Congestion In Wireless Sensor Networks Research Articles

An efficient reconfigurable geographic routing congestion control algorithm for wireless sensor networks

<span lang="EN-US">In recent times, huge data is transferred from source to destination through multi path in wireless sensor networks (WSNs). Due to this more congestion occurs in the communication path. Hence, original data will be lost and delay problems arise at receiver end. The above-mentioned drawbacks can be overcome by the proposed efficient reconfigurable geographic routing congestion control (RgRCC) algorithm for wireless sensor networks. the proposed algorithm efficiently finds the node’s congestion status with the help queue length’s threshold level along with its change rate. Apart from this, the proposed algorithm re-routes the communication path to avoid congestion and enhances the strength of scalability of data communication in WSNs. The proposed algorithm frequently updates the distance between the nodes and by-pass routing holes, common for geographical routing. when the nodes are at the edge of the hole, it will create congestion between the nodes in WSNs. Apart from this, more nodes sink due to congestion. it can be reduced with the help of the proposed RgRCC algorithm. As per the simulation analysis, the proposed work indicates improved performance in comparison to conventional algorithm. By effectively identifying the data congestion in WSNs with high scalability rate as compared to conventional methods</span>

Energy efficient congestion control scheme based on Modified Harris Hawks Optimization for heavy traffic Wireless Sensor Networks

The performance of Wireless Sensor Networks (WSNs), a subset of Wireless Ad-hoc Networks, is significantly influenced by the application, lifetime, storage capacity, processing power, changes in topology, communication medium, and bandwidth. These restrictions call for a strong data transport control in WSNs that takes into account quality of service, energy efficiency, and congestion management. Wireless networks face a significant difficulty with congestion which impacts on the loss rate, channel quality, link utilization, the number of retransmissions, traffic flow, network lifetime, latency, energy, and throughput are all negatively impacted by congestion in WSNs. Since the routing problem has been shown to be NP-hard and it has been realized that a heuristic based method delivers better performance than their traditional counterparts, routing is one of the most popular methods for reducing the energy consumption of nodes and increasing throughput in WSNs. This research provides a Rate Aware Congestion Control (RACC), an effective congestion avoidance method that enhances network performance by applying Modified Harris Hawks Optimization (MHHO). Nodes are initially clustered using the DBSCAN clustering algorithm. When compared to existing approaches, the simulation outcomes of the developed technique indicate superior service, low delay, high energy, packet delivery ratio and increased living nodes.

Software Defined Networking Controller (SDNC): a robust security architecture for SDN-based 5G networks

The efficacy of Wireless Sensor Networks (WSNs), a subset of Wireless Ad-hoc Networks, is significantly impacted by application, lifetime, storage capacity, processing power, topology changes, communication medium, and bandwidth. These constraints necessitate a robust data transport control in WSNs that considers service quality, energy efficiency, and congestion management. Congestion is a significant issue for wireless networks. Congestion in WSNs has deleterious effects on loss rate, channel quality, link utilization, number of retransmissions, traffic flow, network lifetime, latency, energy, and throughput. The predominance of WSNs necessitates the development of more efficient congestion control algorithms. Since it has been demonstrated that the routing problem is NP-hard and that heuristic-based methods outperform their traditional counterparts, routing is one of the most prevalent techniques for reducing the energy consumption of nodes and increasing throughput in WSNs. This study presents Rate Aware Congestion Control (RACC), an efficient method for avoiding congestion that improves network performance by employing Modified Harris Hawks Optimisation (MHHO). Initially, nodes are clustered utilizing the DBSCAN clustering algorithm. The simulation results of the developed technique indicate superior service, low latency, high energy, a high packet delivery ratio and an increasing number of living nodes when compared to existing approaches.

Congestion-Free Cluster Formation and Energy Efficient Path Selection in Wireless Sensor Networks using ButPCNN

Today, network congestion is a common occurrence that needs to be focused on and effectively addressed, particularly in Wireless Sensor Networks (WSN) for packed type networks. The main causes of congestion in WSN are a lack of channel capacity and energy waste. This study's major goal is to develop Energy Efficient Congestion Free Path Selection Protocol (ECFPSP) protocol, which aims to reduce network congestion. By selecting the most appropriate main cluster head (PCH) and secondary cluster head (SCH), the ECFPSP protocol is proposed to decrease end-to-end delay time and extend the network lifetime. The suggested protocol implements a routing protocol that provides security by avoiding hostile nodes and reducing data loss. It also routes the nodes. Hence, a Congestion-Free Cluster Formation is provided to increase the lifetime of the network by proposed ButPCNN approach. To decrease packet loss and conserve energy, this research also uses brand-new cluster-based WSNs. In comparison to other standard protocols, the simulation results reveal that ButPCNN has a reduced packet drop rate, which increases the ratio of packet distribution, network life, and residual energy. As a result, the suggested method enhances congestion control performance while using less energy and a recently developed strategy is suggested to successfully enhance network performance. The proposed ButPCNN gives 25 percent improvement to optimize traffic on overloaded node than the other traditional approaches.

Effective and Enhanced Congestion Control Technique using Adaptive Routing Protocol to Improve the Performance of Crammed WSN

Nowadays, congestion on the network becomes a usual fact which is to be focused and to be addressed appropriately especially in Wireless Sensor Networks (WSN) for crammed type networks. Limited capacity on channel and wastage of energy are the root cause of congestion in WSN. The effects of congestion implies on QoS parameters, queue length, data arrival rate etc. Furthermore, data packets should be transmitted energy-efficiently to the sink node. In this regard, an Energy-Efficient Routing Protocol is offered to efficiently transmit the nodes to their end node or destination. To control congestion, an Adaptive Buffer trade-off and Improved Trust-based Energy Efficient Routing protocol are first presented, this method identifies the congestion free paths and the Buffer trade-off handles the buffer effectively. To route the protocol, a Cross-Layer Security-Based Fuzzy Logic Energy Efficient Packet Loss Preventive Routing Protocol has been developed. The proposed protocol routes the nodes and the protocol adopts a routing protocol that imparts security in terms of avoiding malicious nodes and preventing data loss. Consequently, to improve the lifetime of the network, a Density Aware Optimal Clustering Approach is presented. The proposed method is evaluated based on the Matlab software and the QoS performance metrics are Energy Consumption, Packet Delivery Ratio, Trust Value Computation, latency, reliability, energy efficiency, end-to end delay, Average Throughput, accuracy and network lifetime. The effectiveness of the research is evaluated by comparing it with other existing techniques, including Trust Aware Secure Routing Protocol (TASRP), Artificial Flora Algorithm Based Support Vector Machine (SVM-AF), Well-Organized Trust Estimation Based Routing Scheme (ETERS), Lion Fuzzy Bee, and Bat Fuzzy Bee Algorithm. Accordingly, the suggested method’s performance is higher than the existing methods for Packet delivery ratio, throughput, network lifetime, energy efficiency, and reliability. Consequently, the proposed method improves the congestion control performance in an energy-efficient manner, in future; a recently advanced technique is proposed to effectively improve the network performance respectively.

A Machine Learning-Based Intelligence Approach for Multiple-Input/Multiple-Output Routing in Wireless Sensor Networks

Computational intelligence methods play an important role for supporting smart networks operations, optimization, and management. In wireless sensor networks (WSNs), increasing the number of nodes has a need for transferring large volume of data to remote nodes without any loss. These large amounts of data transmission might lead to exceeding the capacity of WSNs, which results in congestion, latency, and packet loss. Congestion in WSNs not only results in information loss but also burns a significant amount of energy. To tackle this issue, a practical computational intelligence approach for optimizing data transmission while decreasing latency is necessary. In this article, a Softmax-Regressed-Tanimoto-Reweight-Boost-Classification- (SRTRBC-) based machine learning technique is proposed for effective routing in WSNs. It can route packets around busy locations by selecting nodes with higher energy and lower load. The proposed SRTRBC technique is composed of two steps: route path construction and congestion-aware MIMO routing. Prior to constructing the route path, the residual energy of the node is determined. After that, the residual energy level is analyzed using softmax regression to determine whether or not the node is energy efficient. The energy-efficient nodes are located, and numerous paths between the source and sink nodes are established using route request and route reply. Following that, the SRTRBC technique is used for congestion-aware routing based on buffer space and bandwidth capability. The path that requires the least buffer space and has the highest bandwidth capacity is picked as the optimal route path among multiple paths. Finally, congestion-aware data transmission is used to minimize latency and data loss along the route path. The simulation considers a variety of performance metrics, including energy consumption, data delivery rate, data loss rate, throughput, and delay, in relation to the amount of data packets and sensor nodes.

Rate aware congestion control mechanism for wireless sensor networks

The sensor nodes in wireless sensor networks (WSNs). inherit low processing, shorter range and low power features with the miniature size to offer wireless transmission. Therefore, in order to fulfil the long-time data sensing need, several Quality of Service (QoS) parameters such as: lesser delay, higher throughput and packet delivery ratio (PDR) with minimum overhead must to be improved. In recent years extensive research efforts have been made to ameliorate these parameters to achieve optimum QoS. However, the rate adaptation and congestion control in WSNs are still least explored areas. The traffic congestion in WSNs is the main reason that results in higher delay and low throughput. In this paper, a new rate aware congestion control (RACC) mechanism has been proposed which defines three levels of congestion based on which the data rate, throughput, overhead and the delay. RACC at the transport layer, improves congestion by source rate regulation at the specific hotspot areas. Further, RACC has been applied to different modulation schemes like: 16 QAM (Quadrature Amplitude Modulation), BPSK (Binary Phase Shift Keying) and QPSK (Quadrature Phase Shift Keying) to test the optimum modulation scheme for the proposed approach. The testing is done to ensure the that the data can be sent to the longer distant sensors using appropriate modulation technique suitable for the congestion model (RACC). The simulation outcomes in NS2 tool confirms the improvement of RACC over existing techniques (Delay-aware congestion control protocol (DACC) and Joint energy replenishment and load balancing (J-ERLB)) in WSNs. The overall improvement for RACC over existing techniques follows an improvement percentage 17% for throughput parameter, for packet delivery ratio, it is 8.35%, while for normalized routing overhead it shows 0.56% and for MAC Overhead, average end to end delay, and average remaining energy it shows 0.64%, 2.04% and 59.28% improvement respectively.

Triangular Fuzzified Support Vector Regression Based Multivariate Rocchio Robust Boost Classification for Traffic and Congestion Aware Data Delivery in WSN

Wireless sensor network (WSN) is a huge set of sensor nodes distributed geographically with limited power supply. These nodes are used to sense environmental changes, gather information and send the collected data to a remote destination for data analysis and decision making. During continuous data transmission, the network traffic causes congestion. Congestion in WSN is a major cause for packet loss and it degrades the network performance. In order to improve the congestion-aware routing, a novel technique called as the Triangular Fuzzified Support Vector Regression Based Multivariate Rocchio Robust Boost Classification (TFSVR-MRRBC) technique is introduced. The proposed TFSVR-MRRBC consists of two processes such as route path identification and congestion aware data delivery. Initially, route paths are constructed by estimating node residual energy. After that, Support Vector Regression is applied to analyze the energy using the fuzzy Triangular membership function. Thus, energy-efficient nodes construct the multiple paths between sources and sink node through control message distributions namely route request with route reply is identified. Then the Multivariate Rocchio adaptive robust boost technique is applied for congestion aware data delivery based on the buffer space and bandwidth capacity. The route path with low buffer space and higher bandwidth capacity is selected as the best route among the multiple paths. Finally, Delay and packet loss is reduced by achieving congestion-aware data delivery. The simulation is achieved by various performance factors namely, energy consumption, packet delivery ratio, packet loss rate, and end-to-end delay with versus to amount of data packets and sensor nodes. Experimental results of proposed TFSVR-MRRBC technique improves data packet delivery and energy consumption is minimized, packet loss rate, delay than the conventional routing techniques.

Traffic and Energy Aware Optimization for Congestion Control in Next Generation Wireless Sensor Networks

Congestion in wireless sensor networks (WSNs) is an unavoidable issue in today’s scenario, where data traffic increased to its aggregated capacity of the channel. The consequence of this turns in to overflowing of the buffer at each receiving sensor nodes which ultimately drops the packets, reduces the packet delivery ratio, and degrades throughput of the network, since retransmission of every unacknowledged packet is not an optimized solution in terms of energy for resource-restricted sensor nodes. Routing is one of the most preferred approaches for minimizing the energy consumption of nodes and enhancing the throughput in WSNs, since the routing problem has been proved to be an NP-hard and it has been realized that a heuristic-based approach provides better performance than their traditional counterparts. To tackle all the mentioned issues, this paper proposes an efficient congestion avoidance approach using Huffman coding algorithm and ant colony optimization (ECA-HA) to improve the network performance. This approach is a combination of traffic-oriented and resource-oriented optimization. Specially, ant colony optimization has been employed to find multiple congestion-free alternate paths. The forward ant constructs multiple congestion-free paths from source to sink node, and backward ant ensures about the successful creation of paths moving from sink to source node, considering energy of the link, packet loss rate, and congestion level. Huffman coding considers the packet loss rate on different alternate paths discovered by ant colony optimization for selection of an optimal path. Finally, the simulation result presents that the proposed approach outperforms the state of the art approaches in terms of average energy consumption, delay, and throughput and packet delivery ratio.

Improving The Performance Of Wireless Sensor Network With Multi Factor Strategies

In the design of wireless sensor network (WSN), packet delivery ratio(PDR) is an import parameter to be maximized. PDR plays an important role for improving the performance of WSN. With the existing schemes, a secure zone-based routing protocol was implemented for life time improvement in WSNs. In multi - hop communication, a new routing criterion was formulated for packet transmission. Security against message tampering, dropping and flooding attacks were incorporated in the routing metric. The approach skipped risky zones as a whole from routing and choose alternative path to route a packet in secured manner with less energy consumption. Though energy conservation and attack resilience are achieved, congestion in WSN is increased and because of it packet delivery ratio is diminished. To address this problem, we propose a solution to improve the packet delivery ratio with a multi factor strategy involving routing, differentiation of flows, flow-based congestion control with retransmission and redundant packet coding. Detailed analysis and simulations are undertaken to evaluate the efficiency of the contemplated work compared to the existing solutions.

Implementation of Improved TOPSIS Method for Congestion Control in WSN

In wireless sensor networks, sensor nodes are limited in memory, battery power and computational power. Wireless Sensor Networks (WSN) are a specific category of wireless adhoc networks where their performance is highly affected by application, lifetime, storage capacity, topology changes and the communication medium and bandwidth. Whenever the traffic load in the network increases, buffer at a node becomes full and it cannot handle any more data packets. This causes packet loss and retransmission which affects delivery ratio, energy and decreases network lifetime. Congestion is an important issue in wireless networks. Congestion in WSN severely affects loss rate, channel quality, the number of retransmissions, traffic flow, network lifetime, delay, energy as well as throughput. The congestion can be resolved in WSN either by reducing the data rate or by increasing the resources to form alternative paths. In this paper, an Implementation of Improved TOPSIS Method for congestion control in WSN known as Improved Hierarchical tree Alternative path protocol has been proposed which provides an improved performance over the basic HTAP protocol.

IRCTGP: A Novel Intra-node Resource Control Protocol Based on Traffic Priority and Evolutionary Games Theory for Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a network which is made up of resource-constrained wireless sensors with restricted capabilities. Quality of Service in Wireless Sensor Networks is still an open area of research, due to the fact that several problems need to be solved using resource constrained WSN nodes. The probability of congestion occurrence and packet loss in Wireless Sensor Networks is high, which makes it difficult to satisfy different qualities of service requirements. Congestion in WSN needs to be controlled to improve the quality of service such as throughput and packet loss ratio along with the packet delay. In particular, we proposed the use of evolutionary game theory in the design of a QoS-aware Intra-node congestion control protocol. Intra-node congestion control protocol is a non-cooperative asymmetric game that adjusts the queue accepting rate and queue service rate such that higher priority classes have lower loss rate and lower queuing delay. Simulation results show that the proposed protocol decreases packet dropping and improves network throughput and network quality of service performance.

Improving Packet Delivery Ratio in Wireless Sensor Network with Multi Factor Strategies

In the design of wireless sensor network (WSN), packet delivery ratio is an import parameter to be maximized. In existing schemes, a secure zone-based routing protocol was implemented for life time improvement in WSNs. In multi - hop communication, a new routing criterion was formulated for packet transmission. Security against message tampering, dropping and flooding attacks was incorporated in the routing metric. The approach skipped risky zones as a whole from routing and chooses alternative path to route a packet in secured manner with less energy consumption. Though energy conservation and attack resilience are achieved, congestion in WSN is increased and because of it packet delivery ratio is diminished. To address this problem, we propose a solution to improve the packet delivery ratio with a multi factor strategies involving routing, differentiation of flows, flow-based congestion control with retransmission and redundant packet coding. Detailed analysis and simulations are undertaken to evaluate the efficiency of the contemplated work compared to the existing solutions.

Game Theory Based Congestion Control for Routing in Wireless Sensor Networks

The Congestion in wireless sensor networks can lead to the loss and retransmissions of packets, and excessive energy consumption. Since network congestion can deteriorate the throughput, the quality of service (QoS) in data transmission, and network lifetime, congestion control is a critical problem. In this paper, the data transmission process is firstly presented based on the data type. The cooperation game theory is used to model the transfer behavior of different type data. Further, a cooperation game based congestion control (CGCC) is proposed. The different type data cooperates with each other to use link capacity. And then Ant colony Routing algorithm combined with CGCC (ARCGCC) is designed. According to the congestion degree and QoS requirement of each type data, the routing path is built in the routing algorithm. When congestion is detected, the congestion information is exchange. In the light of the data traffic and priority, the nodes adjust the sending rate to the next hop node to mitigate the congestion. Experimental results show that our mechanism and algorithm have better performance than some other mechanism and algorithms in terms of the throughput, reliability, delay, and energy consumption.

Mitigating congestion in wireless sensor networks through clustering and queue assistance: a survey

A network of randomly deployed sensor nodes which shares limited resources like bandwidth, buffer, queue, and battery powered nodes is known as wireless sensor network. Such network must have energy, to avoid the chances of congestion because congested network degrades the performance of network. Congestion may occur due to several reasons like data packet collision, transmission channel contention and buffer overflow. A congestion control protocol must acquire the functionalities that can increase the lifetime and efficiency of network which are major responsibilities of wireless sensor network. In this paper traffic oriented, resource oriented and a hybrid approach with some additional functionalities of controlling congestion are discussed in a wide manner. The hybrid approach is best as per this survey as it integrates various factors of wireless sensor networks to control and mitigate the situation. A comprehensive analysis is also done on these factors to justify the nature of different approaches.

Presentation and interaction of Internet of Things data based on augmented reality

In recent years, AR (Augmented Reality) technology has gradually entered people’s horizons and has become a research hotspot in the field of image processing. At the same time, the mobility and interactivity of AR have also been valued. This paper studies the interaction technology in the AR scene, and proposes a scheme to detect the user’s gaze at the cone perspective. The Leapmotion body sensor was used for gesture extension, and the hardware connection and data transmission scheme of Leapmotion and Holo Lens was designed. Based on the three input modes of sight, gesture and voice, the interaction logic between the user and the system in the AR scene is designed. The network of the Internet of Things is dominated by wireless sensor networks (Wireless Sensor Networks). This paper studies the problem of congestion in wireless sensor network sensing data transmission. WSNs nodes need to transmit the sensed data or preprocessed sensed data to sink nodes. When the data packets (or packets) received by a sensor node exceed its upper limit for forwarding or processing data packets, the excess data packets need to be buffered. When the memory of cached packets is full, excess packets are discarded, causing buffer overflows and causing node-level congestion. This paper proposes an energy balance strategy based on feedback control. The feedback control algorithm is used to adjust the node’s transmit power to realize the redistribution of node load in the network, solve the problem of node energy imbalance in wireless sensor networks, and further extend the sensor network.

Fuzzy logic rate adjustment controls using a circuit breaker for persistent congestion in wireless sensor networks

Congestion control is necessary for enhancing the quality of service in wireless sensor networks (WSNs). With advances in sensing technology, a substantial amount of data traversing a WSN can easily cause congestion, especially given limited resources. As a consequence, network throughput decreases due to significant packet loss and increased delays. Moreover, congestion not only adversely affects the data traffic and transmission success rate but also excessively dissipates energy, which in turn reduces the sensor node and, hence, network lifespans. A typical congestion control strategy was designed to address congestion due to transient events. However, on many occasions, congestion was caused by repeated anomalies and, as a consequence, persisted for an extended period. This paper thus proposes a congestion control strategy that can eliminate both types of congestion. The study adopted a fuzzy logic algorithm for resolving congestion in three key areas: optimal path selection, traffic rate adjustment that incorporates a momentum indicator, and an optimal timeout setting for a circuit breaker to limit persistent congestion. With fuzzy logic, decisions can be made efficiently based on probabilistic weights derived from fitness functions of congestion-relevant parameters. The simulation and experimental results reported herein demonstrate that the proposed strategy outperforms state-of-the-art strategies in terms of the traffic rate, transmission delay, queue utilization, and energy efficiency.

WIRELESS SENSOR NETWORKS CONGESTION AND ROLE OF ARTIFICIAL INTELLIGENCE

The occurrence of the congestion has an extremely deleterious impact on the performance of Wireless Sensor Network (WSNs). Some novel blockage control method utilizing computerized reasoning for remote sensor systems. Remote Sensor Systems are a class of remote systems estimated for checking objective and ecological wonders. Our paper provides opening to wireless sensor network as well as for artificial intelligence, which aims to act both proactively, in order to avoid the creation of congestion in WSNs, and reactively, so as to mitigate the diffusion of upcoming congestion through alternative path routing. We make use of Artificial Intelligence (AI) which play an important role in our society, give rise to systems that can manage themselves. Many advanced AI techniques can be utilized to improve the performance and reliability. Investigating the AI algorithm applied to WSN may improve network management, security or routing in WSN which may result in a more reliable network. We will look at AI algorithm applied in WSN and discuss the possible use of these AI in WSN to address the WSN challenge and improve its performance and reliability. And due to the constraint on data processing and power consumptions, use of artificial intelligence has been historically discarded in these kind of network. The use of the AI in WSN makes system error free, fast and efficient in almost many aspects. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes.

Energy Efficient Hierarchical Multi-Path Routing Protocol to Alleviate Congestion in WSN

Congestion easily occurs in wireless sensor networks (WSN) due to it's centralized traffic pattern. In order to achieve high energy efficiency, network longevity, better fairness and quality of service, it is important to detect congestion in WSN in a timely manner. In this paper, an energy efficient hierarchical multi-path routing protocol to alleviate congestion and to balance energy is proposed. The algorithm is designed by partitioning the network into equal sized zones to achieve complete network connectivity and to reduce packet transmissions. The zone leaders selected are shifted on different nodes based on network dynamic conditions to avoid hotspots and to provide energy balancing. For an efficient data transmission, quicker and optimal multiple paths are established using merged zone and hierarchical network topology structure. The proposed algorithm detects the congestion by monitoring the path quality. The detected congestion is a result of overloaded links or nodes on the path. In addition, the algorithm proactively controls the congestion by dynamically shifting the transmission paths on their quality and alleviate it reactively using traffic splitting approach. The goal of this approach is to control resources instead of controlling the network load. The simulation results demonstrate that the proposed algorithm performs better as compared to other congestion control algorithms in terms of throughput, energy consumption and packet delivery ratio in a resource constraint wireless sensor network.

A Congestion-Aware Clustering and Routing (CCR) Protocol for Mitigating Congestion in WSN

Wireless sensor networks (WSN) have been investigated as a powerful distributed sensing application to enhance the efficiency of embedded systems and wireless networking capabilities. Although WSN has offered unique opportunities to set the foundation for using ubiquitous and pervasive computing, it suffered from several issues and challenges such as frequently changing network topology and congestion issue which affect not only network bandwidth usage but also performance. The main objective of this study is to introduce a congestion-aware clustering and routing (CCR) protocol to alleviate the congestion issue over the network. The CCR protocol is proposed to decrease end-to-end delay time and prolong the network lifetime through choosing the suitable primary cluster head (PCH) and the secondary cluster head (SCH). The experimental results demonstrate that the effectiveness of the CCR protocol to satisfy the quality of service (QoS) requirements in increasing the network lifetime and raising the number of packets sent alike. Moreover, the CCR outperforms other state-of-the-art techniques in decreasing the overflow of data, and thus the network bandwidth usage is reduced.