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Low-rise vernacular masonry buildings are vulnerable to damage from extreme flooding. This paper presents findings from a study based in Sri Lanka assessing the structural resilience of non-engineered single-storey houses constructed with either fired clay brickwork or cement block masonry walls. A structural survey of flood-damaged houses enabled characterisation of masonry materials, and development of proposals to improve the lateral load capacity of masonry walls. The structural survey revealed that a combination of low-quality masonry units and variable thickness of mortar beds may have contributed to flexural failure due to the hydrostatic pressures during flood events. An experimental programme investigated the impact of differing masonry units and mortar thickness on masonry properties under dry and saturated conditions. A simple retrofit method has been developed, using reinforced renders, that enhances sectional flexural capacity by over 11 times, offering greater resilience to lateral flood loads. The impact of this research study will have direct welfare benefits for Sri Lankan people living in flood-risk areas, reducing the risk of structural collapse and enabling people to remain safely in their homes during flood events.

Software-defined wireless sensor networks (SDWSNs) can be functionally affected by malicious sensor nodes that perform arbitrary actions, e.g., message dropping or flooding. The malicious nodes can degrade the availability of the network due to in-band communications and the inherent lack of secure channels in SDWSNs. In this paper, we design a hierarchical trust management scheme for SDWSNs (namely TSW) to detect potential threats inside SDWSNs while promoting node cooperation and supporting decision making in the forwarding process. TSW evaluates the trustworthiness of involved nodes and enables the detection of malicious behavior at various levels of the SDWSN architecture. We develop sensitive trust computational models to detect several malicious attacks. Furthermore, we propose separate trust scores and parameters for control and data traffic, respectively, to enhance the detection performance against attacks directed at the crucial traffic of the control plane. Furthermore, we develop an acknowledgment-based trust recording mechanism by exploiting some built-in SDN control messages. To ensure the resilience and honesty of the trust scores, a weighted averaging approach is adopted, and a reliability trust metric is defined. Through extensive analyses and numerical simulations, we demonstrate that TSW is efficient in detecting malicious nodes that launch several communications and trust management threats such as black-hole, selective forwarding, denial of service, bad mouthing, and ON-OFF attacks.

Link-flooding attacks (LFAs) are a new type of distributed denial-of-service (DDoS) attacks that can substantially damage network connectivity. LFAs flows are seemingly legitimate at the origin. But their cumulative volume at critical links causes congestion. We propose RL-Shield, a reinforcement learning based defense system against LFAs. It mitigates LFAs and, at the same time, effectively forwards data traffic in the network. RL-Shield introduces a new detection algorithm for monitoring IP behaviors using the Dirichlet distribution and Bayesian statistics. It monitors the interplay of LFAs and traffic engineering and identifies source IPs that persistently react to rerouting events. The detection algorithm controls two reinforcement learning based routing algorithms that use a hop-by-hop technique to connect related node pairs. We evaluate RL-Shield on various network topologies by simulating several attack strategies. The simulation results demonstrate the effectiveness and high-accuracy of RL-Shield.

Blockchain allows for secure management of a shared ledger by agreement protocols, where transactions are validated over network without central authorities. Although the agreement protocol has been thoroughly conducted of propagation and consensus researches, mobility of nodes in unstructured overlay networks has not received much attention. Besides, current dynamic propagation schemes waste travel hops and are low of delivery ratio. In this paper, we propose a social characteristic-based propagation-efficient protocol NefSBFT to agree on system state plus consensus mechanisms in public blockchains. We devise a propagation technique (travel hops of at least $\frac{1}{3}$ savings, delivery ratio above $0.93$ , etc.) for message multicasting when exploiting real nodes' social characteristics of intermittent connectivity and frequent partitions. This propagation technique is executed in the improved FastBFT to achieve transaction ordering and block verification, thus, no controllable mobility is required during the whole system's execution. NefSBFT achieves fast propagation, small message complexity and few resource consumption of travel hops and running nodes for complete protocol execution. We analyze NefSBFT's security against DDOS attack of non-primary failure. The experiments show the performance tradeoff under different parameters, compare the propagation efficiency with Erlay and Flooding, and clarify NefSBFT's impact on the whole system performance through comparison.

Quality of Service (QoS) in a fifth-generation communication network is leveraged through its interoperable information communication technologies. This interoperability improves the scalable and adaptable level of heterogenous users by maximizing the radio and network resources reliably. Denial of service (DoS) turns out to be a threat in granting communication quality and reliability in service responses due to periodic flooding and invariable traffic flows. This manuscript introduces a differential flow management scheme (DFMS) for the 5G communication network for thwarting the impact of DoS adversaries. This scheme classifies the request/ response flow traffic as continuous and discrete and addresses the discrete flow as a sub-optimal differential problem. In this optimization problem, the goal is to converge the time of adversary detection and to re-formulate resource allocation as a continuous flow based on the remaining flows. The invariable flow is modeled for the persistence time based on service and transmission intervals to retain the user equipment's response rate. The experimental results show the proposed scheme's consistency by achieving less adversary detection time, maximizing the request delivery ratio, and retaining the response rate, respectively.

Applications such as collaborative mapping motivate a need for network-wide broadcasting in Mobile Ad Hoc Networks (MANET) with low-rate long-range links. Existing MANET protocols are inadequate in such low-capacity regimes due to their high use of control packets. We present a novel protocol called ECHO that constructs and maintains a broadcast backbone without using any control packets. Instead, using a field in the data packet header, a node transmits and listens for an "echo" of the specific packet to determine its membership in the backbone. ECHO is deterministic, source-independent, fully distributed, accommodates mobility, and balances battery consumption across nodes. We prove ECHO's correctness and show that its communication complexity is lower than that of Multi-Point Relay (MPR) and Flooding, and comparable to that of Opportunistic Announcement (OA). Simulations over random mobile low-capacity networks show that ECHO provides at least a 30% better delivery ratio than Flooding, MPR, and OA for 50-node networks and dramatically reduces the communication load. Experiments on a 12-node testbed of goTenna mobile mesh networking devices show that ECHO reduces transmissions by about 3x and increases battery life by more than 50% over Flooding. ECHO's performance advantages are crucial for scalable broadcast in low-power, low-capacity wireless networks.

Carbon dioxide-enhanced oil recovery (CO2-EOR) has gained widespread attention in light of the declining conventional oil reserves. Moreover, CO2-EOR contributes to the reduction of the global emissions of greenhouse gases through CO2 sequestration in subsurface geologic formations. This method has been largely used in the petroleum industry for several decades especially for extracting oil from light-to-medium oil reservoirs approaching an advanced state of maturity. Traditionally, CO2 is used in a continuous flooding scheme for EOR. However, continuous CO2 flooding tends to be problematic due to unfavorable mobility, viscous fingering/channeling and early breakthrough of CO2, especially in the presence of reservoir heterogeneities. In this paper, recent developments in the methods used to overcome these problems are reviewed. These developments include CO2 water-alternating-gas (WAG) injection, polymer-assisted CO2 injection, surfactant-assisted CO2 mobility control (CO2-foam injection), and nanoparticle-assisted CO2 flooding. Each method addresses, to an extent, one or more of the problems associated with conventional CO2 flooding. Furthermore, incorporating more than one method can provide better performance in terms of CO2 mobility control and oil recovery. In comparison with CO2-WAG and CO2-foam injection methods, the use of polymers and nanoparticles with CO2 flooding is relatively new. These two new methods were mostly investigated experimentally, at the laboratory level, and they still need further development prior to field implementation.

Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal condential information of the learning models through public queries. Though there are some protection options such as differential privacy (DP) and monitoring, which are considered promising techniques to mitigate this attack, we still nd that the vulnerability persists. In this paper, we propose an adaptive query-ooding parameter duplication (QPD) attack. The adversary can infer the model information with black-box access and no prior knowledge of any model parameters or training data via QPD. We also develop a defence strategy using DP called monitoring-based DP (MDP) against this new attack. In MDP, we rst propose a novel real-time model extraction status assessment scheme called Monitor to evaluate the situation of the model. Then, we design a method to guide the differential privacy budget allocation called APBA adaptively. Finally, all DP-based defences with MDP could dynamically adjust the amount of noise added in the model response according to the result from Monitor and effectively defends the QPD attack. Furthermore, we thoroughly evaluate and compare the QPD attack and MDP defence performance on real-world models with DP and monitoring protection.

This article proposes a new three-stage stochastic framework for dealing with predictable two-phase natural disasters in distribution systems. This framework is a multiobjective optimization, in which the amount of curtailed energy, the number of switching actions, and the vulnerability of operational components are selected as the main criteria for decision-making process. The optimization problem is formulated in the form of a stochastic mixed-integer linear programming (MILP) problem. In this article, a windstorm event followed by flooding is analyzed as a two-phase natural disaster. In this regard, the uncertainties associated with gust-wind speed, floodwater depths, and load demands are taken into account by the proposed framework. The initial configurations of islands are formed just ahead of the storm event (first stage), and their borders are changed in the second stage, which is associated with the storm event and its aftermath. The final configurations of islands are determined by the third stage once the uncertainties of floodwater depths are revealed. In the proposed framework, the emergency generators (EGs) are assumed to be prone to flooding, and a novel approach is proposed for quantifying the flood-related failure probability of EGs. Likewise, overhead distribution structures are recognized as vulnerable components to storms. The proposed framework is implemented on a test system, and its effectiveness is investigated and verified through seven case studies.

Improving the resilience of complex engineered and engineering systems (CES) includes planning for complex resilience situations, in which there may be multiple threats, interactions, and disruptions. One challenge in the modeling of CES is the identification of how interactions in a complex situation occur and their combined influence on CES resilience. This article presents a resilience holon that can be used to analyze complex resilience situations. It is made up of 24 elements (defining types of resilience, threats, interactions, and disruptions), which have varying importance to specific situations. Holons can be linked together hierarchically or in a network. An application of the resilience holon to a documented real-world resilience situation, widespread flooding in a city, illustrates its use. Pathways taken by threats and disruptions, as the flood effects cascaded through the city, are shown as connections between holons. The resilience holon could be used to decompose diverse resilience situations involving CES, to identify where critical vulnerability points are and how the whole resilience situation could be improved. The visual nature of the resilience holon could be used in an interactive way, allowing stakeholders to better understand the full resilience picture of CES that they use or operate.

Climate change is one of the greatest challenges for modern societies. Its consequences, often associated with extreme events, have dramatic results worldwide. New synergies between different disciplines including Artificial Intelligence (AI), Internet of Things (IoT), and edge computing can lead to radically new approaches for the real-time tracking of natural disasters that are also designed to reduce the environmental footprint. In this article, we propose an AI-based pipeline for processing natural disaster images taken from drones. The purpose of this pipeline is to reduce the number of images to be processed by the first responders of the natural disaster. It consists of three main stages, (1) a lightweight auto-encoder based on deep learning, (2) a dimensionality reduction using the t-SNE algorithm and (3) a fuzzy clustering procedure. This pipeline is evaluated on several edge computing platforms with low-power accelerators to assess the design of intelligent autonomous drones to provide this service in real time. Our experimental evaluation focuses on flooding, showing that the amount of information to be processed is substantially reduced whereas edge computing platforms with low-power GPUs are placed as a compelling alternative for processing these heavy computational workloads, obtaining a performance loss of only 2.3x compared to its cloud counterpart version, running both the training and inference steps.

Flood events are the most frequent cause of damage to infrastructure compared to any other natural hazard, and global changes (climate, socioeconomic, technological) are likely to increase this damage. Transportation infrastructure systems are responsible for moving people, goods and services, and ensuring connection within and among urban areas. A failed link in these systems can impact the community by threatening evacuation capability, recovery operations and the overall economy. Bridges are critical links in the wider urban system since they are associated with little redundancy and a high (re)construction cost. Riverine bridges are particularly prone to failure during flood events; in fact, the risks to bridges from high river flows and erosion have been recognized as crucial at global level. The interaction of flow, structure and network is complex, and not fully understood. This study aims to establish a rigorous, multiphysics modeling approach for the assessment of the hydrodynamic forces impacting inundated bridges, and the subsequent structural response, while understanding the consequences of such impact on the surrounding network. The objectives of this study are to model hydrodynamic forces as demand on the bridge structure, to advance a performance evaluation of the structure under the modeled loading, and to assess the overall impact at systemic level. The flood-prone city of Carlisle (UK) is used as a case study and a proof of concept. Implications of the hydrodynamic impact on the performance and functionality of the surrounding transport network are discussed. This research will help to fill the gap between current guidance for design and assessment of bridges within the overall transport system.

In Named Data Networking, cache pollution, cache poisoning and interest flooding are three popular types of attacks that can drastically degrade the network performance. However, previous methods for mitigating these attacks are not sufficiently effective or efficient. Also, they cannot simultaneously handle the three attacks, or the case that core routers or edge routers are compromised. To handle these problems, we propose an effective and lightweight countermeasure scheme. It consists of token-based router monitoring policy (TRM), hierarchical consensus-based trust management (HCT), and popularity-based probabilistic caching and caching replacement policy (PPC). In TRM, each edge router monitors and evaluates each data requester's probability of launching the cache pollution attack and each data provider's probability of launching the cache poisoning attack, and accordingly assigns, rewards and penalizes tokens to them to control their data request and data provision activities. Thus, the interest flooding attack can also be mitigated by limiting the consumption of tokens. In HCT, each core router manages its directly connected edge routers using TRM, and the core routers trust each other through adopting the concept of consensus in Blockchain. Thus, the edge and core routers executing monitoring and evaluation are trustable. PPC uses probabilistic caching and caching replacement based on the popularity of received content to further mitigate the attacks and reduce caching and data verification overhead. Results from simulation experiments demonstrate that our proposed scheme has better performance, in terms of interest satisfaction ratio and average end-to-end delay than current mechanisms.

With the proliferation of smart mobile devices, there is now an ever increasing craving for higher bandwidth for end user satisfaction. Increasing mobile traffic leads to congestion of backhaul networks. One promising solution to this problem is the mobile edge network and consequently mobile edge caching. There is an emerging paradigm shift toward the use of unmanned aerial vehicles (UAVs) to assist the traditional cellular networks and also to provide connectivity in places where there are no small base stations or faulty ones as a result of some natural disaster such as flooding. Hence, UAVs can be used to assist in content caching as well. This work proposes the use of human centric features, random waypoint user mobility model, and deep reinforcement learning to predict the location of the UAVs and the contents to cache at the UAVs. We formulated our problem as a Markov decision problem (MDP) and proposed a dueling reinforcement learning-based algorithm to solve the MDP problem. Our simulation results prove that our algorithm converges to an optimal solution and performs better than other baseline reinforcement learning algorithms in terms of quality of experience satisfaction, transmit power, and cache resource utilization

Cloud Computing provides an auto-scaling feature for dynamic resource utilization to cope with their customers’ requirements and charge as ‘pay-per-use’. Attackers get the benefit of this auto-scaling feature by flooding DDoS attacks on the VMs or instances in the cloud environments. Such DDoS attacks give financial damages to the customers because of massive amount of resource utilization, known as the Economic Denial of Sustainability (EDOS) attack. Transmission Control Protocol (TCP) SYN flooding attack is known to be the most challenging attack resulting EDOS attack. Software Defined Network (SDN) being a cost-efficient solution for cloud service providers, uses the OpenFlow switches and flow tables to make rules for each incoming flow. In SDN, it is feasible to classify an incoming flow as an attack and block forwarding of this flow to the targeted VM. This research work proposes an SDN based fast and computationally cost-efficient statistical anomaly detection model, named EDOS-TCP SYN mitigation model (EDOS-TSM) to mitigate TCP SYN flooding attack from a single user and spoofed IPs. EDOS-TSM uses binomial probability, TTL field value of IP packet header and multi-TCP SYN requests to detect source-based and spoofing based attacks. The proposed model is implemented, and the performance is evaluated on an OpenStack production-based cloud with real-time traffic and attack generation. The attack mitigation results are compared with existing models in the literature. The results show a large number of false negatives in existing models and efficiency of EDOS-TSM is proved in both source based and spoofing attacks.

This paper concentrates on tracking multiple targets using an asynchronous network of sensors with different sampling rates. First, a timely fusion approach is proposed for handling measurements from asynchronous sensors. In the proposed approach, the arithmetic average fusion of the estimates provided by local cardinalized probability hypothesis density filters is recursively carried out according to the network-wide sampling time sequence. The corresponding intersensor communication is conducted by a partial flooding protocol, in which either cardinality distributions or intensity functions pertinent to local posteriors are disseminated among sensors. Moreover, both feedback and non-feedback fusion-filtering modes are provided to meet the performance and real-time requirements, respectively. Second, an extension of the timely fusion approach referred to as robust bootstrap approach is presented, which can deal with unknown clutter and detection parameters by exploiting a local bootstrap filtering scheme. Finally, numerical simulations are performed to test the proposed approaches.

The link flooding attack (LFA) has emerged as a new category of Distributed Denial of Service (DDoS) attacks in recent years. Along with the massive deployment of low-cost insecure Internet of Things (IoT) devices, the fast proliferation of IoT botnets dramatically increases the risk of LFAs. However, how to efficiently defend against LFAs in IoT still remains as an open problem. To overcome this challenge, we model the interaction between an LFA attacker and the network manager as a two-person Bayesian game in this paper to precisely characterize the behaviors of both sides. Then, the rational behaviors of the attacker and the optimal strategies of the defender are unveiled by deriving the Bayesian Nash Equilibrium (BNE). Inspired by the obtained BNEs, a cost-effective decision framework is proposed for the defender to make defense decisions. Furthermore, we numerically analyze the effect of all the related factors and present feasible suggestions to deter attack motivations fundamentally. Experimental results demonstrate that the proposed method not only consistently outperforms baseline methods in terms of the defender’s utilities under different attack intensities, but also is robust to the changes in important parameters, including the value of benign traffic and the latency of traffic scrubbing.

Floods are the most common disaster in the world and cause deaths, damages to houses, buildings, and possessions, as well as disruption to communications. In such dynamic scenarios, it is needed to search and track mobile objects transported by the water flows, such as humans, animals, vehicles, and debris. The use of Unmanned Aerial Vehicles (UAVs) is essential in disaster scenarios to help first responders determine correct procedures in terms of searching, tracking, and rescuing the victims, as well as in defining the actions to minimize the risks in a sustainable and timely manner. However, the tracking of mobile objects and the delivery of real-time video sequences from UAVs with energy constraints toward first responders is still a hard task. Therefore, it is necessary to orchestrate a swarm of UAVs for searching and tracking objects while reacting fast to frequent changes in the topology and trajectory, as well as distributing real-time video flows with quality level support to the ground team. This article proposes an energy-aware swarm-based and mobility prediction scheme for UAVs, called SUAV. SUAV provides a unique UAV-based system for rescue scenarios with mobile objects and with support for route and path planning, searching and tracking procedures, mobility prediction and multi-hop communication management, video delivery with Quality of Service (QoS)/Quality of Experience (QoE) support, and energy-efficiency. Simulation results show the efficiency of SUAV in transmitting real-time video flows with QoS/QoE support while minimizing the energy consumption in flooding scenarios with mobile targets.

This work presents a multidimensional attention learning-based lightweight convolutional neural network for very-high-resolution (VHR) remote sensing imagery recognition, which incorporates channel attention, spatial attention, and saliency sampler attention into the backbone to improve its recognition performance. Specifically, channel attention can alleviate the interference of original feature cube by adaptively giving different coefficient weights to different feature channels. Spatial attention can emphasize the discriminative regions by using the sum of activation value in different locations of the image to weight the original feature cube. Saliency sampler attention can increase the influence of interesting regions on the final representation according to the saliency priors. In general, different from the existing methods, this work utilizes the constraints rather than the model scale to improve the recognition performances of the network. In addition, the novel flooding loss is used to optimize the network, which can improve the performance of the framework by alleviating the severe overfitting problem.

Security issues of a system are factors that need to be considered in the operation of information systems, which are intended to prevent threats to the system and detect and correct any damage to the system. Distributed Denial of Services (DDOS) is a form of attack carried out by someone, individuals or groups to damage data that can be attacked through a server or malware in the form of packages that damage the network system used. Security is a mandatory thing in a network to avoid damage to the data system or loss of data from bad people or heckers. Packages sent in the form of malware that attacks, causing bandwidth hit continuously. Network security is a factor that must be maintained and considered in an information system. Ddos forms are Ping of Death, flooding, Remote controled attack, UDP flood, and Smurf Attack. The goal is to use DDOS to protect or prevent system threats and improve damaged systems. Computer network security is very important in maintaining the security of data in the form of small data or large data used by the user.

An effective online fault diagnosis system is of great significance to improve the reliability of fuel cell vehicles. In this paper, a fault diagnosis model for proton exchange membrane fuel cells is proposed. Firstly, the tests of electrochemical impedance spectroscopy under different fault types (flooding, drying, air starvation) and fault degrees (minor, moderate, severe) are carried out, and each polarization loss of the fuel cell is denoted by an equivalent circuit model (ECM). Then, the parameters of the ECM are identified by the proposed random mutation differential evolution algorithm. Furthermore, the parameters identified under different fault conditions are used to train and test a probabilistic neural network-based fault diagnosis model. The fault diagnosis model achieves diagnosis accuracies of 100% for the fault type and 96.67% for the fault degree. By setting operating conditions with different fault degrees, the fault diagnosis model proposed in this paper can realize the fault type and fault degree diagnosis, effectively avoiding the misjudgment of fault types, and is effective for improving the reliability of the fuel cell system.

Security and protection of information is an ever-evolving process in the field of information security. One of the major tools of protection is the Intrusion Detection Systems (IDS). For so many years, IDS have been developed for use in computer networks, they have been widely used to detect a range of network attacks; but one of its major drawbacks is that attackers, with the evolution of time and technology make it harder for IDS systems to cope. A sub-branch of IDS-Intrusion Alert Analysis was introduced into the research system to combat these problems and help support IDS by analyzing the alert triggered by the IDS. Intrusion Alert analysis has served as a good support for IDS systems for many years but also has its own short comings which are the amount of the voluminous number of alerts produced by IDS systems. From years of research, it has been observed that majority of the alerts produced are undesirables such as duplicates, false alerts, etc., leading to huge amounts of alerts causing alert flooding. This research proposed the reduction alert by targeting these undesirable alerts through the integration of supervised and unsupervised algorithms and approach. The research first selects significant features by comparing two feature ranking techniques this targets duplicates, low priority and irrelevant alert. To achieve further reduction, the research proposed the integration of supervised and unsupervised algorithms to filter out false alerts. Based on this, an effective model was gotten which achieved 94.02% reduction rate of alerts. Making use of the dataset ISCX 2012, experiments were conducted and the model with the highest reduction rate was chosen. The model was evaluated against other experimental results and benchmarked against a related work, it also improved on the said related work.

Optical burst switching networks are vulnerable to various threats including Burst Header Packet Flooding attack, Circulating Burst Header attack, Address Spoofing, and Replay attack. Therefore, detecting such threats play a key role in taking appropriate security measures. One of the major challenges in identifying the risks of Burst Header Packet flooding attacks is the lack or insufficiency of reliable historical data. In this paper, firstly, Burst Header Packet flooding attacks are classified into four categories, Misbehaving-Block, Behaving-No Block, Misbehaving-No Block and Misbehaving-Wait, using Naive Bayes and K-Nearest Neighbor algorithms. Using performance metrics obtained both after testing on the same set and after applying 10-fold cross validation, the performance of Naive Bayes and K-Nearest Neighbor algorithms is compared based on commonly used performance metrics. As the results show, compared to Naive Bayes, K-Nearest Neighbor algorithm is more suitable for predicting Burst Header Packet Flooding attacks.

In this study, the efficiency of clay nanoparticles-assisted polymer (CNAP) flooding in enhancing oil recovery was numerically scrutinized and compared with the conventional polymer flooding. Thus, using a three-dimensional chemical flood simulator, comprehensive numerical simulations were carried out to find out the most suitable scenario for ameliorating oil production from a stratified reservoir with a high permeability contrast. Therefore, the effects of reservoir heterogeneity, well arrangement, and salinity on the simultaneous flow of crude oil and the nanofluid in the layered reservoir were explored. The results disclosed that, by increasing the rock heterogeneity index, the ultimate oil recovery during water flooding was reduced from 41 to 34% of original oil-in-place (OOIP). However, for the case of polymer flooding and CNAP flooding, the ultimate oil recovery was reduced by a factor of 3% and 1%, respectively. In addition, for the cases studied here, the inverted-two-5-spot pattern was proposed as the best well-placement strategy to boost oil recovery from layered formations. Due to the greater degree of salt tolerance of the clay nanoparticles dispersed in polymer (CNDP) solution, compared with polymer flooding, the CNAP flooding in the reservoir with the highest formation salinity showed a better macroscopic sweep efficiency and improved oil recovery by a factor of 15% of OOIP. Interestingly, the output of numerical simulations proved that the amount of required polymer to gain an ultimate oil recovery of more than 60% of OOIP can be reduced by half when the injectant has clay nanoparticles (NPs).

In some Vehicular Ad Hoc Networks (VANETs) applications, the geocast routing protocol is used for data transmission from a source vehicle to a group of vehicles located in a common region. Efficient data transmission to the destination region is one of the critical challenges of geocast routing protocols. In this research, the geocast routings are considered that exploit rateless coding to improve the reliability, and so packet delivery ratio. Some of these geocast routing methods use flooding schemes to deliver the messages to the destination region. However, in order to cut high overheads caused by flooding schemes, the routing protocols that use unicast routes for data delivery have been taken into account. In this way, recent geocast routing protocols exploit on-demand unicast routing methods such as Ad-hoc On-Demand Distance Vector (AODV) to deliver the packets to the destination region and then broadcast them in that area. However, the packet delivery ratio and the delay of those methods are respectively lower and higher than flooding-based methods. This paper proposes to exploit the table-driven Optimized Link State Routing (OLSR) protocol to deliver the messages to the destination region. To customize the OLSR protocol for geocasting, we propose a number of modifications to message flows and data exchanges. Compared to on-demand geocast protocols, OLSR imposes lower message delay and delivers more messages to the destination region at a higher overhead expense. To overcome this overhead, we also propose algorithms to adjust the control message intervals of the OLSR protocol in each node. Simulation results show that our OLSR-based protocol demonstrates better performance in terms of delay and packet delivery ratio than those of the traditional AODV-based method and CALAR-DD protocol regarding various vehicles' densities, vehicles' velocities, message sizes, and destination region sizes. Compared to the traditional OLSR, using the tuned OLSR-based method has also significantly reduced the signaling overhead costs.

Smart communication systems improve VANETs with the assistance of intelligent software systems. Such electronic tools allow automatic traffic control services to reduce road injuries and to classify routes effectively. Such automated traffic control networks are vulnerable to many kinds of attacks. Denial of service attack is a type of attack when the malicious node disrupts the network’s entire communication by flooding the packets. Many methods of detecting DoS attacks in VANETs are available in the literature, but they are still hard to detect and mitigate attacks. Due to these attacks, the vehicle user cannot transmit emergency alert messages and disseminate the traffic data to the destination vehicle in due time. A new detection algorithm is developed and suggested in this analysis to detect and mitigate a malicious node using a game-theoretical approach. Stackelberg’s game technique is designed to identify attacks, reduced the packet drop ratio by 12.45%, and increased the throughput by 94.21% compared to the Packet Detection Algorithm, Attacked Packed Detection Algorithm, and Enhanced Attacked Packet Detection Algorithm.

In today's technological world, distributed denial of service (DDoS) attacks threaten Internet users by flooding huge network traffic to make critical Internet services unavailable to genuine users. Therefore, design of DDoS attack detection system is on urge to mitigate these attacks for protecting the critical services. Nowadays, deep learning techniques are extensively used to detect these attacks. The existing deep feature learning approaches face the lacuna of designing an appropriate deep neural network structure for detection of DDoS attacks which leads to poor performance in terms of accuracy and false alarm. In this article, a tuned vector convolutional deep neural network (TVCDNN) is proposed by optimizing the structure and parameters of the deep neural network using binary and real cumulative incarnation (CuI), respectively. The CuI is a genetic-based optimization technique which optimizes the tuning process by providing values generated from best-fit parents. The TVCDNN is tested with publicly available benchmark network traffic datasets and compared with existing classifiers and optimization techniques. It is evident that the proposed optimization approach yields promising results compared to the existing optimization techniques. Further, the proposed approach achieves significant improvement in performance over the state-of-the-art attack detection systems.

Advances in technology allowed the fast and easy creation and configuration of industrial alarms. The growth in the number of alarms, however, brought some problems to the alarm management systems, for example, alarm flooding. In this paper, it is propose a new method to detect the causal relationships between industrial alarm variables using Transfer Entropy theory and the structural learning of the Bayesian networks K2 Algorithm as its fundamental basis. This work argues that the detection of these relationships can help to reduce the number of alarms in the alarm management systems, avoiding the overloading of operators in case of plant failures. To validate the proposal, a case study using the well-known plant-wide simulator Tennessee Eastman Process was performed. In this study case the graph of the causal relationships obtained by the proposed method was compatible with the system functioning.

Chemical flooding is one of the effective methods to recover large volumes of oil from sandstone formations after primary depletion. However, silica dissolution often occurs during Alkaline-Surfactant-Polymer (ASP) flooding, affecting the petro-physical properties of the formation. To address this issue, samples from Berea sandstone formations were treated with various brine solutions, through static tube tests and core flooding experiments. Analytical tests such as DR/2800 spectrophotometer and scanning electron microscope were used to evaluate the silica solubility and the alteration in mineral content. The results indicated that the silicate contents decreased after the saturation due to silica solubility in the solution. Increasing brine salinity to 40,000ppm and introducing Magnesium and Calcium ions to the solution, reduces the silicate contents by 5.03 % and 7.32 %. Moreover, saturating the samples with ASP solution, further reduced the silicate contents by 14.86 %. This reduction is associated with a relative increase in silica solubility and pH of the solution. Silica dissolution affects the pore microstructure, which resulted in increasing the porosity and pore volume after the core flooding. The injection of the ASP solution increased the porosity by 5.83%, thus the pore volume increased from 17.72 to 18.76cc. This is associated with the high silica solubility and the increase of solution pH in the ASP solution. The permeability of the samples generally reduced after the core flooding, due to the silica solubility. However, injecting the ASP solution, resulted in a major reduction of the permeability by more than 75%. These changes in the petro-physical properties can lead to severe formation damage, and affect hydrocarbon production. This study assists in understanding the impact of silica dissolution during ASP treatment and addresses the factors involved. Efficient utilization of chemical flooding can help mitigating silicate scaling within the formation, and extend field productivity.

Legacy industrial control systems (ICSs) are not designed to be exposed to the Internet and linking them to corporate networks has introduced a large number of cyber security vulnerabilities. Due to the distributed nature of ICS devices, a detection-in-depth strategy is required to simultaneously monitor the behaviour of multiple sources of ICS data. While a detection-in-depth method leads to detecting attacks, like flooding attacks in earlier phases before the attacker can reach the end target, most research papers have focused on anomaly detection based on a single source of ICS data. Here, we present a detection-in-depth method for an ICS network. The new method is called automated flooding attack detection (AFAD) which consists of three stages: data acquisition, pre-processing, and a flooding anomaly detector. Data acquisition includes data collection from different sources like programmable logic controller (PLC) logs and network traffic. We then generate NetFlow data to provide light-weight anomaly detection in ICS networks. NetFlow-based analysis has been used as a scalable method for anomaly detection in high-speed networks. It only analyses packet headers, and it is an efficient method for detecting flooding attacks like denial of service attacks, and its performance is not affected by encrypted data. However, it has not been sufficiently studied in industrial control systems. Besides NetFlow data, ICS device logs are a rich source of information that can be used to detect abnormal behaviour. Both NetFlow traffic and log data are processed in our pre-processing stage. The third stage of AFAD is anomaly detection which consists of two parallel machine learning analysis methods, which respectively analyse the behaviours of device logs and NetFlow records. Due to the lack of enough labelled training datasets in most environments, an unsupervised predictor and an unsupervised clustering method are respectively used in the anomaly detection stage. We validated our approach using traffic captured in a factory automation dataset, Modbus dataset, and SWAT dataset. These datasets contain physical and network level normal and abnormal data. The performance of AFAD was compared with single-layer anomaly detection and with other studies. Results showed the high precision of AFAD in detecting flooding attacks.

In this study, a model is developed for short-term flood control of a complex multi-reservoir system located on one of the largest basins in Turkey. The managing body of the reservoirs requires that short-term real-time flood control is used in conjunction with the long-term operation for energy maximization. For this purpose, a novel operating framework is developed to couple short-term and long-term decisions. The resulting optimization problems are a large scale and nonlinear. Hence, the solution is maintained by developing an improved epoch-based particle swarm optimization (PSO) method. The proposed solution is general and can be used for any watershed with different layouts of several reservoirs having anticipated inflows and pre-defined release and storage constraints. It is the first time PSO is used for such a complex large-scale problem within flood control context. The developed optimization model was applied to a sample basin having 10 reservoirs with general layout of the mixed structures. The results obtained were compared to the real operational data and it was found that a solution with less flooding and more energy production would have been possible if the proposed methodology were utilized.

The Internet of Things (IoT) concentrates on content dissemination and retrieval, so it is significant to achieve efficient content delivery. However, the Internet focuses on end-to-end communications, which might degrade the content retrieval performance in mobile environments. By contrast, the content-centric mechanism might be an ideal method for achieving efficient content delivery although it suffers from flooding and reverse-path disruptions. Therefore, we are motivated to exploit the content-centric mechanism to achieve IoT-based content delivery, and employ the address-centric anycast to overcome the limitations of the content-centric mechanism. Inspired by the idea, we propose a content-centric framework for IoT. The experimental results show that the proposed framework reduces the content communication cost and improves the content acquisition success rate.

We present a method for accelerated optimisation of CO2 injection into petroleum reservoirs. The optimisation assumes maximisation of the net present value by coupling reservoir models with the calculation of cash flows. The proposed method is based on the construction of a hierarchy of compositional reservoir models of increasing complexity. We show that in dimensionless volumes, the optimal water and gas slugs are very close for the 1-D and 2-D areal reservoir models of the water-alternating-gas (WAG) process. Therefore, the solution to the 1-D optimisation problem gives a good approximation of the solution to the 2-D problem. The proposed method is designed by using this observation. It employs a larger number of less computationally expensive 1-D compositional simulations to obtain a good initial guess for the injection volumes in much more expensive 2-D simulations. We suggest using the non-gradient optimisation algorithms for the coarse models on low levels of the hierarchy to guarantee convergence to the global maximum of the net present value. Then, we switch to the gradient methods only on the upper levels. We give examples of the algorithm application for optimisation of different WAG strategies and discuss its performance. We propose that 1-D compositional simulations can be efficient for optimising areal CO2 flooding patterns.

Over the last two decades, peer-to-peer systems have proven their vital role in sharing various resources and services to diverse user communities over the internet. The unstructured P2P network is the most popular topology, and the resources are fully distributed among participating peers. Therefore, searching is a challenging issue due to the absence of control over resource locations. Intelligent decisions should be made to select a particular number of neighbors that can hold relevant resources for queries instead of selecting neighbors randomly. In this paper, an intelligent neighbor selection (INS) algorithm is introduced that uses a reinforcement learning approach, ‘Q-learning’. The main objective of this algorithm is to achieve better retrieval effectiveness with reduced searching costs by fewer connected peers, exchanged messages, and less time. To achieve this, INS relies on Q-learning, which makes a Q-table in each peer and stores the Q-values gathered from the results of previously sent queries, and uses them for the forthcoming queries. The cold start issue during the training phase is also addressed in this research, which allows INS to improve its results continuously. The simulation results show a significant improvement in searching for a resource with compression to controlled flooding and learning processes after sufficient training. Here, retrieval effectiveness, search cost in terms of connected peers, and average overhead are 1.23, 104, 167, respectively.

A nuclear power plant operation requires reliability safety systems. Therefore, an accidental scenario such as the LOCA becomes a specific attention. This relates to a countercurrent flow phenomenon which permits the occurrence of a flooding regime. This study aims to investigate the effets of I/D ratios of the riser on the onset of flooding and time-series voltage fluctuations of differential pressure signals of the flooding regime on a complex geometry representing a PWR hot leg. The test section contains a combination of a horizontal, an elbow and an inclined pipes. Three I/D ratios containing 1.9 (R1), 3.9 (R2) and 8.3 (R3) were assesed. The tabulated data are expressed in the term of a flooding curve. Meanwhile, the statistical features of the time-series voltage fluctuations of the signals are presented on both PDF and PSD graphs. The results obtained show that the assesed I/D ratios do not conduct significance effects on the onset of flooding. On the other hand, there are trends which can be obtained from the time-series signals with the increase of the I/D ratio. Those trends can also be observed in the such statistical features; the PDF and PSD graphs, respectively.

Alkali–surfactant–polymer (ASP) flooding holds a critical position in oil development. This paper is concerned with the distributed parameter system (DPS) modeling and development scheme optimization of ASP flooding. Owing to the neglect of coupling among three-dimensional (3-D) spatial information, most of the existing data-driven modeling methods fail to provide a high-precision model for this type of strongly nonlinear and spatiotemporal coupling 3-D DPS. To overcome this shortcoming, an integrated spatiotemporal modeling method (ISM) is proposed, which reconstructs temporal–spatial relationship to avoid spatial mapping distortion based on canonical polyadic decomposition (CPD), and a spatiotemporal MSSO-LS-SVM algorithm is presented to compensate the nonlinear dynamics and decrease truncation error. Moreover, the synchronous optimization of ASP injection strategy and oil wells switching scheduling is an urgent industry requirement of intelligent oilfield decision system, which can be described a mixed-integer optimal control/dynamic programming problem. This paper first puts forward a mixed-integer approximate dynamic programming (MIADP) algorithm, dedicated for solving this class of optimal control problem with integer control subset requirements. In the MIADP, a parallel mixed-integer policy improvement framework is developed, where (i) the continuous control weight updates by the temporal difference (TD) error, (ii) the quantum evolution mechanism is designed to learn the integer control, and (iii) the two parts share information interactively and iterate to approach the optimal mixed-integer policy. At last, a case study on ASP flooding is taken, and the superior performance metrics illustrate the effectiveness of the proposed ISM method and MIADP algorithm.

Wireless Ad Hoc Networks are self-organizing networks deployed without any centralized infrastructure. Flooding based Denial-of-Service (DoS) attacks were targeting the constrained resources of mobile nodes as well as the network. Route Request (RREQ) flooding attack is one of the prominent DoS attacks launched from the network layer in which the attacker sends a huge number of spoofed Route Request (RREQ) packets which not only overflows the target buffer but also creates network congestion. In this work, novel methods were suggested for defending RREQ flooding attack in Wireless Ad Hoc Network using two well-known frameworks in uncertain reasoning namely Bayesian Inference and Dempster-Shafer (D-S) evidence theory. The present work reports the modeling of RREQ traffic and developed an optimum algorithm for the detection of persistent RREQ flooding attack using Bayesian Inference. The algorithm was further refined for the detection of high rate and low rate pulsed RREQ flooding attack using D-S evidence theory. Based on the comprehensive evaluation using mathematical modeling and simulation, the proposed method successfully defended any type of flooding based DoS attack in Wireless Ad Hoc Network with lower communication and memory overhead.

A water level sensor is critical to measure water levels at strategic points in the river. The sensor location directly impacts the quality of the collected data sent to the flood early warning system. To prevent and minimize the risk of flooding, it is crucial to determine the optimal locations for water level sensor placement. This research proposes a Mixed-Integer Linear Programming (MILP) model for water level sensor placement considering multi-sensor and multi-disaster areas. In addition, this model was applied in a case study in Tikala River, Manado, Indonesia. The results indicated that all disaster areas could be covered by at least one single sensor. A sensitivity analysis was performed by running the model under several different budget scenarios. When the budget increases, the number of sensors and the coverage performance are getting larger. Thus, the proposed MILP model was able to determine the optimal locations for sensor placement under a limited budget.

Network caching in named data networks (NDN) is essential for improving the potentialities of the conventional IP networking. The concept of network caching is necessary for achieving optimal bandwidth utilization and location independent data access during multipath data dissemination. However, network caching in NDN makes it highly vulnerable to security breaches such as access content packets violation, flooding or malicious injection of packets and content cache poisoning. In this paper, a dynamic forest of random subsets-based one-time signature-based capability enhancing security architecture (DFORS-CSA) is proposed for attaining distributed data authentication. This DFORS-CSA security architecture leverages the potential in exploring the access privileges of the packets disseminated in the network. It includes the capability through which the routes can perform authentication of packets forwarded in NDN. It supports a significant verification strategy through which the routers can ensure the packet timeliness for resolving the problems that get introduced through unsolicited packets exchanged during flooding-based denial of service attacks. The simulation experiments of the proposed DFORS-CSA is conducted using the open source CCNs platform and Planetlab simulator. The results of the proposed DFORS-CSA confirmed its predominance in minimizing overall delay and time incurred in the bit vector generation by 16.74 and 15.63%, excellent to the baseline approaches. The results of the proposed DFORS-CSA also conformed a mean improvement in the precision rate by 10.21%, true positive rate by 8.94% and F-measure by 7.62% with decreased false positive rate of 8.56%, during the process of detecting content cache poisoning attack.

In empty well killing, in order to save the time and cost of killing the well, the dynamic replacement method is often used to kill the well. The main problem of the dynamic replacement method for killing wells is how to avoid terrible working conditions caused by flooding, such as gas carrying fluid, killing fluid being brought to the wellhead. Based on the principle of flooding formation and the basic tenets of flooding correlation experiment and dynamic replacement method, this paper incorporates the kill fluid viscosity, surface tension, droplet diameter, inclination angle, drill pipe joint outer diameter, and drill pipe eccentricity into the calculation range and establishes a new mathematical model suitable for dynamic replacement kill. Based on the calculation results, the influencing factors of flooding are analyzed, and the following conclusions are drawn: the increase of dynamic viscosity, gas density in the well, casing pressure, well angle, the outside diameter of drill pipe, the outer diameter of drill pipe joint, and eccentricity of drill pipe can promote the occurrence of flooding; The increase of surface tension, well-killing fluid density, and casing inner diameter have an obstacle to flooding.

Wireless sensor network occupies an inevitable role in various applications that requires data collection and transmission. The tiny wireless sensor nodes are responsible for data collection and transmission. However, these randomly deployed nodes are vulnerable to attacks such as selective forwarding attacks, flooding attacks, etc., These attacks directly affect the network performance and probe the network security. In this research work, an efficient trust-based attack detection module is presented to detect denial of service attacks such as selective forwarding and flooding attacks. Multi-dimensional trust parameters are extracted and estimated through the proposed approach to determine the packet forwarding activity from the sensor nodes. The proposed DoS attack detection model performance is verified through simulations and the results validate the better performance in terms of throughput, energy consumption, packet delay, and accuracy. Compared to conventional detection techniques proposed attack detection model outperforms well in all aspects.

A large number of Internet of Things (IoT) devices such as sensor nodes are deployed in various urban infrastructures to monitor surrounding information. However, it is still a challenging issue to collect data in a low-cost, high-quality, and reliable manner through IoT technique. Although the recruitment of mobile vehicles (MVs) to collect urban data has proved to be an effective method, most existing data collection systems lack a trust detection mechanism for malicious terminal nodes and malicious vehicles, which should lead to security vulnerabilities in practice. This paper proposes a novel data collection strategy based on a layered trust mechanism (DC-LTM). The strategy recruits MVs as data collectors of the sensor nodes based on the data value in the city, evaluates the trustworthiness of the data reported by the nodes, and records the results to the cloud data center. Furthermore, in order to make the data collection system more efficient and trust mechanism more reliable, we introduce unmanned aerial vehicles (UAVs) dispatched by data centers to actively verify the core sensor node data and use the core sensor data as baseline data to evaluate the credibility of the vehicles and the trust value of the whole network sensor nodes. Different from the previous strategies, UAVs adopts the DC-LTM method to obtain the node data while actively obtaining the trust value of MVs and nodes, which effectively improves the quality of data acquisition. Simulation results show that the mechanism effectively distinguishes malicious vehicles that provide false data in exchange for payment and reduces the total cost of system recruitment payments. At the same time, the proposed incentive mechanism encourages vehicle to complete the evaluation task and improves the accuracy of node trust evaluation. The recognition rates of false data attacks and flooding attacks as well as the recognition error rate of normal nodes are 100%, 98.9%, and 3.9%, respectively, which improves the quality of system data collection as a whole.

Flooding is now becoming one of the most frequent and widely distributed natural hazards, with significant losses to human lives and property around the world. Evacuation of pedestrians during flooding events is a crucial factor in flood risk management, in addition to saving people’s lives and increasing time for rescue. The key objective of this work is to propose a shortest evacuation path planning algorithm by considering the evacuable areas and human instability during floods. A shortest route optimization algorithm based on cellular automata is established while using diagonal distance calculation methods in heuristic search algorithms. The Morpeth flood event that occurred in 2008 in the UK is used as a case study, and a highly accurate and efficient 2D hydrodynamic model is adopted to discuss the flood characteristics in flood plains. Two flood hazard assessment approaches [i.e., empirical and mechanics-based and experimental calibrated (M&E)] are chosen to study human instability. A comprehensive analysis shows that extreme events are better identified with mechanics-based and experimental calibration methods than with an empirical method. The result of M&E is used as the initial condition for the Morpeth evacuation scenario. Evacuation path planning in Morpeth shows that this algorithm can realize shortest route planning with multiple starting points and ending points at the microscale. These findings are of significance for flood risk management and emergency evacuation research.

Internet of Things (IoT) is a heterogeneous environment where multiple devices/consumers can interest/request for the same chunk of content at the same time with the need for source authenticity. According to the current NDN policy, only one user can verify the content authenticity using a digital signature. However, the digital signature is generated for one particular user whose public key and identity have been used. As the content can be cached and multicast to all the users whose interest is awaiting in the Pending Interest Table (PIT). Unfortunately, only one user is able to verify the content authenticity while the rest can just receive the content. To solve this, we designed a novel multi-cast signature solution for NDN-based IoT networks in which the consumers can verify the source of the content provider. The proposed scheme obeys the forwarding and caching policy of NDN. Besides, the designed scheme can prevent cache pollution attacks, content poisoning attacks, denial of service attacks, and interest flooding attacks. For efficiency, we compared our newly designed scheme with some Identity-Based Signature (IBS) in terms of computation time and communication overhead. The results show significant improvements in both computation time and communication overhead. In addition, we also validate the designed scheme using the familiar security validation tool AVISPA. Finally, we provide a robust application scenario of the designed scheme on NDN-based IoT.