Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks

This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the NP-complete multicast routing problem (MRP). The main contribution is the extension of PSO from continuous domain to the binary or discrete domain. Firstly, a novel bi-velocity strategy is developed to represent possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP where 1 stands for a node being selected to construct the multicast tree while 0 stands for being otherwise. Secondly, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the OR-library (Operation Research Library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly as it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on GA, ACO, and PSO

Review of Optimization Problems in Wireless Sensor Networks

International audienc

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

A Hybrid Optimized Weighted Minimum Spanning Tree for the Shortest Intrapath Selection in Wireless Sensor Network

Wireless sensor network (WSN) consists of sensor nodes that need energy efficient routing techniques as they have limited battery power, computing, and storage resources. WSN routing protocols should enable reliable multihop communication with energy constraints. Clustering is an effective way to reduce overheads and when this is aided by effective resource allocation, it results in reduced energy consumption. In this work, a novel hybrid evolutionary algorithm called Bee Algorithm-Simulated Annealing Weighted Minimal Spanning Tree (BASA-WMST) routing is proposed in which randomly deployed sensor nodes are split into the best possible number of independent clusters with cluster head and optimal route. The former gathers data from sensors belonging to the cluster, forwarding them to the sink. The shortest intrapath selection for the cluster is selected using Weighted Minimum Spanning Tree (WMST). The proposed algorithm computes the distance-based Minimum Spanning Tree (MST) of the weighted graph for the multihop network. The weights are dynamically changed based on the energy level of each sensor during route selection and optimized using the proposed bee algorithm simulated annealing algorithm

Energy Efficient Error Rate Optimization Transmission in Wireless Sensor Network

Wireless Sensor Network is a collection of independent nodes and create a network for monitoring purposes in various scenarios like military operation, environmental operation etc. WSN network size is increasing very rapidly these days, due to large network size energy consumption is also increased and it has small battery, lifetime of network   decreases due to early death of nodes and it impact the overall system performance. Clustering is a technique for enhance the network lifetime in WSN. Here in this paper we propose a new multi-objective adaptive swarm optimization (MASO) technique for clustering and computes the maximum number of clusters, which is best suited for the network. Each cluster has cluster head and cluster members and performed the task of local information extraction. Cluster head gathers all the extracted information from member nodes and send it to the base station, where base station performed global information extraction from all the cluster head nodes and generate some useful result. In MASO technique, object is used to find the best global position for the node and compare with existing position value. If new value is better than the old value, than node moves to a new position and object update their table for this new position. We are considering error probability in transmission of data packet in one hop communication. Here obtained the results are compared with other research in terms of overall network lifetime and effect on network lifetime when the size of the network is changed. We have fine tuned the node’s decay rate and throughput of the network

A New Intelligent Approach for Optimising 6LoWPAN MAC Layer Parameters

Fairness, low latency, and high throughput with low energy consumption are desired attributes for Medium Access Control (MAC) protocols. The IEEE 802.15.4 standard defines the MAC and physical (PHY) layers standard for IPv6 over Low power Personal Area Network (6LoWPAN). When non-appropriate parameter setting is used, the default MAC parame-ters generate excessive collisions, packet losses, and high latency under high traffic when a large number of 6LoWPAN nodes being deployed. A search of the literature revealed few studies which investigate the impact of optimising these parameters to achieve high throughput with minimum latency. This paper proposes a new intelligent approach to select the optimal 6LoWPAN MAC layer parameters set, the introduced mechanism depends on Artificial Neural Networks (ANN), Genetic Algorithm (GA) or Particles Swarm Optimisation (PSO) to select and validate the optimised MAC parameters. The obtained simulation results showed that utilising the optimal MAC parameters improved 6LoWPAN network throughput by 52-63% and reduced the end-to-end delay by 54-65% in which the enhancement percentage depends on the number of deployed sensor nodes in the network

A Dynamical Relay Node placement Solution for MANETs

Network deployment in wireless networks implies the distribution of the communication nodes to improve some key operational aspects, such as energy saving, coverage, connectivity, or simply reducing the network cost. Most node placement approaches are focused on static scenarios like WSNs, where the topology of the network does not vary over time. Nevertheless, there exist certain situations in which the network node locations can continuously change. In this case, the use of special nodes, so-called Relay Nodes (RNs), contributes to supporting, maintaining or recovering communication in the network. The present work introduces a multi-stage dynamical RN placement solution to lead the RNs to their time-varying optimized positions. The approach, named Dynamical Relay Node placement Solution (DRNS), is based on the use of Particle Swarm Optimization (PSO) algorithms and is inspired by Model Predictive Control (MPC) techniques following a bi-objective optimization procedure, where both network connectivity and throughput are jointly maximized. DRNS is validated in both simulated and real environments composed of mobile robotic nodes, the results showing its goodness and operational suitability for real MANET environments