Lecture Series in Mobile Telecommunications and Networks (1583KB)

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From wireless networks to sensor networks and onward to networked embedded contrtol time, we would collide. Knowledge of time is therefore important in cyberphysical systems. What these things do is that they presage a movement towards not just event-based computing, but time-cum-event-based computing. However, no two clocks in the world agree and the question is, how do you synchronise clocks? What are the limits to synchronisability? The traditional approach to clock synchronisation is really simple. Let us say that this is your route clock, and here is your network, and then these two nodes talk to each other. Essentially, this clock finds out how much ahead of that clock it is, the offset, and then these two clocks talk to each other, with this one saying, ‘I’m this much ahead of you’, and so on. This node [on slide] then finally adds up all these offsets and gets its estimate of time and perspective as well. Spatial smoothing in random networks Random multi-hop network n nodes – Common range for network connectivity – Multiplicity of paths can reduce error – Distributed algorithm Theorem: Synchronization error can be kept bounded in large wireless networks – Lends support for the feastibility of time-based computing in large distributed wireless It turns out that each of these conversations is a little noisy and so a certain error is introduced. Basically, the standard deviation of the error is the sum of the errors along the diameter of the graph, if you will, which grows like the square root of the diameter. If there are n nodes arranged in a plain, then the diameter is [square root n] and so basically the error is growing like [n to the one-fourth]. This means that, in a large network, your synchronisation error will increase and that is not good if you want to build applications. So can we do better? (Giridhar & K ’05) It turns out that you can, and I just want to show you an 15/34 idea. Let us suppose that I have a random network, and suppose that all nodes choose some range with which the network is connected. In a connected graph, there is a multiplicity of paths from one node to another and you can average out the errors over all these paths and actually build very simple distributed algorithms so that – and this is a fundamental result – the standard deviation of error is bounded. We can actually prove that we can keep synchronisation errors bounded in large networks of arbitrary size. That lends support to the feasibility of time-based computation in wireless networks. Object tracking by directional sensors Object moving at constant velocity Domain Directional Sensor Sensor locations and directions are unknown Object locations, tracks and speeds are unknown Only times of crossing are known Goal: Estimate trajectories od all abjects as well as all sensor lines – Optimization problem is highly non-convex – Use adaptive basis tuned to motions of the first two objects (Plarre & K ’05) 16/34 Let me show you one application of tracking an object purely through measurements of time. We have a domain and let us suppose that, in that domain, I throw down a highly directional sensor which could be like a laser beam. Whenever anybody trips the laser beam, I record the time when they crossed the laser beam, so I have time measurements of crossing – but I do not know where the object crossed, in what direction or at what speed. I do not know any of those things – just the time. Let us suppose that I randomly throw down a bunch of such directional sensors, and that objects cross the domain at constant speed. So this object crosses and, as it crosses, I get the measurements of the times of crossing. Then there is another object that crosses and, again, I have measurements of time of crossing and so on. These objects would be moving at different speeds but each object has a constant velocity. Let us suppose that everything about this network is unknown – I do not know the sensor locations or directions, I just ‘air drop’ them. I do not know about object locations, tracks, speeds – none of that, but only the times of crossings are known. However, I want to estimate everything – I want to estimate the positions of all sensors and of all objects and everything. Of course, it is impossible to solve this problem because we have to agree on a co-ordinate system – so we can solve it up to a co-ordinate system, but all these co-ordinate systems are equivalent. The Royal Academy of Engineering 11
Implementation with laser pointers, motes and Lego car 12 The Royal Academy of Engineering I want to show you an application and there is another reason why I have introduced it. It turns out that these kinds of technologies will have a fundamental impact on our university curricula. This is a particularly simple experiment which requires minimal hardware to set it up – in fact, it uses paper cups, a Lego car and these laser beams, and a few of these Berkeley modes, as you see. [Shows video – model vehicles in lab] You have these Berkeley modes and these paper cups and you have a small Lego car with a little flag. When the flag is up, it trips these laser beams and when the flag is down it does not. You just move it up and down the domain. Initially, I was a little sceptical about whether this experiment would work, given the possibilities for numerical error, but I was surprised. If this is the actual path of the object, these black dots indicate the estimates. Also, the system picks up how the laser beams are pointed. I believe that similar things should find their way into our curricula at universities. The third generation of control systems Let me move on to control, and set the historical stage here. We are at the cusp of the third generation of control systems and so, if we think of the first generation of control systems as analogue control systems, the technology for that was electronic feedback amplifiers. The technology created a great deal of need for theory, which was admirably met by people like Bode, Evans, Nyquist and so on. In fact, there is a beautiful book on the history of technology by a professor at MIT, David Mindell, which shows how the fields of computing and communication control all arose together about 60 or so years ago. Starting in about 1960, we had the second generation, which is digital control. This was when digital computers came along, and you could do a little computation before you closed the loop. I believe that people like Rudy Kalman and so on happened to come along at the right time which needed a certain theory, which was a states based theory, and that was supplied by several researchers. At the computer science end, this technology was supported by developments in real-time scheduling which took place in some pioneering work in Illinois, with the work of Leonard Lewin. However, things have changed over the last 40 years and computers have become much more powerful and embedded in all kinds of devices. The whole fields of wireless and wire LAN networking have come about and software has also become much more powerful. This is actually leading to a revolution which I call network embedded control systems. Challenge of abstractions and architecture I believe that the first and most important challenge for this dual technology is to decide what are the appropriate abstractions and what is the right architecture. Let me make the case for why abstractions and architecture are important for the evolution of technology. Let me start with the architecture of the internet – and many of you may be familiar with this. There is a layer in the hierarchy. If you are a person in communication theory who knows a great deal about modulation, then you would work on the physical layer problems here. If you were a graft theorist, you would work on the networking layer over here, and if you were working on HTTP or something, then you would work up here. So there is a hierarchical segregation of tasks.
Page 1 and 2: Lecture Series in Mobile Telecommun
Page 3 and 4: Foreword This compilation brings to
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Page 7 and 8: From wireless networks to sensor ne
Page 9 and 10: As far as the routing of packets is
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Page 17 and 18: adds a great deal of discrete compl
Page 19 and 20: Questions & Answers Mike Walker: Th
Page 21 and 22: Human beings will also evolve and w
Page 23 and 24: Welcome and introduction Professor
Page 25 and 26: ComReg Collection - Start 16/Apr/20
Page 27 and 28: evidence that people give in favour
Page 29 and 30: chunks of spectrum where you are in
Page 31 and 32: As an aside, I was reading a few of
Page 33 and 34: Of course, it is not just one mask,
Page 35 and 36: I apologise for hopping between top
Page 37 and 38: Questions & Answers Michael Walker:
Page 39 and 40: Nobody - believe you me, it came ou
Page 41 and 42: The second aspect of my comment is
Page 43 and 44: want to call it - that sits there a
Page 45 and 46: From plain old Telephony to flawles
Page 47 and 48: From plain old Telephony to flawles
Page 49 and 50: You can see that there is much room
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Page 57 and 58: Questions & Answers Michael Walker:
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