Compressive sensing data recovery is a terminology used for salvaging data from Wireless Sensor Networks. These systems are mainly based on recursive algorithms like the optical flow, pattern matching, normalization, matrix approximation and other forms of compressive sensing functions. The cyber and cloud based data storage devices use this type of recovery technology extensively. Major parameters to be considered in these types of recovery are loss rates, performance levels and data bandwidth. All the algorithms work towards optimizing these parameters so that faster and effective data recovery procedures can be adopted across wireless networks. Optimization of bandwidth is one of the main criteria which can keep the speed of backup and recovery on par with the everyday data transactions without affecting their performance levels.
When you wish to achieve highest levels of consistency during backup and recovery processes in wireless networks, it is recommended that you take the help of data recovery professionals. We have a system of trained technicians, backed by high end hardware and software infrastructure for achieving and sustain the goals which have been listed above.
Wireless sensitive algorithms
Optical flow algorithm
Wireless systems use this type of algorithm for sensing the images of previously accumulated data and comparing it with the current set of data that is stored on the network databases. Once this difference is calculated and estimated, it is possible for the system to set two types of parameters into the recovery process.
Pre-filter values: – They are the values set between two consecutive points of data storage in the disk drives of wireless network server. Normally their base values are measured from -2 to +2. Pre-filters evaluate the bitwise data from disks in vertical positions. Single dimensional temporal derivatives are evaluated by the recovery mechanism before arriving at the exact nature of data to be restored from damaged tracks and sectors. Once this figure is arrived, the system starts recovering data and transferring it to a local storage device.
Derivative filters: – They are used for the collection of sensitive data from faulty disk drives in the wireless network servers as well as workstations. After collecting this information, they use it for interpolating them with the images created by pre filter values in a process called triangulation. Spatial and temporal derivatives are calculated based on these values and provision is made for the faster and effective methods of data recovery from the servers as well as workstations. However, the technician will be able to select the location of data restoration after it has been recovered from the server and workstations.
Once the data has been recovered from the server and workstation disk drives, it is subject for re scanning to detect any logical errors in them. Once the logical errors are detected and their nature is determined, the technician uses software applications to set right these errors. After setting these errors right, the process of data recovery is said to be complete. However the important stage of diagnosing the Disk drive in the wireless network servers and workstations still remain inconclusive, which needs to be carried out. Once this process is complete, the disks are either repaired or replaced and the recovered data is once again restored back to the servers and workstations. This procedure calls for some of the native types of disk drive testing which has been already discussed before. The following explanation reaffirms the same points with reference to physical evaluation of disk drives.
Optical diagnosis of physical disks
This is one of the basic types used for diagnosing the disk failure symptoms by drilling down to the bit level of data present in the tracks and sectors of the server disk drive. The bitwise scanning operation is conducted in all the platters of the disk from zero till the last platter. While the recursive search is in progress, an image of the disk is created by the probing software based on the bitwise data collected. This image can be used to determine the probable causes and effects of disk failure.
The flow of algorithm needs a starting set of points which are standardized. Grid-data is one such function which is used to create a liner path which passes through all the sectors and tracks in the failed disk and determines the data points. These data points are compared with the standard values of normal data storage. The resulting line will be straight as long as the existing data points are matching with the standard values. Once an error is found in the data storage pattern (It may start from boot sector, master file table or the data areas), the straight line experiences a gradient. The angle and intensity of gradient will depend on the nature and intensity of the error encountered during the scanning operation. Once the complete disk diagnosis is complete, the graph is submitted to the analyzing software. The application will create a diagrammatic view of the errors and based on that it will determine the nature and intensity of errors in the disk drive. Various values are allocated for logical failures and physical damages. Based on these values, the exact nature of damage is determined and displayed on the user interface screen.
Pattern matching: – This type of algorithm is used for matching the file patterns based on bit comparison method. It can be used to determine the errors in file allocation tables and NTFS partitions, apart from comparing many other types of errors in the HFS, HFS plus and other file system patterns. It will also determine the nature and intensity of bad sectors within the tracks scanned by the software application. Once the variations in patterns with respect to the standard patterns are listed out, this will be fed to the diagnosis part of the software application. The system derives at a report which shows all the errors and displays them on the user interface screen. The technician will be able to generate a diagnosis report based on the listing. This report will be highly useful for repair and restoration planning.