Congestion Avoidance and Fair Event Detection in Wireless Sensor Network

Congestion in Wireless Sensor Network (WSN) occurs when traffic load allocated to any sensor node is beyond its capacity. It is necessary to control the congestion traffic to support traditional quality of service (such as packet loss ratios, packet delay, wasting energy and throughput) especially, multimedia application of Wireless Multimedia Sensor Network (WMSN). Congestion control in WSN has been shown to be very effective with respect to extending lifespan of system. Hence to avoid the congestion in WSN, we proposed an efficient protocol called Priority-based Congestion Control Protocol (PCCP) which prevents an upstream congestion in WSN. The PCCP creates priority table based an importance of each node, and then sends this information to all the nodes within the network. Secondly, the major function of PCCP is to measure congestion level in the network using ratio of packet inter-arrival time along over packet service time. PCCP uses to control upstream congestion along with congestion degree and packet table. PCCP is hop-by-hop upstream congestion control protocol for WSN. PCCP provides work for packet-based computation to optimize congestion control. It can work under both single path & multipath routing. PCCP ensures guarantee of packet loss as well as delay, resulting each node can avoid unfairness and achieve flexible throughput. In this paper, we have proposed that PCCP provides better QoS by controlling network recourse management and network traffic to consuming efficient energy.

Wireless sensor networks (WSNs) have become widespread in recent years due to their uses in healthcare, infrastructure monitoring, environmental sensing, tactical surveillance, and defense. However, their inherent vulnerabilities pose significant security threats and performance challenges. WSNs are highly exposed to wormhole attacks and congestion which affects the reliability and efficiency of a network. Current routing protocols often lack comprehensive trust mechanisms to address these challenges effectively. This study aims to evaluate trust-based multi-path routing protocols to counter wormhole attacks and minimize congestion, enhancing security and performance in Wireless Sensor Networks (WSNs). It focuses on six key trust attributes to identify the most impactful factors. A survey of 29 network security experts of various experience levels examined six key trust attributes: encryption, authentication, route disjointedness, observation similarity, packet delivery, and end-to-end latency. Statistical techniques including descriptive statistics, correlation analysis, and principal component analysis were used to assess each attribute’s impact. The similarity of observation and route disjointedness were identified as the most crucial factors, with mean scores of 55.52 and 53.31. While authentication was valued, opinions varied, suggesting it should be part of a broader security framework. No differences were found in trust attribute evaluations based on qualifications or experience, indicating consensus among experts. The study shows that trust-based multi-path routing can curb wormhole attacks and detect congestion in WSNs.

Congestion control and avoidance in Wireless Sensor Networks (WSNs) is a subject that has attracted a lot of research attention in the last decade. Besides rate and resource control, the utilization of mobile nodes has also been suggested as a way to control congestion. In this work, we present a Mobile Congestion Control (MobileCC) algorithm with two variations, to assist existing congestion control algorithms in facing congestion in WSNs. The first variation employs mobile nodes that create locally-significant alternative paths leading to the sink. The second variation employs mobile nodes that create completely individual (disjoint) paths to the sink. Simulation results show that both variations can significantly contribute to the alleviation of congestion in WSNs.

Consistent data delivery in constrained networks such as wireless sensor networks (WSN), which has a significant role in Internet of Things (IoT), is vital due to the high probability of packet loss, not only because of the unreliable channel but also from link and buffer congestions. Thus, a reliable transport protocol should be used. Data caching at intermediate nodes is one way of improving the reliability performance of transport protocols in WSNs. However, it is not yet known up to what extent data caching alone can alleviate congestion in WSNs. This study investigates and analyses how a cache-based transport protocol, that has no congestion control mechanism and has a fixed transmission window, performs under congestion states. The transport protocol, called DTSN, is evaluated under link and buffer congestion states and the optimal performance is determined in terms of goodput, transmission cost and cache hit metrics. The results of our analysis reveal that intermediate caching was able to perform significantly during congestions in WSN. However, the amount of traffic being sent and intermediate node's cache memory should be taken into account. The optimum caching performance during link contentions is reached at higher transmission window and cache size values, while for buffer congestions, it is achieved at lower transmission window but higher cache size values, respectively. These values obtained can guarantee optimum usage of cache while ensuring congestion avoidance. In addition, the correlation that is established can serve as a basis for developing congestion window management for transport protocols in WSN that employ data caching.

Most wireless sensor networks performs a data collecting task, which means every node collects data of the environment such as temperature, humidity and shaking, then send them to the sink. In this case, all nodes would frequently send small packets to the sink, when the network is large, it will lead to heavy traffic load, especially in the area near the sink, nodes will suffer great congestion and packet loss, which makes them the bottleneck of the entire network. Here we propose a simple method to reduce congestion in wireless sensor networks, we call it packet combining protocol (PCP). When running PCP, a node doesn't forward the packets that were routed to it immediately, but extract the payloads as well as some source node information from the original packet, then the node put a certain number payloads into one new packet and route them toward the sink, PCP takes no extra calculations or transmissions at all. By running PCP, we not only reduced the total number of packets transmitted in the network, but also reduced the total data length transmitted to the sink. The result of the simulation shows that our method could reduce packet congestion by about 45%-85% and increase the successful packet delivery rate by about 5% to 25%.

The congestion is one of the major challenge in wireless sensor network (WSN). Congestion occurs due to buffer-overflow, channel-contention, packet-collision, reporting-rate, may be dynamic time varying wireless channel condition. Its causes overall channel quality to degrade, raise packet loss which lead to buffer drops and increased delays and consume excess energy. Therefore, it needs to be control the congestion in WSN to support quality-of-services and extend lifespan of system. Hence, to address this challenge, we proposed an efficient protocol which mitigate congestion and allocate appropriate source-rate to each-node in WSN. The proposed protocol is modified SB priority-based congestion control protocol (MSBPCCP) to avoid an upstream congestion in WSN. With the help of ratio of packet inter-arrived-time along with packet-service-time, PCCP measure the congestion level/degree. Secondly, it creates priority-index based on importance of each-node. Along with congestion-degree and priority-index MSBPCCP control upstream congestion. The MSBPCCP may avoid packets drops due to traffic congestion and improve the network throughput by providing the better quality of services.

A Wireless Sensor Network (WSN) is collection of wireless sensors with limited memory, processing and energy supply. Based on application, sensors distribute in a wide geographically area in order to collect information and transmit the collected data packets toward a base station also called Sink. Due to the relatively high node density and source-to-sink communication pattern, congestion is a critical issue in WSN. Congestion not only causes packet loss, but also leads to excessive energy consumption as well as delay. To address this problem, in this paper we propose a new fuzzy logic based mechanism to detect and control congestion in each grid in WSN. In the proposed approach, sink select one node in each grid as Monitor Node. In addition, sink defines congestion candidate grids. Each Monitor Node in congestion candidate grids continually monitors the network and fetches the fuzzy controller inputs in order to determine level of congestion in each grid. Based on the congestion level, packets forward through the grid or relay nodes. Simulation results show that our approach has higher packet delivery ratio and lower packet loss than existing approaches.

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.

The intensive traffic loads and high data rate in wireless sensor networks (WSNs) can cause congestion and have negative impact on the performance such as increased delay and energy consumption by sensor nodes. Moreover, the information provided by sensors may have different importance. The data packets which have more priority should reach sink nodes with reduced delay compared to low priority traffic. In this paper, we propose a delay-aware priority based routing scheme, called DAPR to alleviate congestion in wireless sensor networks. Our proposed mechanism uses queuing model at each sensor node which differentiates high-priority (HP) and low-priority (LP) data packets and transmits them in different way so that HP packets can be reached at sink nodes with reduced delay compared to LP packets. DAPR uses multipath routing technique which helps to alleviate congestion at certain places in the network, balances the load of traffic across several sensor nodes, and maximizes the network lifetime by reducing the energy consumption by sensor nodes. Simulation results show that our protocol achieves lower average end-to-end delay, more energy savings by sensor nodes, and less sensitive due to the impact of node failure than EQSR and MCMP protocols.

Congestion in Wireless Sensor Networks has negative impact on the Quality of Service. Congestion effects the performance metrics, namely throughput and per-packet energy consumption, network lifetime and packet delivery ratio. Reducing congestion allows better utilization of the network resources and thus enhances the Quality of Service metrics of the network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks reduces congestion by considering one hop neighbor routing in the network. This paper proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for congestion control in wireless sensor networks based on two hop neighbor information. On detection of congestion, the algorithm forwards data packets around the congestion areas by spreading the excessive packets through multiple paths. The path with light load or under loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the algorithm is to build path to the destination using two independent potential fields depth and queue length. Queue length field solves the traffic-aware problem. Depth field creates a backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential field to make dynamic decision for data forwarding. Network Simulator used for simulating the algorithm is NS2. The proposed algorithm performs better.

A Wireless Sensor Network (WSN) is composed of resource-constrained sensors with limited capabilities. Wireless Sensor Networks are subject to more packet loss and congestion. For efficient congestion control, an accurate and timely estimation of resource demands by measuring the network congestion level is necessary. To mitigate congestion, either the available resources have to be increased (resource control) or the source transmission rate should be restricted (traffic control). Most congestion control in WSNs has focused on traffic control. We proposed a Dynamic Resource Control Protocol (DRCP) to control congestion in wireless sensor networks. DRCP utilizes multiple resources to control congestion. DRCP alleviates congestion by controlling transmission power. Simulation results show the performance of the proposed protocol that improves system throughput, and decreases packet dropping, while saving energy.

Transmission rate is one of the contributing factors in the performance of wireless sensor networks. Congested network causes reduced network response time, queuing delay, and more packet loss. To address the issue of congestion, we have proposed transmission rate control methods. To avoid the congestion, we have adjusted the transmission rate at current node based on its traffic loading information. Multiclassification is done to control the congestion using an effective data science technique, namely support vector machine (SVM). In order to get less miss classification error, differential evolution (DE) and grey wolf optimization (GWO) algorithms are used to tune the SVM parameters. The comparative analysis has shown that the proposed approaches DE‐SVM and GWO‐SVM are more proficient than other classification techniques. Moreover, DE‐SVM and GWO‐SVM have outperformed the benchmark technique genetic algorithm–SVM by producing 3% and 1% less classification errors, respectively. For fault detection in wireless sensor networks, we have induced four types of faults in the sensor readings and detected the faults using the proposed enhanced random forest. We have made a comparative analysis with state of the art data science techniques based on two metrics, ie, detection accuracy and true positive rate. Enhanced random forest has detected the faults with 81% percent accuracy and outperformed the other classifiers in fault detection.

In recent years, wireless sensor networks have attracted the attention of researchers and network users. The main sensor nodes are battery-powered, which makes energy conservation a major factor in this area. Wireless sensor networks require reliability and timeliness for a successful detection of various events. To demand WSN networks, congestion is a problem and must be a bigger problem with a larger system that is capable for processing audio, video and has several base stations. Overloading base stations is a serious problem, even for simple base stations, when traffic enters base stations. This solution uses back-pressure, which reduces the rate of transmission of source nodes, eliminates less important messages, and uses planning to avoid as many collisions as possible, further complicating congestion. Too much of overloading will cause packet loss, which, in turn, will lose power and shorten the life of the node. Flow is a very important problem. This does not only affect the reliability of transportation, but also useful energy resources. We present several congestion control algorithms that aim to minimize congestion in wireless sensor networks

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.

Successful event detection in Wireless Sensor Networks (WSN) requires reliability and timeliness. When an event occurs, the base station (BS) is particularly interested about reliable and timely collection of data sent by the nodes close to the event, and the data sent by other nodes have little importance. In this paper, we propose Congestion Avoidance and Mitigation (CAM) protocol that performs the function of a routing protocol as well as the function of a transport protocol. During routing, it avoids congestion by avoiding congested routes. It also mitigates congestion by utilizing an accurate data-rate adjustment when congestion occurs. Experimental results show that CAM is capable of avoiding and mitigating congestion effectively, and performs better than similar known techniques in terms of reliable and timely event detection.