International Journal of Wireless and Mobile Computing (28 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.
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 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.
Artificial bee colony algorithm for energy efficiency optimisation in massive MIMO system
by Fatma Bouchibane, Messaoud Bensebti
Abstract: This paper deals with antenna selection for multi-user massive MIMO systems, with the aim of maximising energy efficiency. Massive MIMO technology, by employing a large number of antennas at a base station, provides huge improvements in throughput and energy efficiency. However, the increased number of antennas leads to additional energy consumption due to RF chains and signal processing circuit. The main purpose of the paper is to determine the optimal subset of antennas at the base station that should be activated to serve a given number of active user devices. This idea is implemented using an artificial bee colony algorithm, which has proven its efficiency by specifying the best control parameters.
Keywords: 5G; massive MIMO; energy efficiency; antenna selection; artificial bee colony.
Research on the logistics robot task allocation method based on improved ant colony algorithm
by Xue Fei, Tingting Dong
Abstract: In this paper, multiple logistics robot task allocation in an intelligent warehouse system is studied. First, the task allocation model is developed based on two objectives of time balancing among logistics robots and the correlation between tasks. Then, the model is solved using the improved ant colony algorithm which innovates from the updating rule of pheromone and the setting of the heuristic function. Finally, by simulation experiment, on the one hand, the improved ant colony algorithm can be used to solve the logistics robot task allocation and get a task allocation scheme. On the other hand, the validity of the improved ant colony algorithm can be verified by comparing the improved and original ant colony algorithms.
Keywords: task allocation; logistics robot; intelligent warehouse system; ant colony algorithm.
A improving clustering algorithm for order batching of an e-commerce warehouse system based on logistic robots
by Xue Fei, Tingting Dong, Zixiang Qi
Abstract: In this paper, the batching model and strategy of orders in an e-commerce warehouse system based on logistic robots are studied. First, different order-picking patterns are put forward by analysing the operation process of logistic robots in the e-commerce warehouse system. Then the order-batching model is established based on the two objectives of the minimisation of the total picking and travelling time of logistic robots and the minimisation of the longest picking time used among all the picking stations. The model is solved using the improved clustering algorithm. Finally, the results show that the picking pattern of batching first and combining last has the advantages of higher put-out-storage efficiency by simulating experiment and the comparison analysis of order-picking efficiency corresponding to different order-picking patterns.
Keywords: logistic robots; e-commerce warehouse system; order batching; clustering algorithm.
Demand forecasting for transportation service network of food cold chain based on a combined model of trend double exponential smoothing and improved grey methods
by Xing Xu, Ren-wang Li, Yun Zhao, Xin-li Wu, Timo Nyberg
Abstract: In a competitive market, the accurate forecasting of short-term transport demand is critical to the transportation service network of a food cold chain. In this paper a model that combines trend double exponential smoothing and improved grey forecasting methods is proposed to predict the short-term cold chain transport demand of transportation service networks of a food cold chain, showing changes in trends and seasonal fluctuations having irregular periods. The combined model is constructed to fit the changing trends and the featured seasonal fluctuation periods. In order to improve forecasting accuracy and model adaptability, the combined model is modelled repeatedly to fit the remnant tail time series of the main combination model until forecast accuracy is achieved. The modelling approach is applied to the freight companies engaged in the transportation of the food cold chain in China. The results demonstrate that the proposed modelling approach produces acceptable forecasting results and goodness of fit, also showing good model adaptability in an uncertain environment. This fact makes the modelling approach an option for predicting the short-term transportation demands of the food cold chain transportation service network.
Keywords: demand forecasting; transportation service network; combination model; time series analysis.
PAPR reduction in OFDM using various coding techniques
by Priyanka Mishra, Mehboob Ul Amin
Abstract: OFDM is a transmission scheme that offers diversity in frequency selective fading environments but suffers from high PAPR. Many researchers have studied the appropriate coding scheme to reduce PAPR and offer good error control properties as well. This article reviews the major results obtained up to date for the reduction of PAPR. Various scrambling techniques, such as PTS and SLM, have been combined with clipping-filtering and DCT to reduce PAPR. The various coding techniques, such as block coding and Reed Muller codes, have been evaluated in this paper to obtain significant reduction in PAPR. CCDF graphs depict the performances of the proposed techniques.
Keywords: OFDM; PTS; SLM; CCDF;DCT.
Research on parallelisation of collaborative filtering recommendation algorithm based on Spark
by Yang Yongli, Ning Zhenhu, Cai Yongquan, Liang Peng, Liu Haifeng
Abstract: More and more people become conscious of the recommendation system to make good use of the data through their inherent advantages faced with the large amount of data on the internet. The collaborative filtering recommendation algorithm cannot avoid the bottleneck of computing performance problems in the recommendation process. In this paper, we propose a parallel collaborative filtering recommendation algorithm, RLPSO_KM_CF, which is implemented based on Spark. Firstly, the RLPSO (Reverse-learning and local-learning PSO) algorithm is used to find the optimal solution of particle swarm and to output the optimised clustering centre. Then, the RLPSO_KM algorithm is used to cluster the user information. Finally, effective recommendations are made to the target user by combining the traditional user-based collaborative filtering algorithm with the RLPSO_KM clustering algorithm. The experimental results show that the RLPSO_KM_CF algorithm has a significant improvement in the recommendation accuracy and has a higher speedup and stability
Keywords: collaborative filtering recommendation algorithm; RLPSO algorithm; K-means algorithm; Spark.
Energy consumption prediction model of plane grinder processing system based on BP neural network
by Yan Zhou, Hua Zhang, Wei Yan, Feng Ma, Gongfa Li, Wentao Cheng
Abstract: According to the processing characteristics of high energy consumption and low efficiency of China's CNC surface grinding machine, this paper studies the process parameters influence on the energy consumption of the processing system, determines the wheel speed, feed speed of worktable and grinding depth as the main parameters, in which the grinding depth has the greatest influence on energy consumption. Then, the prediction model of the energy consumption of the processing system based on BP neural network is established, and the above three main factors are used as input and the additional load loss power. After training the model, the energy consumption ratio of the grinding machine system can be predicted. The prediction results show that the accuracy of the model is high, and it can predict the energy consumption of the grinder in the process well.
Keywords: processing system energy consumption; process parameters; BP neural network; prediction model.
Optimal base station location in LTE heterogeneous network using non-dominated sorting genetic algorithm II
by Ouamri Mohamed Amine, Zenadji Sylia, Khellaf Sylia, Azni Mohamed
Abstract: The main objective of radio network planning is to provide a cost-effective solution for the radio network in terms of coverage, capacity and quality of service. The network planning process and design criteria vary from region to region depending on the dominant factor, which could be capacity or coverage. However, the optimisation of base stations is an important and crucial process in cellular network planning. It represents a major challenge for mobile operators and is considered an NP-hard problem. In this work, we study the placement of the base station and configuration with an optimisation approach. In addition, a mathematical model based on set covering problem is suggested to solve the base station positioning. The main objectives of model are to maximise the coverage and minimise the financial cost. Non-Dominate Sorting Genetic Algorithm (NSGA II) is applied to find a suitable solution. Simulation results and discussions on the performance of suggested algorithm are provided.
Keywords: base station; cellular network planning; antenna; genetic algorithm;.
A feature selection method based on effective range and SVM-RFE
by Yifei Mao, Yuansheng Yang
Abstract: Identification of discriminative features from information-rich data with the goal of clinical diagnosis is crucial in the field of biomedical science. Support Vector Machine Recursive Feature Elimination (SVM-RFE), an efficient feature selection method, has been widely applied in the domain and has achieved remarkable results. However, biological data are usually class-imbalanced and contain outliers, which largely affect the feature ranking in SVM-RFE. This paper proposes a new feature selection method based on SVM-RFE and Effective Range (SVM-RFE-ER). The proposed method ranks the features by means of combining the SVM weight and the feature weight based on the effective ranges. Experiments on the simulated and real datasets have shown that SVM-RFE-ER is robust, especially against outlier and imbalanced data, and it is effective in identifying biologically meaningful biomarkers for disease study.
Keywords: feature selection; imbalanced data; outlier data; effective range; SVM-RFE.
Performance analysis of efficient power consumption protocols in wireless sensor networks using RELSEP and TSEP
by Ramkrishna Ghosh, Dipak Kumar Jana
Abstract: Wireless sensor networks have attracted worldwide attention. Wireless Sensor Networks (WSNs) and Wireless Multimedia Sensor Networks (WMSNs) consist of wirelessly interconnected sensor nodes organised arbitrarily or deterministically in an environmental area, which can gather, distribute and route information in different application areas. Power consumption in these networks is the foremost problem. WSNs are a recent generation of sensor networks and have an ample range of applications, and their expansion and application will have an extensive impact in human life and construction of all areas. Some of the applications include diaster management, landslide detection, glaciar monitoring, wildlife tracking, health care, military applications, environmental monitoring, security surveillance, industrial process control and a large number of applications to robotics, including Internet of Things (IOTs) projects. This paper will demonstrate the elementary description of WSN followed by different energy-efficient power consumption protocols. Here we have performed the comparative performance analysis of different energy-efficient protocols. In our work we have compared MODLEACH with TSEP followed by TSEP with RELSEP.
Keywords: WSN; LEACH; MODLEACH; SEP; TSEP; cluster head.
Research on conservation planning strategy of historic and cultural site islands in Shanghai based on analytic hierarchy process
by Zhen Wei, Wei Zhang
Abstract: With the continuous development of Shanghai in the process of internationalisation city construction, a variety of urban problems have emerged. The emergence and conservation planning of historic and cultural landscape site islands in Shanghai has become an important research object in academic circles. This paper applies the analytic hierarchy process (AHP) to the conservation planning strategy of historic and cultural site islands in Shanghai. By establishing a protection hierarchy of historic and cultural sites and calculating the weight of elements in each layer, various important factors are ranked. The corresponding protection planning strategy is put forward, which not only provides support for the conservation planning of historic and cultural site islands in Shanghai, but also offers reference for the conservation planning of historic and cultural site islands in other cities.
Keywords: analytic hierarchy process; conservation planning; historic and cultural landscape site islands; Shanghai.
An energy-efficient wireless sensor network routing protocol powered by ambient energy harvesting
by M.A. Mohamed, Abeer Twakol Khalil, Ahmed Hammad
Abstract: Energy and clustering are the most important elements in wireless sensor networks (WSNs). Lifetime, stability period, saving energy, deployment of nodes, fault tolerance and latency are the main challenges in WSNs as a result of their wide range of applications. This paper proposes a routing algorithm using solar-powered nodes in heterogeneous WSN to reach energy-efficient clustering concept. It is shown via simulations that the proposed protocol has better network stability period, network lifetime, total remaining energy, and throughput compared with other protocols, including LEACH, Teen, DEEC, and SEP, with more effective and stability data packet messages.
Keywords: wireless sensor networks; low energy adaptive clustering hierarchy; stable election protocol; threshold sensitive energy efficient sensor network; distributed energy efficient clustering.
Fast computation method for privacy-preserving data aggregation protocol
by Yuan Jiangjun, Jie Wang
Abstract: Recent years have seen the rapid improvement of smart terminals and wireless networks, and a lot of smart applications have come to the fore, such as mobile sensing. Taking advantage of smart terminals, more and more complicated sensing tasks can be taken over and finished. Data aggregation is an important application that is required by many tasks, such as remote health care and smart metering. The sensing devices in mobile sensing systems get time-series data, which can be used for supply forecast and leakage detection. As people nowadays pay more attention to privacy issues, to allow people to use todays data aggregation application, the first task is to solve their privacy concerns. Here, we propose a fast encryption/decryption mechanism for addition operations on keyed hash functions, which is based on multi-threads computation and parallel computation on multi-cores architecture and multi-threads based CPU. We apply the fast encryption/decryption method to the encryption/decryption key generation process by mobile sensors and aggregator, and the decryption process by aggregator. The advanced protocol presented here works better for large systems and requires less time cost for both mobile sensors and aggregators. We implement the fast encryption/decryption method based on the advanced privacy-preserving data aggregation protocol, and the test experiments show the performance benefits of the presented protocol.
Keywords: privacy; mobile sensing; multi-threads; untrusted aggregator.
Transition rate, trend and signal strength based stable routing protocol for mobile ad hoc networks
by P. Gnanasekaran, T.R. Rangaswamy
Abstract: In order to have continuous connectivity for data transmission in mobile ad hoc networks, it is necessary to establish a stable path with a long lifetime. Even though much work carried out by researchers to maintain the path for a long lifetime, it is necessary to give more importance to consider the stability of the nodes during the path discovery phase itself. To increase the stability of the path, this paper proposes a Transition Rate, Trend and Signal strength based Stable Routing Protocol (TTSSRP), considering the transition trend of a node along with signal strength for a predetermined period during the route discovery process. During a predetermined period, several samples are collected from a node. The total number of transitions and rate of change of signal strength with respect to the first position is computed for all sampling times to find the stability of a particular node so as to select one of the nodes in the path. This procedure is followed until the destination is reached. Simulation results confirm the superiority of TTSSRP over the Adhoc On-Demand Distance Vector (AODV) routing protocol and the Relative link and Path stability based Routing (RLPSR) protocol in terms of packet delivery ratio, average end-to-end delay and route lifetime.
Keywords: mobile ad hoc network; transition rate; signal strength; stability.
Maximum match filtering algorithm to defend spectrum-sensing data falsification attack in cognitive wireless sensor networks
by Pinaki Sankar Chatterjee, Monideepa Roy
Abstract: Cognitive wireless sensor networks (CWSNs) are a new technology to provide better bandwidth usage compared with a normal wireless sensor network. CWSNs
use opportunistic spectrum access to transfer data. They transmit data through the primary user's spectrum band when there is heavy traffic in its own network. IEEE 802.22 is the first standard that tells us about the concept of cognitive radio. It also helps the network to eliminate collisions and delays in data delivery. While doing so, however, CWSNs are subject to several security threats, attacks on secrecy and authentication, attacks on network availability, stealthy attacks on service integrity, etc. The attacks on network availability are known as the Denial of Service (DOS) attacks. The Spectrum Sensing Data Falsification (SSDF) attack is a type of DOS attack. In SSDF attack the attackers modify the spectrum sensing report in order to compel the base station to take a wrong collaborative decision regarding the vacant spectrum band in other networks. In this paper, we have proposed a new algorithm for collaborative spectrum sensing and spectrum decision making in CWSNs, named the Maximum-Match Filtering algorithm (MMF). This algorithm is executed at the base station to counter the SSDF attack.
Keywords: cognitive wireless sensor network; denial of service attack; spectrum sensing data falsification attack; multiple linear regression.
BMROMP: a fast algorithm of block compressed sensing
by Yongping Zhang, Lei Wang, Yan Liu, Jun Gao, Qiming Liu, Rong Chen
Abstract: Compressed sensing (CS) is a method of signal sampling that can directly acquire the compressed form of the original signal. However, its signal reconstruction needs to solve the optimisation algorithm with high computational complexity. This paper introduces a redesigned algorithm of block CS, called BMROMP (Block More Relaxed Regularised Orthogonal Matching Pursuit), which is a fast reconstruction algorithm for 2D-signals based on the ROMP algorithm. To reduce the consumption of computational resources when reconstructing 2D-signals with big size, BMROMP uses the method of dividing blocks reconstruction. For the reconstruction of each block signal, like ROMP, BMROMP also uses the least-squares method, but relaxes the calculation of the most relevant atoms. It is realised by a newly defined parameter, regularised-entire-correlation. The parameter can help us obtain a 2D reconstructed signal directly. The experimental results show that BMROMP has great performance advantage in the reconstruction speed, i.e., it can drastically reduce the execution time of the optimisation algorithm. This point is very useful in many scenarios of CS application.
Keywords: block CS; fast algorithm; least-square optimisation; regularised-entire-correlation.
Stochastic analysis of DTN routing protocols in delay tolerant network routing hierarchical topology
by El Arbi Abdellaoui Alaoui, Khalid Nassiri
Abstract: We propose in this work a topology adapted to the routing in the delay tolerant networks (DTN). This topology plays a very important role in the design and the implementation of routing protocols in this type of network devoid of any infrastructure and any centralised administration with an intermittent connectivity. Indeed, we develop a DTN routing hierarchical topology (DRHT) which incorporates three fundamental concepts: ferries messages, ferries routes, and clusters. The intra-cluster routing is managed by the cluster head, while the inter-cluster routing is managed by the ferries messages. This approach allows us to improve the performances of DTNs. In addition, we present a modelling and an analysis of the process of the bundles distribution in a DTN through a Markov process of birth and death in continuous time; our modelling takes into account the characteristics of the DTN. The simulation results have shown that our DRHT solution proves to be effective and adequate in the context of the DTNs compared with other existing approaches.
Keywords: delay tolerant networks; bundles; stochastic processes; hierarchical cluster; DRHT; TSP.
Throughput enhancement of SISO parallel LTE turbo decoders using floating point turbo decoding algorithm
by M. Parvathy, R. Ganesan
Abstract: A floating point decoding algorithm for advanced turbo decoders is proposed in this paper. The analysis of the proposed algorithm is done as an alternative to the Tail Overlapped Decoding (TOD) algorithm. This article is described as a comparative analysis of the TOD and floating point turbo decoding algorithms for the throughput enhancement of the fourth generation mobile networks. The latter one is proved to be better for the throughput enrichment and latency reduction with reduced power consumption for long term evolution networks. The decoder with odd-even interleaver considerably reduces the computational complexity and thus enhances the throughput.
Keywords: fourth generation mobile networks; tail overlapped algorithm; fully parallel turbo decoder; throughput; latency; odd-even interleavers.
PQISEM: Bayesian network's structure learning based on partial qualitative influences and SEM algorithm from missing data
by Yali Lv, Jian’ai Wu, Tong Jing
Abstract: The structure learning of Bayesian network (BN) is an important issue for probabilistic inference by BN modelling. In general, based on domain knowledge, experts can give easily the partial qualitative influences between variables in BN.
Thus, in this paper, for missing data, a structure learning algorithm of BN, named PQISEM algorithm, is proposed by making full use of these partial qualitative influences and SEM algorithm. Specifically, firstly, we address the problem of how to modify BN's parameters, based on qualitative influence knowledge for complete data by EM algorithm, which makes the parameters of BN that have the best structure to meet the given qualitative constraint relationship in each iteration. Then, based on qualitative influences, we give random search operators in a hill climbing strategy,
and then analyse the selection rule of the initial network and selection strategy of candidate networks based on these operators. Further, the PQISEM algorithm of BN's structure learning is proposed, based on partial qualitative influences and SEM algorithm, and its complexity and convergence are analysed and discussed. Finally, the learning performance of the PQISEM algorithm is verified by comparing with other algorithms on standard networks and analysing on different sample sizes and different missing value proportion.
Keywords: Bayesian network; missing data; partial qualitative influences; qualitative influence knowledge; structure learning; SEM algorithm.
Using PSO-TVAC to improve the performance of DV-Hop
by Fang Juanyan, Feng Junhua
Abstract: Particle swarm optimisation with time-varying accelerator coefficients is a novel variant of particle swarm optimisation algorithms. Different from the standard version, the cognitive coefficient and social coefficient are both adjusted with the different generations to balance the exploitation and exploration capabilities. In this paper, particle swarm optimisation is incorporated into the methodology of DV-Hop algorithm to estimate the beacon nodes' positions. To show the performance, it is compared with two other algorithms, and simulation results show it is effective.
Keywords: particle swarm optimisation; time-varying accelerator coefficients; DV-Hop algorithm.
Using machine learning methods for detecting network anomalies within SNMP-MIB datasets
by Ghazi Al-Naymat, Mouhammd Al-kasassbeh, Eshraq Al-Harwari
Abstract: The exponential increase in the amount of malicious threats on computer networks and internet services, due to the large number of attacks, means that the network security is at continuous risk. One of the most prevalent network attacks is Denial of Service (DoS) flooding attacks. DoS attacks have recently become the most attractive type of attack to attackers and have posed devastating threats to network services. Hence, there is a need for effective approaches that can efficiently detect any intrusion in a network. This paper presents an efficient mechanism for network attacks detection and types of attack classification, within the Management Information Base (MIB) data, which is associated with the Simple Network Management Protocol (SNMP), through machine learning techniques. This paper also investigates the impact of SNMP-MIB data in the detection of network anomalies. Three classifiers, namely Random Forest, AdaboostM1 and MLP, are used to build the detection model. The use of different classifiers presents a comprehensive study on the effectiveness of SNMP-MIB data in detecting different types of attack. In our approach, we categorised the MIB variables into five MIB groups (Interface, IP, ICMP, TCP and UDP) where each group includes a number of MIB variables that are affiliated to it. The empirical results show that the classifiers can successfully detect and classify the attacks with a high detection rate. The Random Forest classifier achieved the highest accuracy rate with the IP group (100%) and with the Interface group (99.93%). In addition, results show that among the five MIB groups the Interface and IP groups are the groups that are affected the most by all types of attack, whereas the ICMP, TCP and UDP groups are less affected.
Keywords: anomaly detection; DoS attack; SNMP; MIB; machine learning classifier.
An investigation on metaheuristic techniques for solving cell to switch assignment problem
by Mridul Chawla, Manoj Duhan
Abstract: The assignment of cells to switches in a Personal Communication Services Network (PCSN) is a Non-deterministic Polynomial time (NP) hard problem with exponential complexity and is among the hardest combinatorial optimisation problems of great practical importance in wireless communication systems. The objective is to assign cells to switches in an optimal manner, such that the cost comprising of the sum of handoff, cabling and switching cost is minimised with reasonable accuracy in an acceptable time scale, while obeying the constraints that each cell must be assigned to exactly one switch and total load of all the cells which are assigned to a switch is below the capacity of the switch. The aim of this article is to experimentally investigate the performance of three recent metaheuristic algorithms namely Bat Algorithm (BA), Firefly Algorithm (FA), and Flower Pollination Algorithm (FPA) for solving the Cell to Switch Assignment (CSA) problem.
Keywords: metaheuristics; personal communication service network; cell to switch assignment; bat algorithm; firefly algorithm; flower pollination algorithm.
Denial of service attack mitigation addressing all the security attributes in OLSR MANET
by Ramaswamy Bhuvaneswari, R. Ramachandran
Abstract: A mobile ad hoc network is formed instantly by mobile nodes that exist in a particular radio range. The infrastructure for the network can be laid when and wherever required. The nodes themselves serve as routers to forward the data from the source to the destination through multiple hops. The protocols for these ad hoc networks are built in such that every node is considered as trustworthy, and they work cooperatively among themselves. But in the real environment some nodes are malicious and disrupt the actual communication. In this paper. we discuss the security of the OLSR protocol by mitigating various active attacks and the role of elliptic curve cryptography and fictitious nodes in strengthening the security attributes.
Keywords: routing protocols; security attributes; DoS attacks; elliptic curve cryptography; OLSR; network layer attacks.
A flexible visual quality control algorithm for perceptual video encryption based on H.264
by Cao Yuqiang, Gong Weiguo, Bai Sen
Abstract: In order to meet all kinds of video security applications, especially pay-digital-TV system, a perceptual video encryption scheme based on visual quality control strategy is proposed. The optimal syntax elements and sensitive coded elements are chosen to encrypt by using mathematical XOR operations with stream ciphers generated by Chen chaos system. In order to enhance the security, the encryption scheme of this paper synthetically uses three strategies, including MVD encryption, intra-prediction mode encryption and levels of low coefficients encryption. The variable length keys are used to encrypt the syntax elements to improve key usage. Experimental results show that the proposed perceptual encryption scheme can achieve high security at a relatively high compression ratio and bandwidth cost, as well as about 2% coding rate and 6% encoding time increased, at the cost of slightly sacrificing code rate and encoding time. Especially, the degraded visual quality can be controlled gradually with a simple quality factor.
Keywords: video encryption; perceptual video encryption; H.264.
Randomness-driven global particle swarm optimisation for unconstrained optimisation problems
by Zhen Hu, Dexuan Zou, Zichen Zhang, Xin Zhang, Xin Shen
Abstract: This paper proposes a randomness-driven global particle swarm optimisation (R-dGPSO) algorithm to solve the unconstrained optimisation problems. First, an opposition learning strategy is modified and applied to the population initialisation of R-dGPSO, which is helpful to improve the quality of the initial solutions. Second, cosine mapping and random factors are used to adjust the inertia weight and improve the convergence of the algorithm. Third, an impact factor is incorporated into the velocity updating formula in order to regulate the impact of personal best particles and global best particle on particles flight trajectories. Fourth, a new location updating strategy is devised to help R-dGPSO to get rid of local optima. Experimental results show that R-dGPSO can effectively accomplish the task of numerical optimisation in most cases. Furthermore, it can produce better objective function values than the other methods. Therefore, R-dGPSO is an effective numerical optimisation method for solving unconstrained optimisation problems.
Keywords: particle swarm optimisation; randomness-driven; global; unconstrained problems.