A Reliable Error Detection Mechanism in Underwater Acoustic Sensor Networks

Khan, Imtiaz Ahmed; Yun, Nam-Yeol; Muminov, Sardorbek; Park, Soo-Hyun

doi:10.1007/978-4-431-54216-2_21

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…It will be added to the least significant bit (LSB) of the checksum. There are examples where the four checksum methods are implemented [32] [33]. The effectiveness of checksums in detecting error during data transmission is presented in [34].…”

Section: Fig 4 Checksum Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Overhead Interspersing of Redundancy Bits Reduction Algorithm by Enhanced Error Detection Correction Code

Tolentino¹,

Valenzuela²,

Juan³

2019

JESTR

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Additional check bits, which are commonly attached to the message's input data, are normally used to minimize the error during data transmission. The receiver system implements a checking algorithm to determine if an error was occurred in the received data. This algorithm will correct a corrupted bit and recover the original message. An enhanced error detection correction code was presented to better detect and correct the corrupted conveyed bits. It improves the existing limitations of utilizing cyclic redundancy checking (CRC), Hamming code, and other checksum techniques. Also, it reduced the length of the redundancy bits which exists in CRC, the overhead of interspersing of the redundancy bits in a typical Hamming code, and the system resources such as processor time and bandwidth in checksum techniques. This paper was synthesized and simulated using the Xilinx Spartan 6 (XC7Z020-2CLG4841) FPGA. Results show that the resource utilization of the designed memory architecture using EEDC is lower compared to CRC, Hamming, and Checksum algorithms.

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Section: Fig 4 Checksum Methodsmentioning

confidence: 99%

“…In the simplest way, a checksum is formed by computing the binary values in a data block using some algorithm and keeping the outputs with the same data. Single-Precision, Double-Precision, Honeywell, and Residue Checksum methods [18] [33] are the four methods that can be seen in the Checksum Encoder/Decoder.…”

Section: Checksum Codesmentioning

confidence: 99%

Overhead Interspersing of Redundancy Bits Reduction Algorithm by Enhanced Error Detection Correction Code

Tolentino¹,

Valenzuela²,

Juan³

2019

JESTR

View full text Add to dashboard Cite

show abstract

“…Different error checking mechanisms are used depends on the system design. According to our previous study [7], one byte CRC performs better among checksum, parity bit, longitudinal redundancy check (LRC), vertical redundancy check (VRC) different FEC and ARQ mechanisms.…”

Section: Introductionmentioning

confidence: 94%

Nibble-CRC for Underwater Acoustic Communication

Khan

Yun

Park

2012

Lecture Notes in Computer Science

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Underwater acoustic sensor networks (UWASNs) are complex in design due to the characteristics of underwater medium. The underwater communication environment and the medium vary on time, geographical location and depth. Sensor nodes are also born with some limitations such as fixed amount of energy, small size memory and node mobility. Signals travel inside a noisy acoustic channel which required checking errors after receiving. Secure communication is one of the main characteristics of any networks. UWASNs also maintain a security protocols in each layer. Moreover, data link layer performs the main task of error check. To protect unauthorized access to our network and for a common line of defense against the errors, it requires attentions during the protocol design. Depends on the several error detection mechanisms, cyclic redundancy check (CRC) performs better than other mechanisms in respects of the different underwater factors. In this paper, we discuss about the CRC: how it is different from other mechanisms in terms of carrying functions that helps to check double error (running sum) but correct checksum. The trade-offs between error detection system designs (using a nibble size CRC) and underwater communication system designs.

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“…The two methods of dealing with erroneous packets in the data link layer are error detection and error correction. In [38], data link layer error detection methods are explored with a focus on the specific considerations necessary for underwater development. Standard checksums are a good last "last line of defense" method of error detection in channels with minimal error rates, however in a channel as volatile as the underwater acoustic domain, it is exceptionally weak.…”

Section: Data Link Layermentioning

confidence: 99%

Software-defined acoustic underwater mesh networks

Turco

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While terrestrial IoTs have been extensively explored, both academically and commercially, IoUTs have had significantly fewer developments. The underwater channel presents unique challenges which cause commonly utilized communication methodologies in the RF domain to become nonviable. This thesis presents an underwater mesh network designed to address the challenges faced in the network, data link, and physical layers of the underwater acoustic domain. This underwater mesh network implements a proactive routing protocol for maintaining routing topology, allowing for Dijkstra's algorithm to be utilized as an efficient method of determining the next hop for a data packet. Endpoints across an on-board Ethernet socket will be capable of joining the mesh network, allowing for cross-medium data forwarding. A CRC-32 was implemented as the primary methodology for error detection. Two MAC schemes were implemented for this underwater mesh network: a Pure ALOHA scheme and a repetitive burst retransmission scheme. The routing table discovery process and the implemented MAC schemes were evaluated through testing with hardware in a real-world oceanic environment (located in the Boston harbor). Utilizing a ZP-OFDM communication scheme, the underwater mesh network's discovery process was able to establish topological consensus on the state of the routing table quickly and accurately in this real-world setting. The repetitive retransmission scheme greatly outperformed the Pure ALOHA scheme due to the greater number of collisions (from acknowledgement packets in the shared channel) in the Pure ALOHA scheme. Through real-world testing, the protocols and systems implemented within this underwater mesh networks were shown to be highly successful at establishing and maintaining a network at varying levels of data traffic.

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A Reliable Error Detection Mechanism in Underwater Acoustic Sensor Networks

Cited by 4 publications

References 7 publications

Overhead Interspersing of Redundancy Bits Reduction Algorithm by Enhanced Error Detection Correction Code

Overhead Interspersing of Redundancy Bits Reduction Algorithm by Enhanced Error Detection Correction Code

Nibble-CRC for Underwater Acoustic Communication

Software-defined acoustic underwater mesh networks

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