Congestion In Sensor Networks Research Articles

Harnessing PSO and GA for Congestion Control in High-speed Wireless Sensor Networks

Sensor and sink nodes create wireless sensor networks. Traffic congestion caused by WSN data transfer causes bigger packet losses, low throughput and excessive energy consumption. This work proposes a hybrid congestion management approach using genetic algorithm and particle swarm optimization. The new approach is simulated and compared to established methods. The suggested system dramatically improved performance metrics. The suggested system of interest increased to 94% detection efficiency, 91% network lifespan, 106 J energy usage, and 22 packet loss rate. The hybrid approach avoided wireless sensor network congestion and managed traffic.

Artificial Intelligence Based Sensor Network Congestion Fuzzy Control Algorithm

The topology control of sensor sensor network was studied based on fuzzy control algorithm. Aiming at the dynamic changes of the topology of large-scale and heterogeneous artificial intelligence sensor networks and the incomplete information between nodes, a smart network-based congestion control algorithm for sensor networks was proposed and the performance of fuzzy control algorithms was analyzed. Based on this, a fuzzy control algorithm was designed. The algorithm fully considered the residual energy of nodes and the distribution of nodes in the network. Therefore, the reasonable election of the cluster head can be realized through the game between nodes, which effectively avoided energy holes, made the network energy consumption more uniform, prolonged the network life cycle, and optimized the network topology.

A Comprehensive Analysis of Congestion Control Models in Wireless Sensor Networks

Congestion control is an important factor for performance improvement in wireless sensor networks (WSNs). Congestion occurs due to various reasons including a variation in the data rate between incoming and outgoing links, buffer size, flooding attacks and multiple inputs and minimum output capability. Various outcomes of congestion in sensor networks include immense packet loss or packet drop, fast energy depletion, unfairness across the network, reduced node performance and increased delay in packet delivery. Hence, there is an extreme need to check channel congestion in order to enhance the performance with better congestion management. The job of choosing a suitable congestion control technique is a challenging task for the network designer. In this article, the authors traverse through the underlying conceptual ideas on congestion control schemes which come under six unique models. This article highlights a survey on the existing works done so far on congestion control domains in sensor networks. A comparative analysis based on Quality of Service parameters has been discussed.

Novel dynamic data aggregation scheme for WSN based intelligent vehicle systems

The data aggregation with multi-resources traffic information is a very important issue for intelligent vehicle systems (IVS). It can not only collect the most critical data from the traffic environment, but also prevent sensor network congestion. Unfortunately, we so far have no suitable solutions for WSN based IVSs. This paper presents an efficient approach for traffic data aggregation, applied in IVSs. At first, our approach adopts a hybrid network structure which is the combination of the chain structure and the unequal clusters structure. In this structure, all sensor nodes from the same cluster use parameters such as leading code and geographical position etc. to guarantee secure data aggregation. Furthermore, a method is introduced depending on creditability evaluation and reliability allocation so that the application layer can calculate aggregation results accurately, and then makes the decisions. Finally, the performance of the proposed scheme has been verified using simulation, showing that it is superior to similar protocol VLEACH (an improvement on LEACH) and ESDA (Efficient and Secure Data Aggregation protocol) such as the sensor nodes energy consumption and aggregation precision. The analysis result indicates that our scheme is effective and feasible in the next generation of sensor technologies of ITSs.

RELIABLITY CONTROL USING LOSS RECOVERY RATIO IN WIRELESS SENSOR NETWORK

In Wireless Sensor Network congestion leads to the degradation of communication links that result in the decreased the reliability and waste of energy which one of the scarcest resources of sensor network. In cluster based environment each sensor senses the information and forwarded to its cluster head and cluster head will forward that data packet towards the sink. But the cluster heads are one hop from sink node so that they acts as intermediate nodes and hence there are more chances of congestion and leads to packet drop i.e. nothing but data loss. In my model I recover this data loss by using my loss recovery model where I attach the database to the cluster heads and those packets which are drop at cluster head due to congestion are push into the database and re-transmitted to the sink with high priority.

Dynamic Service Execution in Sensor Networks

Sensor networks face a number of challenges when deployed in unpredictable environments under dynamic, quickly changeable demands, and when shared by many partners, which is often the case in military and security applications. To partially address these challenges, we present a novel target tracking algorithm that can be deployed on various sensor nodes and invoked dynamically when needed by the presence of targets. We also demonstrate that an auction-based mechanism can be used to provide efficient and localized wireless sensor network congestion management for bursty traffic of abstract services based just on user-assigned priorities to different services and the quality of information provided by the services. We present results from using this auction mechanism to resolve congestion caused by packets from competing target tracking missions.

Congestion-aware, loss-resilient bio-monitoring sensor networking for mobile health applications

Many elder patients have multiple health conditions such as heart attacks (of various kinds), brain problems (such as seizure, mental disorder, etc.), high blood pressure, etc. Monitoring those conditions needs different types of sensors for analog signal data acquisition, such as electrocardiogram (ECG) for heart beats, electroencephalogram (EEG) for brain signals, and electromyogram (EMG) for muscles motions. To reduce mobile-health (m-health) cost, the above sensors should be made in tiny size, low memory, and long-term battery operations. We have designed a series of medical sensors with wireless networking capabilities. In this paper, we report our work in three aspects: (1) networked embedded system design, (2) network congestion reduction, and (3) network loss compensation. First, for networked embedded system design, we have designed an integrated wireless sensor network hardware / software platform for multi-condition patient monitoring. Such a system integrates ECG/EEG/other sensors with Radio Frequency Identification (RFID) into a Radio Frequency (RF) board through a programmable interface chip, called PSoc. Second, for network congestion reduction, the interface chip can use compressive signal processing to extract bio-signal feature parameters and only transmit those parameters. This provides an alternative approach to sensor network congestion reduction that aims to alleviate ?hot spot? issues. Third, for network loss compensation, we have designed wireless loss recovery schemes for different situations as follows. (1) If original sensor data streams are transmitted, network congestion will be a big concern due to the heavy traffic. A receiver-only loss prediction will be a good solution. (2) If the signal parameters are transmitted, the transmission loss mandates a 100% recovery rate. We have comprehensively compared the performance of those schemes. The proposed mechanisms for m-health system have potentially significant impacts on today's elder nursing home management and other mobile patient monitoring applications.

TARA: Topology-Aware Resource Adaptation to Alleviate Congestion in Sensor Networks

Network congestion can be alleviated either by reducing demand (traffic control) or by increasing capacity (resource control). Unlike in traditional wired or other wireless counterparts, sensor network deployments provide elastic resource availability for satisfying the fidelity level required by applications. In many cases, using traffic control can violate fidelity requirements. Hence, we propose the use of resource control: increasing capacity by enabling more nodes to become active during periods of congestion. However, a naive approach to increase resources without a careful consideration of the type of congestion, traffic pattern, and network topology make the situation worse. In this paper, we present TARA, a topology-aware resource adaptation strategy to alleviate congestion. The core of TARA is our capacity analysis model, which can be used to estimate capacity of various topologies. Detailed performance results show that TARA can achieve data delivery rate and energy consumption that is close to an ideal offline resource control algorithm.

Sink-to-sensors congestion control

The problem of congestion in sensor networks is significantly different from that of conventional ad-hoc networks and has not been studied to any great extent thus far. In this paper, we focus on providing congestion control from the sink to the sensors in a sensor field. We identify the different reasons for congestion from the sink to the sensors and show the uniqueness of the problem in sensor network environments. We propose a generic framework that addresses congestion from the sink to the sensors in a sensor network. Through ns2 based simulations, we evaluate the proposed approach and compare its performance with three baseline approaches.