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It also shows how those files are included by modules implementing SWCs and, by general, non-component code. The application header file defines the “interface” between a component and the RTE. The interface consists of the RTE API for the component and the prototypes for runnable entities. The definition of the RTE API requires that both relevant data structures and API calls are defined. The data structures required to support the API are defined in the RTE Types header file. This enables the definitions to be available to multiple modules to support direct function invocation.
The new cloud-based model makes the user into a “thinclient,” only creating data and consuming reports. This change will greatly influence the business model for many current software packages vendors. So in principle, the suppliers would have to move from a “transactional” relationship with the customers to a tailored “service” relationship . In cloud computing many forms of service can be provided from centralized locations. Included would, of course, be updates and upgrades.
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Companies such as IBM, Cisco Systems, and Google have realized that for “Internet of Things” to be successful, standards for objects linked to the web need to be agreed upon. The CISCO-led IPSO (Internet Protocol for Smart Objects) Alliance has been formed to speed up these developments. The starting point for the cloud notion, as described before, is a large “mainframe”-like centralized enterprise-computing facility (the server farms) connected through the Internet to a large set of thin clients (the things). In this “cloud computing” architecture, the data and services principally reside in massively scalable data centers that can be universally accessed from any connected devices over the Internet. Most services (Apps) will be provided in the cloud, some (see Chapter 7) will be most efficiently provided in a local “sensory” cloud.
The Book It provides conceptually a one-stop entry into the world of intelligent sensory networks, as we envision them to exist in relationship to the computing cloud(s). This is the “ambient intelligence” equivalent of what “cloud computing” means to business intelligence, and therefore the new generation of large-scale sensory networking systems. Cloud computing usually refers to the accessibility from the network of a well-orchestrated data warehouse. Clearly, cloud computing concepts also apply to networks of embedded sensors. The book discusses concepts, problems, and initial solutions for the latter, while also providing illustrations from a wide field of practice. Becoming aware of the basic problems and solutions helps the readers to prepare for innovation based on distributed intelligence in their own area of technological expertise. This book provides new theory on the design of wireless sensory networks. It provides a step-by-step discourse on building case studies to capture the requirements, taking into account practical limitations to creating ambient intelligence. Notably, it pays attention to topological and communication constraints, while adding intelligence, security, and safety to the overall desired system functionality. Such aspects will be highlighted by reviewing applications in a variety of typical sensory/cloud network scenarios.
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Of late, we also see migration, where a container is reconfigured to a different server because of utility considerations such as being faster, having better peripherals, and so on. The access to the Cloud has a relatively low bandwidth. Similarly, the computational energy is centered on the cloud, not on the access. This gives the Cloud unlimited power in storage and computation, but is by the same reasoning hard to access. This should not be a problem if the Cloud means to provide a service, not just the result of a computation. This means that the Cloud reduces the required bandwidth by providing an abstraction. The user may query in loose terms on a concept. The cloud then computes several meanings of that query and returns some initial results. In turn, the user may improve his query on which the clouds terminates some searches and narrows down the potential meaning to continue computing in more promising directions. This continues till user and cloud start to agree on the rightly formulated query at the same moment as the right answer becomes available. This constitutes an “asymmetric function” similar to the ones that are applied in creating secure transmission.
Lee is basically addressing this issue when discussing cyberphysics. The reality is enhanced by adapting physical behavior to the desired one; in other words, nature is compensated for its frivolity. This architecture has stimulated futuristic thinking a lot, and underlies such technophile studies as by Ray Kurzweil and Hans Moravec . The popular example of an anticipated practical application is illustrated in the famous “7 of 9” from Starship Enterprise (the Voyager), whose brainpower is enhanced by implanted supercomputing. Nevertheless, the biorelated concept remains heavily debated and issues in system reliability may make it impractical. The state of the art contributes already in situations, where a model of desired behavior can be enforced on the (non-living) physical reality, such as the automotive power train . From the overall needs of the car, it can be deduced what the power train needs to deliver. Unfortunately, this behavior cannot be built in conventional electromechanical technology at acceptable cost. The experienced technician can probably make an acceptable part, given enough time and permanent dedication. But the salary costs make this impossible to pursue, even if sufficient amounts of such technicians would exist to support mass production.
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But the story does not stop with phones. All kind of products are turning to wireless, if not only to get rid of the cables. The doorbell connects wirelessly to the phone and allows you to answer the doorbell, even when you are not at home. The car seat for the children warns for dehydration of the baby. And in all these new devices, we have computing platforms ranging from simple controllers to graphic accelerators. Similar networking trends such as in wireless consumer communities are visible in smart energy grids, homeland security systems, and environmental networks. These sensory networks are meant to create awareness in space and time. They may be measuring the presence of an object or a condition, characterizing an object stream or a situational pattern, or even detect abnormalities that are to occur. Different types of sensors can be used for similar purposes, allowing for enhanced system reliability and safety, but they tend to be preferentially attached to the network. For the network functionality, one can distinguish between cloud, and even swarm and flock computing, the difference being the distribution of the decision control.
For the cloud of mobile devices the trend is to build applications on such platforms by moving software to where it is needed instead of having it pre-installed. Both make the Connection-Set Architecture (CSA) to a key ingredient, assuming that measures are present to allow migrating software to “glue onto” judicious nodes. The CSA works on and with abstract connections with the goal of making functionality in software movable. For this to work, we see the need to replace the scheduler way of moving software to that of the skeleton injector. The skeleton injector works on abstract connections between software, and is a master in the sense that it tells the nodes what they should do and not the other way around. The injector can be a slave to another injector, and it takes this role regularly when spreading onto the nodes in a network. In the spreading of the injector and functionality onto a network, complexity is lowered by its abstraction of the networks topology.
The injector encapsulates the parts of the software that are still handled by the scheduler. These packages we call compartments, and the injector technology works on the level on how to place and connect these. The compartments are created during design time to the level as needed. Compartments can be of many different sizes, but they do not split computations and functions. This means a slight performance loss for a single-core CPU, but at the gain of shorter development time and lower maintenance cost.
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Embedded systems not only interact with their environment, but are nowadays also equipped with communication interfaces. For a long time, this digital communication was domain- or even supplier-specific, but the standards developed in the classical computing world are rapidly getting more popular.
This is one of the price(s) to be paid for as a result of cloud-centeredness. Access to the data in the cloud will need to be carefully managed. Again this is one of the price(s) to be paid for. Also once the database is centrally located, synchronized access control needs to be provided. The cost model needs to contain charges for the computation, as well as for some of the price(s) indicated above. That is it should contain charges for provided maintenance, security, and access control, among others.
The operation of a single neuron. A multilayer feed-forward network. Direct (a) and Indirect (b) Neural control. Hierarchical alarm handling in rudder control . The general human driver/car control system (a), the neural network/car control system (b), and a human driving behavior identification model. The steering behavior of the human driver compared with the neurocontroller.
On top of the hardware lies conventionally the operating system. It operates directly on the underlying hardware, but masks the details away for the application programmer. A number of platforms can be supported using an abstraction of the underlying hardware, the virtual machine. This Physical-to-Virtual transformation can be done in software as well as hardware. Popular examples of a software technique on the compiler level are the P0 layer for Pascal compilation and the Java Virtual Machine (JVM) for executing Java program. The original virtual machine was compiler-driven and did not provide a clear transparent view on the underlying hardware. It was therefore severely limited in its application. Already as early as 1967, IBM has shown how also the supervisor mode could be handled.
Sun Microsystems during their recent comeback has created systems highly supportive of cloud computing, in their BlackBox (containers for server farms) and SPOTS (sensors for monitoring the low maintenance BlackBox). Most recently, Oracle has acquired Sun Microsystems. The point of network-based pervasive computing is that the question on the nature of the computer seems actually moot. Anything in the embedded world is a computer, even and including a network. And there are so many nodes that most of them could be inactive at a given moment. Given further that through nomadicity the network structures will constantly change and we see that any network is a computer. Ad hoc networks use this point of view to their advantage. For instance, where buoys are drifting through the Great Barrier Reef , their wireless intercommunication network to reach eventually the support center on the mainland uses an organization based on the current location of the individual buoys. But the ad hoc network does not always have to be homogeneous. Embedded products come with a variety of resources, and this will not stop them from having ad hoc arrangements.
Large server farms have been established by companies such as Google, Microsoft, and IBM. Since power consumption is an important issue for the large computing facilities, proximity to cooling opportunities such as rivers, as well as to cheap power such as by hydro-electric dams, has been crucial. The server farms are generally constructed out of homogeneous clusters of commodity processors packaged in racks. To minimize the cost of facilities and their infrastructure, the racks have been assembled in containers. The containers are selfcontained in power, cooling, and cabling. Sun Microsystems’ Blackbox is a good example of this kind of packaging. Curiously, the box also contains a cadre of sensor nodes (Sun SPOTs) used for measuring acceleration (in transport), temperature, humidity, light, and location. The use of containers facilitates also the quick and efficient assembling of a server farm; no specific building of facilities needs to take place, except for cooling, power, and cabling. The original use for the server farms was to speed-up web searches, the use of the same facilities for on-demand computing was a consequence of its capabilities and also of its utilization levels.
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A next speed-up comes when several computers are clustered together with a fast interconnect. This allows building a supercomputer from a number of standard computing blades, each containing multiple processing nodes. Where the single computing node has gradually been equipped with a number of threads in which the processing node performs a program partly in parallel with the others, the cluster allows many more simultaneous threads. The cluster concept is limited by the scalability of the interconnect network and the memory architecture. A shared-memory architecture is commonly applied to facilitate that all nodes may work coherently on the same problem. If the coherency requirement is easy to fulfill, the architecture seems reasonably scalable and has gradually found its place on the chip level as well. A necessary, but not sufficient, condition is well-behaved timing on the internal memory bus. In this sense, it helps to have all nodes of the same technology, for instance, on one board or on one chip. A typical example is the IBM Cell cluster of Synergistic Processing Elements. Extending this architectural concept over more than one Cell chip adds variability to the memory access timing and consequently provides coherency problems.
The goal was to create computing networks and systems that – like the biological analog – hide complexity from the user and deliver much greater value than today’s systems can. These new systems need to be self-managing, selfconfiguring, self-healing, self-protecting, and continuously self-optimizing.
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The network will then provide the opportunity to share resources and data. In addition, it provides for executing tasks remotely and to forward messages farther into the network. In so-called MANETs (Mobile Ad Hoc Networks), the location of the various network elements is not fixed; in addition, the number of participating network elements will vary. Since the network elements are mobile, it is hard to pre-assign functionality to the elements. A MANET will be set up incrementally by growing the net and distributing software accordingly. In opportunistic computing, this growing of the net takes place two-nodes-at-atime between nodes that find themselves close enough for reliable communication. The realized network is amorphic and expresses the mobile nature of the underlying (in particular social) networks.
Specialized, high-speed routers are required to shift the optimum. Clearly, such hardware devices are part of the grand scheme of universal worldwide communication and therefore not required in personal-area, body-area, or tiny-area sensory networks. Web-based appliances are often extremely thin, in the sense that they have only a basic web support and need the help of the gateway to provide a fully functional presence on the net. As a consequence, the networks need to be designed on basis of the anticipated information flow within an architecture that is meant to provide a functionality which is more than the sum of the parts.
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Let us take for example a network of vision sensors to supervise an area. Then it is required that the sensors cover the intended area without any holes. This is a maintenance issue, as the mechanical placement of cameras cannot be relied on. The ideal is then self-calibration. Two main situations can be discerned. When all sensors should be able to see exactly the same object but from different angles, we have to ensure that all angles are covered. The object will be nonlinearly transformed in a way that is angle-dependent. Coverage can be ensured by analyzing this perspective. In a typical WiCa system (more details to be provided in Section 4/5), the foundation will be handled by the 8051 controller while the processing is performed by the IC3D intelligent vision processing chip.
We expect research steps similar to the RAMP program to be performed in order to select a practical set of network topologies, starting with and including the three sensor-collector networks described by Spaanenburg . The notion of polytopol computing is derived from the area of sensory networks. Multiple unattended sensors can be distributed over an area of interest in a subset of operational (poly-topological) configurations, while a (directing/collecting) computer system will (manage) traverse the sensor network to harvest the results. The central (multicore) computer will collect and analyze the data for subsequent updates of the network and/or transmission of the external actionable data items.
And in digital cameras, we already encounter super-computing components to provide the best picture quality ever! Finally, the increasing number of applications where the installed computing potential created products that otherwise would not be possible – at least not with the same efficiency and at similar costs – have kindled the fire and a new technological area arose, the embedded systems. Its applications are everywhere and in amazing quantities around us, giving it names like pervasive, ubiquitous, and ambient, but the common thread is the embedded technology.
The already maturing “cloud computing” concept allows for the integration of data collection networks into a combined cloud computing/ambient intelligence environment. The notion of “ambient intelligence” is based on the development of information/awareness spread over a distributed sensor environment. Classically, this is performed on the mainframe; but in the ubiquity of the sensors and especially their networks, this can also be supported by swarming, the forming of a local cloud.
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The next step is the limitation of the variety in the procedure parameter lists. This leads to the concept of the virtual bus, an approach that facilitates the construction of extendable systems. A complex system is extendable when it is constructed of a minimal (core) system that provides a basic but bare functionality, while the internal buses can be externally accessed by procedures to enrich or bridge the core functions. As exemplified by the AUTOSAR project, a minimal system can be released much sooner than the complete one and also be repaired much easier. The component programming dogma states that, faced with the complexities of a heterogeneous, distributed world, the right thing to do is to break the applications into pieces that can be developed, shipped, and upgraded independently; can inter-operate with other pieces; and can be separated from their original application context. Such pieces, software components, are often likened to hardware-integrated circuits. Each integrated circuit provides a discrete, well-defined set of functions that can be used by any number of printed circuit boards. Similarly, each software component provides a discrete, well-defined set of functions that can be used by any number of applications.
In polytopol computing, the relationship between the sensors and the central computers will be similar to those in “cloud computing”: a set of thin clients (sensors) and a “software-as-a-service” focal point (the directing/collecting central computer). Another way to look at the concept is from the computational perspective. The network takes a large number of nodes that can each contain one or more cores, supporting multiprocessing both in the nodes and in the sensor network. In order to accelerate innovation in the area of multicore computers, the UC Berkeley RAMP program has developed an FPGA-based standard research platform (the BEE3, (Berkeley Emulation Engine version 3)) for evaluation and testing of large multicore systems and especially its software development environment. The program originally started by the definition (after analysis ) of the initial seven so-called “dwarfs,” a form of Model of Computation. After that initial study, the list was augmented with six more for a total of thirteen computational structures, somewhat domain-specific. A new approach to code optimization and generation, so-called Auto-tuners, is to produce optimized C code to be compiled for that particular computing structure. Similar as in the 1960s “Fixed-Plus-Variable (F + V) Structure” studies , the RAMP ParLab studies program will determine its computational structures from analysis.
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Again it seems that the fabrication technology is pushing the state-of-the-art in computer engineering. And together with this comes physical size. Where the paper tape reader had to be tightly integrated in the computer frame, current networks may perform a single computational task over a very wide area. If this is just a series of continuing improvements, what then makes for the regular surprises? It seems that microelectronics is a major driving force, but not the only one! On reflection, we see that the historical review described before is one of changing emphasis. In the first instance, the interest was in creating access to the platform for as many users as possible. Then the focus shifts to interaction, allowing the user to work without being aware of the other people at the same system. Subsequently, the issue of data storage arises to have the user work in real time with real data. We pose here that as a direct consequence the current focus is on ownership.
This preferential attachment worked nicely in the past for the Internet, where third parties competed on the market to develop ever better network structures and services. In other words, the growth of the network implies an expensive, closed, and labor-intensive process of continuous renewal, which can only be paid from the exponential increase of commercial value of the resulting network.
Caring, Comforting and Concerned (C3) Home, Home Automation and Healthcare will boom (see also Section 4/3.2). A network, also one comprising of sensors, is a collection of cooperating objects. Some division of labor has to be introduced to bring structure in the seeming chaos. The guiding rule to divide a network into parts emphasizes one of more of the outstanding system characteristics. Such a choice is made during the historical development of computer networks in a continuous stream of small improvements. But a network of appliances will have to be designed in the presence of a wide variety of device functionalities, taking all into account at once. Within the sensor network, each sensory node is equipped with a network interface and will therefore become part of a digital network, indistinguishable from other computing elements with a network interface. Such could be another Webbased appliance, but also a Personal Computer. Some of the elements have a special task. They perform a service for the network community and are therefore called Servers.
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To manage a large distributed system requires a stable blue-print. A detailed analysis of the blue-print will then provide the control for the system. Already for large software systems, this will be hard to reach, and – even if one could get there – the maintenance is hard to do. Things get only more complex for large, geographically distributed systems. Therefore, the ideal is to get away from all these things and on bio-inspiration to come up with an architecture that can manage itself by configuring around the failures to achieve a high degree of system health. Most of large systems develop by autonomous growth and preferential attachment. For instance, a production line is based on machines that are individually optimized in the expectation that this will bring a healthy overall system.
In other words, the virtual caller moves into the home network, communicates with me and leaves again. Clearly, such a scenario requires a careful design and a raised level of security. With the requirement to move software in place comes the need for distribution. In the network we have a multitude of sensors and devices, and in hardware we get a network of cores. Polytopol computing brings these areas together by introducing an abstraction of the network topology, enabling computation to be executed on the network rather than on the nodes. It is here that the new field of function migration comes in and provides together with polytopol computing the infrastructure that enables flow of functionality on a topology. This hides the network structure and therefore the location of software functionality. Enabling technologies are the skeleton injector as a replacement of the task scheduler, the polytopol computing topology abstraction of the network, virtualization applied on the connection of software to software, and the introduction of a connection set architecture.
All software parts are created as components, and are therefore modular and cohesive. Similar to hardware, components communicate through ports defined on its interface. When it provides functionality to another component, it is given a provider interface, and consequently the provider component becomes encapsulated into the consumer component. On the other hand, the consuming component is given a consumer interface, stating which provisions are required to be encapsulated. From the reliance on interface specifications comes the potential for components to be substituted. Components that deliver functionality according to the same provider interface can be substituted in the consuming component to update and/or otherwise improve functionality. As a general rule of thumb, component B can immediately replace component A, if component B provides at least what component A provided, and requires no more than what component A required. Reusability is an important characteristic of a high-quality software component. A software component should be designed and implemented so that it can be reused in many different programs. It takes significant effort and awareness to write a software component that is effectively reusable.
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Our concept of “polytopol computing” is derived from the notion that both in the multicore central processor (the cloud) as well as in the distributed sensors (the thin-clients) network topological aspects have been pursued though with differing orientations. In multicore system development, software is related to SystemC-like “Models of Computation,” hiding details of the procedural interconnect from the application code. In distributed sensor networks, such as found in ZigBee-based networks, a relatively small set of basic interconnect patterns (star, mesh, and cluster tree) are generally applied. Figure 1/12 illustrates that both in the multicore central processor (the cloud) as well as in the distributed sensor (the thin-clients) network topological aspects have been pursued though with differing orientations.
Multiple small sensors can be distributed over an area of interest. Again information, such as vehicle passage, can be gained by fusing data from individual sensors. The sensors will be communicating over an amorphous distributed network. Issues concerning the tradeoff between processing and communication, especially in regards to power consumption, will need to be addressed. Some form of power harvesting from the environment will be required for long-term deployment.
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It points out that what becomes important is not the location but the relationship between the elements, and this means division of labor. Since it is a top down approach, it needs the functions of the software to become migratable . Function migration makes software movable and the effect of this is that functionality becomes more stable. Function migration is a top down approach and can be applied to the degree of gradualism needed. At the same time, function migration is bottom up on the scale global/local. This duality from the program point of view comes from the fact that we are not working on the network nodes, but with the network onto a topology. This means that location is a concern of the network and function that of the program. Software migration started when the amount of hardware grew explosively and software had to migrate. Function migration need comes in at a time where the devices attached to hardware are growing in number, ubiquitous, and pervasive. What this means is that the hardware is forming a network around us and this changes the demands on software.
Where the past is governed by a limited amount of and therefore application-specific parts, we have argued that in current days ubiquity and generality rules. We are rapidly moving toward swarms of sensory devices, where the computing power may be local but where a supporting (server) farmer may also be at hand. The consequence of all this is that a function is not closely coupled to a platform.
They are generally powered or selfpowered, amorphous (not on a grid) with generation-and-attrition, field reprogrammable, and sensor plug-and–play-able. The sensors, in principle, should provide no continuous “Big Brother” observations; they should, as much as possible, be limited to anomaly/change detection. In addition to data collection, analysis, and reporting, it is important to emphasize the need for providing increased safety, integrity, and trustability to the various sensor network data-collecting elements. Oberheide describes an innovative technique for virtualized in-the-cloud security services for mobile devices, a Security-as-a Service approach for sensor-based thin-clients (see Section 5/5 for more details). The concept of “polytopol computing” is highly applicable in security applications where actionable intelligence is required. Technology will need to be included to access working status of network elements, including self-test, tampering, adversity spoofing, and maliciousness all the way to “Byzantine Generals” network analysis . It is extremely important to access the integrity of the collected data, in addition to collecting the data itself. This level of security technology is a missing element in many currently applied sensor networks (see Section 6/2 for more details). In polytopol computing the abstraction comes from the use of topology. This separates the partitioning of labor between collector and sensor from design and construction of them.
Consequently, an application router is required to plan the message paths for the single interactive application. We will describe how we will deal with software distribution and execution in sensors-in-the-cloud systems. Subsequently in Chapters 5 and 6, we will describe how to deal with new requirements such as safety, security, and integrity in cloudcentric sensory system applications.
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In the “cloud computing” architecture, the data and services reside in massively scalable data centers that can be universally accessed from any connected devices over the Internet. The emphasis in our cloud-computing-based use of sensor network processing is a top-down approach to sensors network development. In principle, the high-performance centralized (directing/collecting) computer and its Internet communication/ infrastructure capabilities will come first; the thin-client sensor elements at first only collect and forward data. The resulting division of labor will maximize compute capabilities, minimize communication and total system power requirements. In order to support the development of such extremely large sensor networks, we suggest the development of a processing infrastructure for the collection, analysis, and visualization of large data sets from sensor network based on the cloud computing paradigm.
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In dedicated architectures, it is possible to connect them in a unique and not reusable manner, but it has become good practice to use a more universal and extendable means. This results in a network of processing nodes. There is a world of difference between having many small computers and a few big ones in a processing network. Though the network connectivity brings an amount of reuse and scalability, the real innovation comes about when some additional parameters such as variability and accessibility are added. In this section, we will argue that the processing network will become the new design unit, the next abstraction beyond the computer. At this level, the software and hardware components on which the processing functionality is implemented will become so integrated that they will not be separately discernable again. It will make the network a cloud of embedded computers, similar to the computer-farm being a cloud of servers.
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With some small differences, we see a lot of similar techniques under different names. It seems to show the firm preferences of some companies: Pervasive Computing (IBM), Ubiquitous Computing (Microsoft, Intel, Xerox), Sentient Computing (AT&T), Active Badge (HP), Cooltown (HP) and Ambient Computing (Philips).
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IEEE Secure Private 1(5):19–25 9. Waugh A, Williams GA, Wei L, Altman RB (January 2001) Using metacomputing tools to facilitate large scale analyses of biological databases. In: 6th Pacific symposium on biocomputing (PSB2001), Kamuela, HI, pp 360–371 10. Weiser M (September 1991) The computer for the 21st century. Sci Am 265(3):94–104 11. Reeves B, Nass C (1996) The media equation: how people treat computers, television and new media like real people and places. CSLI Publications, Stanford, CA 12. Bondarenko O, Kininmonth S, Kingsford M (October 2007) Coral reef sensor network deployment for collecting real-time 3-D temperature data with correlation to plankton assemblies. In: International conference on sensor technologies and applications (SENSORCOMM2007), Valencia, Spain, pp 204–209 13. Zhang L, Malki S, Spaanenburg L (May 2009) Intelligent camera cloud computing.
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Such examples show the existence of networked intelligence. In sensory networks, we raise such concepts to a higher abstraction level, involving not mere exchange of data but also exchange of software. This covers both a data reduction process that reduces the bandwidth requirements as well as a collaboration process that has the opposite effect. Modular neural and machine intelligence techniques are proposed, which allow the provision of an efficient balance while keeping the platform constraints of the sensory nodes in check. The promise of networked interaction is the emergence of distributed control from the basic controllers in the network nodes . The mutual adaption to raise the quality of the task learns the nodes to work together in a strategically sound way. This plays an important role in the effort to optimize the maintainability of the system.
There is a small common part in the privileged area (called “bare metal” when it is considered to be absolutely minimal) and the rest is a virtualization process in the user area that will be instantiated per guest. The added advantage is that virtualization can be easily (and even dynamically) tuned for individual applications. When such individual needs are brought in, the mechanism is called paravirtualization. This allows building virtualization on top of one another, each interface being optimally suited for optimal performance. The state of the art supports even real-time performance in a transparent platform. This being the case, hardware is by itself not a differentiation and a distributed sensory network is also multicore in sharing a software distribution problem. The static solution balances the load by optimally pre-assigning tasks to nodes. Such is known to work for parallelizable computational problems, but that is where it stops. The dynamic solution allocates processes to nodes when and where this comes to pass.
We have envisioned the implementation of “cloud computing” as a large “mainframe”-like centralized enterprise-computing facility with an Internet-connected large set of thin-client sensor nodes. Sensory devices are domain transformers. Events in different physical or physiological domains are sampled and turned into an electrical signal. Sensors are of a wide technological variety, though microelectronic sensors are rising in popularity, as they promise a further integration of the sensory element and the signal-processing system. For instance, a temperature sensor can be based on the temperature sensitivity of electron/hole pair generation. This flow of charge carriers constitutes a current that can be handled as an analog value and therefore easily converted into the digital domain. The presence of some level of digital signal processing is gradually moving from remote computer boards into the sensors. The driving force behind this move is the need for programmability, that is, the structured reuse in time of scarce resources. The basic notion of Instruction Set Architecture (ISA) arose from the desire to perform different computations on the same hardware. At first sight, this does not seem applicable to a sensor.
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This approach will result in effective performance of the “virtual” architecture, with maximum parallelism and throughput. To the system programmer the “virtual” (middleware) machine will become its programming environment. Programming and code generation of the actual “virtual” machine will make use of conventional software tools, such as compilers and assemblers, specific to the “dwarf” in question. An example of this approach, although limited to Data Flow (its Virtual Processor Architecture), can be found in the Mitrion-C or Vector Fabrics toolset. The Mitrion Virtual Processor lets the user run software in their FPGA-based “Virtual” Middleware Concept. Some years ago, in an interview with Electronics Weekly (9 May 20059 ), Wim Roelandts, President and CEO of Xilinx, made the following observation: The next step is really to make FPGAs disappear. Today our customers are hardware engineers. But FPGAs are programmable devices. If we can create a level of abstraction that appeals to software engineers, we can increase our customer base by at least 10x. That is really where our future is. As long as you have a set of interfaces that you can program to, you don’t have to know what the hardware looks like.
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The question then remains how this can be achieved by dynamic configuration. In principle, there are two change strategies: extension and replacement. Replacement is probably the easiest option. A function is replaced by a new version. Room must exist for the new version; otherwise replacing other parts must create room. A minimal requirement will be that the new version is not an enlargement but rather a limitation of the original functionality. Extension requires the existence of a point of adhesion.
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This approach would also benefit from any heterogeneity of alternate computational configurations present in the cloud. In addition to updating/upgrading of software in the cloud, it would also be possible to expect a variety of computational architectures to be available in the cloud. An example is the neuromorphic simulation engine as designed as part of the 6th Framework “Fast Analog Computing with Emergent Transient States” Integrated Project FACETS.
The point is that a function can be part of a node, can use some nodes, and can also be handled at a server farm. The function is executed on some resources and we do not want to specify immediately where these resources are located and how much of them are needed. It should be moving dependent on the need, and we do not want to know that and where it moved. In other words, the network becomes the cloud! The nature of the network has also changed, though the backbone is still cabled. However, the introduction of wireless technology has caused the computer to become mobile. And from there, we see other computerized mobile equipment coming into fashion, notable the mobile telephone. Yearly 1/5 billion camera phones are sold! The rich set of user interactions is currently being augmented with inter-device interactions, making the camera phone a flexible node in a community network. Being based on preferential attachments, its function and construction changes with the whim of the participants rather than on pre-composed orchestration.
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