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To address the two problems of unclear delineation of sub‐catchment and complicated and cumbersome parameter rate determination in the Storm Water Management Model (SWMM), this study proposes a rapid construction method of SWMM based on the principle of single urban functional area combined with K‐means clustering algorithm, The research area is the southern part of Jinshui District, Zhengzhou City. The Hydrological Response Unit (HRU) contains only a single urban functional area, divided by combining the natural and social attributes of the urban surface. Calibrated uncertain parameters from 76 papers were selected as samples, and the K‐means clustering algorithm was used to cluster and calculate the parameter values, to improve the SWMM model, selecting three typical rainfall runoff processes for validation application. The results show that simulated runoff is consistent with measured runoff trends, with the NSE and R2 value scores of the flow processes of the three floods above 0.86 and the, locations and numbers of flooded nodes are consistent with the actual research. This provides a new idea and technical support for the construction of urban flood models in flood prevention and mitigation. The relevant results can provide scientific decision‐making reference for urban flood forecasting and warning.

This study investigates the effect of structural modification actions on the existing stormwater collecting system in Eastern Tehran to increase the hydraulic capacity and create suitable conditions for the passage of runoff in the critical points of the canal. First, the hydraulic conditions within the stormwater collecting system were simulated using the Stormwater Management Model (SWMM) model before/after the modification to investigate the rehabilitation results. Three critical locations along the main canal were recognized as the most vulnerable points. Then, based on field visits and brainstorming sessions, rehabilitation methods were presented, and three practical solutions, including canal deepening, canal widening, and their combination, were investigated for each. Then, local investigating based on the rehabilitation alternatives for each critical location was conducted using the HEC-RAS. Finally, the SWMM model was used again to evaluate the overall operational performance of the stormwater collecting system after the rehabilitation. The results revealed that it is necessary to implement two alternatives of deepening and widening to provide adequate transmission runoff capacity during rainfalls with various return periods. More specifically, the localized redesign of the eastern flood diversion canal had an acceptable improvement in reducing flooding problems so that for floods with a return period of 10 years, the number of node flooding dropped from 4 to 0, inundated areas from 17% to 0, and the overflow volume from (10–45) to 0. Moreover, the proposed local rehabilitation reduced the overflow volume from (30–65), (43–74), and (70–92) in the status quo to (4–12), (11–27), and (24–36) for rainfall with 25, 50, and 100-year return periods.

An increase in stormwater frequency following the rapid development of urbanization has drawn attention to the mitigating strategies in recent decades. For the first time, the present study aims to conduct a local rehabilitation in stormwater collecting systems by (i) detecting the critical nodes along with the canal network and (ii) redesigning the critical canal reaches using ant colony optimization (ACO) to create maximum capacity for flood discharge with minimum reconstruction cost while considering the probability of exceedance of the flood as a constraint. Hence, using the SWMM model, the flow in the collection system was simulated, and the inundation points in the study area in the eastern Tehran metropolis were determined. After determining the critical points, the hydraulic stimulation model for the selected canal flows was developed using HEC-RAS software to accurately simulate each critical bridge's flow. Then, the optimal parameters for the canal bed width and canal depth were obtained using ACO and defining a probability objective function using the flood probability exceedance as the redesign constraint. The results from the optimizer were compared with those of the LINGO nonlinear model. Finally, the operational performance of the redesigned system was evaluated using the optimal selected parameters. The results showed that in redesigning the studied canals, the two widening and deepening options are needed to obtain a discharge with sufficient flow capacity in various return periods (RPs). The optimization results for the first to third critical sections for a design discharge with a 100-year RPs showed that the calculated cost was 19.765(*106), 13.327(*106), and 43.139(*106) IR rials (1 USD = 202000 IRR), respectively. For the selected sections, the optimal bed width is 6.97, 8.97, and 10.93 m, and the optimal depth is 3.68, 4.81, and 4.04 m, respectively. The results indicate that the local modification in the eastern flood control canal adequately improved inundation problem reduction in various RPs – i.e., for a 10-year RP, the number of node flooding dropped from 4 to zero, the inundated area from 17% to zero, and the overflow volume from (10–45) to zero. It also reduced overflow volume from (30–65), (43–74), and (70–92) in the status quo to (4–12), (11–27), and (24–36) percent for precipitations with 25, 50 and 100-year RPs, respectively.

During data transmission, a decentralised Mobile Ad Hoc Network (MANET) might result in high Energy Consumption (EC) and a short Network Lifetime (NLife). To address these difficulties, an on-demand Power and Load-Aware multipath node-disjoint source routing is presented based on the Enhanced Opportunistic Routing (PLAEOR) protocol. This unique protocol aims at using power, load, and latency to manage routing costs depending on control packet flooding from the destination node. However, the exchange of control packets from the target to all nodes may impact network efficiency. As a result, the PLAEOR is designed with a Multichannel Cooperative Neighbor Discovery (MCND) protocol to locate the nearby cooperative nodes for each node in the routing path during control packet transmission. Furthermore, when the packet rate of CBR is 20 packets/sec, the simulated results show that the PLAEOR-MCND achieves 120 sec of NLife and 20 J of EC than the state-of-the-art protocols.

The trust model is widely used in the opportunistic social network to solve the problem of malicious nodes and information flooding. The previous method judges whether the node is a cooperative node through the identity authentication, forwarding capability, or common social attribute of the destination node. In real applications, this information does not have integrity and does not take into account the characteristics and dynamic adaptability of nodes, network structures, and the transitivity of social relationships between nodes. Therefore, it may not be effective in solving node non-cooperation problems and improving transmission success rate. To address this problem, the proposed node social features relationship evaluation algorithm (NSFRE) establishes a fuzzy similarity matrix based on various features of nodes. Each node continuously and iteratively deletes the filtered feature attributes to form a multidimensional similarity matrix according to the confidence level and determines the weights under different feature attributes. Then, the social relations of nodes are further quantified. The experimental results show that, compared with the traditional routing algorithm, NSFRE algorithm can effectively improve the transmission success rate, reduce transmission delay, ensure the safe and reliable transmission of information in the network, and require low buffer space and computing capacity.

Flood-based communication protocols are attractive due to their easy and fast network startup, resiliency to communication or node loss, and node mobility. Concurrent transmissions are flooding-based protocols that enable low-latency, network-wide communication synchronously. They also provide energy-efficient data dissemination with higher delivery reliability over traditional flooding. This paper proposes the Good Flood Bed (GFB) method to reduce further the energy consumption of networks operating based on concurrent transmission by preventing flood in parts of the network where there is no need for flood data. GFB starts with a distributed leader-election process to choose a root node. Then, the root node builds a spanning tree among nodes. The tree establishes a parent-child relationship and forms a new controlled flood bed. We show the effectiveness of the proposed method by simulation and experimenting on a BLE5 small-scale test bed. Experiments prove that this method has the potential to cut network energy consumption by 50%.

In a mobile ad hoc network, packets are lost by interference occurrence in the communication path because there is no backup information for the previous routing process. The communication failure is not efficiently identified. Node protection rate is reduced by the interference that occurs during communication time. So, the proposed reliability antecedent packet forwarding (RAF) technique is applied to approve the reliable routing from the source node to the destination node. The flooding nodes are avoided by this method; the previous routing information is backed up; this backup information is retrieved if any interference occurred in the communication period. To monitor the packet flow rate of every node, the straddling path recovery algorithm is designed to provide an interference free-routing path. This path has more number of nodes to proceed with communication. These nodes have a higher resource level and also used to back up the forwarded data; since sometimes routing breakdowns occurred, data are lost, which is overcome by using a backup process. It improves the network lifetime and reduces the packet loss rate.

Due to the widespread application of wireless sensor networks in fields such as healthcare, the battlefield, etc., security has become a prime concern for transmitting information without any data manipulation. For this concern, we introduce a Three-Level Weighted Trust evaluation-based Grey Wolf Optimization (3LWT-GWO) approach for the effective detection of misbehaving nodes and provide an optimal secure route through trusted nodes for delivering the data securely to the destination. The proposed model is categorized into three phases: (a) trust-based clustering, (b) cluster head selection, and (c) optimal data routing. Initially, the sensors are deployed randomly in the region in which the nodes have the initial same energy. Then the clustering of nodes is performed in the first phase by computing the Overall Trust Score (OTS) for each node based on the factors like direct trust, indirect trust, energy trust, Long-term neighbor Recommendation Trust, authentication trust, and link quality trust. This OTS helps to identify unsafe nodes. After the identification of unsafe nodes, clustering is performed. In the second stage, the weight of each node is calculated based on the residual energy, node distance, and energy. Then the node that has the highest weight is nominated as Cluster Head. Next, optimal routing is performed based on the GWO algorithm by computing the Trust Satisfactory degree, distance, energy, and delay. Based on the estimated route, the packet is delivered from the source node to the destination. The performance of the 3LWT-GWO method delivers better results when compared with the prevailing techniques in terms of energy consumption, throughput, network lifetime, accuracy, detection rate, and delay.

The Internet of Things (IoT) is an emerging technology in underwater communication because of its potential to monitor underwater activities. IoT devices enable a variety of applications such as submarine and navy defense systems, pre-disaster prevention, and gas/oil exploration in deep and shallow water. The IoT devices have limited power due to their size. Many routing protocols have been proposed in applications, as mentioned above, in different aspects, but timely action and energy make these a challenging task for marine research. Therefore, this research presents a routing technique with three sub-sections, Tri-Angular Nearest Vector-Based Energy Efficient Routing (TANVEER): Layer-Based Adjustment (LBA-TANVEER), Data Packet Delivery (DPD-TANVEER), and Binary Inter Nodes (BIN-TANVEER). In TANVEER, the path is selected between the source node and sonobuoys by computing the angle three times with horizontal, vertical, and diagonal directions by using the nearest vector-based approach to avoid the empty nodes/region. In order to deploy the nodes, the LBA-TANVEER is used. Furthermore, for successful data delivery, the DPD-TANVEER is responsible for bypassing any empty nodes/region occurrence. BIN-TANVEER works with new watchman nodes that play an essential role in the path/data shifting mechanism. Moreover, achievable empty regions are also calculated by linear programming to minimize energy consumption and throughput maximization. Different evaluation parameters perform extensive simulation, and the coverage area of the proposed scheme is also presented. The simulated results show that the proposed technique outperforms the compared baseline scheme layer-by-layer angle-based flooding (L2-ABF) in terms of energy, throughput, Packet Delivery Ratio (PDR) and a fraction of empty regions.

A novel approach is proposed to detect protocol misbehavior using state-of-the-art machine learning frameworks and entropy. Nodes in Vehicular Ad Hoc Networks (VANETs) use broadcast protocols to efficiently disseminate safety information, but nodes do not always behave according to the routing protocols. Misbehavior can be caused by a targeted attack, where an attacking vehicle can intentionally send or route malicious packets to harm. Due to the dynamic nature of nodes in VANETs and routing complexity, unintentional misbehavior can also happen due to hardware or software failures in the vehicle. We are not concerned with the targeted attacks, but rather explore how the unintentional misbehavior, which can cause statistical multi-hop routing protocols to operate as basic flooding protocols, can be detected and accurately classified. These methods and detection techniques are based on the IEEE 802.11p MAC layer and weighted p-persistence multi-hop routing protocol. The linear classification was done using the TensorFlow framework and evaluations were performed using the VEINS simulator using the p-persistence broadcast protocol in a US city area.