End Delay Research Articles

End to end delay distribution analysis and interference aware high throughput for Wireless Mesh Networks

The wireless mesh network is an innovative and efficient wireless communication approach with immense potential to transform future wireless networks. This cutting-edge wireless technology provides a wide range of wireless services for personal, local, campus, and metropolitan environments. The wireless mesh network consists of a cluster of mobile wireless nodes that can dynamically and instantaneously create random and temporary network topologies, forming a wireless ad-hoc network. Ad hoc wireless networks are particularly advantageous due to their cost-effectiveness and ease of deployment. In order to further optimize the performance of ad-hoc networks, different routing algorithms such as Ad-hoc On-Demand Distance Vector Routing (AODV), Dynamic Source Routing Algorithm (DSR) and Destination Sequenced Distance Vector (DSDV) have been thoroughly researched via simulation.

Evaluation of the Unslotted CSMA_CA in Zigbee Networks

A Zigbee network with 12 nodes (end device) and a coordinator is developed to evaluate the performance of the un-slotted CSMA_CA protocol for the two frequency bands 2.4GHz and 900 MHz by varying the BE (Backoff Exponent) parameter . The simulation is effected for BE=1, 2 and 3 to observe the End to End delay which characterizes the response speed of the network. The network implementation and his simulation were effected under Opnet 14.5.

Privacy preservation in wireless sensor network using energy efficient multipath routing for healthcare data

Every day, WSNs (Wireless Sensor Networks) based healthcare systems is witnessing improvement in providing advice to the health condition and living habits of people. Healthcare applications based on WSNs face crucial problems of security and are vulnerable to assaults. In this study, a privacy-preserving method using the multipath routing and principles of secret keys are suggested for healthcare applications based on WSNs. Multiple paths to the sink will be computed using ECSO (Enhanced Cuckoo Search Optimization). The proposed algorithm utilizes distance and also the traffic rate of paths in the form of a parameter for discovering the optimum paths utilizing ECSO. Among these chosen paths, just one ideal path is chosen based on node energy using BO (Butterfly Optimization) that helps in decreasing the energy demands of the network. Encrypted data will be sent to the sink using the selected path. It is found from the results that the secret key merged with multipath routing is helpful in achieving security of privacy in healthcare systems based on WSNs. The results of simulation performance metrics are throughput, End to End delay, PDR, Energy Consumption, encryption time.

Optimized Back Propagation Neural Network Using Quasi-Oppositional Learning-Based African Vulture Optimization Algorithm for Data Fusion in Wireless Sensor Networks.

A Wireless Sensor Network (WSN) is a group of autonomous sensors geographically distributed for environmental monitoring and tracking purposes. Since the sensors in the WSN have limited battery capacity, the energy efficiency is considered a challenging task because of redundant data transmission and inappropriate routing paths. In this research, a Quasi-Oppositional Learning (QOL)-based African Vulture Optimization Algorithm (AVOA), referred to as QAVOA, is proposed for an effective data fusion and cluster-based routing in a WSN. The QAVOA-based Back Propagation Neural Network (BPNN) is developed to optimize the weights and threshold coefficients for removing the redundant information and decreasing the amount of transmitted data over the network. Moreover, the QAVOA-based optimal Cluster Head Node (CHN) selection and route discovery are carried out for performing reliable data transmission. An elimination of redundant data during data fusion and optimum shortest path discovery using the proposed QAVOA-BPNN is used to minimize the energy usage of the nodes, which helps to increase the life expectancy. The QAVOA-BPNN is analyzed by using the energy consumption, life expectancy, throughput, End to End Delay (EED), Packet Delivery Ratio (PDR) and Packet Loss Ratio (PLR). The existing approaches such as Cross-Layer-based Harris-Hawks-Optimization (CL-HHO) and Improved Sparrow Search using Differential Evolution (ISSDE) are used to evaluate the QAVOA-BPNN method. The life expectancy of QAVOA-BPNN for 500 nodes is 4820 rounds, which is high when compared to the CL-HHO and ISSDE.

Block chain based IAS protocol to enhance security and privacy in cloud computing

Cloud computing has become increasingly popular for data storage and processing, but security and privacy remain major concerns due to the potential for malicious attacks on wireless and mobile communication networks. In order to address these issues, researchers have proposed an IAS protocol that incorporates identity, access control, and secure authentication. To ensure the authenticity and security of data transmission in cloud computing, the proposed IAS protocol is implemented on top of blockchain technology. This allows for decentralized key management for recovery and revocation, as well as identity verification. To evaluate the effectiveness of this approach, the proposed model using Block Chain based Identity Management, Access Control and Secure Sharing (BC-IAS) is simulated on the cloud environment. The simulation assesses primary performance key metrics, including Data Access Rate, Message Delivery Ratio, End to End Delay and Energy consumption. Overall, the proposed BC-IAS protocol on top of blockchain technology shows promise for enhancing security and privacy in cloud computing. The proposed BC-IAS protocol on top of blockchain technology presents a promising solution for enhancing the security and privacy of cloud computing. The decentralized nature of blockchain technology ensures that data is stored in a tamper-proof and transparent manner, while the use of smart contracts allows for automated enforcement of access control policies. Moreover, the incorporation of identity verification and secure authentication ensures that only authorized users can access sensitive data, reducing the risk of data breaches and cyber-attacks.

Intelligent energy aware optimization protocol for vehicular adhoc networks

Vehicular adhoc network (VANET) plays a vital role in smart transportation. VANET includes a set of vehicles that communicate with one another via wireless links. The vehicular communication in VANET necessitates an intelligent clustering protocol to maximize energy efficiency. Since energy acts as an essential factor in the design of VANET, energy-aware clustering protocols depending upon metaheuristic optimization algorithms are required to be developed. This study introduces an intelligent energy-aware oppositional chaos game optimization-based clustering (IEAOCGO-C) protocol for VANET. The presented IEAOCGO-C technique aims to select cluster heads (CHs) in the network proficiently. The proposed IEAOCGO-C model constructs clusters based on oppositional-based learning (OBL) with the chaos game optimization (CGO) algorithm to improve efficiency. Besides, it computes a fitness function involving five parameters, namely throughput (THRPT), packet delivery ratio (PDR), network lifetime (NLT), end to end delay (ETED) and energy consumption (ECM). The experimental validation of the proposed model is accomplished, and the outcomes are studied in numerous aspects with existing models under several vehicles and measures. The simulation outcomes reported the enhanced performance of the proposed approach over the recent technologies. As a result, it has resulted in maximal NLT (4480), minimal ECM (65.6), maximal THRPT (81.6), maximal PDR (84.5), and minimal ETED (6.7) as average values over the other methods under all vehicle numbers.

Secure Cluster-based Routing using TCSA and Hybrid Security Algorithm for WSN

Wireless Sensor Network (WSN) is operated as a medium to connect the physical and information network of Internet-of-Things (IoT). Energy and trust are two key factors that assist reliable communication over the WSN-IoT. Secure data transmission is considered a challenging task during multipath routing over the WSN-IoT. To address the aforementioned issue, secure routing is developed over the WSN-IoT. In this paper, the Trust-based Crow Search Algorithm (TCSA) is developed to identify the Secure Cluster Heads (SCHs) and secure paths over the network. Further, data security while broadcasting the data packets is enhanced by developing the Hybrid Security Algorithm (HSA). This HSA is a combination of the Advanced Encryption Standard (AES) and Hill Cipher Algorithm (HCA). Therefore, the developed TCSA-HSA avoids malicious nodes during communication which helps to improve data delivery and energy consumption. The performance of the TCSA-HSA method is analyzed using Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, End to End Delay (EED), and throughput. The existing methods namely Optimal Privacy-Multihop Dynamic Clustering Routing Protocol (OP-MDCRP) and Secure and Energy-aware Heuristic-based Routing (SEHR) are used to evaluate the TCSA-HSA performances. The PDR of TCSA-HSA for 100 nodes is 99.7449%, which is high when compared to the OP-MDCRP and SEHR.

Performance Investigation of IP Security Virtual Private Network in Ipv6 Environment using Quality of Service

Different types of data are transmitted through the internet, these data such as Email, HTTP, FTP and VoIP conversation might contain important and sensitive information which can be read or seen by other users. Many attacks such as man in the middle, packet sniffing and phishing are available and used by many attackers, as a way to access the protected information. Based on this fact, security and privacy become a necessity in the field of private network (intranet) and public network (extranet). IP sec VPN is considered to be the strongest technique for protecting packets from any kind of attack. In this paper, an analytical study is accomplished using opnet simulation software to design and analyze the proposed network. The proposed network represents four sites in different cities are communicating using the technique of IPsec VPN, different scenarios are created and studied to investigate the performance of network using IPsec VPN in IPV6 environment with QoS. The investigated metrics were packet end to end delay, jitter, packet delay variation, download and upload response time.

An Integrated Grasshopper Optimization Algorithm with Artificial Neural Network for Trusted Nodes Classification Problem

Wireless Body Area Network (WBAN) is a tool that improves real-time patient health observation in hospitals, asylums, especially at home. WBAN has grown popularity in recent years due to its critical role and vast range of medical applications. Due to the sensitive nature of the patient information being transmitted through the WBAN network, security is of paramount importance. To guarantee the safe movement of data between sensor nodes and various WBAN networks, a high level of security is required in a WBAN network. This research introduces a novel technique named Integrated Grasshopper Optimization Algorithm with Artificial Neural Network (IGO-ANN) for distinguishing between trusted nodes in WBAN networks by means of a classification approach, hence strengthening the safety of such networks. Feature extraction process is done by using Linear Regression-Based Principal Component Analysis (LR-PCA). The test results demonstrated that the proposed IGO-ANN method attains the greatest performance in terms of accuracy, end to end delay and packet delivery ratio regarding trusted WBAN nodes classification than certain existing methods.

Performance evaluation of dynamic source routing protocol with variation in transmission power and speed

<span lang="EN-US">Mobile ad-hoc network (MANET) is a set of mobile wireless nodes (devices) which is not rely on a fixed infrastructure. In MANETs, each device is responsible for routing its data according to a specific routing protocol. The three most common MANET routing protocols are: dynamic source routing protocol (DSR), optimized link state routing protocol (OLSR), and ad-hoc on-demand distance vector (AODV). This paper proposes an efficient evaluation of DSR protocol by testing the MANETs routing protocol with variation in transmission power at different speeds. The performance analysis has been given using optimized network engineering tools (OPNET) modeler simulations and evaluated using metrics of average end to end delay and throughput. The results show that the throughput increases as the transmission power increases up to a certain value after which the throughput decreases, also the network work optimally at a certain transmission power which varied at different speed.</span>

Secure cluster-based routing using multi objective-trust centric artificial algae algorithm for wireless sensor network

<p>Nowadays, wireless sensor network (WSN) is developed as a key technology to observe and track applications over a wide range. However, energy consumption and security are considered as important issues in the WSN. In this paper, the multi objective-trust centric artificial algae algorithm (M-TCAAA) is proposed to accomplish a secure broadcasting over the WSN. The proposed M-TCAAA is used to choose the secure cluster head (SCH) as well as routing path, based on the distinct fitness measures such as trust, communication cost, residual energy, and node degree. Hence, the M-TCAAA is used to ensure a secure data transmission while decreasing the energy consumed by the nodes. The performance of the M-TCAAA is analyzed by means of energy consumption, packet delivery ratio (PDR), throughput, end to end delay (EED), normalized routing load (NRL), and network lifetime. The existing researches namely energy aware trust and opportunity-based routing with mobile nodes (ETOR-MN), grey wolf updated whale optimization (GUWO), secure cluster-based routing protocol (SCBRP), secure routing protocol based on multi-objective ant-colony-optimization (SRPMA) and multi objective trust aware hybrid optimization (MOTAHO) are considered for evaluating the M-TCAAA. The PDR of the M-TCAAA for 100 nodes is 99.87%, which is larger than the ETOR-MN, GUWO, SRPMA and MOTAHO.</p>

Energy aware Clustered blockchain data for IoT: An end-to-end lightweight secure & Enroute filtering approach

Internet of things (IoT) include smart homes, smart buildings and smart factories, in which all the devices are connected over the internet through wireless network for data transmission or sharing. IoT faces many security challenges due to sharing the data in an open source. Security and energy are the most focusing challenges in IoT due to its resource constrained nature. IoT devices have low energy that is wasted by the attackers, and the data also gets hacked by the attacker, which leads to poor security. This paper proposed CLIENT (Clustered blockchain IoT with En-route) to provide energy efficient and secure communication. For that, this research proposed five processes. Such as Chaotic map based Elgamal authentication, credit score based clustering, capuchin search optimization based packets routing, signature based Enroute filtering and deep leaning based anomaly packet detection. First process is Chaotic Map based Elgamal Authentication, in which chaotic map is used in Elgamal key generation. Second process is Credit score based clustering which minimizes the complexity and overhead during packet transmission. Third process is Capuchin search optimization based packets routing which is used to select optimal route from cluster member to cluster head (CH). Fourth process is signature based Enroute filtering which used to detect and drop false report from the network. Edwards signature based digital signature algorithm (EdDSA) is used for verification purpose. And final process is deep learning based anomaly packets detection, this process used Katz centrality based feed forward small world neural network (FFSWNN) for packet classification (normal or malicious). All the transactions are stored in blockchain to enhance security. The proposed system implemented by using NS3. The result shows that proposed system achieves superior performance compared to existing works in terms of energy consumption, packet loss rate, end to end delay, routing overhead and network life time, precision, recall, accuracy, false alarm rate, encryption time, decryption time and security strength.

HFLFO: Hybrid fuzzy levy flight optimization for improving QoS in wireless sensor network

In IoT-based WSN applications, energy is a vital parameter since limited residual energy of a node often leads to packet loss, diminished network lifetime, and end-to-end delay. IoT nodes are often resource-constrained, with limited computational and storage capabilities, communication range, and energy-saving potential. To offer an improved Quality of Service (QoS) guarantee, the network lifetime and energy consumption of the WSN-based IoT applications need to be improved. QoS is an important parameter in time-sensitive IoT applications. Hence this paper introduces a novel QoS improvement framework for the WSN-IoT networks, and the objective of this work is twofold. In the first phase, we utilize an Aquila optimizer (AO) for an efficient and reliable cluster head selection which is based on different factors such as node level, distance to the sink node, energy level, etc. The AO algorithm enhances the lifetime of the WSN network by electing the QoS-aware relay node as the cluster head. In the next phase, the Hybrid Fuzzy Levy Flight Particle swarm optimization algorithm (HFLPSO) is presented to enhance the QoS of the network even further by identifying a set of optimal paths to route the packets and measure the reliability of each path via different metrics (packet loss rate, residual energy, end-to-end delay, link stability, and the number of hops). The simulation experiments were conducted using different metrics such as End to End delay, Packet loss rate, and Packet delivery ratio to prove that the proposed model is capable of improving the QoS of the WSN-IoT networks when compared to the different state-of-art methodologies. When compared to the existing techniques, the proposed model offers a 10% improvement in the packet loss rate and a 17% improvement in the packet delivery rate.

Analysis of OLSR Routing Protocol Performance Based on Gauss-Markov Mobility and Random Walk in Mobile Ad-Hoc Network (MANET)

MANET is included in the network without infrastructure from a collection of nodes that are interconnected to communicate. Routing protocol performance is a measure of the performance of how the ability of the routing protocol works. A routing protocol is needed where the routing table information must be kept up to date at regular intervals such as Optimized Link State Routing (OLSR). MANET is dynamic, so node mobility increases the risk of node failure. Gauss-Markov and random walk are mobility that has a significant impact on the performance of the routing protocol. This study analyzes the effect of mobility on the OLSR protocol in MANET topology using Network Simulator 3.25 with packet delivery ratio (PDR) parameters, end to end delay and routing overhead. The test scenario is done by varying the number of nodes as many as 20, 40, 60, and 80 nodes as well as the minimum and maximum speed of nodes 0-5 m/s, 5-10 m / s, 10-15 m/s, and 15-20 m/s. PDR results show the highest value on Gauss-Markov with 80 nodes and a minimum and maximum speed of 10-15 m/s of 69.36%. The best end to end delay results is seen in Gauss-Markov with 20 nodes and a minimum and maximum speed of 15-20 m/s of 10.22 ms. The routing overhead results display the best value on a random walk with 20 nodes and a minimum and maximum speed of 0-5 m/s of 8100 packets.

Enhanced On-demand Distance Vector Routing Protocol to prevent Blackhole Attack in MANET

Wireless networks are becoming increasingly popular. Mobile ad hoc networks are one category among the different types of wireless networks that transmit packets from the sender node to the receiver node without the use of abase station or infrastructure, as the nodes serve as both hosts and routers. These networks are referred to as mobile because they are movable. MANET is an ad-hoc network that can change positions at any time, and nodes can join or leave at any moment, making it vulnerable to attacks such as Blackhole. Existing solutions, in some ways, led to more memory space consumption, while others led to an overhead. This research proposes an Enhanced On-demand Distance Vector (AODV) routing protocol to prevent Blackhole attacks on MANETs using Diffie Hellman and Message Digest 5 (DHMD), implemented using Network Simulator 2 (NS2). The performance of the proposed protocol was evaluated using the following parameters: Packet Delivery Ratio, throughput, End to End (E2E)Delay, and routing overhead. It was concluded that DHMD has reduced network over head as it resulted to 23% while AODV resulted at 38%and memory consumption for DHMD gave 0.52ms compared to AODV that gave 0.81msdue to Blackhole prevention. This research will help to mitigate the effect of blackhole attacks in a network and increase network performance by reducing overhead and memory consumption.

A Novel Context-Aware Reliable Routing Protocol and SVM Implementation in Vehicular Area Networks

The Vehicular Ad-hoc Network (VANET) is an innovative technology that allows vehicles to connect with neighboring roadside structures to deliver intelligent transportation applications. To deliver safe communication among vehicles, a reliable routing approach is required. Due to the excessive mobility and frequent variation in network topology, establishing a reliable routing for VANETs takes a lot of work. In VANETs, transmission links are extremely susceptible to interruption; as a result, the routing efficiency of these constantly evolving networks requires special attention. To promote reliable routing in VANETs, we propose a novel context-aware reliable routing protocol that integrates k-means clustering and support vector machine (SVM) in this paper. The k-means clustering divides the routes into two clusters named GOOD and BAD. The cluster with high mean square error (MSE) is labelled as BAD, and the cluster with low MSE is labelled as GOOD. After training the routing data with SVM, the performance of each route from source to target is improved in terms of Packet Delivery Ratio (PDR), throughput, and End to End Delay (E2E). The proposed protocol will achieve improved routing efficiency with these changes.

A selfish node trust aware with Optimized Clustering for reliable routing protocol in Manet

Reliable routing in MANET is a pivotal element in real-world scenarios. The former works introduced a novel routing protocol namely, Trustable Energy-based Clustering and Optimized Routing (TECROP). However, this failed to concentrate on the selfish nodes. The proposed research focuses on the progress of a routing protocol that could handle the presence of selfish nodes. The proposed Selfish Node aware Trustable and Optimized Clustering-based Routing (SN-TOCRP) follows hierarchical clustering for creating node clusters. The selection of cluster Heads (CH) is done through the nature-inspired Fuzzy based Crow Search Algorithm. Selfish node detection is done by using introducing the authentication method which will authenticate the cluster heads. Bandwidth-aware Trust-based Routing Protocol (BTRP) is deployed to detect as well as isolate both misbehaving as well as faulty nodes. This also helps in diagnosing common misbehaviour attacks like a wormhole, Sybil, and selfishness with better results. Trust estimation and trust-based routing will be based on Modified Glow swarm optimization. The detailed experimental analysis done on the NS2 simulator proves that the proposed work achieves improved results with Packet delivery ratio of 96%, loss ratio of 0.045%, average delay of 0.325 ms with throughput is 76 Kbps, end to end delay of 0.425 ms and of energy consumption of 88 mJ.

Modified Ring Routing Protocol for Mobile Sinks in a Dynamic Sensor Network in Smart Monitoring Applications

The stationary hierarchical network faces considerable challenges from hotspots and faster network breakdowns, especially in smart monitoring applications. As a solution to this issue, mobile sinks were recommended since they are associated with huge and balanced ways to transfer data and energy across the network. Again, due to the mobile sink node advertisement around the network latency and the energy utilization overheads introduced across the network, ring routing reduces the control overhead while preserving the benefits of the mobile sink, thereby optimizing the energy and improving the network life span. Consequently, we suggested a novel, distributed advanced ring routing strategy, in this work, for the mobile wireless sensor network. Extensive simulations and performance evaluation, in comparison to previous distributed mobile approaches, reveal a 37% and 40% boost in the network throughput and end-to end delay, respectively. Additionally, the lifespan of a network is determined by the control overhead and energy demand.

Opportunistic Routing with Multi-Channel Cooperative Neighbour Discovery

Due to the scattered nature of the network, data transmission in a distributed Mobile Ad-hoc Network (MANET) consumes more energy resources (ER) than in a centralized network, resulting in a shorter network lifespan (NL). As a result, we build an Enhanced Opportunistic Routing (EORP) protocol architecture in order to address the issues raised before. This proposed routing protocol goal is to manage the routing cost by employing power, load, and delay to manage the routing energy consumption based on the flooding of control packets from the target node. According to the goal of the proposed protocol technique, it is possible to manage the routing cost by applying power, load, and delay. The proposed technique also manage the routing energy consumption based on the flooding of control packets from the destination node in order to reduce the routing cost. Control packet exchange between the target and all the nodes, on the other hand, is capable of having an influence on the overall efficiency of the system. The EORP protocol and the Multi-channel Cooperative Neighbour Discovery (MCCND) protocol have been designed to detect the cooperative adjacent nodes for each node in the routing route as part of the routing path discovery process, which occurs during control packet transmission. While control packet transmission is taking place during the routing path discovery process, the EORP protocol and the Multi-channel Cooperative Neighbour Discovery (MCCND) protocol have been designed to detect the cooperative adjacent nodes for each node in the routing. Also included is a simulation of these protocols in order to evaluate their performance across a wide range of packet speeds using Constant Bit Rate (CBR). When the packet rate of the CBR is 20 packets per second, the results reveal that the EORP-MCCND is 0.6 s quicker than the state-of-the-art protocols, according to the findings. Assuming that the CBR packet rate is 20 packets per second, the EORP-MCCND achieves 0.6 s of End 2 End Delay, 0.05 s of Routing Overhead Delay, 120 s of Network Lifetime, and 20 J of Energy Consumption efficiency, which is much better than that of the state-of-the-art protocols.

An intelligent base link recovery management and path stability in clustered randomly distributed mobile ad hoc networks

MANETs are networks of mobile nodes that are randomly distributed and play a major role in data transmission, route discovery, and route maintenance. Connection recovery and path stability are significant issues in the MANET, making data transfer problematic. When mobile nodes move out of range or when the node lacks adequate energy to maintain connection or path stability, data packet loss happens. We suggest a Clustered Intermediate System-Clustered Intermediate System Local Connection Failure Recovery Algorithm (Clustered Intermediate System-Clustered Intermediate System LLFRA) routing migration technique that builds the node while simultaneously repairing the broken link. Also utilised to look at node energy, energy- drained nodes, and route stability is Dolphin Partner Optimisation (DPO). The suggested approach's primary goal is to guarantee route stability while reducing packet loss. The suggested method is put to the test using the NS-2 simulator. The proposed recovery protocol beats the current technique in charge of PDR, end to end delay, throughput and node failures, according to experimental outputs.