An Introduction to Algorithms and the Big O Notation

Chivers, Ian; Sleightholme, Jane

doi:10.1007/978-3-319-17701-4_23

Cited by 62 publications

(21 citation statements)

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“…An example of multiple pollution sources using our blockchain with a DAG approach is also provided in the Supplementary section . Details of the big O notation can be found in Avigad and Donnelly [ 39 ] and Chivers and Sleightholme [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework

Lin

Mukhtar

Huang

et al. 2020

Sensors

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Real-time identification of irrigation water pollution sources and pathways (PSP) is crucial to ensure both environmental and food safety. This study uses an integrated framework based on the Internet of Things (IoT) and the blockchain technology that incorporates a directed acyclic graph (DAG)-configured wireless sensor network (WSN), and GIS tools for real-time water pollution source tracing. Water quality sensors were installed at monitoring stations in irrigation channel systems within the study area. Irrigation water quality data were delivered to databases via the WSN and IoT technologies. Blockchain and GIS tools were used to trace pollution at mapped irrigation units and to spatially identify upstream polluted units at irrigation intakes. A Water Quality Analysis Simulation Program (WASP) model was then used to simulate water quality by using backward propagation and identify potential pollution sources. We applied a “backward pollution source tracing” (BPST) process to successfully and rapidly identify electrical conductivity (EC) and copper (Cu2+) polluted sources and pathways in upstream irrigation water. With the BPST process, the WASP model effectively simulated EC and Cu2+ concentration data to identify likely EC and Cu2+ pollution sources. The study framework is the first application of blockchain technology for effective real-time water quality monitoring and rapid multiple PSPs identification. The pollution event data associated with the PSP are immutable.

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Section: Methodsmentioning

confidence: 99%

Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework

Lin

Mukhtar

Huang

et al. 2020

Sensors

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“…pacing_gain = P down 3: return 4: end if Phase 3: next six cycles 1: if pacing gain == 1.0 then 2: return 3: end if Algorithm 1 gives the pseudo code of BBR-GC, which is an improvement of BBR algorithm. The algorithm complexity analysis uses the big O notation [39,40], which can be divided into time complexity and space complexity. This method uses another function (usually simpler) to describe the asymptotic upper bound of the order of magnitude of a function.…”

Section: Design Motivationmentioning

confidence: 99%

Improvement of RTT Fairness Problem in BBR Congestion Control Algorithm by Gamma Correction

Pan

Tan

et al. 2021

Sensors

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Google proposed the bottleneck bandwidth and round-trip propagation time (BBR), which is a new congestion control algorithm. BBR creates a network path model by measuring the available bottleneck bandwidth and the minimum round-trip time (RTT) to maximize delivery rate and minimize latency. However, some studies have shown that there are serious RTT fairness problems in the BBR algorithm. The flow with longer RTT will consume more bandwidth and the flows with shorter RTT will be severely squeezed or even starved to death. Moreover, these studies pointed out that even small RTT differences will lead to the throughput of BBR flows being unfair. In order to solve the problem of RTT fairness, an improved algorithm BBR-gamma correction (BBR-GC) is proposed. BBR-GC algorithm takes RTT as feedback information, and then uses the gamma correction function to fit the adaptive pacing gain. This approach can make different RTT flows compete for bandwidth more fairly, thus alleviating the RTT fairness issue. The simulation results of Network Simulator 3 (NS3) show that that BBR-GC algorithm cannot only ensure the channel utilization, but also alleviate the RTT fairness problem of BBR flow in different periods. Through the BBR-GC algorithm, RTT fairness is improved by 50% and the retransmission rate is reduced by more than 26%, compared with that of the original BBR in different buffer sizes.

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“…Such an evaluation was made by analyzing the theoretical complexity of the events classification (Algorithm 1) and the events features transformation (Algorithm 2), as previously formalized. The complexity is expressed according to the Big O notation [96], which is aimed to define the upper bound of an algorithm, since it bounds a function only from above.…”

Section: Computational Complexitymentioning

confidence: 99%

A Local Feature Engineering Strategy to Improve Network Anomaly Detection

et al. 2020

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The dramatic increase in devices and services that has characterized modern societies in recent decades, boosted by the exponential growth of ever faster network connections and the predominant use of wireless connection technologies, has materialized a very crucial challenge in terms of security. The anomaly-based intrusion detection systems, which for a long time have represented some of the most efficient solutions to detect intrusion attempts on a network, have to face this new and more complicated scenario. Well-known problems, such as the difficulty of distinguishing legitimate activities from illegitimate ones due to their similar characteristics and their high degree of heterogeneity, today have become even more complex, considering the increase in the network activity. After providing an extensive overview of the scenario under consideration, this work proposes a Local Feature Engineering (LFE) strategy aimed to face such problems through the adoption of a data preprocessing strategy that reduces the number of possible network event patterns, increasing at the same time their characterization. Unlike the canonical feature engineering approaches, which take into account the entire dataset, it operates locally in the feature space of each single event. The experiments conducted on real-world data showed that this strategy, which is based on the introduction of new features and the discretization of their values, improves the performance of the canonical state-of-the-art solutions.

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An Introduction to Algorithms and the Big O Notation

Cited by 62 publications

References 0 publications

Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework

Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework

Improvement of RTT Fairness Problem in BBR Congestion Control Algorithm by Gamma Correction

A Local Feature Engineering Strategy to Improve Network Anomaly Detection

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