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Experimental Applications of Mapping Services in Wireless Sensor Networks James Shuttleworth, Mohammad Hammoudeh, Elena Gaura, Robert Newman Cogent Computing Applied Research Centre Department of Creative Computing Coventry University, Coventry, UK. CV1 5FB {j.shuttleworth, aa2792, e.gaura, r.m.newman}@coventry.ac.uk be processed, aggregated, distilled and acted upon within the network, possibly with only selected data being reported back to a monitoring user. The wireless sensor nodes are usually more than capable of performing these tasks, as long as the ap- plication developer creates the required software, constructing specific functions from what has become increasingly generic hardware. There has been a consistent effort to change the mechanism of use of certain capabilities in wireless sensor networks, to simplify and abstract them, turning them into services of the network rather than being the result of coordinating the services of individual nodes. TinyDB’s retrieval of data [1], for example, uses the abstraction of SQL to effectively hide the details of data collection, buffering, and transmission. Following on from such work and moving to a slightly higher level of abstraction, we propose a new network service: map generation. The primary purpose of wireless sensor networks is to collect and transmit data, but other capabilities have arisen to support this goal. Just as clustering, routing and aggregation allow for more sophisticated and efficient use of Fig. 1. Height map from USGS data covering the Grand Canyon the network resources, a mapping service would support other network services and make many more applications possible with little extra effort. Abstract— Wireless sensor networks typically gather data at anumber of locations. However, it is desirable to be able to design In this paper, we present our first steps towards a generalapplications and reason about the data in more abstract forms mapping service. We define a simple service, discuss ourthan points of data. This paper examines one way in which this simulation of networks that implement it and present ancan be done. example application built on the service. By bestowing the ability to predict inter-node values uponthe network, it is proposed that it will become possible to buildapplications that are unaware of the concrete reality of sparse II. B ENEFITS OF AN IN -N ETWORK M APPING S ERVICEdata. This interpolation capability is realised as a service ofthe network. We present an implementation of this service and We predict many benefits to result from the development ofdiscuss its merits and shortcomings. an efficient and flexible in-network mapping service. Additionally, we present an initial application of the service Networks of nodes are built to solve problems and manyin the form of isopleth generation. That is, the delineation of problems are essentially problems of interpolation betweencontours of constant parameter value. Finally, we discuss the improvements required to create more points and it is this set of problems that we intend to addresssophisticated applications and services and examine the benefits with a mapping service. Defining lines of constant height orthese improvements would bring. pressure, contours and isobars, for example, requires knowl- edge not just of measurements at a few scattered locations, I. I NTRODUCTION but also the likely values between them. Contours and isobars The purpose of any wireless sensor network is to gather are, in fact, specific types of isopleth and a very simple earlydata. In the most simple systems, the collected data might implementation of isopleth determination is presented later inbe instantly reported. In more complex systems, the data will this paper.

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Another problem that is at heart simply a problem of inter- of geometric parameter interpolation has been shown to workpolation is surface reconstruction. While mesh-based models well for reconstructing underlying geography when the entireare feasible and useful for specific applications int he con- network has been queried [2]. However, It does not extendtext of WSNs [2], an interpolation-based model of surface well to variable surfaces or overlapping local mapping, sincereconstruction offers benefits such as being locally applicable, it requires a complete data set to define the surface.of arbitrary detail and easily subjected to image processing A more general method is interpolation by inverse distancetechniques. and, specifically, Shepard [4] interpolation which improves on Other aspects of wireless sensor networks that would benefit it.from such a service include clustering, in which geographical The simple inverse distance algorithm, used in WSN appli-context might be useful; deployment, for which the mapping cations before [5], is defined as:service could provide information on network density related  Nto terrain or phenomenon complexity, and so on. d−u zi    i=1 i   As well as these easy to identify benefits, it is likely that  if d = 0 for all Di    Nhaving such a service would make new applications obvious, f (P ) = −u dijust as there has been as recent increase in new Internet    i=1applications combining existing services to produce new and   until now unthought-of “mashups”.    Of course, the efficacy and efficiency of applications based zi if di = 0 for some Dion such a service are tied to how well the service is imple- Where, P is the point at which the interpolated value ismented. required, di is the distance from P to the point numbered i The implementation of the mapping service presented in in the N known points and zi is the known value at pointthis paper, and the example application built upon it, are not i. The exponent, u, is used to control the smoothness of theoptimal. In short, they are inefficient and na¨ve. However, ı interpolation. High values lead to sharp edges between regionswhile more sophisticated implementations of the algorithm while low values lead to soft edges.are being devised and tested, this simple approach gives us Shepard [4] devised a number of improvements to this basican opportunity to investigate the usefulness and validity of its algorithm to limit the effect of distant points, make use of theapplication. direction of the relationship between known points and the point to be interpolated and to incorporate information on the III. A LGORITHMS FOR M AP G ENERATION slope between known points. The algorithm, including the first Map generation is essentially a problem of interpolation two of these refinements, has been implemented to interpolatefrom sparse and irregular points. Given a set of known data between sensor readings and is discussed further here.points representing the nodes’ perception of a given measur- IV. S IMULATION WITH S EN S ORable parameter of the phenomenon, what is the most likelycomplete and continuous map of that parameter? In order to implement our mapping approach and study In the field of computer graphics, this problem is known as its properties, we have extended an in-house sensor networkan unorganised points problem, or a cloud of points problem. simulator called “SenSor” [6]. SenSor is a realistic andThat is, since we assume that the position of the points in xy scalable Python based simulator, in which each sensor nodeis known, the third parameter can be thought of as height and runs in its own thread and communicates using the samesurface reconstruction algorithms can be applied. protocols as its physical counterpart. Sensors have a fixed API, Simple algorithms use the point cloud as vertices in the with customisable internals. This enables us to experimentreconstructed surface. These are not difficult to calculate, but with different algorithms for managing the network topology,can be inefficient if the point cloud is not evenly distributed, simulating fault management strategies and so on, within theor is dense in areas of little geometric variation. same simulation. Sensors are modelled as a pool of concurrent, Approximation, or iterative fitting algorithms define a new communicating threads. Individual sensors are able to:surface that is iteratively shaped to fit the point cloud. Al- 1) Gather and process data from a model environmentthough approximation algorithms can be more complex, the 2) Locate and communicate with their (geographically orpositions of vertices are not bound to the positions of points otherwise) nearest neighboursfrom the cloud. For applications in WSNs, this means that we 3) Determine whether they are operating ”correctly” andcan define a mesh density different to the number of sensor act accordingly to alter the network topologynodes, and produce a mesh that makes more efficient use of Separate interfaces gather information from the networkthe vertices. Self organising maps are one of the algorithms and display it. This partitioning allows us to experiment withthat can be used for surface reconstruction [3]. This method different ways of processing individual node data into infor-uses a fixed number of vertices that move towards the known mation. Using this simulation framework, we implement ourdata. mapping services. For our experiments, we provided SenSor Note that surface reconstruction on typical non-overlapping with interpolation capabilities and developed a plug-in to doterrains is equivalent to sparse-data interpolation. This kind draw the isopleths.

3.
For the purposes of the experiment reported here, nodesare designated into logical groups called clusters where eachcluster is managed by a master node called cluster-head,as in LEACH [7]. The use of clusters improves networkperformance and reliability by localising network traffic. Also,cluster-head nodes run a time aggregation function to reduceredundancy and minimise energy consumption by decreasingthe number of transmissions. In this algorithm, robustness isachieved by storing multipath and electing backup node(s) thatcan substitute for the cluster-head when the current clusterround ends or in some failures. During the set-up phase nodeswill select to belong to the nearest cluster-head using thenumber-of-hops metric. The use of this metric will result ina more fair distribution of nodes among clusters and establishshorter paths, hence reducing the energy expenditure sincebridging the distance between the node and its cluster-head Fig. 2. SenSor, the WSN simulator, showing information transmissionwill be less expensive. Because of the random transmission between nodes and a visualisation of the interpolated surfacedelays and synchronisation errors, we use a back-off waitingscheme, similar to one used in [8], to lessen these effects. When a node receives an advertisement message it waits fora constant time to gather more set-up messages; when the timerexpired it rebroadcasts the message with the best metric andresets the back-off timer. The number-of-hops metric togetherwith the back-off time will help to reduce the set-up messagesand create more uniform clusters. During the transmissionphase, sensing nodes transmit data to their cluster-head whichretransmit messages to the querying node. All nodes weregiven a virtually infinite supply of energy and the protocol wasallowed to run until it converged. Since the energy is unlimitedwe have used other metrics to measure energy consumptionlike the number of messages sent. For our experiments, wecreated an 600-node network in such a way that nodes arescattered randomly in area of 256 × 256 m2 . Figure 2 shows arandom topology of 600 nodes scattered over a terrain wherethe edges represent communication neighbours. The power of the sensor radio transmitter is set to cover allnodes within a 50m radius. The processing delay for transmit- Fig. 3. Interpolated field generated from 600 simulated nodes randomlyting a message is randomly chosen between 0 and 5 seconds, distributed on the surface from Figure 1simulating real-world characteristics of low-power radio trans-mission. Using this network configuration we gathered the datato interpolate the landscape and draw the isopleths from the V. E XPERIMENTAL A PPLICATIONinterpolated output. The determination of isopleths or contours is useful in a The landscape image is fed to the simulator and sensors number of applications and the ability to find the “edges”will “perceive” the colour intensity at the xy position of of a phenomenon has been put forward before as a usefulthe landscape image corresponding to their coordinates as operation [9]. Such an ability would enable the systematicgathered sense data. After cluster-heads are selected and paths finding and delineation of the borders of phenomena suchare established, an external observer can choose to query any as gaseous emissions or freezing conditions, creating heightnode to collect the information necessary to interpolate the contour maps, calculating lines of sight, and so on.surface from the network. Every node responds to the query To highlight this potential use of an in-network mappingmessage by sending its sense value and position through the service, we present an implementation of isopleth generation.cluster head. This collected information could then be used to This is a simple implementation, with little regard for effi-build the network surface using the interpolation algorithm. ciency, but nevertheless provides a genuinely useful output for the purposes of visualisation or phenomenon edge delineation. With this framework in place, we were able to experiment With no optimisation or refinement to reduce communica-with isopleth generation.

4.
Fig. 4. A contour at 116 units drawn over Figure 1 Fig. 5. A contour at 116 units drawn on the interpolated field in Figure 3tion costs, the algorithm is very simple. A request is made to In a more advanced implementation, this algorithm wouldany node, giving the value of the required isopleth. A height of be replaced by one that begins with an efficient search for the120 metres, for example, if contours of height were needed. first matching value and then a process of extending the searchThis node is then responsible for collecting the information along the isopleth as it is discovered.needed to produce the result. A threshold figure is also given, VI. N EXT S TEPSt, so that values ±t are included. The simple algorithm presented here causes this node to We have presented, above, our very early research intothen query every other node in the network for their value mapping and the applications of mapping in wireless sensorof the parameter in question. Once all data is collected, the networks. Leading directly from the work in this paper, thereinterpolation is performed as described in Section III, and are a number of avenues that need to be followed.every value matching the required isopleth is recorded as being The na¨ve approach above collects all of the data in a central ıpart of that isopleth. location and processes it there. In networks of a few nodes, This algorithm has been implemented using the simulation this might even be the most sensible solution. However, insoftware SenSor and the simple API described in Section IV. networks of nodes large enough to be useful, the number ofFigure 1 shows a height map of a section of the Grand Canyon, transmissions needed to accomplish the task would become sotaken from data recorded by the US Geological Survey [10]. If large as to require a more intelligent solution.we apply the isopleth generation algorithm to this data directly, The cost of communication in this initial system is verywe are left with a result such as that shown in Figure 4. This high because the interpolation step, the mapping service, iscontour was generated for a height of 116 units and a threshold fairly simple and requires that all data is collected to a point.of 1. For the most simple case, in which the node collecting data is To present the result of generating isopleths on interpolated in direct communication with all other nodes in the network,fields as described above, Figure 5 shows the result of gener- the number of hops is simply N − 1, for N nodes. However,ating an isopleth with the same parameters as in Figure 4, on this is an unlikely situation in any real world application. Thethe interpolated field shown in Figure 3. likelihood is exponential growth with N . Clearly the interpolated terrain is similar to the real surface. Although the cost, in terms of power of transmission of dataDetermining exactly how close the similarity is, and what the in wireless sensor networks far outweighs the usual processingalgorithmic limits to the accuracy of the representation, is a load, the nodes are most often power-limited devices with,problem we are currently investigating. Consider, though, that at best, a fixed schedule of battery replacement, and sothe information used to reconstruct the surface in Figures 3 computational drain on this resource cannot be completelyand 5 is just 600 points, while the original is recorded as overlooked. For small collections of data of around 50 nodes65,536. Taking the position of the nodes into account as extra interpolating 65,536 points (a square field 256 × 256) takesinformation, the reconstruction is built using less than 3% of very little time, just a few seconds, on a desktop computerthe original data. and would take just a little more on modern sensor nodes.

5.
In a real deployment, the number of sensor nodes might We feel that this area of research is pertinent to moderneasily exceed 1,000, depending upon the required density, wireless sensor networks, and in this paper we have takendrastically increasing computational expense. The acceptabil- initial steps towards exploring it. We have identified appli-ity of such a computational load in a wireless sensor network cations, such as isopleth generation, that the service wouldis debatable, but in this case would probably be unacceptable make simple to develop, and challenges that need to be metfor most deployments. before such a service is feasible. Challenges such as local The simplest improvement is to develop a complete imple- interpolation, required to make the service efficient enough tomentation of Shepard’s refinements and assess how this affects be deployed.the accuracy of the interpolated field when compared to a This paper is not the result of a completed project, but theknown real surface or phenomenon. Although the algorithm exposition of the start of one. The problems and limitationspresented by Shepard [4] is decades old, it is often only described above are the opportunities we intend to take andimplemented in its most simple form and it would be of the lines we intend to follow.great benefit to have empirical evidence of the performance of The work presented above has been useful in a numberthe various improvements to this suggested by Shepard, when of ways. Firstly, it allowed us to extend SenSor so that itapplied to data likely to be perceived by sensor networks. is capable of simulating networks with complex predefined A particularly interesting feature of the refinements is that variations of parameters over space. This is a useful advance,they deal with limiting the data included in the weighted since it allows for precise control over the parameters of theaverage, or adjusting weights based on context. These im- virtual deployment.provements might be instantly applicable as ways to reduce Secondly, it gave us an opportunity to examine the appli-communication costs as well as improve the quality of the cability of Shepard interpolation in the reconstruction of ainterpolation. That is, if local information is required, then parameter map from sparsely sampled data. The experienceonly local nodes need be queried. gained here is proving to be invaluable in developing more Taking this idea further, we arrive at the idea of a mapping sophisticated implementations of the service and its applica-service that allows for local querying. Our proposed solution, tions.currently being developed, is an application of the more Finally, we have been able to identify limitations andadvanced mapping service outlined above. problems as well as potential extensions and solutions that An isopleth for a given parameter at a given value can be would otherwise have been missed until later iterations.found by a step-by-step search for the next point in the map at R EFERENCESthe same level. That is, a single point is used as the start of the [1] J. M. Hellerstein, W. Hong, S. Madden, and K. Stanek, “Beyond average:isopleth and the next is found by searching the neighbourhood Towards sophisticated sensing with queries,” in Proceedings of the 2nd International Workshop on Information Processing in Sensor Networksin progressively more detail. The process is repeated until the (IPSN ’03), 2003.end of the isopleth moves out of the mapped region or meets [2] J. K. Shuttleworth, E. I. Gaura, and R. M. Newman, “Surface re-the beginning. The highest level of detail that is searched will construction: Hardware requirements of a som implementation,” in Proceedings of the ACM Workshop on Real-World Wireless Sensordetermine how fine the isopleth is. Networks (REALWSN’06), June 2006, pp. 95–96, aCM ISBN: 1-59593- The same improvements intended to decrease the cost of 431-6.transmission also reduce the cost of computation. The change [3] Y. Yu, “Surface reconstruction from unorganized points using self- organizing neural networks,” in Proceedings of IEEE Visualization,from global to local area interpolation reduces the number of 1999, pp. 61–64.transmissions and makes the task of interpolation significantly [4] D. Shepard, “A two-dimensional interpolation function for irregularly-smaller. spaced data,” in Proceedings of the 1968 23rd ACM national conference. New York, NY, USA: ACM Press, 1968, pp. 517–524. Additionally, since the local interpolation could be per- ´ ´ [5] R. Tynan, G. OHare, D. Marsh, and D. OKane, “Interpolation forformed by local nodes, the computational cost is not just wireless sensor network power management,” in Proceedings of thereduced, but also distributed. That is, rather than individual International Workshop on Wireless and Sensor Networks (WSNET-05). IEEE Press, June 2005.nodes taking big losses in battery life and becoming unusable [6] S. Mount, R. Newman, and E. 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Koivo, “Distributed sensor network data fusion using image processing,” in Proceedings of Systems Communi-application. cations, August 2005, pp. 383–388. Future work may apply the mapping services to other [10] USGS and C. McCabe, “Grand canyon terrain,” Georgia Insti-network exposures such as barriers that might cause discon- tute of Technology Large Geometric Models Archive. http://www- static.cc.gatech.edu/ projects/large models/ index.html, 1998.tinuities in the interpolated map, contours, or many othersophisticated applications.