Proceedings of a Workshop on Statistics on Networks (CD-ROM) (2007) / Chapter Skim
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Dynamic Networks--Embedded Networked Sensing (Redux?)--Deborah Estrin, University of California at Los Angeles
Dynamic Networks--Embedded Networked Sensing (Redux?)--Deborah Estrin, University of California at Los Angeles
Pages 120-168
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Dynamic Networks 120
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Embedded Networked Sensing (Redux?
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I do know that they are very interesting systems and data sources, and through this talk, recognize that I am an engineer so, most of what I focus on is the design of new and, hopefully, useful functionality and unlike the other examples here -- power networks, communication networks, social networks, even, biological networks, neural networks -- these networks are just now beginning to be prototyped. There are things that can retrospectively be called sensor networks that have been around for a long time, like our seismic grids.
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Remote sensing has been around for a long time, and generates lots of very interesting data that continues to be a real interesting source of information for scientists, and a source of interesting algorithmic challenges. In situ sensing is a complement to that, where, as everyone knows, a pixel in a remote sensing image represents an average over a very large area.
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When that is the case you actually want to be able to embed sensing up close to the phenomenon, with the hope that it will reveal things that we were previously unable to observe. For us that means that embedded network sensing or sensor networks -- I use that term, embedded network sensing -- you already get a little bit of a clue that I don't think it is so much about the network.
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Embedded network sensing is the embedding of large numbers of distributed devices, and what we mean by large has changed over time. These get deployed spatially, in a spatially dense manner, and in a temporally dense manner, meaning you are able to take measurements continuously, although there are many interesting systems where you might do this over a short period of time, go out and do a survey to develop a model, and you don't necessarily need the system to be there and live for a very long period of time.
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FIGURE 6 The fact is that there is a tremendous amount of potential data, and there are many modalities that you end up deploying that causes these systems to be fairly interesting, even when we are not talking about tens of thousands, or necessarily even thousands of individual devices. Most of the examples I will be describing are from things we are doing now with scientists, although the expectation is that, over time, the engineering enterprise, and even health-related applications will end up dominating.
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FIGURE 8 In particular, we have mature first generation examples of basic routing, of reliable transport, time synchronization, energy harvesting. Many of these nodes are operating based on batteries without any form of continuous recharge of that battery although, as I will be describing, in all the systems we deploy, we have some nodes that are larger and more capable, but energy harvesting largely refers to solar.
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Fortunately, I was wrong in the sense of identifying that as something that would be a non-starter if we didn't achieve it, because doing automated localization is as hard as all the others. You have to solve all the sensor network problems in order to do automated localization.
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First of all, early themes, we talked a lot about the thousands -- actually, I think I even had slides early on that said tens of thousands -- of small devices, and the focus was absolutely on minimizing what every individual node, what each individual node had to do, and exploiting those large numbers, and making sure that the system was completely self configuring because, obviously, at that scale, you have to make the system self configuring. Hopefully, all of those will come back as being key problems to solve.
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At the smallest level you have little micro controller based devices that fit micro controllers that operate off of 130
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We build networks that the data hops maybe three or four of these low band-width wireless hops, before they get to an Internet 131
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The micro servers in the network form that is more of a peer to peer, sort of any to any network, because they have the resources to do that, but in these clouds of motes, they tend to be doing very simple tree forwarding, localized processing, just based on an individual node and passing that data forward, with the rich interface in terms of what a micro server can tell a mote to do. That is interesting because we are starting to put more interesting sensors on our motes themselves -- and I will describe one in a little bit -- such as image sensors or acoustic sensors -- so the image and acoustic sensors, we are not actually wanting to get an image feed or an acoustic feed.
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FIGURE 13 I am going to go back for a moment. One of the shifts here is that it is when you take this heterogeneous collection of devices and you decide, both at design time and at run time what should be done where, who should be doing the processing and the adaptation, we take this perspective now that it is really about this whole system optimization, not just looking at an individual node and seeing how to optimize its particular energy usage.
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The insight that Greg Potti and Bill Kaiser had was that the way you move around more easily and the way you navigate more easily is by using infrastructure, be it roads or rails or what have you. They put the robots up on aerial cables, and the robotic devices are still robots and they are autonomously moving around, but they are elevated above these complex environments in which we tend to deploy things, and they can also lower and elevate sensing and sampling devices.
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The ability to combine these higher end imaging and data collection tools with a statically deployed, simpler sensor ray, has turned out to be of great interest to them, and this is being used by folks who are studying, as I used the example before, of nitrate run off into streams. This is being used not just above ground, but actually to study what is going on inside in the stream as well.
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We do this with a public health faculty member and a graduate student who is trying to get a thesis out of this and actually needs usable data. One of the things that we have come up with is a calibration procedure, whereby this robotic device comes up and dips itself into some fresh water to clean itself off, dips itself into a known solution of nitrate level to do a calibration level, dips itself back, and then continues on its run.
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The last form of heterogeneity that I wanted to mention, and again, I chose these things -- I forgot to say something. In this robotic context, one of the reasons I chose to say this, aside from its importance in center networks, is because there is a lot of opportunity for doing statistics in the network here.
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FIGURE 18 Yet web cams and phone cameras, which are all over the place are not embeddable, because they largely collect rather high data rate images and continuously send them back. What you need is to take that little camera off your cell phone and put it on a small low powered device and program into it simple computations that can allow it to quickly, or not so quickly, locally analyze those images.
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It certainly gives us something that we want to be able to program with very tightly crafted analyses. If you put these on a graph where you have high position cameras, or clusters of web cams, we are talking about down here where we have really small image sizes, really low algorithmic complexity, but the potential of putting out multiple of these things you can actually deal with things like occlusion and multiple perspectives pretty easily.
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FIGURE 20 The last thing that I wanted to mention, which could just be called another form of heterogeneity in these systems, is what I referred to before separately as this issue of moving from completely autonomous to interactive systems, is that last sort of tier in our system which is actually the user. In retrospect, this is completely obvious.
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FIGURE 21 finding that our scientist customers are finding it very useful to look at these systems as systems that allow them to interact with their experiments and their set ups and their measurements interactively, sort of converting this, if you will, their experimental modes form being batched to interactive. Now what we are trying to put them with is the ability to go out there in the field, have access to their various GIS models and data, to remote sensing data, to their statistical models, to their statistical models.
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I don't know how much research there is to do, but there is definitely system building they need for equipping them with pretty nice statistical tool kits that they can take out into the field. Obviously, this in situ data by itself, these points that I say we are always under-sampling, are not most valuable by themselves.
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There are lots of places to follow up such as conferences in the field. How important are statistics to sensor networks?
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I can give you specific answers. For the ecologist it depends on who was asking that question about micro climates and how changes in forest canopy will end up changing the life structure and the micro climates that the ground plant species will end up experiencing, there the relevant scales are cubic meters.
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Of course we are not going to have uniformity with cubic meters. You are talking about some experimental design that is not uniform.
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Since many of these pathways interact with one another, the interacting pathways are called signaling networks. I come from a biology background, and I am trying to use network analysis to understand cellular functions such as those shown in Figures 1 and 2.
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One of the nicest examples of physiological consequences of information processing in single neurons comes from the work of Wendy Suzuki and Emery Brown and others, who published a paper in Science showing that, in live monkeys, during the learning process, there are changes in spike frequency of individual neurons that correlate with learning.
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The overall organization of the hippocampal neuron is summarized in Figure 4. The organization indicates that that receptors that receive extracellular signals regulate the levels and/or activity of the upstream signaling molecules such as Calcium, cyclic AMP, and small GTPases that in turn regulate the activity of the key protein kinases.
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Neurons, T cells and pancreatic beta-cells. If we are studying electrical properties of neurons and how they change in an activity dependent manner, one might focus on early biochemical modifications such as phosphorylation of channels.
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All of the analysis has been done by one graduate student, Avi Maayan. The sort of biology area that I come from, everybody keeps saying, the days of individual laboratories are finished, you need this massive network biology.
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There are edges that are positive -- that means that A stimulates B -- and negative -- that means A inhibits B Then there are these neutral edges, that important for cellular networks because they represent interactions with anchors and scaffolds.
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We constructed a network in silico using a function-based approach. For this we have focused on binary interactions since the validity of these interactions is clear from the biological literature.
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Such distal relationships are not always known and there is some ambiguity about pathways within networks. What we did was to use a function based approach to identify direct interactions and develop a network as a series of binary interactions.
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FIGURE 13 There are groups who study changes in the activity of channels, and the NMDA receptors. Others study changes in gene expression and translation, and actually even the way the spines are formed.
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You can parse these nodes out in these functions, as shown in Figures 14 and 15. FIGURE 15 Figure 15 is a modified Pajek diagram of the CA1 neuron network with triangles as nodes and the size of the triangles indicative of the density of associated edges.
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In cell signaling systems information transfer occurs through chemical reactions. Typically information processing by motifs involves changing the input/output relationship such that there is a change in the amplitude of the output 156
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I really like this motif since it gives you two for the price of one. One, it allows for redundancy of pathways, which is very important in these systems to ensure reliability of signal flow, and two, it essentially works as a positive feedback loop that allows for persistence of the output signal, which almost always alters the interpretation of the signal for the mounting of functional responses.
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Eight is the average number for going from receptor to effector. When we are tracking paths from ligand-receptor interactions to channels or transcription factors we can identify the regulatory motifs that emerge as connectivity propagates through the network.
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As we start at the receptor, at the outside of the cell, the first few steps yield many more negative feedback and feed-forward loops, which would tend to limit the progression of information transfer. As you go deeper into the system, in each of these cases -- with glutamate, norepinephrine, we pick up the positive feedback loops and positive feedforward motifs.
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So, the presence of these regulatory motifs can have real functional consequences. FIGURE 22 The next analysis was to simulate the formation of motifs as signals propagated from the receptor to an effector protein.
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When neurons become potentiated, they behave differently in response to stimuli. The pesudodynamic analyses showed that for norepinephrine, when the number of feed forward and feedback loops were counted for whether they were positive or negative, far more positive loops were engaged with increasing numbers of steps.
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This statistical analysis gives us insight into how the configuration of motifs can affect state change in cells. If we engage more positive feedback and feed forward loops than negative loops we can induce state change (i.e plasticity)
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This type of analyses indicates that we are likely to have a series of dynamic functional modules. The properties of these functional modules are summarized in Figure 26.
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FIGURE 26 Second, we were interested in figuring out what these highly connected nodes do as part of the network. Avi started out with a system of mostly unconnected nodes and then asked the question, what happens to the system if we add nodes with four, five, six links, iteratively.
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FIGURE 29 What we found is that the highly connected nodes disproportionately contribute to the formation of regulatory motifs. Eighty percent of the feedback loops and feed forward motifs occurred as these highly connected nodes come into play.
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A detailed distribution of the various types of motifs in the interaction space between receptor and effector proteins is shown in Figure 31. FIGURE 30 FIGURE 31 Detailed analyses for the location of the various motifs indicate that the motifs are densely clustered around the functional center of the network as shown in Figure 31.
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Thus description of the statistics of networks for somebody like me who works at a cellular level, is very useful in providing an initial picture of the regulatory capabilities of the cellular network. It allows me to design experiments to test which of these regulatory motifs are operative and develop an overall picture that I would never get from a bottom up approach, if I was just studying one feedback loop or two feedback loops at a time.
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2007. "Compartment specific feedback loop and regulated trafficking can result in sustained activation of ras at the golgi." Journal of Biophysics 92:808815.
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