A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks

Wang, Hao; Wang, Shilian; Bu, Renfei; Er-yang, Zhang

doi:10.3390/s17081821

Cited by 22 publications

(16 citation statements)

References 31 publications

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“…Note that a large portion of the SIMD and FLIX speedup is due to the speedup achieved by the table lookup of the TIE inverse, i.e., the left-most set of bars in Figure 6b. Importantly, it can be observed from Figures 5 and 6 that 8-bit, i.e., GF(2 8 ), and 16-bit, i.e., GF(2 16 ), operations give essentially the same numbers of computing cycles and therefore, speedups. This is because the Tensilica LX5 can be flexibly configured to process 8-bit or 16-bit data.…”

Section: Clock Cycles and Speedupmentioning

confidence: 94%

“…This optimization reduced the total gate count by almost one-third for the SIMD with 8-bit MAC. On the other hand, for the SIMD with 16-bit MAC for GF(2 16 ), this pre-load register optimization was counterbalanced by additional circuitry to manage the 16-bit processing with elementary NAND-2 gates (processing 8-bits). Thus, the 11,245 NAND-2 gate count for SIMD with 16-bit MAC was slightly more than eight times the gate count for the 16-bit MAC.…”

Section: Fixed Instruction-set Processor Benchmarkmentioning

confidence: 99%

“…The plain C inverse computation directly inverts to GF(2 8 ) (or GF(2 16 )) through elementary Galois field multiplication operations. On the other hand, the TIE inverse computation transforms each given GF(2 8 ) element into two GF(2 4 ) elements (or four GF(2 4 ) elements for GF(2 16 )) (see Section 3.3) and finds the inverse values through table lookups. From examining the inversion speed-up on the order of 10 4 closer, it can be observed from Figure 5b that the plain C inverse with lookup table requires about two orders of magnitude fewer clock cycles than the plain C inverse with elementary multiplication operations.…”

Section: Clock Cycles and Speedupmentioning

confidence: 99%

“…RLNC reduces the number of transmissions needed to achieve a prescribed resilience level (probability of packet delivery) and has the potential to reduce transmission delays. RLNC is well suited for wireless data transmissions [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], data storage [20][21][22][23], and content distribution [24][25][26][27][28][29][30]. The practical usage of RLNC for the wide range of communication applications in emerging cyber-physical systems (CPS) and the internet of things (IoT) as introduced by fifth generation (5G) wireless systems requires high RLNC encoding and decoding throughput while consuming only low amounts of energy.…”

Section: Introductionmentioning

confidence: 99%

“…• Throughput: We evaluated the GF(2 8 ) and GF(2 16 ) RLNC encoding and decoding throughputs as the size (g • m) of the generation in bytes divided by the required computing time, obtained as the number of computing cycles times the inverse of the clock frequency. We report the encoding and decoding throughputs in units of MiB/s, whereby 1 MiB = 2 20 bytes.…”

mentioning

confidence: 99%

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Hardware Acceleration for RLNC: A Case Study Based on the Xtensa Processor with the Tensilica Instruction-Set Extension

et al. 2018

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Random linear network coding (RLNC) can greatly aid data transmission in lossy wireless networks. However, RLNC requires computationally complex matrix multiplications and inversions in finite fields (Galois fields). These computations are highly demanding for energy-constrained mobile devices. The presented case study evaluates hardware acceleration strategies for RLNC in the context of the Tensilica Xtensa LX5 processor with the tensilica instruction set extension (TIE). More specifically, we develop TIEs for multiply-accumulate (MAC) operations for accelerating matrix multiplications in Galois fields, single instruction multiple data (SIMD) instructions operating on consecutive memory locations, as well as the flexible-length instruction extension (FLIX). We evaluate the number of clock cycles required for RLNC encoding and decoding without and with the MAC, SIMD, and FLIX acceleration strategies. We also evaluate the RLNC encoding and decoding throughput and energy consumption for a range of RLNC generation and code word sizes. We find that for GF ( 2 8 ) and GF ( 2 16 ) RLNC encoding, the SIMD and FLIX acceleration strategies achieve speedups of approximately four hundred fold compared to a benchmark C code implementation without TIE. We also find that the unicore Xtensa LX5 with SIMD has seven to thirty times higher RLNC encoding and decoding throughput than the state-of-the-art ODROID XU3 system-on-a-chip (SoC) operating with a single core; the Xtensa LX5 with FLIX, in turn, increases the throughput by roughly 25% compared to utilizing only SIMD. Furthermore, the Xtensa LX5 with FLIX consumes roughly four orders of magnitude less energy than the ODROID XU3 SoC.

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Section: Clock Cycles and Speedupmentioning

confidence: 94%

Section: Fixed Instruction-set Processor Benchmarkmentioning

confidence: 99%

Section: Clock Cycles and Speedupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Hardware Acceleration for RLNC: A Case Study Based on the Xtensa Processor with the Tensilica Instruction-Set Extension

et al. 2018

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Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Gola

Arya

2023

Concurrency and Computation

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SummaryWireless information routing beneath the ocean, or usually underwater, has supplied the most excellent technical methods of oceanic observations. Traditionally, ocean bottoms have been monitored by placing oceanographic sensors that collect data at specific and defined ocean zones. When the duties are performed, the oceanographic instruments are recovered. Because there is no collaborative exchange of collected data between the collecting point and the monitoring end, data cannot be observed remotely. Underwater sensor networks can be wirelessly connected with sensors and monitors without extensive cable. Underwater wireless sensor networks are what they are called (UWSNs). This article investigates the various aspects of UWSNs, such as their significance, applications, network design, demands, routing, deployments and challenges.

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Multivariate fuzzy logic‐based ad hoc on‐demand distance vector protocol under the impact of node mobility in wireless ad hoc networks

Mehta

2022

Int J Communication

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Efficient routing of generated packets through the network with minimal overhead in path discovery and subsequent route maintenance is the fundamental objective required in the modern wireless network operation. Recently, the application of autonomic computational learning techniques for design and optimization of routing protocols in ad hoc networks is substantially gaining the research interest. The commonly deployed soft computing methodology of fuzzy inference system is capable of handling uncertain and imprecise networking information related to the frequently changing states of generic mobile technologies. In this paper, we propose a novel fuzzy logic-based ad hoc on-demand distance vector (FL-AODV) routing protocol employing the multivariate cross-layer design architecture to optimize the multiple performance parameters in wireless ad hoc networks. This fuzzy optimization framework essentially applies the header length from data link and physical layers, route timeout from network layer, and node mobility speed from application layer as inputs to the fuzzification interface. Besides, bit rate for application layer and communication range parameter for data link layer are scrutinized as the fuzzy outputs derived from the defuzzifier. The designed adaptive routing protocol is extensively assessed through simulation experimental analysis under the varying effects of node mobility conditions.Various network performance attributes including the reception cache hit, packet delivery ratio, packet errors, ping loss rate, mean throughput, and delay are computed and analyzed for comprehensive comparison between the presented fuzzy-based FL-AODV and classical AODV routing mechanisms.Finally, we compare our fuzzy routing model with previous algorithms to demonstrate its efficacy in terms of key performance metrics of throughput and delay.

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A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks

Cited by 22 publications

References 31 publications

Hardware Acceleration for RLNC: A Case Study Based on the Xtensa Processor with the Tensilica Instruction-Set Extension

Hardware Acceleration for RLNC: A Case Study Based on the Xtensa Processor with the Tensilica Instruction-Set Extension

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Multivariate fuzzy logic‐based ad hoc on‐demand distance vector protocol under the impact of node mobility in wireless ad hoc networks

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