Higher Packet Delivery Ratio Research Articles

Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

ABSTRACTIn the context of Mobile Ad Hoc Networks (MANETs), the dynamic and decentralized topology poses significant challenges like unreliable connectivity, limited bandwidth, node mobility, and vulnerability to security threats from malicious nodes. Ensuring secure and energy‐efficient data transmission in such environments is crucial for mission‐critical applications. This research addresses these pressing challenges by introducing a robust routing protocol capable of detecting and mitigating malicious nodes, thereby enhancing MANET's Quality of Service (QoS). The proposed approach, the Dendritic Cell with Adaptive Trust Q‐learning Protocol (dDC‐ATQP), integrates several innovative techniques to tackle these issues. Firstly, the trust evaluation mechanism assesses the behavior of nodes to identify potential malicious actors, mitigating the effect of malicious nodes on system execution. Secondly, the adaptive routing strategy optimizes data transmission paths based on real‐time network conditions, reducing latency and packet loss. To evaluate the effectiveness of this approach, extensive simulations are conducted using a range of performance metrics. The results demonstrate significant improvements over existing methods, including a throughput increase of (79.2% in 50 s), lower end‐to‐end‐delay (0.075 s for 20 nodes), energy consumption of (38.55/J), higher packet delivery ratio (98% for 20 nodes), reduced packet loss ratio (5% for 100 nodes), enhanced security (80% for 70 nodes).

Adaptive Mobility and Reliability-based Routing Protocol for Smart Healthcare Management Systems in Vehicular Ad‐hoc Networks

Ambulances enable digital and widespread remote care for older people in the healthcare sector. The Vehicular Ad Hoc Network (VANET) is critical for allowing the digital and Intelligent Transportation System (ITS) for the smart healthcare world. Enhancing the dependability and flexibility of an adaptive routing algorithm improves vehicular communications' Quality of Service (QoS) effectiveness. An essential concern in vehicular technology is enabling drivers to make reliable judgments. Developing an effective routing system that ensures a suitable QoS takes time and effort. The vehicular network ecosystem is characterized by limited mobility, fast vehicle speeds, and constantly changing configurations. This study makes four distinct contributions. Initially, it presents adaptive routing protocols, capable of recognizing changes in the network environment, aware of the mobility of nodes, and dependable in delivering data packets. The experimental configuration is implemented inside a discrete Network Simulator (NS-3) scenario to enhance the QoS. Mobility-aware routing algorithms can provide ITS with satisfactory mobility, vital dependability, high Packet Delivery Ratio (PDR), high throughput, and low End-to-End (E2E) Delay. The proposed method has an average throughput of 664.3 kbps, a delay of 151.3 ms, and a packet delivery ratio (PDR) of 94.23%.

Energy Efficient Secured PSO Optimized Clustering and Data Aggregation Routing Protocol for Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), sensor nodes are placed to sense and collect data. Due to the energy constraint nature of WSN, optimising the energy during the data dissemination is a major concern. To solve this problem, data aggregation may be used to bring down the redundant transmission of packets in WSN. In most of the previously available techniques, security is also a major concern during data aggregation and routing process with optimized energy. Many data aggregation-based routing systems are subject to security attacks during the data transfer from sensors to source to Clustered Heads (CHs) and then to sink with data aggregation process. Moreover, the existing data aggregation-based routing protocols suffer from data redundancy with less accuracy in aggregated data. For handling such issues order to overcome these issues, an Energy Efficient Secured Clustered Particle Swarm Optimization (PSO) oriented Data Aggregation Routing Protocol (EESCPSO-DARP) that can provide efficient authentication during data aggregation-based routing is introduced in this paper. Moreover, the proposed protocol enhances the rate of the data transmission by efficient prevention of false data injection and other attacks through node authentication and data encryption. This proposed protocol minimizes the energy usage by minimizing the retransmissions by eliminating the possible redundant transmissions of data during data aggregation-based routing. Moreover, the introduced protocol minimizes both communication and computational overhead through optimal clustering and routing with PSO and provides and efficient routing system. This proposed EESCPSO-DARP protocol has been developed by using the NS3 simulator. The results of this protocol showed improved security, higher packet delivery ratio and enhanced network throughput with reduced energy and delay.

FloatingBlue: A Delay Tolerant Networks-Enabled Internet of Things Architecture for Remote Areas Combining Data Mules and Low Power Communications.

Monitoring vast and remote areas like forests using Wireless Sensor Networks (WSNs) presents significant challenges, such as limited energy resources and signal attenuation over long distances due to natural obstacles. Traditional solutions often require extensive infrastructure, which is impractical in such environments. To address these limitations, we introduce the "FloatingBlue" architecture. This architecture, known for its superior energy efficiency, combines Bluetooth Low Energy (BLE) technology with Delay Tolerant Networks (DTN) and data mules. It leverages BLE's low power consumption for energy-efficient sensor broadcasts while utilizing DTN-enabled data mules to collect data from dispersed sensors without constant network connectivity. Deployed in a remote agricultural area in the Amazon region, "FloatingBlue" demonstrated significant improvements in energy efficiency and communication range, with a high Packet Delivery Ratio (PDR). The developed BLE beacon sensor achieved state-of-the-art energy consumption levels, using only 2.25 µJ in sleep mode and 11.8 µJ in transmission mode. Our results highlight "FloatingBlue" as a robust, low-power solution for remote monitoring in challenging environments, offering an energy-efficient and scalable alternative to traditional WSN approaches.

Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6TiSCH Environments.

The 6TiSCH protocol (IEEE 802.15.4e) is crucial for the Industrial Internet of Things (IIoT), utilizing a time-slotted channel hopping (TSCH) mode based on node distribution. In this study, we propose an innovative cell allocation strategy based on node position clustering using the K-means algorithm, specifically designed to address congestion and optimize resource distribution in the 6TiSCH network. Our mechanism effectively groups nodes into clusters, allowing for dynamic adjustment of cell capacities in congested areas by analyzing traffic patterns and the spatial distribution of nodes. This clustering approach enhances the efficiency of slot frame utilization and minimizes communication delays by reducing interference and improving routing stability. The proposed strategy leverages the clustering results to improve cell usage efficiency and reduce communication latency between nodes. By tailoring cell allocation to the specific traffic needs of each cluster, we significantly reduce packet loss, manage congestion more effectively, and enhance data transmission reliability. We evaluated the clustering method using the K-means algorithm through experiments with the 6TiSCH simulator. Additionally, we considered using objective functions in Routing Protocol for Low-Power and Lossy Networks (RPL), such as OF0 and MRHOF, to assess clustering results and their impact on throughput and packet delivery. Our method resulted in significantly improved average performance metrics. Under the OF0 routing protocol, we achieved a 30.01% latency reduction, a 15.95% faster joining time, an 8% higher packet delivery ratio, and a 13.82% throughput increase. Similarly, we observed a 12.34% improvement in packet delivery ratio, 21.06% latency reduction, 12.68% faster joining time, and 25.97% higher throughput speed with the MRHOF routing protocol. These findings highlight the effectiveness of the improved cell allocation strategy in congested 6TiSCH environments, offering a better solution for enhancing network performance in IIoT applications.

NEMa: A Novel Energy-Efficient Mobility Management Protocol for 5G/6G-Enabled Sustainable Vehicular Networks

As the advancement of 5G and the emergence of 6G technologies unfolds, it is anticipated to support a diverse set of technologies, services, energy resources, and storage capabilities, all while maintaining a high quality of service to users and balancing the load on network devices; however, challenges such as energy consumption, network densification, and rapid mobility of vehicles need to be addressed A critical aspect of this evolution is the mitigation of mobility management, communication, and tracking of vehicles’ rapid mobility in network-dense environments, which currently consumes significant power resources. Consequently, existing mobility management protocols must be adapted and reconfigured to meet the requirements and limitations of energy-constrained vehicular networks. In this article, we introduce a novel energy-efficient mobility management protocol, NEMa, specifically designed for vehicular networks. This protocol optimizes network resources and vehicles’ on-board sensing to minimize energy consumption while maintaining a high packet delivery ratio and minimal interruption time. We have evaluated the performance of the proposed protocol and compared it to existing benchmark mobility management solutions in terms of network overhead, latency, and energy efficiency. Our results demonstrate that our proposed protocol surpasses existing benchmark solutions, thereby contributing to the enhancement of 5G/6G and beyond vehicular networks by addressing the challenges of energy efficiency and network performance.

JamholeHunter: On detecting new wormhole attack in Opportunistic Mobile Networks

This paper first shows that Prophet, Spray and Wait, Epidemic, and First Contact routing protocols in Opportunistic Mobile Networks (OMNs) are vulnerable to the Jamhole attack. In Jamhole attack, an attacker, Eve, compromises two different locations in OMNs by (i) jamming the GPS signal of victim nodes in these locations and (ii) by establishing a pair-wise hidden wormhole tunnel among these locations to route packets and to achieve high packet delivery ratio. The Jamhole attack enables Eve to disrupt the routing, obtain more packets of victim nodes, and possibly launch more severe attacks like packet modification, packet dropping, and packet injection attacks.In this paper, the impact of Jamhole attack on OMNs routing protocols is investigated using different attack parameters (i.e., area of compromised locations, attack frequency, and attack duration). To identify the Jamhole attack, this paper proposes JamholeHunter, a detection protocol that employs nodes’ wireless ranges, velocities, and last available GPS locations. The paper measured the impact of Jamhole attack and evaluated the JamholeHunter technique through extensive simulation experiments using synthetic and real-world mobility traces. The results demonstrate that (i) the Jamhole attack can cause a serious impact on the OMNs routing protocols, (ii) the effectiveness of JamholeHunter in identifying Jamhole attack with Detection Rate (75% to 100%) depending on various attack parameters with ∼95% Accuracy, and low False Positive Rate (≤ 3.7%), and (iii) the reliability of JamholeHunter in real-world scenarios of OMNs under different attack parameters, mobility models, and nodes velocity.

Intelligent Packet Priority Module for a Network of Unmanned Aerial Vehicles Using Manhattan Long Short-Term Memory

Unmanned aerial vehicles (UAVs) are becoming more common in wireless communication networks. Using UAVs can lead to network problems. An issue arises when the UAVs function in a network-access-limited environment with nodes causing interference. This issue could potentially hinder UAV network connectivity. This paper introduces an intelligent packet priority module (IPPM) to minimize network latency. This study analyzed Network Simulator–3 (NS-3) network modules utilizing Manhattan long short-term memory (MaLSTM) for packet classification of critical UAV, ground control station (GCS), or interfering nodes. To minimize network latency and packet delivery ratio (PDR) issues caused by interfering nodes, packets from prioritized nodes are transmitted first. Simulation results and evaluation show that our proposed intelligent packet priority module (IPPM) method outperformed previous approaches. The proposed IPPM based on MaLSTM implementation for the priority packet module led to a lower network delay and a higher packet delivery ratio. The performance of the IPPM averaged 62.2 ms network delay and 0.97 packet delivery ratio (PDR). The MaLSTM peaked at 97.5% accuracy. Upon further evaluation, the stability of LSTM Siamese models was observed to be consistent across diverse similarity functions, including cosine and Euclidean distances.

An adaptive intelligent routing algorithm based on deep reinforcement learning

A mobile ad hoc network (MANET) is a self-organizing network composed of multiple mobile nodes. These nodes achieve distributed communication and wireless resource sharing through autonomous collaboration. However, typical routing protocols for MANETs suffer from uneven network load, low network throughput, poor anti-destruction performance, and low transmission reliability. In order to meet the routing protocol characteristics of distributed adaptability, reliability and anti-destruction, balance of load and energy consumption and guarantee of high QoS in MANETs, this paper proposes an adaptive intelligent routing algorithm based on the PER-D3QN model of deep reinforcement learning. To achieve better routing strategy selection, the routing problem is first modeled, a distributed multi-agent reinforcement learning routing framework is designed, and a deep reinforcement learning reward function considering multiple factors is used. Next, the implementation process of the adaptive intelligent routing algorithm using the PER-D3QN model is described in detail. Finally, a mobile self-organizing network environment was constructed based on Networkx, and corresponding data was collected to simulate the algorithm. The simulation results show that the adaptive intelligent routing algorithm proposed in this paper can balance the energy consumption of the network and the load of nodes, its average remaining energy has increased by more than 10% and PL is reduced by at least 10% compared to other algorithms, while guaranteeing high QoS performance of routing, that is, reducing the average end-to-end delay of data packet transmission by over 22% compared to other algorithms and maintaining a high packet delivery ratio.

Energy‐Efficient Routing Algorithm for Optimizing Network Performance in Underwater Data Transmission Using Gray Wolf Optimization Algorithm

Due to the aquatic nature of communication in the underwater world, the underwater acoustic sensor network (UASN) is commonly used. However, it has inherent limitations, such as limited bandwidth, high transmission energy, long propagation delays, void regions, and expensive battery replacement. Improving network lifetime (NL) is the primary objective since replacing batteries in UWSN is very expensive and challenging. NL is improved by having a high packet delivery ratio (PDR), reduced dead nodes, and reduced energy consumption (EC). If two more node batteries are depleted, they become dead nodes, causing partitions on the network and resulting in a void region problem. Void regions occur when a node has no forwarder node to forward data packets toward the sink node. Void nodes affect the routing techniques’ overall performance regarding end‐to‐end delay (EED), data loss, and EC. So, the primary objective of this work is to avoid void regions. For the same, this paper proposes a void hole detection algorithm. The algorithm selects the best next hop node based on the fitness function calculated by the gray wolf optimization (GWO) algorithm, considering only the vertical directions despite horizontal directions, further reducing the EED. The proposed approach is simulated using MATLAB, and the evaluation is based on data broadcast copies, PDR, EC, dead node number (DNN), average operational time (AOT), NL, and EED. The paper has presented a comparison with weighting depth and forwarding area division depth‐based routing (WDFAD‐DBR) routing protocol for underwater acoustic sensor network (UASN) and energy and depth variance‐based opportunistic void avoidance scheme (EDOVS) for UASN. WDFAD‐DBR avoids void holes by selecting forwarding nodes and taking the weighting sum of depth differences of two hops; in comparison, EDOVS considers not only the depth parameters but also the normalized residual energy. The proposed paper contributes to developing an energy‐efficient routing algorithm that removes void nodes by selecting the appropriate forwarder node in void regions based on the GWO algorithm. The proposed work increases the network lifetime by avoiding void regions and balancing the EC. The simulation results show that the proposed algorithm gains more than 20% PDR, less EC, and 60% less broadcasted copies of data packets and has more NL than the WDFAD‐DBR, and 10% improved PDR and lesser broadcasted copies were sent than EDOVS along with the enhanced NL, by varying the transmission range the proposed algorithm showing the better performance in terms of EC, PDR, and DNN along with the values of 61.6, 0.97, and 35 (scenario node 60 and transmission range 600 m) and 64.1, 0.89, and 25, respectively, by variable network size.

Cost‐Efficient Network Design in Multichannel WSNs With Power Control: A Grey Wolf Optimization Approach to Routing and Clustering

Wireless sensor networks (WSNs) require optimized energy consumption and an improved packet delivery ratio (PDR) for optimal performance. Clustering and routing strategies are frequently used to reduce energy usage, while multiradio (MR) multichannel (MC) systems improve PDR levels. Transmission power control (TPC) features in both energy consumption reduction and PDR improvement. While the mentioned techniques are used in previous studies, they were investigated separately and without a holistic approach. This study discusses the challenge of obtaining high‐PDR, energy‐efficient, and cost‐effective clustering and routing in WSNs. As a means of reducing deployment costs, a heterogeneous setting with energy‐constraint normal sensors for environmental monitoring as well as some high‐energy, TPC‐enabled, MR super nodes is assumed. The super nodes are considered as CHs and are responsible for collecting the sensed data from the normal sensors. Installing additional radios on super nodes enables static channel assignment (CA), which yields high PDR with low imposed overhead on the network. The mentioned TPC‐based MC heterogeneous setting, which yields cost‐effectiveness, high PDR, and energy efficiency, was not investigated in previous studies. The considered problem is decoupled into two phases of configuring the super nodes and normal sensors, which are solved using the grey wolf optimization (GWO) algorithm. The performed simulations show an average improvement of our proposed algorithm in PDR, energy consumption, consumed energy per delivered bit, and network lifetime by 9%, 30%, 65%, and 44%, respectively.

Optimizing Path Selection in Dynamic Autonomous Mobile Ad Hoc Networks: A Clustering Approach for Variable Nodes

The dynamic autonomous mobile ad hoc networks (MANETs) allow nodes to move everywhere quickly and communicate without the support of a network infrastructure. The movement of mobile nodes in MANETs leads to several issues, such as changeable topology, energy limitations, path conservation, and bandwidth ineffectiveness. This would negatively impact the network’s QoS. The primary aim of the present study is to select the optimal path in MANETs using the clustering technique for variable nodes (i.e., 100–1000). The network performance characteristic and energy efficiency of the present ORSMAN protocol are simulated in the ns3 simulator. The research results demonstrate that compared to the current OPSTSA, EAAORS, EERS, and OEERS techniques, the suggested ORSMAN has a higher packet delivery ratio, the lowest latency, the lowest jitters, and superior throughput. The maximum improvement in throughput is 29.81%, and in energy efficiency, it is 9.67% compared to current techniques. Additionally, a trade‐off study between energy efficiency and QoS reveals that the performance characteristics enhance QoS while reducing energy efficiency. This reduction may be attributed to variations in throughput, jitter, and latency. The total QoS performance was improved by 22.16% for the present study compared to the current techniques.

Self-Configuration and Self-Healing Framework Using Extreme Gradient Boosting (XGBoost) Classifier for IoT-WSN

In most Internet of Things (IoT) systems, Quality of service (QoS) must be confirmed with respect to the requirement of implementation domain. The dynamic nature of the IoT surroundings shapes it to complicate the fulfilment of these commitments. A wide range of unpredictable events endanger the quality of service. While execution the self-adaptive schemes handle with system’s unpredictable. In IoT-based Wireless Sensor Networks (WSNs), the significant self-management objectives are self-configuration (SC) and self-healing (SH). In this paper, Self-Configuration and Self-healing Framework using an extreme gradient boosting (XGBoost) Classifier are proposed. In this framework, the IoT traffic classes are categorized as several types under XGBoost classifier. In SC phase, the IoT devices are self-configured by allocating various transmission slots, contention access period (CAPs) on the basis of its categories with priorities. In SH phase, the source node cardinally establishes a confined route retrieval method if the residual power in-between node is truncated or the node has displaced far away. The proposed framework is executed in NS-2 and the results exhibit that the proposed framework has higher packet delivery ratio with reduced packet drops and computational cost. Therefore, the proposed approach has attained 24.7%, 28.9%, 12.75% higher PDR, and 16.8%, 19.87%, and 13.7% higher residual energy than the existing methods like Self-Healing and Seamless Connectivity using Kalman Filter among IoT Networks (SH-SC-KF-IoT), Provenance aware run-time verification mechanism for self-healing IoT (PA-RVM-SH-IoT), and Fully Anonymous Routing Protocol and Self-healing Capacity in Unbalanced Sensor Networks (FARP-SC-USN) methods, respectively.

An enhanced MSU‐TSCH scheduling algorithms for industrial wireless sensor networks

SummaryThe reliability and latency requirements of wireless sensor network‐based smart grid communications are met in large part by MAC protocols. Developed by IEEE, time slotted channel hopping (TSCH), a method that is effective, dependable, and predictable. However, the TSCH standard does not enable mobility and does not offer any solution for scheduling, especially in IEEE 802.15.4 TSCH, the most recent generation of extremely dependable and low‐power MAC protocols. Recently, MSU‐TSCH a time‐frequency communication schedule was proposed to provide mobility to TSCH. Despite its distinctive qualities, MSU‐TSCH has the drawback of computing the TSCH schedule at each node independently of its traffic load, which can significantly increase the communication delay. Due to this limitation, MSU‐TSCH is not suitable for some delay‐sensitive smart grid applications. In this article, we provide an improved MSU‐TSCH‐based TSCH protocol, called Mobility‐TSCH, that dynamically adjusts time slot assignments based on traffic volume and latency requirement. Moreover, the protocol Mobility‐TSCH adapts to topology changes and supports mobility. Additionally, it optimizes time slot allocation by prioritizing nodes closest to the sink. The performance and evaluation analysis of Mobility‐TSCH compared to the original MSU‐TSCH, reveal that the communication delay is greatly reduced, decreases the average end‐to‐end latency, the high packet delivery ratio (PDR), Increase the performance of both metrics, parent connectivity ratio (PCR) and convergence time (CT), while maintaining a minimal overhead signaling.

Energy efficient greedy tree based algorithm for data aggregation in wireless sensor network

Secure data aggregation is essential in wireless sensor networks for lowering the amount of data transmitted and extending the lifetime of the network. The foundation underlying significantly greater industrial internet of things applications is primarily wireless sensor nodes. Sensors that have already been integrated within can be used to sense data in any form of real-time IoT application. In real-time physical surroundings, sensors utilize as little power as feasible to conduct operations including sensing, communicating, and processing data. Many investigations are being carried out to improve sensor node energy efficiency and network lifetime. To save energy, more attention must be paid to the clustering and routing aspects of communication. In this paper, we introduce Energy Efficient Greedy Tree based Data Aggregation (EE–GTDA) algorithm for efficient data aggregation with increased reliability and reduced energy consumption. It is a two-fold homogeneous technique that supervises safe energy-efficient connectivity and data aggregation with the greedy tree based solution that emphasizes multi-objective function. These methodologies are based on minimizing sensor energy consumption to maximize network lifetime simultaneously decreasing communication overhead. A trade-off between energy and safety is accomplished in order to increase efficient energy consumption with a higher packet delivery ratio.

An efficient self-attention-based conditional variational auto-encoder generative adversarial networks based multipath cross-layer design routing paradigm for MANET

Battery life is often a helpful resource for portable devices in MANET (Mobile Ad Hoc Network). When creating control methods, energy efficiency with network lifespan is considered. However, creating a routing method that is energy-efficient requires taking into account a variety of nodes from various layers, including channel assumptions, total traffic load, and remaining energy. The traditional layered technique cannot tackle power-relevant problems that may affect every stack layers. In this manuscript, Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Networks is proposed for enabling cross-layer routing over Mobile Ad Hoc Network (SACVAEGAN-MCLD-MANET). This Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Networks (SACVAEGAN) choose the optimum path by deeming given objective functions: residual energy cost of communication, Data Success Rate (DSR), mobility. In which, energy, DSR, mobility are computed through network layer. MAC layer's hesitating fuzzy linguistic bi-objective clustering technique (HFLBC) reduces the energy consumption brought on by contentions. The nodes with lesser residual energy must complete the immediate data transfer, while the path with greater contention must wait. As a result, this HFLBC is used to reduce energy use and enhance data transmission. Adaptability is produced using the knowledge that is shared among the different layers. The proposed method is done in Network Simulator-2.34, its efficacy is analyzed under certain performance metrics, like energy consumption, Packet Delivery Ratio, end-end delay, routing overhead, network lifetime, PLR, throughput. The proposed approach provides 23.21%, 15.64%, 19.96% higher packet delivery ratio and 16.63%, 17.29%, 6.47% lower delay compared with existing methods, like Sustainable Multiple path Routing for Enhancing Cross-Layer Presentation utilizing Energy Centric Tunicate Swarm Approach in MANET (ECTSA-MCLD-MANET), Cross-Layer adaptive data scheduling policy in mobile ad-hoc networks (ADSP-MCLD-MANET) and Cross-Layer Design Based N-ary Huffman Coding for DSDV Routing Protocol Analysis in mobile ad-hoc networks (NAHC-MCLD-MANET) respectively.

Dual-discriminator Conditional Generative Adversarial Network Optimized with Hybrid Momentum Search Algorithm and Giza Pyramids Construction Algorithm for Cluster-based Routing in WSN Assisted IoT

Wireless sensor network (WSN) efficiently sends and receives the data on the internet of things (IoT) environment. As a large-scale WSN's nodes are powered by batteries, it is essential to create an energy-efficient system to decrease energy consumption and increase the network's lifespan. The existing methods not present effectual cluster head (CH) selection and trust node computation. Therefore, dual-discriminator conditional generative adversarial network optimized with a hybrid Momentum search algorithm and Giza Pyramids Construction algorithm for Cluster Based Routing in WSN Assisted IoT is proposed in this manuscript, for securing data transmission by identifying the optimum CH in the network (DDcGAN-MSA-GPCA-CBR-WSN-IoT). Initially, the proposed method is acting routing process via cluster head. Therefore, Dual-Discriminator conditional Generative Adversarial Network (DDcGAN) is considered to select the CH depending on multi-objective fitness function. The multi-objective fitness function, such as energy, delay, throughput, distance among the nodes, cluster density, capacity, collision, traffic rate, and cluster density. Based on fitness function, CH is selected. After cluster head selection, a malicious node depends on three parameters: trust, delay, and distance. These three parameters are optimized by hyb MSA-GPCA for ideal trust path selection. The proposed DDcGAN-MSA-GPCA-WSN-IoT technique is activated in PYTHON and network simulator (NS2) tool. Its effectiveness is analyzed under performance metrics, such as number of alive nodes, dead nodes, delay, energy consumption, packet delivery ratio, a lifetime of sensor nodes, and total residual energy. The simulation outcomes display that the proposed method attains lower delay, higher packet delivery ratio and high network lifetime when comparing to the existing models.

A Low-Cost Low-Power LoRa Mesh Network for Large-Scale Environmental Sensing

Sustainability and climate monitoring efforts create a need for long-term in-situ sensing of large geographic areas. However, environmental monitoring in remote areas of developing countries remains impeded by a lack of low-cost, scalable IoT solutions. Whereas IoT systems for in-situ sensing abound, they mostly are either low-cost or suitable for large areas, but not both. In this paper, we present a low-cost low-power network solution for in-situ sensing of areas up to hundreds of square kilometers. Taking advantage of LoRa technology, we develop a self-organizing mesh network that can be scaled to a hundred and more nodes. Scalability is achieved by developing methods that mitigate packet collisions during data collection. We present a protocol, called CottonCandy, with which nodes self-organize in a spanning-tree network topology in a distributed fashion. A power profile on a custom-built circuit board shows that CottonCandy nodes can run thousands of duty cycles on 2 AA batteries, sufficient to operate for years in many applications. Using off-the-shelf components, the cost of a CottonCandy node is less than US-$ 15. Evaluations by simulation show that CottonCandy networks with 100 nodes achieve a packet delivery ratio of >90%. Measurements of an outdoor deployment with 15 nodes corroborate the high packet delivery ratio in a real-life setting.

A robot-assisted adaptive communication recovery method in disaster scenarios

Communication recovery is necessary for rescue and reconstruction scenarios including earthquakes, typhoons, floods, etc. The rapid and stable communication link can provide efficient victims’ real-time information for the rescue process. However, traditional centralized communication links cannot traverse the further victims with information-sharing requirements. And the even communication link distribution leads to a load burden on the crowded victim area. Thus, we propose a three-layer architecture consisting of the emergency communication vehicle, backbone links, and branch links to rapidly recover communication via mobile robots. Then, considering victims’ distribution, an improved MaxMin distance algorithm is presented as the basis of robot dispatch. The relay probability of the link is also estimated with closed formulae. Finally, simulation results verify that our proposed algorithm can recover communication with lower delay and higher packet delivery ratio.

Ant with Artificial Bee Colony Techniques in Vehicular Ad-hoc Networks

A VANET faces many problems due to dynamic changing of networks with certain requirements such as low delay, high (PDR) packet delivery ratio, low routing overhead and throughput. However, numerous routing protocols have been suggested to meet the demands of Quality of Service (QoS), but none of them can consistently maintain the highest level of QoS simultaneously. The proposed method Ant with Artificial Bee Colony Techniques provides better performance when compared to the existing techniques. This work is compared with latest developed Techniques in VANET to find the best path and different performance metrics are used to check the performance. This work premeditated the comparative analysis of Quality of services made by the performance of latest emerging techniques in VANET and will provide the best solution for the recognition problem in finding the best path based on the evaluation of performance of Quality of Service. Simulation results imply the benefits of the proposed Ant with Artificial Bee Colony Techniques (AABC) produces better result when compare to the other conventional method and Ant Colony Techniques(ACT)in terms of high packet delivery ratio, less end-to-end delay and less energy consumption level. The performance is evaluated by using Ns2 simulator and results shows that the AABC successfully achieve the optimal routes.