Throughput Optimization for Mobile Sensor Networks under Rayleigh Multipath Fading (original) (raw)
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PSO based Throughput Optimization for Mobile Sensor Networks
This paper addresses the problem of optimal route selec- tion and throughput optimization using heuristic computation in realistic communication environments for mobile sensor networks. The strategy is developed in two phases: communication aware optimization that se- lects the optimal route by using communication quality as the metric for routing decision; and position aware optimization that identifies the bottleneck link on the selected route, then enables route reconfiguration by relocating the bottleneck involved relay to the optimal position with higher throughput. The optimal position is found using particle swarm optimization and mobility of the relay is governed by a feedback control. We demonstrate through simulations that proposed strategy provides better routes in terms of end-to-end throughput as compared to other routing strategies based on ideal communication models.
Particle Swarm Optimization Based QoS Aware Routing for Wireless Sensor Networks
Efficiency in a Wireless Sensor Network can only be obtained with effective routing mechanisms. This paper uses Particle Swarm Optimization (PSO, a metaheuristic algorithm to perform the process of routing. Since PSO does not have a defined fitness function, it is flexible to incorporate user defined QoS parameters to define the fitness function.
International Journal of Intelligent Systems Design and Computing, 2018
Power and resource limitations of the sensor nodes, the possibility of packet loss and delay are the requirements should be considered while designing routing protocol for the wireless sensor network. To meet and achieve these requirements, several routing techniques have been proposed. Clustering-based routing protocol puts a network structure to satisfy energy efficiency, stability and scalability of the network. In such protocols, the network is organised into clusters in which one node will be selected as a cluster head for the cluster. Selecting cluster head and forming the clusters are the key issues in these protocols, as a result, many routing protocols-based clustering have been proposed. With the objective of solving of these issues, reducing the energy consumption and extending the lifetime of the network, in this paper, energy efficiency-based clustering and particle swarm optimisation (EECPSO) method is proposed. EECPSO performance is evaluated and justified through extensive analysis, comparison and implementation. The results show that the proposed method is highly efficient and effective in term of balancing the consumption of energy and prolonging network lifetime.
A Novel Routing Algorithm for Wireless Sensor Network Using Particle Swarm Optimization
IOSR Journal of Computer Engineering, 2012
Wireless sensor network is becoming a progressively Important and challenging research area. Advancement in WSN enable a wide range of environmental monitoring and object tracking system. Wireless sensor networks consists of small low cost sensor nodes, having a limited transmission range and their processing, storage capabilities and energy resources are limited. We consider energy constrained wireless sensor network deployed over a region. The main task of such a network is to gather information from node and transmit it to base station for further processing. Generally, it needs a fixed amount of energy to receive one bit of information and an additional amount of energy to transmit the same. This additional amount depends on the transmission range. So, if all nodes transmit directly to the BS, then they will quickly deplete their energy. To perform routing in wireless sensor network with this limitation of low power, energy and storage capabilities is a major problem. Many solutions has been proposed where energy awareness is essential consideration for routing. The LEACH, PEGASIS, GROUP, Ant colony optimization etc has provided elegant solutions and has shown very effective results. In this paper, we have proposed a Particle Swarm Optimization based Routing protocol (PSOR) where we have taken energy efficiency as major criteria for performing routing and deriving optimized path for data forwarding and processing to base node. The PSOR generates a whole new path of routing by taking energy as fitness value to judge different path and choose best optimized path whose energy consumption is less as compared to other routing paths. We concluded with the result obtained by performing experiment on our proposed algorithm PSOR and comparing its result with Genetic Algorithm which shows better result as compared to Genetic Algorithm and the experiments performed are done using NS2 simulator.
An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm
Wireless Communications and Mobile Computing, 2022
Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.
Wireless Personal Communications, 2018
One of the most effective approaches to increase the lifetime of wireless sensor networks (WSNs), is the use of a mobile sink to collect data from sensor. In WSNs, mobile sinks implicitly help achieving uniform energy-consumption and provide load-balancing. In this approach, some certain points in the sensors field should be visited by the mobile sink. The optimal selection of these points which are also called rendezvous points is a NPhard problem. Since hierarchical algorithms rely only on their local information to select these points, thus the probability of selecting an optimal node as rendezvous point will be very low. To address this problem, in this paper, a new method called particle swarm optimization based selection (PSOBS) is proposed to select the optimal rendezvous points. By applying PSO, the proposed method is capable of finding optimal or near-optimal rendezvous points to efficient management of network resources. In the proposed method, a weight value is also calculated for each sensor node based on the number of data packets that it receives from other sensor nodes. The proposed method was compared with weighted rendezvous planning based selection (WRPBS) algorithm based on some performance metrics such as throughput, energy consumption, number of rendezvous points and hop count. The simulation results show the superiority of PSOBS as compared with WRPBS, but it increases the packet loss rate in comparison with WRPBS.
Sensors, 2020
Mobile wireless sensor networks (MWSNs), a sub-class of wireless sensor networks (WSNs), have recently been a growing concern among the academic community. MWSNs can improve network coverage quality which reflects how well a region of interest is monitored or tracked by sensors. To evaluate the coverage quality of WSNs, we frequently use the minimal exposure path (MEP) in the sensing field as an effective measurement. MEP refers to the worst covered path along which an intruder can go through the sensor network with the lowest possibility of being detected. It is greatly valuable for network designers to recognize the vulnerabilities of WSNs and to make necessary improvements. Most prior studies focused on this problem under a static sensor network, which may suffer from several drawbacks; i.e., failure in sensor position causes coverage holes in the network. This paper investigates the problem of finding the minimal exposure paths in MWSNs (hereinafter MMEP). First, we formulate th...
Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2000
Wireless sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Many issues in WSNs are formulated as multidimensional optimization problems, and approached through bio-inspired techniques. Particle swarm optimization (PSO) is a simple, effective and computationally efficient optimization algorithm. It has been applied to address WSN issues such as optimal deployment, node localization, clustering and data-aggregation. This paper outlines issues in WSNs, introduces PSO and discusses its suitability for WSN applications. It also presents a brief survey of how PSO is tailored to address these issues.
Mobile base station for wireless sensor networks using particle swarm optimization
2010
Wireless sensor networks are a family of networks in wireless communication system and have the potential to become significant subsystem of engineering applications. In view of the fact that the sensor nodes in wireless sensor networks are typically irreplaceable, this type of network should operate with minimum possible energy in order to improve overall energy efficiency. Therefore, the protocols and algorithms developed for sensor networks must incorporate energy consumption as the highest priority optimization goal. Since the base station in sensor networks is usually a node with high processing power, high storage capacity and the battery used can be rechargeable, the base station can be utilized to collect data from each sensor node in the sensing area by moving closer to the transmitting node. The main objective of this research is to propose an energy-efficient protocol for the movement of mobile base station using particle swarm optimization (PSO) method in wireless sensor networks. Simulation results demonstrate that the proposed protocol can improve the network lifetime, data delivery and energy consumption compared to existing energy-efficient protocols developed for this network.
Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks
TheScientificWorldJournal, 2015
Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-...