Packet Drop Research Articles

HEURISTIC GATEWAY PLACEMENT FOR MINIMAL TRANSMISSION POWER & COLLISION PROBABILITY IN AN INTERNET OF THINGS LOW POWER WIDE AREA NETWORK (HGPMTPIoT-LPWAN)

A variety of long-range (LoRa), low-power, and low bit-rate wireless network technologies have been made possible by the Internet of Things' (IoT) rapid growth. As a radio technology for the implementation of numerous Internet of Things applications, the LoRa low-power wide area network (LPWAN) has gained prominence. LoRa is frequently used in conjunction with the Long-Range Wide Area Network Media Access Control (LoRaWAN MAC) protocol and functions in the Industrial, Scientific, and Medical (ISM) bands, which are unlicensed. As a result of receiving a wide variety of different message sizes from diverse applications, LoRa networks experience scalability issues when the number of end nodes connected to one network is more than the shared number of channels. This results in collisions and packet loss. In this paper, heuristic gateway placement for minimal transmission power & collision probability in an internet of things low power wide area network (HGPMTPIOT-LPWAN) is proposed to increase network efficiency, and improve overall performance by measuring collision probability, which in turn can help to reduce the need for retransmissions and packet drop rate. The things network simulator is used to measure the collisions and packet drop rate. An improved performance for HGPMTPIOT as against the efficient graph-based gateway placement (EGBGP) for large-scale LoRaWAN deployments is achieved for packet drop rate by 6%. Likewise, the simulation results show improvements in terms of decreasing the collision probability for 20 to 60 nodes by 20%.

Anomaly Recognition in Wireless Ad-hoc Network by using Ant Colony Optimization and Deep Learning

As a result of lower initial investment, greater portability, and lower operational expenses, wireless networks are rapidly replacing their wired counterparts. The new technology that is on the rise is the Mobile Ad-Hoc Network (MANET), which operates without a fixed network infrastructure, can change its topology on the fly, and requires no centralised administration to manage its individual nodes. As a result, MANETs must focus on network efficiency and safety. It is crucial in MANET to pay attention to outliers that may affect QoS settings. Nonetheless, despite the numerous studies devoted to anomaly detection in MANET, security breaches and performance difficulties keep coming back. There is an increased need to provide strategies and approaches that help networks be more safe and robust due to the wide variety of security and performance challenges in MANET. This study presents outlier detection strategies for addressing security and performance challenges in MANET, with a special focus on network anomaly identification. The suggested work utilises a dynamic threshold and outlier detection to tackle the security and performance challenges in MANETs, taking into account metrics such as end-to-end delay, jitter, throughput, packet drop, and energy usage.

MHSEER: A Meta-Heuristic Secure and Energy-Efficient Routing Protocol for Wireless Sensor Network-Based Industrial IoT

Several industries use wireless sensor networks (WSN) for various tasks such as monitoring, data transmission, and data gathering. They find applications in the industrial internet of things (IIoT). WSNs are utilized to track and monitor changes in the environment. Since they include multiple small sensor nodes (SN), they are severely constrained, so resource management geared toward energy efficiency is crucial in this kind of network. Minimizing the power to interpret, transmit, and store data between various sensors poses important challenges. Experts have considered various ways to address these issues that unavoidably affect the network’s performance: reducing energy usage while maintaining system throughput remains the primary research issue. Another important concern relates to network security. Specifically, intrusion detection and avoidance are major concerns. In this work, we introduce the meta-heuristic-based secure and energy-efficient routing (MHSEER) protocol for WSN-IIoT. The protocol learns the forwarding decisions using the number of hops, connection integrity characteristics, and accumulated remaining energy. To make the method more secure, the protocol also employs counter-encryption mode (CEM) to encrypt the data. A meta-heuristics study designed to achieve reliable learning is used in the suggested protocol. The protocol consists of two stages. The first stage uses a heuristics method to improve the option for dependable data routing. Security based on a computationally simple and random CEM is accomplished in the second stage. The proposed MHSEER protocol has been compared to the secure trust routing protocol for low power (Sectrust-RPL), heuristic-based energy-efficient routing (HBEER), secure and energy-aware heuristic-based routing (SEHR), and secure energy-aware meta-heuristic routing (SEAMHR) in terms of packet drop ratio, throughput, network delay, energy usage, and faulty pathways. The proposed protocol increases throughput to 95.81% and decreases the packet drop ratio, packet delay, energy consumption, and faulty pathways to 5.12%, 0.10 ms, 0.0102 mJ, and 6.51%, respectively.

An Enhanced QoS-Aware Multipath Routing Protocol for Real-Time IoT Applications in MANETs

Recent transmission of large volumes of data through mobile ad hoc networks (MANETs) has resulted in degraded Quality of Service (QoS) due to factors such as packet loss, delay, and packet drop in multipath routing. To address this issue, a traffic-aware Enhanced QoS-Aware Multipath Routing Protocol (EQMRP) has been proposed for real-time IoT applications in MANETs. EQMRP efficiently switches between multiple paths and monitors traffic conditions to maintain an optimal data transmission rate. The proposed method considers different delay sensitivity levels and link expiration time (LTE) to maintain QoS in each path. Through IoT application data analysis, EQMRP maintains QoS in each path more efficiently than conventional methods. The proposed method has been simulated and validated using MATLAB, and the performance analysis shows that EQMRP achieves a higher packet delivery ratio, lower delay, and reduced packet drop compared to conventional methods. In conclusion, the traffic-aware EQMRP protocol offers a significant improvement in QoS for real-time IoT applications in MANETs.

Spider Web-based Dynamic Key for Secured Transmission and Data-Aware Blockchain Encryption for the Internet of Things

In the developed field of information and communication systems, the utilization of the Internet of Things (IoT) devices is greatly increasing. In their applications, data gathering with limited energy consumption and good security is essentially needed nowadays. The network is energy limited as the sensor nodes are operated using battery power. The nodes in the network are vulnerable to several attacks and the proposed data transmission scheme should provide network security. Security is a main concern in data aggregation. In this work, a secured data aggregation scheme with compression technique and blockchain-based encryption added routing is presented. The data transmitted by the device are added with a hash key generated using a spider web-based dynamic key (SWDK) generation process. The SWDK-added compressed data are transferred in the process of blockchain encryption routing. Then compression-based data aggregation is utilized to reduce the data size and sequentially the transmission cost. The devices in the network transmit data to the base station/server in the network through gateways. The simulation results prove that the proposed work gives reduced transmission cost and energy consumption compared with the existing works. The network throughput increases due to the sharing of security keys, as well as network latency and packet dropping, which are reduced in the proposed work.

Mitigation of Black Hole and Selective Forwarding Attacks through Active Trust Mechanisms in Wireless Sensor Networks

In modern times, security remains a significant concern in wireless networks. Wireless sensor networks are widely employed in different practical situations. WSNs struggle with numerous internal and external threats, and detecting and defending against insider attacks is difficult. Insider attacks, in which intruders discard received data packets selectively, pose a significant risk to WSNs. The presence of black hole nodes within the network causes this issue. This research paper presents a secure and reliable route management methodology for WSNs that selects nodes and routing paths based on trust metrics. The proposed mechanism employs intelligent routing and node selection to defend against various types of assaults encountered during the routing process, including selective forwarding and black hole attacks. As a result, the paper identifies secure routes and establishes secure network routing. This scheme uses throughput, latency, and packet drop ratio as performance metrics. Experimentation demonstrates that the proposed system effectively increases the probability of secure network routing paths while extending the network lifecycle.

ConPoolUBF: Connection pooling and updatable Bloom filter based SYN flood defense in programmable data planes

SYN flood attack is one of the common ways in which attackers take the advantage of TCP’s three-way handshake connection establishment to overwhelm target systems. With the emergence of programmable networks, many promising security functions have been implemented at the network layer. SYN authentication and cookie-based SYN proxy are two significant approaches recommended on programmable switches against SYN flood attacks. However, while the implementation of a cookie-based SYN proxy causes additional delays and packet drops, the SYN authentication approach increases the number of packets required to establish a TCP connection. In this study, two novel functions are implemented on programmable switches using the P4 language, and a new security solution against SYN flood attacks is proposed by combining both functions. The first function is the high-accuracy updatable Bloom filters implemented to track the state of network flows for TCP connection establishment. As the proposed data structure uses a salted input, it is more resistant to target-set coverage attacks. The second is the connection pooling function on programmable switches for requests to the backend servers. In this regard, the TCP three-way handshake is offloaded from the target systems to network switches. Upon the verification of a connection request, dynamic resource allocation is carried out from the connection pool on the P4 switch, enabling the client to connect to the server seamlessly and transparently without the need for additional packets. We implement these functions and demonstrate their feasibility as an effective defense against SYN flood.

A security-friendly privacy-preserving solution for federated learning

Federated learning is a privacy-aware collaborative machine learning method where the clients collaborate on constructing a global model by performing local model training using their training data and sending the local model updates to the server. Although it enhances privacy by letting the clients collaborate without sharing their training data, it is still prone to sophisticated privacy attacks because of possible information leakage from the local model updates sent to the server. To prevent such attacks, generally secure aggregation protocols are proposed so that the server will not be able to access the individual local model updates but the aggregated result. However, such secure aggregation approaches may not allow the execution of security mechanisms against some security attacks to model training, such as poisoning and backdoor attacks, because the server cannot access the individual local model updates and; therefore, cannot analyze them to detect anomalies resulting from these attacks. Thus, solutions that satisfy privacy and security at the same time or new privacy-preserving solutions that allow the server to execute some analysis on the local model updates without violating privacy are needed for federated learning. In this paper, we introduce a novel security-friendly privacy solution for federated learning based on multi-hop communication to hide clients’ identities. Our solution ensures that the forwardee clients in the path between the source client and the server cannot execute malicious activities by altering model updates and contributing to the global model construction with more than one local model update in one FL round. We then propose two different approaches to make the solution also robust against possible malicious packet drop behaviors by the forwardee clients.

SDBGPChain: A decentralized low complexity framework to detect and prevent the BGPattacks using SDN with smart contract based Dendrimer tree blockchain

The Border Gateway Protocol (BGP) helps to exchange the information over the internet. However, BGP is susceptible to malicious attacks and misconfigurations meanwhile BGP does not focus on providing security during routing information advertisement. The secure BGP environment provides constant routing services on the internet, but existing works does not provide an optimal solution for BGP router security. To overcome these issues, we proposed decentralized low complexity framework for providing security to BGP attacks using deep learning and blockchain technologies. The proposed SDBGPChain (SDN based BGP using dendrimer tree blockchain) model whereas the SDN provides network management, flexibility and scalability for BGP environment, the proposed work includes three processes such as entity authentication, low complexity BGP attack detection and prevention, and deep learning-based route update validation. Initially, users and BGP routers are authenticated using Enhanced Blowfish and Elliptical Curve Cryptography (EB-ECC) which generated the unique key using EB algorithm and the unique is encrypted by enhanced ECC for improving security. Every Autonomous System (AS) is constructed as a graph structure using Directed Acyclic Graph (DAG) which improves the scalability and interoperability of the network. Secondly, we perform BGP attack detection using Dual Agent based Twin Actor Twin Delayed Deep Deterministic Policy Gradient (DA-T2TD3) algorithm by performing both proactive and reactive methods. Finally, we perform route update validation by Gated Recurrent Unit (GRU) by considering update message tuples which validate the updates by identifying misconfigurations and errors in the updated messages. Here, updated messages are passed to the blockchain for validation using the DiemBFT consensus algorithm which improves high security. The simulation of the proposed SDBGPChain model is conducted by OMNET++ simulation tool, and the performance of this work is evaluated based on various performance metrics such as Packet drop ratio (0.22), Throughput (39.8 bits/sec), Block validation time (20.2 s), Detection accuracy ((no. of attacks 92.5%) and (no of anomalies 93%)), and Network traffic (27.6%) which proved that the proposed work achieved superior performance compared to state-of-the-art works.

Distributed resource allocation model with presence of multiple jammer for underwater wireless sensor networks

Underwater-wireless sensor network (WSN) are prone to the jamming attacks; mainly in case of reactive jamming. Reactive jamming has emerged as one of the critical security threat for underwater-WSN; this occurs due to the reactive jammer capabilities of controlling and regulating jamming duration. Further reactive jammer possesses low detection probability and high vulnerability; moreover the existing model has been designed in consideration with terrestrial-WSN. Hence these models possesses limited capabilities of detecting the jamming and distinguish among uncorrupted and corrupted packets; also it fails to adapt with the dynamic environment. Furthermore co-operative mechanism of jamming model is presented for utilizing the resources in efficient way; however only few existing work has been carried out through the co-operative jamming detection; especially under presence of multiple jammer nodes. For overcoming research issues this paper presents distributed resource allocation (DRA) model adopting cross layer architecture under presence of multiple jammer. DRA algorithm is designed for allocating resource to jammer user in optimal manner. Experiment outcome shows the proposed DRA model achieves much better detection rate considering multi-jammer environment. Thus aid in achieving much better detection accuracy, packet drop, packet transmission and resource utilization performance.

Resource allocation optimization for mitigating multi-jammer in underwater sensor network

<p class="16">Wireless underwater sensor networks (UWSNs) are used for coastal area monitoring and military monitoring applications, such as tsunami prevention and target tracking. In UWSNs, jamming is considered to be a serious problem where the intruder affects the lifetime of sensor motes and impacts the performance of the packet transmission. This paper considers that the jammer device is capable of reducing battery life and preventing the trustworthy UWSN mote from communication. Considering the presence of multiple jammers, the existing resource utilization model is not effective. This work presents an efficient resource allocation design to mitigate multiple jammers in UWSNs to overcome research problems. The resource allocation (ERA) model adopts a cross-layer design and can interact in a cooperative manner using direct and hop-based communication to maximize the quality of resource use. Compared to existing resource allocation methodologies, considering the presence of multiple jammer motes, the ERA achieves a much better detection rate, resource utilization, packet drop, and energy efficiency performance.</p>

Two efficient Kalman filter algorithms for measurement packet dropping systems under maximum correntropy criterion

Two efficient Kalman filter algorithms for measurement packet dropping systems under maximum correntropy criterion

A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks.

Quick and accurate detection of inside packet drop attackers is of critical importance to reduce the damage they can have on the network. Trust mechanisms have been widely used in wireless sensor networks for this purpose. However, existing trust models are not effective because they cannot distinguish between packet drops caused by an attack and those caused by normal network failure. We observe that insider packet drop attacks will cause more consecutive packet drops than a network abnormality. Therefore, we propose the use of consecutive packet drops to speed up the detection of inside packet drop attackers. In this article, we describe a new trust model based on consecutive drops and develop a hybrid trust mechanism to seamlessly integrate the new trust model with existing trust models. We perform extensive OPNET (Optimized Network Engineering Tool) simulations using a geographic greedy routing protocol to validate the effectiveness of our new model. The simulation results show that our hybrid trust model outperforms existing trust models for all types of inside packet drop attacks, not only in terms of detection speed and accuracy as it is designed for, but also in terms of other important network performance metrics, such as packet delivery rate, routing reliability, and energy efficiency.

Use of Node credibility and Andrews plot to detect and prevent BHA in MANET

The MANET wireless network operates independently of any infrastructure and can establish connections dynamically, making it highly accessible regardless of the environment. However, the network is vulnerable to attacks such as the BHA, which can cause the depletion of the network. This attack introduces MNs into the network, providing false routing information to the source node. This leads to the drop of data packets and ultimately damages the network. To address this issue, we propose a node credibility-based approach that utilizes Andrew's plot to assess node credibility after several transactions. This method enables us to identify highly credible nodes which can be considered trustworthy in the network. We utilized Network Simulator software to create various MANET scenarios and test the effectiveness of our proposed approach against the BHA.

Distributed state estimation with state equality constraints in the presence of packet dropping

In this paper, we investigate distributed Kalman consensus filter with state equality constraints in the presence of packet dropping. Firstly, the unconstrained distributed Kalman filter is designed by utilizing the local measurement information of each sensor node, and then the consensus term is added to it to derive the unconstrained distributed Kalman consensus filter. Secondly, we research the equality constrained distributed Kalman consensus filter based on the projection operator to achieve better filter performance, where the unconstrained estimation gain is calculated by solving a modified algebraic Riccati equation and a Lyapunov equation. Additionally, we design the second type of equality constrained distributed Kalman consensus filter by using time-stamping technology and projection operator to eliminate the effect of the Lyapunov equation in the infinite time domain on the convergence analysis, and its unconstrained estimation gain only requires solving a modified algebraic Riccati equation. Finally, the simulation experiments demonstrate that the equality constrained distributed Kalman consensus filter outperforms the unconstrained filter and that the second type of estimator exhibits superior performance compared to the first.

Performance assessment of a communication infrastructure with redundant topology: A complex network approach

The physical assets within a critical infrastructure system are pivotal to its efficient performance and protection and that of other dependent systems. This is particularly the case for communication systems where network protection strategies usually involve asset redundancy. Although such redundancy is well-modelled in the literature, there is a gap in knowledge from a network science perspective in terms of its implications for network modelling and performance assessment. This paper presents a multilayer complex network framework that takes into account the heterogeneity of the redundant infrastructure for realistic network modelling and further analysis, a step change from using a single network model. Key performance indicators (KPIs) for communication networks (i.e., latency and jitter, bandwidth and throughput, queue depth and packet drops) are redefined to evaluate key important features of a long-haul backbone network such as network capacity and average use. In addition, these KPIs are adapted to deal with the aforementioned redundancy and so inform network managers with values defined over a model closer to the real system. The paper analyses the use case of a nationwide core and metro network infrastructure of one of the main UK internet service providers. The results of the analysis of KPIs showcase the advantage of the proposed multilayer complex network framework over the traditional single network model. Critical network elements within different dimensions of a communication network are identified based on their performance for prioritising network management measures.

An intrusion detection system using Exponential Henry Gas Solubility Optimization based Deep Neuro Fuzzy Network in MANET

The smoothness and dynamic nature of the exploitation are significantly influenced by Mobile Ad hoc Networks (MANETs). The wireless and active environments, on the other hand, are vulnerable to a range of threats. Enhancing the reliable intrusion detection model is crucial to safeguarding the MANET from various malicious attacks. In this paper, an effective intrusion detection method for MANET is presented based on a Deep Neuro Fuzzy Network based on Exponential-Henry Gas Solubility Optimization (EHGSO). The newly designed EHGSO algorithm is used to select the optimal routes in the early phases of safe routing. The fitness metrics that define this approach include energy, distance, neighbourhood quality, and link quality. The proposed EHGSO combines the Henry Gas Solubility Optimization (HGSO) with Exponential Weighted Moving Average (EWMA). The second phase, in which the conveyed data packets are altered and the Knowledge discovery in databases (KDD) features are extracted, starts the intrusion detection phase at the base station. Following the extraction of the KDD features, data augmentation is performed. The Deep Neuro Fuzzy Network is trained using the suggested EHGSO method before performing intrusion detection. The proposed method demonstrates higher performance when compared to all other existing technologies. In without attacks scenario, the values achieved by the proposed method considering the metrics, such as energy, throughput, packet drop, jitter, Performance and Development Review (PDR), precision, and recall is 0.342 J, 134975 kbps, 4.123, 0.086, 95.877%, 0.950, and 0.924, respectively.

A smart packet type identification scheme for selective discard of video packets

In recent years, due to the growth of social networks and development of video sharing platforms, transmission of video content has increased dramatically. This huge amount of data imposes a heavy load on the limited bandwidth of the networks. As a result, data loss will be inevitable. Nevertheless, since some types of video frames, such as B-frames are more resilient to packet loss, discarding them can ease network congestion while maintaining QoE. However, frame-type information is only available in the packet payload. Limited processing power of devices in high-speed networks, data encryption and user credibility issues, make finding the frame types a costly operation. Therefore, an intelligent router with fast and reliable decision-making is proposed to model live video streams structures and recognize the importance of video packets at the network level. This information enables the network routers to discard insignificant video packets ahead of congestion. The proposed method does not need to read the IP layer payload and uses only the packet header data for decision making. Experimental results show an intelligent router, at the worst scenario can classify packet type with better than 95.1% accuracy, as if packet types are marked at the source without any bias. Tests on various HD video, with 1–10% packet drop rates, shows while the proposed packet discarding scheme is 0.8–2.7 dB inferior to ground truth method, it is still 6–10 dB better than dropping packets at random.

A consensus-based approach to reputational routing in multi-hop networks

When it comes to the security of the Internet of Things (IoT), securing their communications is paramount. In multi-hop networks, nodes relay information amongst themselves, opening the data up to tampering by an intermediate device. To detect and avoid such malicious entities, we grant nodes the ability to analyse their neighbours behaviour. Through the use of consensus-based validation, based upon the blockchain's miners, all nodes can agree on the trustworthiness of all devices in the network. By expressing this through a node's reputation, it is possible to identify malicious devices and isolate them from network activities. By incorporating this metric into a multi-hop routing protocol such as AODV, we can influence the path selection process. Instead of defining the best route based upon overall length, we can choose the most reputable path available, thus traversing trustworthy devices. By performing extensive analyses through multiple simulated scenarios, we can identify a decrease in packet drop rates compared to AODV by approximately 48% and 38% when subjected to black hole attacks with 30 and 100 node networks respectively. Furthermore, by subjecting our system to varying degrees of grey holes, we can confirm its adaptability to different types of threats.

Design and analysis of energy aware interior gateway routing algorithm with particle swarm optimization

SummaryIn wireless sensor networks (WSNs), the data must reach a processing node with less energy consumption and delay rate. However, with the specific consideration of the unique properties of sensor networks such as limited power, stringent bandwidth, dynamic topology due to node failures, adding/removing nodes, or even physical mobility, high network density and large‐scale deployments have posed many challenges in the design and management of sensor networks. These challenges have demanded energy awareness and robust protocol design. Efficient utilization of the sensor's energy resources and maximizing the network lifetime were the primary design considerations for the proposed work. As a result, the new interior gateway routing with particle swarm optimization (IGRPSO) is presented with minimal latency and a higher packet delivery ratio in WSN; optimum link weights are calculated in a fraction of the original data to the new protocol. Network entropy maximization is a conceptual framework that guides the development of the protocol and the computational techniques required to determine which traffic distributions are not only best but also realizable through link‐state routing. Therefore, a routing algorithm provides the optimal and economically optimal route. We used the MATLAB simulation environment to compare and contrast the performance of the dynamic source routing (DSR) protocol and enhanced interior gateway routing protocol, DSR protocol with particle swarm optimization with that of the IGRPSO protocol, which has the maximum packet delivery ratio of 75.74% and less packet drop of 25.37%, less energy consumption of 0.3674 J.