Parameter selection and performance comparison of particle swarm optimization in sensor networks localization

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors\u27 memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm

A high-accuracy and low-energy range-free localization algorithm for wireless sensor networks

Abstract As the backbone of the Internet of Things, wireless sensor networks are widely applied to perceive the physical world. Most applications need to associate perception information with a position to generate physical significance. This paper proposes WRCDV-Hop, which has quadruple improvements of the well-known DV-Hop. First, the hop count between a pair of sensor nodes is measured as a continuous value rather than a discrete value. Second, the unknown nodes calculate the average distance per hop by the weighted method. Third, each sensor node only records and relays limited but sufficient beacons of the anchors. Fourth, the unknown nodes apply the whale optimization algorithm to estimate positions. The first two improvements ensure that the distance estimation between a pair of sensor nodes is highly accurate, and the third improvement reduces the energy consumption. The last improvement makes the position estimation more precise. The simulation results show that WRCDV-Hop performs well in terms of localization accuracy and energy consumption

liveness analysis of parallel programs petri net models

Wuhan University; Huazhong Normal University; Wuhan University of Technology; National Technology University of Ukraine; Columbia UniversityThe liveness of Petri net models of parallel programs is a very important property . The existing analysis techniques take Petri net models as a whole to study properties,which is subject to the state explosion problem.In this study,we decompose a parallel programs petri net model into multiple process subnets to study liveness preservation instead of taking it as a whole, which thus mitigates efficiently the state explosion problem to some extent. In this paper, the liveness preservation relation between a MPINet and its process subnets is analyzed in detail. A necessary condition of keeping liveness of a MPINet composed of n live process subnets is given.And a class of CR-restricted MPINets is proposed.Liveness preservation can be efficiently verified based on only their net structures for CR-restricted MPINets. ©2009 IEEE

A Multi-Threading Algorithm to Detect and Remove Cycles in Vertex- and Arc-Weighted Digraph

A graph is a very important structure to describe many applications in the real world. In many applications, such as dependency graphs and debt graphs, it is an important problem to find and remove cycles to make these graphs be cycle-free. The common algorithm often leads to an out-of-memory exception in commodity personal computer, and it cannot leverage the advantage of multicore computers. This paper introduces a new problem, cycle detection and removal with vertex priority. It proposes a multithreading iterative algorithm to solve this problem for large-scale graphs on personal computers. The algorithm includes three main steps: simplification to decrease the scale of graph, calculation of strongly connected components, and cycle detection and removal according to a pre-defined priority in parallel. This algorithm avoids the out-of-memory exception by simplification and iteration, and it leverages the advantage of multicore computers by multithreading parallelism. Five different versions of the proposed algorithm are compared by experiments, and the results show that the parallel iterative algorithm outperforms the others, and simplification can effectively improve the algorithm's performance

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm

DMGA: A Distributed Shortest Path Algorithm for Multistage Graph

The multistage graph problem is a special kind of single-source single-sink shortest path problem. It is difficult even impossible to solve the large-scale multistage graphs using a single machine with sequential algorithms. There are many distributed graph computing systems that can solve this problem, but they are often designed for general large-scale graphs, which do not consider the special characteristics of multistage graphs. This paper proposes DMGA (Distributed Multistage Graph Algorithm) to solve the shortest path problem according to the structural characteristics of multistage graphs. The algorithm first allocates the graph to a set of computing nodes to store the vertices of the same stage to the same computing node. Next, DMGA calculates the shortest paths between any pair of starting and ending vertices within a partition by the classical dynamic programming algorithm. Finally, the global shortest path is calculated by subresults exchanging between computing nodes in an iterative method. Our experiments show that the proposed algorithm can effectively reduce the time to solve the shortest path of multistage graphs

Throughput oriented lightweight near-optimal rendezvous algorithm for cognitive radio networks

© 2018 In cognitive radio networks, secondary users have to dynamically search and access spectrum unused by primary users. Due to this dynamic spectrum access nature, the rendezvous between secondary users is a great challenge for cognitive radio networks. In this paper, we propose a Throughput oriEnted lightweight Near-Optimal Rendezvous (TENOR) algorithm that does not need a common control channel. TENOR has very lightweight overhead and accomplishes near-optimal performance with regard to both throughput and rendezvous time. With TENOR, secondary users are grouped into node pairs that are spread onto different channels in a decentralized manner. The co-channel interference is minimized and the throughput is near optimal. We develop a mathematical model to analyze the performance of TENOR. Both analytical and simulation results indicate that TENOR achieves near-optimal throughput and rendezvous time, and significantly outperforms the state-of-the-art rendezvous algorithms in the literature