International Journal of Wireless and Mobile Computing (39 papers in press)
Expected-mode augmentation method for group targets tracking using the random matrices
by Yun Wang, Guoping Hu, Hao Zhou
Abstract: In order to improve the estimation performance of interactive multiple models (IMM) tracking algorithm for group targets, a new EMA-VSIMM tracking algorithm is proposed in this paper. Firstly, by using the expected-mode augmentation (EMA) method, a more proper expected mode set has been chosen from the basic model set of group targets, which can make the selected tracking models match up to the unknown true mode availably. Secondly, in the filtering process of variable-structure interactive multiple model (VSIMM) approach, the fusion estimation of kinematic state and extension state have been implemented by using classical weighting method and scalar coefficients weighting method, respectively. We use the trace of the corresponding covariance matrix of extension state to calculate the weight coefficient. We calculate the prediction value of the extension state parameter by using a fuzzy reasoning approach to improve the estimation accuracy of the covariance matrix, which takes the elliptical area of extension and its change ratio as the input of the fuzzy controller. The performance of the proposed EMA-VSIMM algorithms is evaluated via simulation of a generic group targets manoeuvring tracking problem.
Keywords: interactive multiple models; expected-mode augmentation; group targets; maneuvering tracking.
A signal fading-aware and data transmission estimation assisted handover mechanism in LTE Networks
by Fu-Min Chang, Hsiu-Lang Wang, Wei-Hong You, Shang-Juh Kao
Abstract: In Long-Term Evolution (LTE) networks, the connection between user equipment and base station may be lost when the user equipment moves out of the signal coverage by its service base station, and this results in data loss. This paper proposes a radio signal fading-aware and data transmission estimation assisted handover mechanism in LTE networks in order to decrease the number of unnecessary handovers and data loss rate. Signal fading-aware mechanism is triggered to filter out interfered signal caused by fading in order to obtain the expected signal strength and thereby determine whether handover should be performed. The amount of data loss associated with signal fading is compared with the data loss associated with the handover in order to determine whether the handover procedure should be executed. Thus, if the user equipment performs a handover that is not triggered by signal fading, the handover wait time can be bypassed, whereas in the event of signal fading, handover is performed with the aim of minimising losses in data transmission. Simulation results demonstrate that 30% average handover times and 8% data loss rate are reduced, compared with standard handover schemes.
Keywords: LTE networks; handover; data sharing; signal fading; data transmission estimation.
Optimum path routing algorithm using ant colony optimisation to solve the travelling salesman problem in wireless networks
by Ankit Verma, Jay Shankar Prasad
Abstract: This paper proposes an enhanced clustering ant colony routing modified algorithm for use in solving standard difficulties of the travelling salesman problem of wireless networks while keeping quality parameters such as optimum time, optimum distance and optimum path designed for comparison with other algorithms. The algorithm works on the field of ant colony optimisation, which is a vast area in swarm intelligence that is widespread for solving complex problems of information technology. The algorithm selects the optimum route by keeping the optimum time and optimum distance as major parameters, and solves the principles of optimum path routing.
Keywords: optimum path routing; wireless sensor network; mobile computing; travelling salesman problem.
Improvement and analysis on the experiment of the third harmonic suppression in the low-voltage distribution power network
by Liu Jiandong, Xin-quan Zhu, Wei Zhanhong, Wang Yequan, He Yihong
Abstract: In order to deal with the problem of the growth of harmonic amount in the low-voltage distribution power network, this paper takes a 315KVA transformer distribution network to design an experimental scheme for retraining the third harmonic amount in the three-phase four-wire system. The scheme can limit the third harmonic under the condition of three-phase four-wire system and measure the impact of the limitation on current, voltage, current distortion rate, active power and distortion power. Experimental results show that limiting the third harmonic current can notably reduce the distortion power of low-voltage distribution power system, thus the electricity quality is improved, but there's no significant power-saving effect.
Keywords: third harmonic; neutral harmonic filter; harmonic suppression; power saving; power quality.
A comparison of different transfer functions for binary version of the Grey Wolf Optimiser
by Shuqin Wang, Gang Hua, Guosheng Hao, Chunli Xie
Abstract: The Grey Wolf Optimizer (GWO) is the recently proposed metaheuristic algorithm inspired by grey wolves. The original version of GWO was proposed to solve problems in continuous search spaces. However, there are many optimisation problems in discrete binary search spaces, such as feature selection and dimensionality reduction. We applied eight transfer functions in our binary GWO and compared their performances. The test functions whose minimum points are obtained at 0 are selected as benchfunctions, but also those functions with minimum values at 1.
Keywords: Grey Wolf Optimiser; binary optimisation; transfer function.
Learning automata based multi-flow opportunistic routing algorithm in wireless mesh networks
by Debasis Das, Amritesh Kumar
Abstract: Multi-hop Wireless Mesh Networks (WMNs) is a promising new technique for communication with routing protocol designs being critical to the effective and efficient use of these WMNs. A common approach for routing traffic in these networks is to select a minimal distance from the source to the destination, as in wireless networks. Opportunistic Routing (OR) makes use of the broadcasting ability of wireless networks and is especially very helpful for WMN because all nodes are static. Our proposed scheme of Multicast Opportunistic Routing (MOR) in WMNs is based on the broadcast transmissions and Learning Automata (LA) to expand the potential candidate nodes that can aid in the process of retransmission of the data. The receivers are required to be in sync with one another in order to avoid duplicated broadcasting of data, which is generally achieved by formulating the forwarding candidates according to some LA-based metric. The most useful aspect of this protocol is that it intelligently learns from past experience and improves its performance. The results obtained via this approach of MOR show that the proposed scheme outperforms existing schemes and is an improved and more effective version of OR in a mesh network.
Keywords: wireless networks; opportunistic routing; routing metric; candidate selection algorithms; candidate coordination.
A fast search strategy to optimise path founding in big data graph computing environments
by Wentian Qu, Dawei Sun
Abstract: In big data graph computing environments, the shortest path problem is very important and widely applied in various scenarios. But sometimes, in order to find the shortest path, there is a lot of cost. So, weighing the various aspects of the problems, its a good choice to find optimal path. In this paper, an improved A-star algorithm is proposed to define the optimal path. A-star algorithm has two kinds of parameter, actual cost and estimated cost, and the second one plays an important part in the algorithm. Based on the traditional A-star algorithm, this paper will propose new parameters and improved heuristic function to enhance the performance of the A-star algorithm. Compared with the traditional A-star algorithm, the simulation experiment shows that the improved A-star algorithm runs in a highly efficient manner. Two factors that affect the best situation are also discussed in the end of this paper.
Keywords: improved A-star; optimal path; estimated cost; graph computing; big data.
Novel authentication mechanism for checking node reliability in web vehicular ad hoc networks
by M. Milton Joe, B. Rama Krishnan
Abstract: Web Vehicular Ad Hoc Networks (WVANET) provide efficient communication performance among the nodes through web technology. This recently introduced WVANET communication model should be enhanced with various security parameters to protect the architecture from security threats. The fundamental security concern in any ad hoc network is authentication. Every vehicle in the WVANET communication architecture should be authenticated for checking the reliability of the nodes efficiently to secure the communication from malicious nodes. In WVANET communication architecture, it is possible for malicious nodes to steal the identity of other nodes to broadcast false information among the nodes in order to collapse the network communication. However, it has been observed that no research on authentication for checking the reliability of the nodes has been carried out in WVANET so far. For the first time, as an initial step, this paper proposes an efficient authentication mechanism to authenticate the nodes WVANET. The proposed authentication mechanism functions with the combination of web-based Road Side Units (RSU) known as WiMAX tower and the authentication centre. The authentication centre assigns the authentication parameter to each node within the network topology and all the vehicles are authenticated in the background at the regular periodic internal time. The assigned authentication parameter would change as the vehicle overtakes another vehicle and the new parameter will be assigned by the authentication centre. All the assigned authentication parameters for each node will be stored in the authentication centre for authenticating the nodes for its trustworthiness. In WVANET, authentication is carried out as three phases, which ensures that the proposed authentication mechanism is secure enough and if a malicious node is found the particular vehicle is temporarily blocked from the communication architecture and such a node is monitored for its further behaviour.
Keywords: web vehicular ad hoc network; VANET; authentication; security; WiMAX; RSU; OBU.
Capacity of dual-branch MRC system over correlated Nakagami-m fading channels with non-identical fading parameters and imbalanced average SNRs
by Pukhrambam Bijaya Devi, Amujao Yengkhom, Mohammad Irfanul Hasan, Gitanjali Chandwani
Abstract: We present the closed-form expressions for the capacity with correlated, non-identical fading parameters and imbalanced average SNRs (signal-to-noise ratio) of dual-branch MRC (Maximal Ratio Combining) diversity system over Nakagami-m fading channels. This capacity is evaluated for ORA (optimum rate adaptation with constant transmit power) and CIFR (channel inversion with fixed rate) schemes. Numerical results have been presented and compared with the available capacity results of ORA and CIFR schemes in the literature. The effect of different practical constraints, e.g. non-identical fading parameters, fade correlation and level of imbalanced in average SNRs, on the channel capacity of the systems is analysed.
Keywords: channel capacity; channel inversion with fixed rate; dual-branch; imbalanced; maximal ratio combining; optimum rate adaptation with constant transmit power.
Group maintenance strategy for stochastic selective maintenance
by Jianchao Zeng, Jing Zhao
Abstract: Selective maintenance is often applied to many industrial environments in which the maintenance actions are performed between sequence missions. When the length of maintenance or work mission time is stochastic and there are multiple maintenance workers with different capacities, previous works that determine maintenance and mission time are unsuitable. Assuming that the time of work mission is random and maintenance time is determined, to maximise system reliability of the next work mission, this paper proposes a stochastic model for a two-state system comprising several series-parallel components with multiple maintenance workers under the maintenance time limit. The optimal maintenance plan is obtained by hybrid intelligent optimisation algorithm. The validity and feasibility of the model are verified by the simulation.
Keywords: stochastic model; stochastic selective maintenance; mission time; maintenance time; group maintenance.
A layer-dividing task cooperation model of multi-robots
by Wu Chun-ying, Ke Wen-de, Li Xiao-guang
Abstract: To tackle the problems of dynamic relative mental layer, learning ability and moving time optimisation in the cooperation of multi-robots, a layer-dividing task cooperation model is proposed. First, the changing features of the mental layer in a dynamic environment is shown through the belief, target, intention and knowledge. Secondly, the strategy learning and task planning under the condition of maximum united rewards is constructed by combining the reinforcement learning and particle swarm optimisation. Thirdly, the robots fast moving method is designed based on the basic movement states and Bezier curve track. The validity of model is proved through experiments.
Keywords: multi-robots; cooperation; layer division; learning.
A dual population based firefly algorithm and its application on wireless sensor network coverage optimization
by Gan Yu
Abstract: Firefly algorithm (FA) has shown good performance on many engineering optimisation problems. Recent studies have pointed out that FA suffers from slow convergence. To enhance the performance of FA, this paper presents a dual population based FA (called DPFA). In DPFA, the entire population consists of two subpopulations. A memetic FA (MFA) and the standard differential evolution are used to generate new solutions in different subpopulations. To verify the performance of DPFA, we test it on nine benchmark functions. Simulation results show that DPFA outperforms MFA and another improved FA algorithms. Finally, we use the proposed DPFA to solve the wireless sensor network coverage optimisation problem. Results show that DPFA can also achieve promising solutions.
Keywords: firefly algorithm; dual population; wireless sensor network optimisation; global optimisation.
An adaptive firefly algorithm for blocking flow shop scheduling problem
by Wenjun Wang
Abstract: In this paper, we present an adaptive firefly algorithm (AFA) for solving the blocking flow shop scheduling problem (BFSSP). It is known that the basic firefly algorithm (FA) works on continuous search space, while the BFSSP is a discrete problem. To handle discrete variables, the smallest position value (SPV) rule is employed. An adaptive parameter strategy is used to reduce the dependence on parameters. Furthermore, two local search operators are used to improve the quality of solutions. To save computational time, a random attraction model is used to decrease the number of attractions among fireflies. Experiments are conducted on a set of Taillard's benchmark instances. Simulation results show that the proposed AFA achieves better solutions than the basic FA and four other algorithms.
Keywords: firefly algorithm; adaptive parameters; random attraction model; flow shop scheduling problem; discrete optimization.
Using GSW FHE to provide stronger privacy protection for searchable encryption
by Huaiyu Zheng, Baojia Zhang
Abstract: Nowadays, more and more people choose to encrypt their data, and then outsource it to the server with increasingly strong awareness of privacy protection. But how to search the encrypted data is a challenging problem in this situation. Many people study this problem, not only for the reason of the fundamental property of this question, but also for a practical issue that the volume of data is so big that it is impractical for users to download the whole data and search on it. One popular solution is to build a inverted index using special algorithm, and support searchable algorithm on the encrypted data. But this causes another problem: the privacy will be leaked to the data owner when the owner and user are not the same person. In this paper, we design a novel scheme to solve this problem. We provide stronger privacy protection for users when they search on the data, the scheme can protect the privacy of inverted index table and content of data. Besides,the data owner cant get any information about the query messages searched by users. Also,we analyse the security of our scheme, and simulation demonstrate the good performance of our scheme.
Keywords: fully homomorphism encryption,searchable encryption,cloud computing; privacy.
An improved memetic differential evolution for college students comprehensive quality evaluation
by Yuan Wang, Zhenguo Ding, Mingchen Zuo, Lei Peng
Abstract: The evaluation for the comprehensive quality of college students is a key problem in the management of college student affairs. In this paper, we present an improved memetic differential evolution algorithm to get the best weights of the College Students Comprehensive Quality Evaluation (CSCQE) problem. The proposed algorithm, called Uniform Memetic Differential Evolution (UMDE), hybridises differential evolution (DE) with a local search (LS) operator and a periodic uniform design re-initialisation scheme to balance the exploration and exploitation. UMDE is compared with five well-known evolutionary algorithms on 21 benchmark functions. The results show that UMDE can obtain better, or at least comparable, results than the compared algorithms. And then, UMDE is used to solve CSCQE problem. The results show that UMDE can find better weights of the index system.
Keywords: differential evolution; uniform design; local search; comprehensive quality evaluation.
Firefly algorithm with dynamic attractiveness model and its application to wireless sensor networks
by Wang Jing
Abstract: Firefly algorithm (FA) is a new population-based meta-heuristic algorithm which has outstanding performance on many optimisation problems. However, in standard FA, the attractiveness will quickly approach to a constant in the middle period of the iterations. It may be very detrimental to the search ability of the algorithm. So we proposed a new variant FA(DFA) with a dynamic attractiveness model, which can linearly adjust the rate of change of attractiveness as the number of iterations grows. Thirteen well-known benchmark functions are used to verify the performance of our proposed method, and the computational results show that DFA is more efficient than many other FA algorithms. We also successfully used DFA to solve the wireless sensor network node distribution optimisation problem. The results of the coverage statistics further validate the effectiveness of the proposed algorithm.
Keywords: firefly algorithm; meta-heuristic algorithm; global optimization; wireless sensor network; node distribution optimisation.
Artificial bee colony with dynamic updating strategy and its application on node localisation
by Xiaoman He, Yijun Liu
Abstract: There are many optimisation problems in the real world. To solve these problems, different intelligent optimisation techniques/algorithms have been developed in the past several years. Among these algorithms, artificial bee colony (ABC) has received much attention. Compared with other similar algorithms, ABC has fewer control parameters. However, ABC shows poor local search ability and slow convergence rate. In this paper, we propose a novel ABC variant, called DyGABC, which employs two strategies including the global best solution guided solution model and a dynamic model for dimension updating. Experiments on 12 optimisation problems show that our DyGABC is better than the standard ABC and another improved ABC algorithm. In addition, DyGABC is used to improve the accuracy performance of node localisation in wireless sensor networks. Simulation results demonstrate that our approach is better than the original distance vector-hop (DV-Hop) algorithm.
Keywords: artificial bee colony; global best; dynamic updating model; optimisation; continuous optimisation.
A sine cosine mutation based differential evolution algorithm for solving node location problem
by Chong Zhou, Liang Chen, Zhikun Chen, Xiangping Li
Abstract: Differential Evolution Algorithm (DE) is known in evolutionary computation. However, DE with DE/best/1 mutation has some drawbacks, such as premature convergence and local optimum. To address these drawbacks, we improve the DE/best/1 mutation operator and propose a sine cosine mutation based differential evolution algorithm, named SCDE. In the proposed method, a new sine cosine mutation operator inspired by sine cosine algorithm (SCA) is adopted to balance exploration and exploitation. In the experimental simulation, the proposed algorithm is compared with three state-of-the-art algorithms on the well-known benchmark test functions. The results of test functions and performance metrics show that the proposed algorithm is able to avoid local optima and converge towards the global optimum. In addition, the proposed algorithm is used to solve sensor node location in wireless sensor network. Results show that our algorithm is effective.
Keywords: differential evolution algorithm; sine cosine algorithm; sine cosine mutation; local optimum; sensor node location problem.
Research on HAS video QoE assessment technology
by Wen-ming Zhu, Qi Hong
Abstract: As internet video applications, especially HTTP Adaptive Streaming (HAS), have gained popularity all around the world, monitoring and assessment technologies of video related quality of experience (QoE) have gained in importance during the last few years. The paper introduces the system architecture and procedures that support HAS video application, and analyses the difference of QoE technologies between traditional streaming video and HAS video. It also reviews the QoE models for current video services, and spots the possible future research directions and challenges, from the points of view of client, network and server, respectively, on how HAS QoE would be built and applied in practical employment.
Keywords: HAS; QoE; HTTP adaptive streaming media.
Reviews and challenges on applying status of the big data technology in the publishing industry
by Yeli Li, Xindong You, Likun Lu, Chuan Zhu
Abstract: The big data era is here, which will make or is making an impact on every walk of our life. The publishing industry is one of the technology intensive industries. The papermaking technology, the printing technology and the internet technology have profoundly impacted it once before. Therefore, the big data technology is making and will make a tremendous impact on the publishing industry, and many publishing industries have applied the big data technology on the different production links during publishing and gain its profit. In this paper, we summarise the current application status of big data technology in the different types of publishing industry in China and abroad (the mass publishing industry, the education publishing industry, the academic speciality publishing industry, news and press publishing industry, new-pattern internet publishing industry). We analyse how they use the big data technology to convert or remake the publishing procedures successfully. We identify the different features of the different publishing industry types, and the different application emphasis on the different links. The possibility of using for references each other is also be discussed. Reviews on the application status of the big data technology will clarify the process to employ the technology on transforming the procedures of the publishing industry, and provide ideas and methods to the publishing industry to use the advantages of the technology.
Keywords: big data; application status; publishing industry; publishing link; publishing procedure.
Firefly algorithm guided by general centre particle and its application in node localisation of wireless sensor networks
by Li Lv, Hongmin Tian, Jia Zhao, Zhifeng Xie, Tanghuai Fan, Longzhe Han
Abstract: Firefly algorithm (FA) is a kind of swarm intelligence algorithm that was developed by simulating the fireflies glowing characteristics. FA shows simple model and high optimisation efficiency and is easily implemented, which now has been widely applied in various optimisation problems. During the evolution process, each firefly would learn from the other fireflies with better performances based on full-attraction model, and thus, FA is strongly targeted and characterised by fast convergence. Nevertheless, only the particles (i.e., the fireflies) with better performances were used, while the particles with poor performances were generally discarded; in particular, the optimal firefly in a population would also be discarded since it showed no information for learning, which would greatly reduce FAs learning and detection abilities. This article proposes an improved FA, namely, firefly algorithm guided by general centre particle (GCPFA), in which the general centre particle (GCP) is generated by sharing each particles historically optimal position information, and each particle would learn from GCP after it learns from the other particles with better performances. Accordingly, after the introduction of GCP, the information sharing and communication among populations are enhanced and the optimisation accuracy is also improved. The simulation results on 12 benchmark test functions also revealed GCPFAa superiority to the other six famous FAs. In order to improve the unreasonable distribution of sensor nodes randomly and improve the network coverage rate, the above algorithm is applied to optimise the coverage of wireless sensor networks and achieve better optimisation effect
Keywords: firefly algorithm; general cente particle; information sharing; guidance.
A hybrid harmony search algorithm for node localisation in wireless sensor networks
by Zhaolu Guo, Shenwen Wang, Baoyong Yin, Songhua Liu, Xiaosheng Liu
Abstract: Harmony search (HS) has been widely used in the field of wireless sensor networks. However, the search strategy of the basic HS has excellent exploration capability but weak exploitation capability. To enhance the search capability of HS, this paper presents a hybrid harmony search algorithm (HBHS) for node localisation in wireless sensor networks. The proposed HBHS employs the best solution to enhance the exploitation capability. Moreover, HBHS uses an adaptive search step-size scheme to further enhance the search capability. To verify the search performance, HBHS is compared with two HS algorithms on a suite of classical benchmark problems. The comparisons confirm that HBHS can achieve better performance than the compared HS algorithms on the most of the benchmark problems. Further, HBHS is applied for node localisation in wireless sensor networks.
Keywords: wireless sensor networks; localisation; harmony search; hybrid strategy.
Distribution route multi-objective optimisation of dangerous goods considering the time reliability
by Ruichun He, Changxi Ma, Xiaoyan Jia, Qiang Xiao, Lei Qi
Abstract: In view of the increasingly prominent security issues of dangerous goods transportation, a distribution route optimisation model of dangerous goods is proposed. Firstly, the travel time reliability model and transportation risk model are established. Besides, the multi-objective optimisation model of dangerous goods route is built based on the travel time reliability. Secondly, a genetic algorithm is designed to solve the optimisation model. Finally, a numerical case is used to verify the feasibility and effectiveness of the model and algorithm. Study results show that the distribution route optimisation model of dangerous goods has an important theoretical value and practical significance of reducing the accident risk, increasing the time reliability, and ensuring dangerous goods transportation safety.
Keywords: distribution route; optimisation; genetic algorithm; dangerous goods transportation.
Rapid identification model of mine water inrush sources based on extreme learning machine
by Ya Wang, Mengran Zhou, Pengcheng Yan, Feng Hu, Wenhao Lai, Yong Yang, Yanxi Zhang
Abstract: In the process of disaster prevention of coal mine water inrush, it is necessary to quickly and accurately identify the types of water inrush sources. Based on the high sensitivity, rapid and accurate monitoring characteristics of laser-induced fluorescence technology, the fluorescence spectra of water samples were collected on the experimental platform of water sample detection. After pre-processing spectra and extracting features, the multi-classification learning model is established by the extreme learning machine (ELM) algorithm. In this paper, we determine the sigmoid function as a hidden layer activation function, and obtain the optimal number of hidden layer nodes by the method of cross-validation. ELM is compared with the conventional neural network classification model in different parts, such as the average time and the average classification accuracy. The average classification accuracy of ELM combined with principal component analysis is about 98% and 93% in the training and testing sets, respectively. The classification learning time is greatly improved. Therefore, the model is more suitable for rapid and accurate classification of water inrush sources.
Keywords: mine water inrush; water sources identification; laser induced fluorescence spectra; principal component analysis; extreme learning machine.
DRSO-EGSM: data replication strategy oriented to automatic energy gear-shifting mechanism
by Xindong You, Yeli Li, Zhenyang Zhu
Abstract: Recently, more and more attention has been paid on research of cloud storage system. However, with the rapid growth of data volume, the problems of high energy consumption and low efficiency have become the key factors for restricting its development. In order to provide better support for the energy gear-shifting mechanism, we designed an energy effective data replicating strategy (DRSO-EGSM). First, data is classified into two categories, based on which the number of replicas is determined. Then the replica placement strategy oriented to energy efficiency is designed. Corresponding performance model and energy consumption model are established for data partitioning strategy. Meanwhile, the energy effectiveness of the DSRO-EGSM is evaluated in GridSim simulator. Compared with current existing similar mechanisms TDCS and the default replication strategy of HDFS, substantive results obtained from simulation experiments show that DSRO-EGSM can have more advantages in reducing energy consumption while guaranteeing the system performance.
Keywords: data classification; data replication; energy efficiency; cloud storage system; energy management.
Quantum flower pollination algorithm for optimal multiple relay selection scheme
by Hongyuan Gao, Yanan Du, Shibo Zhang
Abstract: In order to obtain optimal selection scheme with multiple source-destination pairs and multiple potential relays in cooperative relay networks, an optimal relay selection scheme is designed in cooperative relay networks considering co-channel interference (CCI). It is an integer optimisation problem for selecting suitable relay nodes to obtain optimal system performance. To get the optimal estimation performance with low computing complexity, a quantum flower pollination algorithm (QFPA) is proposed to resolve the difficulties of optimal relay selection, and the proposed optimal relay selection scheme is called as quantum flower pollination algorithm based optimal relay selection (QFPA-ORS). The proposed QFPA combines the advantages of quantum evolutionary theory and flower pollination algorithm (FPA). So QFPA has the capability to search the optimal solution. Simulation results show that QFPA-based relay selection scheme has the ability to search global optimal solution compared with other relay selection schemes based on classical algorithm, and performs better than three other previous intelligent algorithms.
Keywords: cooperative relay networks; multiple relay selection; quantum flower pollination algorithm; co-channel interference.
An enhanced virtual backoff algorithm for wireless sensor networks
by Ramesh Babu Palamakula, Venkata Krishna Parimala, Naga Raju Dasari, Saritha Vankadara, M. Pounambal
Abstract: In wireless sensor networks, the MAC layer plays a crucial role in the performance enhancement. Therefore, this paper develops an enhanced virtual backoff algorithm to route the packets to the queue based on the delay as well as ant colony optimisation algorithm for selecting the best nodes from the available nodes. The node counters are maintained for each node to calculate the success rate, and a sleep schedule is introduced to reduce the energy consumption and to improve the lifetime of the network. The experimental results proved that the proposed model is efficient in preserving the energy, decreasing the node collisions and improving the packet delivery ratio.
Keywords: bioinspired mechanism; virtual backoff algorithm; node collision and channel allocation.
Adaptive fuzzy sliding mode control algorithm simulation for 2-DOF articulated robot
by Feng Du, Ying Sun, Gongfa Li, Guozhang Jiang, Jianyi Kong, Du Jiang, Zhe Li
Abstract: Chattering phenomena occur in conventional sliding mode control algorithm of articulated robots. Therefore a robotic adaptive fuzzy sliding mode control algorithm is proposed, designed with an adaptive single input and output fuzzy system to calculate the control gain. Meanwhile, the mathematical proof is carried out on systems stability and astringency by Lyapunov theorys adaptive law. The adaptive fuzzy sliding mode control is simulated in Simulink, and the simulation results show that when the chattering becomes weaker, the function of the system is promoted. The fuzzy sliding mode controller can be changed and adjusted with the transformation of the systems state. Steady-state convergence is constant, and the adaptive fuzzy sliding mode control algorithm retains good robustness under the conditions that the articulated robots parameters are uncertain and there is external interference.
Keywords: adaptive fuzzy sliding mode control; articulated robot; chattering phenomena; position tracking.
On exploiting MIMO technology for achieving maximum detection probability and mitigating interference in tri-sectored cooperative cognitive radio networks
by Javaid A. Sheikh, Mehboob Ul Amin, Shabir A. Parah, Subba Amin, Ghulam M. Bhat
Abstract: The exponential use of wireless applications has put a lot of constraints on the already over-crowded spectrum which is an important resource. Because a fixed spectrum assignment has led to under-use of spectrum, a great portion of licensed spectrum is not effectively used. This fixed spectrum assignment is one of the major hurdles faced by telecom operators, resulting in poor system performance. To overcome this looming spectrum scarcity, a team of 3GPP is examining the incorporation of cognitive radio in wireless network systems. The main focus has been on cooperative technique based spectrum sensing. A tri-sector based technique has been used to mitigate the interference during spectrum sensing in cognitive radio. In this paper we propose a cooperative framework in which we consider a tri-sectored cellular network served by a base station (primary transmitter) and two types of user (primary users and secondary users). Multiple antennas are placed on both primary users as well as secondary users to enhance the sensing probability of the target. The spectrum is sensed for different combinations of users and a Power Spectral Density(PSD) graph is plotted for each combination. To evaluate the performance of the whole system, the detection probability of the target is calculated for each MIMO configuration and compared with a threshold value. The results shown in this paper prove that the use of MIMO antennas increases the probability of detection and decreases the probability of missed detection of the target. Thus, this technique increases the chance to exploit spectrum resources more efficiently, so that the spectrum scarcity problem is alleviated. We have also calculated capacity and Signal-to-Interference-Noise-Ratio (SINR) for the tri-sectored cellular model, and the CDF plot depicts the enhancement of capacity and SINR for the proposed model. The results have been verified in terms of various graphs and plots besides some tables.
Keywords: cognitive radio; probability of detection; probability of missed detection; primary user; secondary user; multiple-input-multiple-output; MIMO.
Multi-strategy artificial bee colony based on multiple population for coverage optimisation
by Hui Sun, Haihua Xie
Abstract: In order to overcome the shortcomings of weak local search ability and slow convergence speed for the standard artificial bee colony algorithm, this paper proposes an improved multi-strategy artificial bee colony algorithm based on multiple populations (IMSABC). Firstly, the employed bees are randomly divided into three subgroups, corresponding to three evolutionary strategies. If the candidate solution obtained from searching is inferior to the current honey source, the bee is randomly assigned to other subgroups and the search strategy is changed. In this way, it not only facilitates the information exchange between populations, but also balances the global search and local development capabilities of the algorithm since the three search strategies have different characteristics. Secondly, by imitating the particle swarm algorithm, the search strategy of the following bees is improved by using the abundant information contained in the current global optimal honey source and random neighbour honey source. The simulation results of twelve benchmark test functions and 28 CEC2013 functions show that the performance of this algorithm has significant advantages compared with many similar improved algorithms. In order to improve the unreasonable distribution of sensor nodes and improve the network coverage, the above algorithm is applied to optimize the coverage of wireless sensor networks and achieve better optimisation effect.
Keywords: artificial bee colony algorithm; multiple populations; random selection strategy.
Frequency reconfigurable textile antenna using different conducting patch and fabric substrate for 2.45 GHz and 5.8 GHz
by Sumit Charjan, R. Sreemathy, Shruti Terdalkar
Abstract: This paper discusses a reconfigurable textile antenna designed on different fabric materials. The antenna is made up of cloth that can be used for frequency reconfigurability. Many people have developed antenna with FR4, RT duriod, etc. as substrate materials, but very few people have been trying to use a textile as a substrate material for antenna development. The reconfigurable antenna is a key element in smart system design. There is a need to implement reconfigurable textile antenna. The textile antenna is easy to design and test for a different frequency. Fabric materials as a substrate used for this antenna are cotton, polyester, nylon etc. Copper adhesive tape, silver conductive ink, and conductive paper are available in the market and are used as radiating material in the design of the textile antenna. The use of cell phones and other communication devices has increased largely in recent years and they are showing the adverse effect on human body. The textile antenna is mounted on cloth that is close to the human body and may affect human tissue and organs. So, it is necessary to study whether the textile antenna is wearable or hazardous to the human body. In this paper, antennas are designed for 2.45 GHz and 5.8 GHz with frequency switching between these two frequencies. Three switches are used for frequency re-configurability When diodes are in OFF state then the antenna is tuned to 5.8 GHz and when diodes are in ON state then it is tuned to 2.45 GHz. There is a deviation of approximately 0.5GHz in tuning frequency for both the antennas as compare to the simulated result. When actual PIN diodes are mounted on patch substrate, tuning frequency for both antenna designed using FR4 and polyester substrate are shifted toward a lower frequency of 5.4 GHz and 5.23 GHz respectively during diode OFF state.
Keywords: dielectric constant; diode; reconfigurable antenna; specific absorption rate; Textiles.
Analysis on fast training speed of extreme learning machine and replacement policy
by Shi-Xin Zhao, Xi-Zhao Wang, Li-Ying Wang, Jun-Mei Hu, Wei-Ping Li
Abstract: Extreme learning machine is known for its fast learning speed while maintaining acceptable generalisation. Its learning process can be divided into two parts: (1) randomly assigns input weights and biases in hidden layer, and (2) analytically determines output weights by the use of the Moore-Penrose generalised inverse. Through the analysis from theory and experiment aspects, we point out that it is the random weights assignment rather than the analytical determination with generalised inverse that leads to its fast training speed. In fact, the calculation of generalised inverse of hidden layer output matrix based on singular value decomposition (SVD) has very low efficiency, especially on large scale data, and even directly cannot work. Considering this high calculation complexity reduces the learning speed of ELM, a conjugate gradient is introduced as a replacement for the Moore-Penrose generalised inverse, and the conjugate gradient based ELM (CG-ELM) is proposed. Numerical simulations show that, in most cases, CG-ELM achieved faster speed than ELM in the condition of maintaining similar generalisation. Even in the case that ELM cannot work because of the huge amount of data, CG-ELM attains good performance, which illustrates that the Moore-Penrose generalised inverse is not the contribution of fast learning speed of ELM from experiment view.
Keywords: extreme learning machine; generalised inverse; SVD; conjugate gradient method.
Video compression and compensation of moving target image based on redundant wavelet theory
by Gu Gong, Hua Zhu
Abstract: The redundant wavelet transform has the translation-invariant property, and motion estimation can achieve better results in the redundant wavelet domain. This paper proposes the redundant discrete wavelet transformation (RDWT) for motion estimation and compensation in video compression and compensation of moving target image, and a fast adaptive block matching motion estimation algorithm based on redundant wavelet transform. This paper also introduces the methods of image domain search, starting point prediction and motion estimation into the redundant wavelet transform, and the study method based on optical flow, which can enhance the transmission robustness with low redundancy. The experimental results show that the algorithm can effectively reduce the time required for motion estimation and improve the efficiency of motion estimation, and the subjective quality of the motion estimation is very good. The research results can provide technical support for robot vision system.
Keywords: redundant wavelet; pattern recognition; image features; time-frequency spectrum.
Evaluation of environmental performance based on SBM-Tobit two-stage model
by Minmin Teng, Chuanfeng Han
Abstract: Governments are the main institutions of environmental governance, but the investment-output results of environmental governance differ across different governments. This paper uses the SBM model to measure the static performance of environmental governance in eastern China from 2008 to 2014, and then uses Tobit regression analysis to identify factors influencing environmental performance. The results show that the environmental performance of seven provinces has a relatively large variation, and the proportion of environmental governance investment in GDP has the greatest impact on environmental performance.
Keywords: environmental governance; performance evaluation; non-expected output; SBM-Tobit model; eastern China.
The feature extraction of facial expression based on the point distribution model of improved Kinect
by Xiaofeng Cao, Shi Cheng
Abstract: In the facial expression recognition system, owing to the differences of faces, the complexity of the organ distribution and the influence of the outside factors, the facial features location becomes a challenging task. Using the Kinect point distribution, we present a facial extraction algorithm of expression feature, and carry out the related experiments. The experimental results show that the proposed methods have the significant advantage in the extracting features that are with unrelated deformation, non-sensitive to noise, or have a high degree of distinction.
Keywords: Kinect; feature extraction; facial expression; emotional computing.
Cloud outsourcing computing security protocol of matrix multiplication computation based on similarity transformation
by Shanshan Kong, Yongquan Cai, Fei Xue, Haiyang Yu, Ditta Allah
Abstract: With the emergence of the cloud computing paradigm in scientific and business applications, computation outsourcing to the cloud has become a popular application in the age of cloud computing. However, this computing paradigm brings in some new security concerns and challenges, such as input/output privacy and result verifiability. In this paper we use similar transformation of matrix design secure verifiable and practical outsourcing protocol for matrix multiplication calculation. Compared with those existing outsourcing protocols, our protocol has obvious improvements concerning both efficiency and security. In order to check the correctness of the result returned from the cloud, an efficient verification algorithm is employed. Computational complexity analysis shows that our protocol is highly efficient.
Keywords: cloud computing; outsourcing computation; secure outsourcing; multiplication calculation; verification.
A fuzzy priority-based congestion control scheme in wireless body area networks
by Faezeh Pasandideh, Abbas Ali Rezaee
Abstract: Wireless sensor network (WSN) is a technology that has many applications. In healthcare, wireless body area network (WBAN) applications, especially sensors placed on parts of the patient's body, are able to detect the patient's vital signs and transmit them to a medical centre. However, when several sensors begin to simultaneously send the data, congestion might happen. The probability of the congestion to happen is directly related to the number of sensors that constantly send data packets. This can lead to a rise in the packet loss ratio and thus a decline in the efficiency as well as the overall performance of the system, so congestion control is one of the major challenges. Congestion detection and control are essential to such systems. This study proposes a technique similar to the random early detection (RED) active queue management (AQM) to indicate congestion. A two-input-single-output fuzzy logic system is used to dynamically adjust the maximum drop probability (maxp) parameter of the RED algorithm, then two adaptive values, the minimum threshold (minth) and the maximum threshold (maxth), are used to estimate the congestion level of each sensor node. We adjust the send rate of the parent node with a fuzzy logical controller (FLC) based on the child node traffic load, considering the different amounts of data being transmitted. The simulation results show that proposed protocol achieves high performance compared with PCCP and PHTCCP, in terms of packet loss, end-to-end delay and energy.
Keywords: wireless sensor network; healthcare application; congestion control; fuzzy logic controller; active queue management.
Optimisation of the high order problems in evolutionary algorithms: an application of transfer learning
by Guosheng Hao, Gai-Ge Wang, Zhao-Jun Zhang, De-Xuan Zou
Abstract: Evolutionary Algorithms (EAs) have been applied to many optimisation problems, among which those with high order are difficult for EAs. The higher the order, the steeper the curve around the optimum is, therefore the more difficult it is. This paper introduces Transfer Learning (TL) aided EAs to conquer the high order problems more efficiently and effectively by optimum transfer from the low order problem (as source domain) to high order problem (as the target domain). The experiments validated this method by comparison of the average numbers of the convergence generation and an impressive feature was observed: this method is robust against the difficulties of the problems. This method is not only significant for high order problems, but also useful for other difficult problems by borrow optimum from other feature-similar easy problems.
Keywords: optimum; difficult problem; transfer learning; high order.
Design and optimisation of narrow dual bandpass filter using bell-shaped structure for RF receiver system
by Azman Ahmad, Abdul Rani Othman
Abstract: A novel approach for designing a narrow dual bandpass filter (BPF) using a microstrip coupled line resonator (CLR) is presented in this paper. The proposed CLR consists of butterfly radial stub and 45
Keywords: dual BPF; bell-shaped structure; microstrip coupled-line; butterfly radial stub; mitered bend.