PLASMA: A PLAnetary Scale Monitoring Architecture Demet Aksoy

Computer Science Dept. University of California, Davis

Davis, CA 95616 530-752-3601

aksoy@cs.ucdavis.edu

ABSTRACT While sensor networks continue to attract significant interest in various research communities, high impact applications still have a long list of challenges to be addressed. An individual sensor system can provide important observations within a local area. However, local observations alone are not sufficient for some applications that require planetary scale coverage. Monitoring volcanic activity, nuclear disasters, magnetic field changes, migration patterns of species, pandemic disease spread patterns are some examples to such applications. These applications require a close interaction between different sensor networks with in-situ and remotely sensed observations. In this paper we describe our PLASMA (PLAnetary Scale Monitoring Architecture) project to motivate the challenges that need to be addressed at such scale. These include approximations in spatiotemporal attributes due to resource constraints and also multi-attribute visualization to enable a real-time user interface to the system.

Categories and Subject Descriptors H.H.3 [Information Systems]: Information Storage and Retrieval, C.C.2 [Computer Organization]: Computer Communication Networks.

General Terms Management, Design, Human Factors, Standardization.

Keywords Sensor networks, Planetary scale monitoring.

1. INTRODUCTION Recent advances in wireless technology and the development of small, low-cost, low-energy electronics has enabled deployment of unattended wireless sensor networks in highly distributed environments. There are many defense, scientific, and engineering applications that use wireless sensor networks; such as environmental and habitat monitoring, wildlife tracking, or

vehicle management in automated highways. A sensor's basic functionality is to acquire data from the physical environment, process the data, and communicate the information to other sensors or users.

In general there are two basic categorization of sensors: 1) remote-sensing, where information is acquired (e.g., from the Earth’s surface) without actually being in contact with the environment, and 2) in-situ sensing, i.e., the technology that acquires information at the site. Wearable and embedded sensors are of this category. Remote sensing has advanced significantly with NASA’s Earth Sciences Program and is an invaluable tool for scientists to better understand our world. In-situ sensing, however, is still necessary to provide information about certain physical phenomena, which cannot be captured by remote sensing satellites. For instance, remote sensing satellites can acquire information about the water surface such as the temperature; but they do not provide any sub-surface information such as the acoustics and ion contents of the water [9]. An individual in-situ sensor system can provide important observations within a local area. Yet it is even more desirable to correlate information from heterogeneous sensors for applications that require planetary-scale coverage. Such applications include monitoring earthquakes, tsunamis, volcanic activity, nuclear disasters, magnetic field changes, migration patterns of species, pandemic disease spread patterns such as AIDS or SARS. These applications require an integrated in-situ and remote sensing approach for more accurate interpretation of the data. In this paper we describe our PLASMA (PLAnetary Scale Monitoring Architecture) project to identify multimedia challenges that need to be addressed on such a scale. PLASMA aims at an integrated architecture of heterogeneous sensor networks in highly distributed environments to enable scientists to correlate real time observations. Solutions for traditional sensor networks do not apply to such planetary scale monitoring due to a number of challenges. The major research challenge in PLASMA is the scale, and the level of approximation due to the enormous number of in-situ sensors. In particular, due to resource constraints of sensor nodes it is not possible to acquire all the relevant data. Observations from a sensor node represent only a small sample of the physical phenomena at discrete points in time. The observations can be incomplete or involve some uncertainty due to the physical limitations of the network. The previous metaphor that “the sensor networks are a database” and that declarative queries can be applied over the sensor network is no longer appropriate. Visualization is a major requirement for analysis of such incomplete, and approximated data. In general, location and time

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management complexity and accuracy versus cost trade-offs result in many approximations. Even the location of a sensor node needs to be approximated due to resource limitations. Synchronization, localization and spatio-temporal nature of information require novel data management. The rest of the paper is organized as follows. Section 2 provides highlights of PLASMA. Section 3 discusses visualization for PLASMA and spatio-temporal attributes. Section 4 presents preliminary results and Section 5 concludes this paper.

2. PLASMA HIGHLIGTHS PLASMA (PLAnetary Scale Monitoring Architecture) is a unified framework for heterogeneous sensor networks in a widely distributed environment. Within this framework sensors with different processing and communication capabilities cooperate with each other to respond to various queries. Such heterogeneous systems may include wearable as well as embedded sensors. We categorize the nodes as: 1) Query injection points: these nodes are either directly connected to the Internet or they have a satellite receiver antenna. Note that receiver antennas are significantly cheaper and less energy demanding compared to transmitters. Therefore, it is relatively easier to supply a sufficient number of query injection points within a network. Users can directly access these nodes to issue specific queries to eliminate the need to target query routing to a specific content or area in the network. These nodes also assist with network calibration. 2) Data collection points: these nodes, also referred to as sink nodes [4], are either directly connected to the Internet or have satellite transceivers. In open areas satellites can provide a communications backbone. Satellite based networking systems allow a rapid deployment of sensor networks by setting up a satellite communication dish on a mobile platform, interconnected to a set of routers and switches communicating with mobile wireless sensors. The benefits from ubiquitous and pervasive sensor networking can be reaped from interconnecting the sensors across the wide area satellite networking, thus providing mobility and versatility. 3) Landmarks: these nodes have deterministic knowledge about their positions. If deployed in a field, they have GPS antennas to

receive signals from GPS satellites. They are also referred to as anchor nodes. 4) Ordinary nodes: these nodes have limited energy, storage, and processing capabilities. They rely solely on short-range radio communications. These nodes typically operate on batteries, and expire when they consume their battery power. Note that sensors are distributed in order to maximize their exposure while minimizing cost. Note that a node can take upon multiple tasks when equipped with the corresponding resources. However, the majority of nodes within PLASMA are ordinary nodes. These nodes do not have GPS antennas, and can only estimate their locations. These also do not have a direct link to the users, but receive and forward queries/observations based on multi-hop communications. The queries can be injected to PLASMA in two basic forms: 1) continuous queries where the user specifies a long-term query like “what is the average temperature in this geographic region?” or “let me know when the temperature around this point increases by 10%”. We refer to the first type of queries as aggregate queries, and the second type as alert actions. In [DEM04] we discuss how alerts are transmitted using satellite-based broadcast. 2) ad hoc queries that describe a one-time query like “what is the temperature at this point right now?” Ad hoc queries are usually time sensitive queries where the user is trying to isolate a temporal behavior in relation to other observations made in the system. The queries are directly sent to query injection points and are further propagated within the network when necessary.

Data flow in PLASMA can be categorized as: event-based (push) in response to a previously assigned alert being triggered, or demand-based (pull) in response to ad-hoc queries issued by users. Pull and push data flows [3] need to coexist and be dynamically prioritized to enable multiple queries to be supported in the network. Sensor communications even without the introduction of different data flows is a challenge [4][1]. Location aware algorithms use the location information to calculate the distance between the nodes to estimate the energy required for transmission. Location information can also be used for addressing the nodes.

Figure 1. In the figure, an example deployment of query injection (horizontal cylinders), data collection (vertical cylinders), and the landmark points are shown. The user can interactively alter the network presentation according to the observation being collected

and the relationship among the nodes at a particular snapshot in time.

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Our approach is based on location information for queries issued for a specific location. As we will discuss in Section 2, however, only a small portion of the network will be equipped with GPS antennas to receive signals from GPS satellites. The majority of the network will consist of nodes that can only approximate their location. Also due to resource constraints we will not be able to collect all observations from the network. Within this heterogeneous and approximated framework PLASMA aims to:

. • provide easy to follow visualization: the end users, e.g., scientists, are likely to be people without any background in advanced sensor system administration. These users are expected to be able to manage the system with ease. This requires an easy user interface that can help manage user queries and responses in a fuzzy setting with highly approximated information.

. • provide representative spatial and temporal information: in order to accurately interpret the measurements, the time and the position of the observation should be accurate. In this regard, cost efficient spatial and temporal message tagging is of extreme importance. Note that due to its resource-consumption, cost and size, it is not feasible to deploy GPS at every sensor node. Therefore, the sensor locations are estimated using approximation algorithms. In this regard, it is important to demonstrate confidence intervals for correct interpretation of data.

. • be easy to scale and adapt: the topology of sensor networks change dynamically. For instance, the location of the sensor nodes may change over time with voluntary or non-voluntary displacement due to forces of nature. In addition, due to their limited energy supply sensor expire when their batteries die in hostile or dangerous areas. In these cases, it is not preferable or even desirable to replace batteries from time to time. New sensors can be deployed to help improve existing infrastructure when necessary. Similarly, the application requirements also change dynamically. In case of an emergency, for instance, all resources in the network should evolve to accommodate the mission critical tasks.

. • provide quality of service guarantees: observations from the sensors should provide quality of service guarantees. These can be in the form of delay or accuracy guarantees. Accuracy of the collected data is a major concern especially at this scale.

• be secure in the presence of resource limitations: according to the requirements of the application, security and privacy should be considered with cost-efficient implementations.

Even though PLASMA is a general-purpose framework, our project is driven by two significantly different workload characteristics: 1) bursty data: sensors used in this type of applications do not continuously generate data, and are passive for long durations. Examples include seismic activity monitoring. When a seismic event occurs the observations are generated in a bursty fashion. In case of such an activity, observations need to be transferred to the control station without any significant loss for accurate interpretation, 2) continuous data: sensors used in this type of applications are likely to periodically generate data and requires timely acquisition of in situ sensing data. Note that different applications will have a different time constraints, which can be in minutes or even on hours depending on the application requirements. Examples include water quality monitoring which do not need data acquisition in hours unless an unusual activity is monitored. This workload can be categorized with continuous and periodic operations, and remote

control of the sensing devices. Since this application periodically generates data and data loss is less critical [2].

3. CHALLENGES A number of challenges arise for multimedia data support within PLASMA described as the following. The major research challenge in PLASMA is the scale, and the level of approximation due to the enormous number of in-situ sensors deployed for planetary scale monitoring. In such dense topologies, each sensor needs to communicate and cooperate with a large number of other sensor systems over wireless links that have severe bandwidth and range constraints. Such limitations restrict the use of sophisticated multimedia representations of data. Moreover, a sensor system needs to operate with significant power and other resource constraints. This results in an environment where even the location information has to be approximated within the network. In this section we outline the challenges in two categories: visual interface and spatio-temporal approximation.

3.1 A Visual Interface Because of the potential scale and complexity of the planetary scale sensor data, one of the key challenges is to have a powerful end-user interface that is easy to use and that can effectively express the queries. Previous work ignores this important component and falls short in providing the level of expression required in PLASMA. In particular the user should be allowed to interactively and graphically manipulate and summarize the behaviors and output of the sensors. To effectively manage and understand the sensor applications and data, a visualization research component is critically important. We rely on novel information visualization techniques along with appropriate interface designs for both the specification of sensor deployment and the interpretation of the data. Figure 1 plots an example where a subset of the sensor nodes are plotted over an overlay area using their coordinates. In the figure nodes with satellite receiver antenna are represented by horizontal cylinders, the nodes with vertical cylinders and those with a GPS antenna are represented with the rectangular structure. Recall that the observations collected by these nodes and the relationship between them are time-dependent. In this regard, the communication between them can only be plotted as a function of time. Visualization for any large-scale information is already a challenging task. At planetary scale, however, we are also concerned with approximation in two ways. First the visualization tool should give a realistic view of the sensors available in the area the user is targeting for the query. In particular, the number of the sensors in the area, their location and remaining power can only be approximated due to limited capabilities and uncertainties. For instance, even if the exact number of sensors deployed in a region were known at the deployment stage, we would not know if some sensors are deactivated by forces of nature or by battery drainage. In addition, not all sensors would be equal in terms of how they contribute to the application mission. We will refer to the importance of the observation made by a specific sensor, at a specific location, at a specific time as the utility of the observation. The utility of an observation depends on a number of factors such as: the location of the sensor, how many sensors agree with the result, what was the previous history for this observation (i.e., is there an abrupt change in reported values), how do other

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observations, e.g., remote sensing data, agree/disagree with the observations. The user interface targets both the geographical information and the approximate sensor distribution to help analyze the utility of a potential observation prior to issuing a query. The region of interest should be from different angles, map other relevant information (e.g., weather condition) onto the region, and interactively manipulate the sensors. Our interface research largely focuses on assessing and developing different display and interaction mechanisms for specification of the sensors placement and topology including sensor's position, power, etc. based on our last communication. In Figure 2 we plot an illustration of the geographic area targeted query that is also interested in the layout of ordinary sensor nodes that help collect data. Note that the location of these nodes are only approximated and is subject to error.

Figure 2. The user can interactively alter the angle of the

visualization, zoom into an area of interest, and examine the underlying sensor information. The lower picture is a top

view representation of the zoomed in area including ordinary sensor nodes in the region.

Note that the heterogeneous sensor system is expected to include cameras and microphones, as well. Aligning different visual images from cameras deployed in the area, verification of vision with the sound recorded by microphones in the area might be useful. Note that visualization of collected data from heterogeneous sources brings up a new research issue. An analogy can be pattern recognition in frames from multiple pictures/movies in a completely mixed up order.

Figure 3 plots the estimated locations in comparison to the actual coordinates of these nodes to account for the error made in the estimations. As illustrated, since the location of nodes is approximated, there is a significant error margin for data analysis. In this regard, we incorporate the estimation errors made by approximations to enable more reliable data analysis. The new visualization capability in PLASMA can drastically improve the user's ability to dictate the phenomena he/she seeks to record, to understand how the sensors' distribution impacts their functioning, and to interpret large amount of geographically distributed data.

Figure 3. The actual coordinates of the ordinary sensor nodes demonstrated in Figure 2. It is possible that some nodes within

the region are assumed to be elsewhere due to location estimation errors. Similarly it is also possible that some nodes

outside the region are assumed to be within.

One part of our visualization research is concerned with the simultaneous presentation of geographical information and sensor data. The geographical information can be conveniently rendered using the conventional polygon graphics hardware incorporating advanced level-of-details methods developed for large terrain rendering. Focus-context visualization methods enable intuitive and fast information searching [6]. Different sensor attribute data can be displayed using glyphs. Since the sensor data are multidimensional in nature, we investigate multidimensional data visualization techniques to support decision-making.

3.2 Spatio-Temporal Approximation Two essential pieces of information that needs to be reported by an autonomous sensor network is the location and the time of the

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observation made. Unlike traditional multimedia applications, data for the continuous stream might arrive out of order due to adaptive routing within the network. An analogy might be receiving multiple streams from multiple movies and trying to reorder these per movie to be able to accurately display the movie. In case of sensor network applications maintaining these attributes accurately is a major issues by itself. GPS (Global Positioning System) is an established method for determining the location as GPS satellites can obtain the most reliable spatial and temporal information. However, despite recent advances in GPS equipment, it is still expensive and infeasible to deploy all sensors with GPS antennas. In addition to its cost and the GPS antenna size also hinders its use at all sensor nodes within the network. Instead, locations are estimated based on a number of landmarks deployed within the network. A small part of the network consists of nodes that are capable to receive signals from a satellite. These nodes are usually referred to as anchor nodes. Until the cost of GPS equipment drops to reasonable level, it is desirable to keep the number of such nodes at minimum. Localization estimation has been studied in various contexts. Active Badge System [11], and the Bat [12] were among the earliest work for position inference. Active Badge depended on infrared links whereas the Bat system uses a combination of ultrasound and radio to measure the time of flight of a sound pulse. Recent work mostly depends on radio communications. Localization algorithms have two different approaches. In the first approach, anchor nodes, which have known positions, propagate position information to the direct neighbors, and these receiving nodes will forward the position information to their neighbors. As a result, position information will eventually propagate throughout the area. In the second approach, only anchor nodes propagate the position information to the direct neighbors periodically. The second approach requires a dense infrastructure of anchor nodes. Therefore we prefer the first approach for wide-area distribution. We have been studying a wide range of localization algorithms and developing a relative positioning approach for PLASMA. In this section we introduce some basic approaches to motivate the challenges for localization.

3.2.1 DV-Distance DV-Distance [7] is a basic localization algorithm that has motivated many other studies. Radio signal strength is used to estimate the distances from a neighbor. In this algorithm, first, the anchor nodes flood the network with their coordinates. The distance from the anchor node is calculated by summing up the individual distances on the shortest path from the anchor node. Note that unless the nodes along the path are aligned on a straight line, this will be an over estimation of the actual distance between the two nodes. Then, each node computes its position using lateration after hearing from at least three anchors. The main idea of lateration is to find the intersection point of circles around the anchor nodes with a radius of the estimated distance to each anchor node. Note that the intersection point can be solved only if no conflicting information is given which is not possible to avoid

for random ad-hoc topologies. DV-Distance is one major algorithm that has motivated numerous studies that followed. In practice, however, distance estimates based on signal strength are shown to suffer complications due to the nature of radio communications [10][13]. A widely accepted approximation is instead described in the following section. In this study we ignore some recent proposals such as [8] that require additional equipment on each sensor node. Directional antennas also have size concerns that limit their use in sensor nodes.

3.2.2 DV-Hop DV-Hop [7] is similar to DV-Distance algorithm except that distance estimates are based on hop count, i.e., the number of nodes that forward a message along a particular path. Hop count is simply achieved as follows. The first message originating from the anchor node will report a hop count of 0. Each sensor node will record the minimum hop count they have heard from the neighbors, and when forwarding a message from the anchor node, it will increment the hop count by one. In other words all distances are in units of hops. These are later converted to real distance units with the additional information propagated by the anchor nodes. For this purpose, the anchor nodes exchange messages between them to figure out the hop count distance in between them. Since they are aware of their exact locations, they produce a hop distance estimate based on the distance between them. Such estimates are then used to convert hop counts to an actual distance at the sensor nodes. We are currently developing our own localization algorithm. Regardless of the algorithm being used, however, an approximation on the accuracy should be passed on together with the observation for more accurate analysis. In this respect, we have performed a number of simulation experiments to better understand the error bounds in approximate localization techniques. In the following section we demonstrate results from our study.

4. EXPERIMENTS We have implemented a simulator using C++ to compare the two algorithms in various settings. In each setting we feed the simulator with an arbitrary topology and obtain the position estimates of each sensor node. Error in the estimates is initially measured as the Euclidean distance between the real coordinate and the estimated coordinate. For the network topology, we made use of a perfect grid where a node is placed at each integer coordinate. At 100% density, we deploy a sensor at each point in the grid such that all neighbors are equidistance to each other. Then, we gradually deleted random sensors in this grid ensuring that all remaining nodes form a connected network. We alter the grid density and the radio range to study different topologies. We repeated the experiments with different random number generator seeds such that we end up with different topologies each time. In the experiments, we simplify the communication model assuming no message loss or corruption. In practice the communication costs will be higher for all algorithms unless supported by organized scheduling [3].

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Figure 4 (a): Actual X-Coordinates Figure 4 (b): Actual Y-Coordinates

Figure 5 (a): Estimate X-Coordinate for DV-Hop Lateration Figure 5 (b): Estimate X-Coordinate for DV-Hop Lateration

4.1 X and Y Coordinate Estimations In the first experiment, we fixed the location of three anchor nodes at (50,50), (49,49) and (50,48). In 100% density, we deploy 10000 sensors located at each point in the grid such that all neighbors are equidistance to each other. In this experiment, the radio range is set to 5 units. Figures 4 (a) and (b) plot the X and Y coordinates of the sensor nodes in the 100x100 plane. Each node is represented with its coordinates over the x and y-axis. The y-axis of Figure 4 (a) plots the X coordinate that a successful estimate should generate, and the y-axis of Figure 4 (b) plots the Y coordinate, respectively. Figures 5 (a) and (b) plot the X and Y coordinate estimates of the sensor nodes as generated by DV-Hop.

4.2 Estimate Error Representation One major focus of our study is to determine if a node can provide a confidence level in its coordinate estimates. For this purpose, we study the error patterns in relation to the distance from the anchor nodes. For this purpose, we measure the error made relative to the center of gravity of the anchor nodes. We define the error metric as the difference between the actual distances from this reference point to the estimated distance. In this regard positive values indicate an underestimation and negative values indicate an overestimation. In figure 6, we plot the error difference for DV-Hop in the same experiment. When we consider the error difference for DV-Hop, we observe that the estimates are always an underestimation, i.e., the difference is always positive. In addition, we observe a bowl like shape, which suggests that the difference increase as the node is placed further away from the anchor nodes. We did not observe

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a similar pattern for DV-Distance. In fact the error pattern for DV-Distance has both positive and negative values – it either overestimates or underestimates its coordinate relative to the anchor nodes without a specific pattern. In this regard, with DV-Hop, it might be possible to provide an error bound on the real distance to the anchor nodes of estimated position. When we studied larger topologies, we observed that the range of error distance increases directly proportional to the area. Note that in practice errors can be even higher [5]. Currently we are developing a new algorithm, which we observed to provide better estimates and a deterministic error bound. On-going studies for delay tolerant networks (see www.dtnrg.org) can also alter the way these estimations and accuracy propagations are handled in the network.

Figure 6: Error Difference for DV-Hop Lateration

5. CONCLUDING REMARKS We move towards an era of global, persistent, planetary scale sensor networks. Spatio-temporal attributes of data are extremely important and real-time data processing can be the only way to extract meaningful information due to the mass amount of data being generated by sensor networks. In this paper we introduced our PLASMA (PLAnetary Scale Monitoring Architecture) project and discuss the challenges that need to be addressed at such scale. PLASMA aims at an integrated architecture of heterogeneous sensor networks in highly distributed environments to enable scientists to correlate real time observations. The main challenge for PLASMA is the strong time and position dependency of collected information with an inevitable uncertainty and incompleteness factor. In this regard there is a need for novel ways of multimedia data modeling and description. We are currently developing a novel algorithm for position estimation that can realistically represent the network topology based on relative positions of neighbors. Unlike previous work

our focus is on the complete topology rather than individual node position accuracy.

6. ACKNOWLEDGMENTS This work is in part supported by a research grant from CENIC.

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