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Naturally, using a coarse object model also has drawbacks Coarse object models lose some type safety For example, the preceding design requires a BadAssetNumber exception on every operation in case a client supplies a non-existent asset number In the original CCS design, this error condition could never arise because the asset number was implicit in the object reference for each device Coarse object models are not polymorphic Each client must explicitly be aware of all possible types of object and requires modification if more specialized versions of objects are later added to the system Moreover, coarse object models create error conditions that would otherwise be absent For example, the set_nominal operation has a NotThermostat exception because a client might specify the asset number of a thermometer for the operation, but a thermometer does not have a nominal temperature attribute Thermometers and thermostats are no longer stand-alone entities that can be passed from address space to address space Suppose that we have located a thermostat of interest and want to pass the thermostat to another process that adjusts the desired temperature for us With the original CCS design, this is trivial: we simply pass the reference to the relevant thermostat However, with a coarse object model, it is not sufficient to pass only a Thermostat structure Instead, we must pass both the structure and a reference to the ontroller because the receiver of the structure may not know which particular controller is responsible for this particular thermostat If your application has more than one collection manager for a particular type of object, the need to track the associations between the collection managers and their objects can complicate the design considerably In general, the coarse object model approach works well if you do not require polymorphism and if objects are simple, small collections of attributes without behavior In this case, objects provide set/get semantics for only a small number of attributes and so might as well be represented as structures Coarse object models are similar to the fat operation technique in that they reduce messaging overhead However, the main value of coarse object models is that they can improve scalability because they reduce the memory overhead for clients and servers dealing with large numbers of objects 2234 Client-Side Caching Both fat operations and coarse object models enable client-side caching of state After a client has retrieved the state for a particular object, it can keep a local copy of that state Future read operations on the object can be satisfied by returning state from the local copy and so do not require a remote invocation For update operations, the client can send a remote message as usual to update the state information for the object in the server Read accesses typically account for more than 95% of the total number of operations in a distributed system, so client-side caching can result in a dramatic reduction of the number of remote messages that are sent Unfortunately, client-side caching also has a number of drawbacks.



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[31] is a voting-based location estimation technique n this method, the sensor eld is quantized into a grid of cells, and each reference node votes on the cells to which an unknown node may belong Then, the method selects the most voted cell(s) and uses the center of these cell(s) as the estimated location Voting results can be re ned interactively to improve accuracy This method requires few resources and is suitable for current resource constrained sensor nodes 1844 Location Veri cation If all previous techniques fail to provide the required security, it is still possible to check the computed positions using redundant information available on the nodes and in the network This is the core of some proposed works that focus on the reliability of the nal position computations rather than on avoiding or detecting compromised nodes and attacks.

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For instance, LAD [32] uses deployment knowledge, with a group-based deployment model, to check whether the computed positions of the nodes are consistent with the known model and observations In reference 33 an algorithm is proposed for in-region veri cation, in which a certain node can check whether another node really is inside the particular region that it claims to be The proposed protocol, called Echo, uses known physical properties of both radio-frequency and ultrasound to compute distances and check whether a node really can be inside the claimed region These techniques can be used to provide a layer of security for all three localization system components, since they only verify the result of the overall localization system 1845 Secure and Simple Algorithms Localization systems are vulnerable mostly due to the number of components available to be attacked.

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Another way to secure a localization system is to use simple, less In reference 33 an algorithm is proposed for in egion veri cation, in which a certain node can check whether another node really is inside the particular region that it claims to be The proposed protocol, called Echo, uses known physical properties of both radio-frequency and ultrasound to compute distances and check whether a node really can be inside the claimed region These techniques can be used to provide a layer of security for all three localization system components, since they only verify the result of the overall localization system 1845 Secure and Simple Algorithms Localization systems are vulnerable mostly due to the number of components available to be attacked Another way to secure a localization system is to use simple, less Using Barcode printer for VS .

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Tesseract is an OCR engine with support for unicode and the ability to recognize more than 100 languages out of the box. It can be trained to recognize other ...












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