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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...
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... The sensor unit ought to be low-cost, battery-operated, effortless to set up, self-configuring, and put in a location where it will be exposed to severe conditions [3,4]. Thermal and infrared radar are two types of sensors that can detect ambient factors such as temperature, pressure, sound, and humidity [5]. Sensors, on the other hand, are hampered by a lack of memory, power, computing resources, and dependability [5,6]. ...
... Thermal and infrared radar are two types of sensors that can detect ambient factors such as temperature, pressure, sound, and humidity [5]. Sensors, on the other hand, are hampered by a lack of memory, power, computing resources, and dependability [5,6]. In WSN, congestion is a big issue [7][8][9]. ...
Data is sent by multi-hop communication in Wireless Sensor Networks (WSNs), where each sensor node transmits data to its neighbouring nodes until it reaches the target node, which could be a base station or sink node. Congestion is a potential when network size and data traffic both rise. When the network is overloaded with data, it is said to be in congestion, which results in packet collisions, higher latency, and worse network performance overall. Numerous congestion-aware routing techniques have already been created in WSNs to address this limitation. By intelligently routing data traffic through less congested pathways and avoiding densely populated areas, congestion-aware routing systems seek to mitigate congestion-related concerns. However, in order for the network's congestion-aware routing algorithms to collect and distribute congestion information, they need extra overhead or control messages. This additional overhead, particularly in sensor nodes with limited resources, might result in higher energy consumption and worse network performance. This work established a new congestion-aware routing protocol called ButBeeRoute in order to address this key shortcoming. However, ButBeeRoute is designed with dynamically arranged node clusters to offer complete coverage and connectivity. This research also employs novel cluster-based WSNs to reduce packet loss and save energy. When the network is made up of nodes with varying transmission ranges and the least amount of power consumption, however, ButBeeRoute can determine which method is optimal or most dependable. The simulation results demonstrate that compared to other traditional protocols, ButBeeRoute has a reduced packet drop rate, increasing the ratio of packet distribution, network life, and residual energy. Comparing the ButBeeRoute strategy to the current one, it resulted in an overall improvement of 13% in packet loss rate, 20% on average energy consumption, and 18% on average reduced traffic.
... CC approaches can be broadly divided into two categories ie Classical approach and optimization based approach. Various researchers have further 84 DIVYA PANDEY AND VANDANA KUSHWAHA ...
... Thus it gives indication to child node to adjust their traffic rates. Yadav S.L. et al. [84] have suggested resource as well as traffic oriented congestion control technique using a combination of huffman coding and ant colony optimization approach for network performance enhancement. Ant colony optimization has been used to identify numerous paths that are free of congestion. ...
Wireless Sensor Network (WSN) is an engrossing area of research due to its various applications and the adaptability into increasingly sophisticated system frameworks. The challenges experienced in WSN configuration , are mostly related to it's stringent constraints, which incorporate energy, transmission capacity, memory, computational abilities and to the necessities of the specific application. Over the last decade, a substantial number of attempts have been made to examine and address congestion-oriented problems using a range of methods. Owing to the high energy utilization and data processing needs, the use of traditional algorithms has usually been degraded. In this sense, contemporary researchers have begun using optimization approaches in the field of wireless sensor networks. However, efforts for congestion control technique in sensor networks using optimization model has not yet been done to any great extent in the literature. The goal of this study is to explore various prevalent optimization method that have been used for congestion control in WSNs. We have also analyzed the multifaceted nature inborn in various optimization problems, which would also suggest future directions to the researchers working in this ambit.
... The experimental outcomes confirmed that the counseled approach became advanced to the modern day strategies. Using statistics forecasting and classification techniques, the authors of [20] suggest a multiagent device (MAS) approach to sensible city traffic management in Birmingham. The authors of [21] supplied an Intelligent Traffic Management (ITM) technique that mixes two other algorithms, the Modified Neural Network Wavelet Congestion Control (MNNWCC) algorithm and the Treebased totally Congestion Control (TACC) set of rules, to alleviate visitors congestion. ...
... Wireless Sensor Network (WSN) is composed of small and self-directed networked devices known as sensor nodes. Due to the inherent nature of WSN, these nodes sense the event, aggregate data within the predefined architecture, and forward it to the base station via multiple connected nodes [1]. Currently, this technology is extremely attractive because of its vast applications in the domain of monitoring systems, healthcare, climate control, motion detection, and border surveillance, among others [2][3][4][5][6]. ...
... Over the years, network congestion has been explored extensively by several researchers, and multiple algorithms proposed by employing PID control theory for SR optimization of the source node, however, the optimization performance of hybrid multi-objective techniques provides better results [10,12,22,23]. A few latest schemes introduced for mitigating network congestion are reviewed below: -Yadav, et al. [1] have developed a traffic and energyaware congestion control algorithm called ECA-HA (efficient congestion avoidance approach using Huffman coding algorithm and ant colony optimization). It is used to optimize the traffic flow using ant colony optimization (ACO) and Huffman coding. ...
Wireless Sensor Networks have an event-driven nature that includes a colossal count of sensors to generate unpredictable network traffic that causes network congestion Due to immoderate congestion packets remain in the queue for a longer period. This incites increased packet delay and packet losses. Existing congestion control strategies are either inelastic or have limited elasticity. Numerous elastic approaches, such as Cuckoo Fuzzy PID (CFPID), PSO (Particle Swarm Optimization)-neural PID, and PSO Fuzzy PID have been introduced using meta-heuristics and evolutionary algorithms; nevertheless, these are slow and prematurely converge, indicating an NP-hard problem. Moreover, Multi-objective Proportional Integral Derivative (MPID) is articulated as a multi-objective optimization problem, which is followed by many researchers but suffers from overshoot defects. To address these issues, the hybrid controller is proposed using the Non-dominated Sorting Genetic Algorithm III and MPID called N3-MPID to optimize the data transmission rate for the source node. The four indirect objectives that are Integral Square Error, Integral Absolute Error, Integral of Time multiplied Absolute Error, and Integral of Time multiplied Square Error are taken to design a novel optimal MPID fitness function. To merge these objectives into a single goal, an equally weighted procedure is adopted that is efficient and well-distributed. The proposed technique is simulated using a Network Simulator and is compared with CFPID using principal metrics. Simulation results depict that the N3-MPID outflanks existing algorithms by an 11.76% increase in packet delivery ratio while minimizing packet loss, average delay, and deviation of queue length by 43.16%, 20.55%, and 15.05%, respectively.
... To optimize congestion control in wireless sensor networks by considering traffic patterns and energy consumption, protocol [32] dynamically adjusts data rates and routes based on network traffic. The energy levels of individual nodes are supposed to minimize congestion and energy consumption. ...
The increasing usage of several nodes to transfer the massive volume of data to the remotes in wireless sensor networks is a challenging task to reduce the loss. The high volumes of data transmission in wireless sensor networks (WSN) can surpass their capacity, resulting in congestion, latency issues, and packet loss. However, computational intelligence (CI) models can aid in managing and creating intelligent networks in WSN. The WSN congestion issues result in information loss and increased energy usage. CI-based models have been used to resolve this issue, reducing the latency. This paper proposes SoftMax Routing with Deep Neural Network (SRDNN) for efficient routing in WSN. This will route the data packets by choosing the high energy and lower load. It consists of two parts, such as the construction of the routing path, which determines the residual energy of the node. It is analyzed using SoftMax routing to decide whether the node is efficient in energy. The route request and reply established various paths between the source and destination. The path with minimum buffer space and maximum bandwidth is chosen in the optimal routing. The simulation results under the metrics such as energy consumption, data loss rate, throughput, and delay show the proposed model performance.
... A new weighted evidence combination algorithm is developed, and its efficiency is demonstrated through its application in target recognition. Congestion [25] in wireless sensor networks (WSNs) is an inescapable issue in today's environment, where data traffic has exceeded the channel's aggregated capacity. As a result of this, the buffer at each receiving sensor node overflows, resulting in packet losses, a lower packet delivery ratio, and a decrease in network throughput. ...
... Retransmission of every unacknowledged packet is not an energy-efficient approach for sensor nodes with limited resources. Routing [25] is a technique used to minimize energy consumption and improve data transfer in wireless sensor networks (WSNs). This paper proposes an efficient congestion avoidance approach using the Huffman coding algorithm and ant colony optimization to improve network performance. ...
... Some might argue about the implication of the contributed work in this paper to this application to WSN and its ability to detect untrusted nodes. In a WSN [25], each node has a limited buffer space to store incoming data packets. To evaluate the packet loss rate, the buffer entropy is calculated using the maximum entropy principle, which is used when there is a lack of information about arrival and service rates. ...
A theoretical approach for creating power law behaviour in broadband communication networks with GeoM or SGeoM specifications utilizing the Rényian maximum entropy functional formalism is undertaken. When specifying the SGeoM, the special case of / / queue's equilibrium distribution emerges. The suggested method simplifies closed-form equations for a queueing distribution, and the service time density and cumulative distribution functions reduce to the appropriate Rényian, Tsallisian, and Shannonian situations. The concept works well in power law activated − communication networks. Some influential applications of GeoM to WSNs are provided. Keywords-Maximum entropy principle(MEP), Rényian entropy functional, extensive maximum entropy (EME) formalism, WSNs.
... • Node level congestion: When a sensor node's queue is saturated or full by receiving more data than its limited memory capacities, it is said to be congested or overflowed. • Link level congestion: occurs when data traffic increased to aggregated capacity of the channel [4]. This leads to buffer overflow of all receiving nodes using that communication channel. ...
Research in the field of Wireless Sensor Networks aims to develop protocols ensuring minimal energy dissipation. In this work, we propose a lightweight congestion control based sink mobility solution to improve data gathering that considers jointly energy and memory constraints. Thus, we propose a mechanism to avoid and resolve situations when nodes may not receive new messages because of buffer saturation. This can causes data loss and energy waste while retransmitting non-received data by the source node. Indeed, our model moves the sink close to the congested nodes to alleviate their queues once a congestion risk was detected. The performances of our approach were approved by intensive simulations. Obtained results proved that the network lifetime achieved by our strategy is two to five times better than that reached by comparative approaches. We note also a considerable improvement in packet delivery ratio and a high adaptability to increased data traffic.
... Yadav et al. [37] have developed a traffic and energy-aware congestion control algorithm called ECA-HA (efficient congestion avoidance approach using Huffman coding algorithm and ant colony optimization). It is used to optimize the traffic flow using ant colony optimization (ACO) and Huffman coding. ...
... The performance in the optimization algorithm is correlated with objective function parameters, thus this research considers five important and most relevant control parameters. The objective function summarizes the input parameter by using maximization or minimization values for each parameter [24,37]. ...
Wireless Sensor Network (WSN) consists of hundreds of devices with limited resources that collect, analyze, and transmit data to a base station. The carry-send nature and inconsistent transmission rate caused network congestion. Congestion incites decreased throughput, increased packet loss, and energy depletion. The existing congestion control strategies address congestion problems but still lack performance and quality of service issues. The optimal source transmission rate helps to alleviate congestion. The article proposes a Multi-objective Fuzzy Krill Herd Algorithm (MFKHA) control network congestion by optimizing the source sending rate. This innovative multi-objective outflow rate optimization mechanism improves network performance by designing a unique probability-based data differentiation mechanism coupled with an optimal source outflow rate optimization. To minimize network congestion by achieving fast convergence, this optimization algorithm incorporates the five objectives (congestion level, inflow rate, outflow rate, bandwidth, and queue length). To validate the performance of the proposed MFKHA algorithm, extensive simulations are carried out using MATLAB. Moreover, the proposed MFKHA algorithm is compared to those of cutting-edge meta-heuristic algorithms such as ECA-HA, ACSRO, and PSOGSA. The simulation result shows that the proposed MFKHA outperformed all counterparts and specifically improved the sending rate, throughput, and fairness and friendliness index. Furthermore, it has also reduced packet loss, delay, queue size, energy usage, and congestion against ECA-HA.
... In high-traffic settings, however, dynamic time-slot management is Figure. 1 The proposed method is depicted in a block diagram challenging and will impair network throughput. To boost network functionality, an effective congestion avoidance strategy [13] is presented based on Huffman coding algorithm and ant colony optimization. This strategy is an amalgam of resource-based and traffic-based optimization strategies. ...
... The amount of time it takes for information to travel from its source to its destination (sink) In this Eq. (13) is the time at the sink while accepting the data and is the time at the origin while forwarding that data. ...
WSN has been widely used in many sensitive applications and it also has novel possibilities for laying the groundwork for using ubiquitous and pervasive computing, but it has also presented a number of issues and challenges, such as a dynamic network topology and a congestion problem that hinders not only network bandwidth utilisation but also performance. Proficient rate control and fair bandwidth allocation (PRC-FBA) was one of the schemes in the literature to solve issues of WSN by combining the ideas of traffic class priority and bandwidth fairness. However, because of the nature of WSN, the energy of nodes near the sink node is diminished when packets move from lowly congested nodes to highly congested nodes. This paper proposes a proficient rate control with data aggregation and fair bandwidth allocation (PRCDA-FBA) to address this problem by using an effective data aggregation approach for reducing the number of transmissions. In the proposed method, fair bandwidth allocation is simplified by an artificial intelligence-based bandwidth prediction method. Thus, PRCDA-FBA increases the network's durability. Despite having lower bandwidth utilizations, energy-critical sensor nodes require careful power management to avoid being eavesdropped upon. Along with data aggregation and fair bandwidth allocation, the effects of overhearing packets by energy-critical nodes are mitigated through network-wide route adjustments based on the energy level of nodes. Thus, in the proposed method, data aggregation is scheduled based on the availability of bandwidth, energy, queue size and packet priority. The proposed method is named as energy-aware proficient rate control with data aggregation and fair bandwidth allocation (EPRCDA-FBA). The proposed algorithms have been deployed on the Network Simulator 2.35 platform, and a comparative analysis has been performed using several metrics, including throughput, packet loss, End-to-End (E2E) delay and energy utilization. The EPRCDA-FBA method archives highest throughput which is 9.17%, 5.48%, 4.68% and 2.45% higher than congestion control strategies like discrete-time sliding mode congestion controller (DSMC), weighted priority based fair queue gradient rate control (WPFQGRC), PRC-FBA and rate adjustment-based congestion control (RACC).
... This algorithm considers the data's sensing rate, and the channel utilisation for data transmissions is evaluated to estimate the congestion. A congestion avoidance algorithm is introduced in Yadav et al. (2021) for WSNs using Huffman coding and ACO algorithms. This algorithm considers both network traffic and available resources to transmit the data between the nodes to avoid congestion. ...
