position of each node and to provide a confidence level for the estimated position of each node and a respective estimated position adjustment factor for each node, and a spring length between each pair of neighbouring nodes for determining the geometry of the group of nodes using the mass spring model.
16. A system according to claim 15 wherein the system is adapted to determine the geometry of the nodes in a node network comprising a plurality of groups of neighbouring nodes.
17. A system according to claim 15 or claim 16, wherein the system is adapted to set the estimated position adjustment factor based on the confidence level, the estimated position adjustment factor being a function of the confidence level value.
18. A system according to claim 17, wherein the functional relationship is linear.
19. A system according to any one of claims 15 to 18, wherein the determined range is used as the value for the spring length.
20. A system according to any one of claims 15 to 19, wherein the determined range is an average measured range based on at least two range measurements between each pair of neighbouring nodes.
21. A system according to any one of claims 15 to 20, wherein the position of each node is estimated based on statistics and/or means of a positioning device or a manual operation.
22. A system according to claim 21, wherein the system is adapted to determine an initial confidence value based the manner in which the position of each node is estimated.
23. A system according to any one of claims 15 to 22, wherein the nodes are adapted to transmit information to its "one hop" neighbouring node in the node network and/or to a monitoring unit.
24. A system according to claim 23, wherein said information include the determined range between said each node and a neighbouring node, said estimated position, and/or said confidence level.
25. A system according to any one of claims 15 to 24, wherein the system is further adapted to determine forces applied to each node, and to calculate a resultant force of said node based on said determined forces.
26. A system according to claim 25, wherein the system is adapted to provide an output if said resultant force is below a predetermined threshold level.
27. A system according to claim 26, wherein said output includes the estimated position of said node and the corresponding confidence value of said estimated position.
28. A system according to claim 27, wherein the system is adapted to determine said output iteratively at an adaptable iteration rate.
29. A node for use in a node network, said node having a transceiver and a processor adapted to determine a range between itself and neighbouring nodes in said node network, a spring length between itself and each neighbouring node and a respective confidence level for an estimated position of each node.
30. A node according to claim 29, wherein the node is adapted to set an'estimated position adjustment factor.
31. A node according to claim 30, wherein the determined confidence level is used for setting the estimated position adjustment factor, the estimated position adjustment factor being a function of the confidence level.
32. A node according to claim 31, wherein the functional relationship is linear.
33. A node according to any one of claims 29 to 32, wherein the determined range is used as the value for the spring length.
34. A node according to any one of claims 29 to 33, wherein the determined range is an average measured range based on at least two range measurements between each pair of neighbouring nodes.
35. A node according to any one of claims 29 to 34, wherein the position of each node is estimated based on statistics and/or means of a positioning device or a manual operation.
36. A node according to claim 35, wherein the node is adapted to determine an initial confidence value based the manner in which the position of each node is estimated.
37. A node according to any one of claims 29 to 36, wherein the node, when in a node network, is adapted to transmit information to its "one hop" neighbouring node in the node network and/or to a monitoring unit.
38. A node according to claim 37, wherein said information include the determined range between said node and a neighbouring node, said estimated position, and/or said confidence level.
39. A node according to any one of claims 29 to 38, wherein the node is further adapted to determine forces applied to itself, and to calculate a resultant force based on said determined forces.
40. A node according to claim 39, wherein the node is adapted to provide an output if said resultant force is below a predetermined threshold level.
41. A node according to claim 40, wherein the node is adapted to provide said output iteratively at an adaptable iterative rate.
42. A storage medium storing computer executable instructions which, when executed on general purpose computer controlled communication apparatus, cause the apparatus to become configured to perform the method of any one of claims 1 to 14.
43. A computer program product comprising computer executable instructions operable to configure general purpose computer controlled communications apparatus to perform a method in accordance with any one of claims 1 to 14.
44. A method substantially as herein described with reference to any of figures 1 to 3 of the accompanying drawings.