Authentication mechanism over the integrated UMTS network and WLAN platform using the cross-layer bootstrap

The 3G mobile data network provides always-on and ubiquitous connectivity for subscribers. Although the service coverage area in wireless local area network (WLAN) is much smaller than that in a 3G mobile data network, the data transmission rate in WLAN can be from 2 to 54 Mbps, which is much faster than 3G mobile network. Obviously, the relationship between the 3G mobile data network and WLAN is complementary in terms of service coverage and data transmission rate. Therefore integration of 3G mobile network and WLAN can offer subscribers higher speed wireless service in hot spots and ubiquitous connectivity in 3G mobile data network. An authentication mechanism over the loose coupled integration mechanism using a cross-layer bootstrap is proposed. The benefits of the proposed mechanism are (a) integrating Universal Mobile Telecommunication System network and WLAN using the existing protocols denned in 3GPP, IETF and IEEE 802. Hi, (b) the use of the Extension Authentication Protocol authentication method is flexible, (c) reduction of the authentication signalling when a subscriber roams from one access point (AP) to another AP and (d) user identity privacy protection.     

Supporting vertical handover between universal mobile telecommunications system and wireless LAN for real-time services

The provisioning of the seamless handovers for real-time services in-between UMTS/ WLAN loose-interworking by the network-layer technologies has been studied. Issues are how to execute such a vertical handover in a fast and efficient manner as well as how to achieve it in a viable way. To resolve such issues, a novel scheme called 'designated crossover point' (DCP) within the existing IP end-to-end quality of service (QoS) architecture has been proposed. The DCP is an agent at the edge router in the customer's premise to join the old RSVP signalling path and new RSVP signalling path. With the DCP in place, the vertical handover can be greatly accelerated. Besides, by enabling the DCP to handover to the edge router in the customer's network currently visited by the mobile, lower charging cost can be achieved. Finally, there is no modification required for the devices in the provider's network and thus the viability is much improved. It is then end up with a topology for resource management in IP backbone for UMTS/WLAN loose-interworking to support the idea of DCP. To determine when to perform the rerouting to remove the possible derouting caused by DCPs, use of an adaptive rule has been suggested. Simulation results show in general that the adaptive rule is effective.     

Optimal Coverage Multi-Path Scheduling Scheme with Multiple Mobile Sinks for WSNs

Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method.     

Efficient unmanned aerial vehicle formation rendezvous trajectory planning using Dubins path and sequential convex programming

Trajectory planning of formation rendezvous of multiple unmanned aerial vehicles (UAVs) is formulated as a mixed-integer optimal control problem, and an efficient hierarchical planning approach based on the Dubins path and sequential convex programming is proposed. The proposed method includes the assignment of rendezvous points (high level) and generation of cooperative trajectories (low level). At the high level, the assignment of rendezvous points to UAVs is optimized to minimize the total length of Dubins-path-based approximate trajectories. The assignment results determine the geometric relations between the UAVs’ goals, which are used as equality constraints for generating trajectories. At the low level, trajectory generation is treated as a non-convex optimal control problem, which is transformed to a non-convex parameter optimization and then solved via sequentially performing convex optimization. Numerical experiments demonstrate that the proposed method can generate feasible trajectories and can outperform a typical nonlinear programming method in terms of efficiency.     

Combining social network and collaborative filtering for personalised manufacturing service recommendation

Owing to the rapid proliferation of Web service technologies in cross-enterprise manufacturing collaborations, information overload is becoming a major barrier that hinders the effective discovery of the shared manufacturing services provided by collaborative partners for supply chain deployment. Thus, we aimed to identify a different approach for discovering manufacturing services by making personalised service recommendations that are suited to the specific needs of active service users based on usage data from previous retrievals made by past service users. The proposed approach combines social network and collaborative filtering techniques in a unified framework to predict the missing Quality of Service (QoS) values of manufacturing services for an active service user, thereby improving the effectiveness of personalised QoS-aware service recommendations. The social network explores the usage of preference and tagging relationships among service users and manufacturing services in making personalised recommendation, which alleviates the data sparsity and the cold start problems that hinder the traditional collaborative filtering techniques. A case study and experimental evaluation demonstrate that the proposed approach can achieve the practicality and accuracy to personalised manufacturing service recommendations in a real application.     

Hybrid Metamodeling/Metaheuristic Assisted Multi-Transmitters Placement Planning

With every passing day, the demand for data traffic is increasing, and this urges the research community not only to look for an alternating spectrum for communication but also urges radio frequency planners to use the existing spectrum efficiently. Cell sizes are shrinking with every upcoming communication generation, which makes base station placement planning even more complex and cumbersome. In order to make the next-generation cost-effective, it is important to design a network in such a way that it utilizes the minimum number of base stations while ensuring seamless coverage and quality of service. This paper aims at the development of a new simulation-based optimization approach using a hybrid metaheuristic and metamodel applied in a novel mathematical formulation of the multi-transmitter placement planning (MTPP) problem. We first develop a new mathematical programming model for MTPP that is flexible to design the locations for any number of transmitters. To solve this constrained optimization problem, we propose a hybrid approach using the radial basis function (RBF) metamodel to assist the particle swarm optimizer (PSO) by mitigating the associated computational burden of the optimization procedure. We evaluate the effectiveness and applicability of the proposed algorithm by simulating the MTPP model with two, three, four and five transmitters and estimating the Pareto front for optimal locations of transmitters. The quantitative results show that almost maximum signal coverage can be obtained with four transmitters; thus, it is not a wise idea to use higher number of transmitters in the model. Furthermore, the limitations and future works are discussed.     

Mobile agent based approach for QoS routing

A mobile agent based on-demand quality of service (QoS) unicast routing scheme for supporting multimedia applications is proposed that considers bandwidth, delay and packet loss as QoS metrics for feasible path computation. A mobile agent is employed to find multiple QoS paths and select a best path among them to preserve resources so as to increase call success ratio and network bandwidth utilisation as well as adapt to network dynamics. The scheme is simulated in various network scenarios (sparse and dense networks) to verify performance and operation effectiveness, and compared with RSVP-based QoS routing by using an internet routing protocol. The results demonstrate significant improvements in call success ratio and network bandwidth utilisation compared with RSVP-based QoS routing, both in case of sparse and dense networks. Benefits of the agent-based scheme are adaptability, flexibility, and support for component-based software engineering features such as software reuse, customisation and maintainability     

An analytical solution based on mobility and multicriteria optimization for access selection in heterogeneous environment

Providing global connectivity with high speed and guaranteed quality at any place and any time is now becoming a reality due to the integration and co-ordination of different radio access technologies. The internetworking of existing networks with diverse characteristics has been considered attractive to meet the incredible development of interactive multimedia services and ever-growing demands of mobile users. Due to the diverse characteristics of heterogeneous networks, several challenges have to be addressed in terms of quality of service (QoS), mobility management and user preferences. To achieve this goal, an optimal network selection algorithm is needed to select the target network for maximizing the end user satisfaction. The existing works do not consider the integration of utility function with mobile terminal mobility characteristics to minimize ping-pong effects in the integrated networks. An integrated multicriteria network selection algorithm based on multiplicative utility function and residual residence time (RRT) estimation is proposed to keep the mobile users always best connected. Multiplicative weighted utility function considers network conditions, application QoS and user preferences to evaluate the available networks. In this paper, the proposed scheme is implemented with two mainstreams (pedestrian users and high-velocity users). For high-velocity users, RRT and adaptive residence time threshold are also considered to keep the probability of handover failures and unnecessary handovers within the limits. Monte-Carlo simulation results demonstrate that the proposed scheme outperforms against existing approaches.     

Artificial Intelligence Based Reliable Load Balancing Framework in Software-Defined Networks

Software-defined networking (SDN) plays a critical role in transforming networking from traditional to intelligent networking. The increasing demand for services from cloud users has increased the load on the network. An efficient system must handle various loads and increasing needs representing the relationships and dependence of businesses on automated measurement systems and guarantee the quality of service (QoS). The multiple paths from source to destination give a scope to select an optimal path by maintaining an equilibrium of load using some best algorithms. Moreover, the requests need to be transferred to reliable network elements. To address SDN’s current and future challenges, there is a need to know how artificial intelligence (AI) optimization techniques can efficiently balance the load. This study aims to explore two artificial intelligence optimization techniques, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), used for load balancing in SDN. Further, we identified that a modification to the existing optimization technique could improve the performance by using a reliable link and node to form the path to reach the target node and improve load balancing. Finally, we propose a conceptual framework for SDN futurology by evaluating node and link reliability, which can balance the load efficiently and improve QoS in SDN.     

Efficient UAV Communications: Recent Trends and Challenges

Unmanned Ariel Vehicles (UAVs) are flying objects whose trajectory can be remotely controlled. UAVs have lot of potential applications in the areas of wireless communications, internet of things, security, traffic management, monitoring, and smart surveying. By enabling reliable communication between UAVs and ground nodes, emergency notifications can be efficiently and quickly disseminated to a wider area. UAVs can gather data from remote areas, industrial units, and emergency scenarios without human involvement. UAVs can support ubiquitous connectivity, green communications, and intelligent wireless resource management. To efficiently use UAVs for all these applications, important challenges need to be investigated. In this paper, we first present a detailed classification of UAVs based on factors such as their size, communication range, weight, and flight altitude. We also explain the hardware system configuration and uses of these UAVs. We present a brief overview of recent work done related to three major challenges in UAVs. These challenges include trajectory control, energy efficiency and resource allocation. We also present three open challenges and future opportunities for efficient UAV communications. These include use of learning algorithms for resource allocation and energy efficiency in UAVs, intelligent surfaces-based communications for enhanced reliability in UAVs, and security algorithms to combat malicious attacks against UAVs.     

Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

The Internet of Vehicles (IoV) is a networking paradigm related to the intercommunication of vehicles using a network. In a dynamic network, one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion. Therefore, optimal path selection to route traffic between the origin and destination is vital. This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access. Firstly, this work proposed a novel use of the Ant Colony Optimization (ACO) algorithm and formulated the path planning optimization problem as an Integer Linear Program (ILP). This integrates the future estimation metric to predict the future arrivals of the vehicles, searching the optimal routes. Considering the mobile nature of IOV, fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path. The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path. Thus, this work strongly supports its use in applications having stringent Quality of Service (QoS) requirements for the vehicles.     

一种支持多跳无线网QoS业务的MAC层接入协议

提供一定的服务质量保证(QoS)是多跳无线网络研究中的热点和难点,本文基于TDMA/FDMA机制提出了一种支持多信道多跳无线网络话音级QoS业务的MAC层接入协议RA/SRN。该协议以RBRP协议思想为基础,针对无线信道中隐藏、暴露终端造成的信道利用率降低和分组冲突问题进行改进设计,减小了信道冲突概率,提高了信道利用率,降低了信道接入时延,为网络层QoS路由协议提供了良好的支持。     

Multi-Criteria Fuzzy-Based Decision Making Algorithm to Optimize the VHO Performance in Hetnets

Despite the seemingly exponential growth of mobile and wireless communication, this same technology aims to offer uninterrupted access to different wireless systems like Radio Communication, Bluetooth, and Wi-Fi to achieve better network connection which in turn gives the best quality of service (QoS). Many analysts have established many handover decision systems (HDS) to enable assured continuous mobility between various radio access technologies. Unbroken mobility is one of the most significant problems considered in wireless communication networks. Each application needs a distinct QoS, so the network choice may shift appropriately. To achieve this objective and to choose the finest networks, it is important to select a best decision making algorithm that chooses the most effective network for every application that the user requires, dependent on QoS measures. Therefore, the main goal of the proposed system is to provide an enhanced vertical handover (VHO) decision making program by using a Multi-Criteria Fuzzy-Based algorithm to choose the best network. Enhanced Multi-Criteria algorithms and a Fuzzy-Based algorithm is implemented successfully for optimal network selection and also to minimize the probability of false handover. Furthermore, a double packet buffer is utilized to decrease the packet loss by 1.5% and to reduce the number of handovers up to 50% compared to the existing systems. In addition, the network setup has an optimized mobility management system to supervise the movement of the mobile nodes.     

Modeling and Analysis of UAV-Assisted Mobile Network with Imperfect Beam Alignment

With the rapid development of emerging 5G and beyond (B5G), Unmanned Aerial Vehicles (UAVs) are increasingly important to improve the performance of dense cellular networks. As a conventional metric, coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous environment. In recent years, stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network systems. In this paper, an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been proposed. An assumption was considered that all users are distributed according to Poisson Cluster Process (PCP) around base stations, in particular, Thomas Cluster Process (TCP). Using this model, the impact of beam alignment errors on the coverage probability was investigated. Initially, the Probability Density Function (PDF) of directional antenna gain between the user and its serving base station was obtained. Then, association probability with each tier was achieved. A tractable expression was derived for coverage probability in both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) condition links. Numerical results demonstrated that at low UAVs altitude, beam alignment errors significantly degrade coverage performance. Moreover, for a small cluster size, alignment errors do not necessarily affect the coverage performance.     

A beacon-based handover scanning mechanism with QoS support in WiMAX femtocell architecture

WiMAX femtocell architecture has gained a lot of attention because of its better indoor service provision through traffic sharing between WiMAX and femtocell. However, since the signal of a WiMAX base station (BS) is usually stronger than that of a femtocell base station (fBS), the handover procedure of mobile stations may not be triggered due to the stronger signal association. And, a huge number of fBSs deployed within a WiMAX BS coverage area will require extra power to scan all fBSs for a handover determination. In this article, we propose a beacon-based handover scanning mechanism with Quality of Service (QoS) support. Through the modified beacons and MOB_NBR-ADV messages, a neighbor cell list with QoS parameters is generated automatically at each mobile station. The decision criteria for handover triggering and an appropriate target cell selection are also presented. The QualNet simulation results demonstrate that along with increased fBSs, the proposed mechanism can improve the total system throughput from 37% to 51% and obtain higher bandwidth utilization in the case of increasing number of fBSs, as compared with Full Scan and Neighbor Cell Information Mechanisms.     

Comparison between hybridized algorithm of GA–SA and ABC,GA, DE and PSO for vertical-handover in heterogeneous wireless networks

Genetic algorithms (GAs) and simulated annealing (SA) have emerged as leading methods for search and optimization problems in heterogeneous wireless networks. In this paradigm, various access technologies need to be interconnected; thus, vertical handovers are necessary for seamless mobility. In this paper, the hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user’s preferences. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithms that avoid slow and massive computations. This was to, specifically, solve two major problems in GA optimization, i.e. premature convergence and slow convergence rate, and the facilitation of simulated annealing in the merging populations phase of the search. The hybrid algorithm was expected to improve on the pure GA in two ways, i.e., improved solutions for a given number of evaluations, and more stability over many runs. This paper compares the formulation and results of four recent optimization algorithms: artificial bee colony (ABC), genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Moreover, a cost function is used to sustain the desired QoS during the transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR, and monetary cost. Simulation results indicated that choosing the SA rules would minimize the cost function and the GA–SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the ‘Ping-Pong’ effect.     

Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment

Many extensive UAV communication networks have used UAV cooperative control. Wireless networking services can be offered using unmanned aerial vehicles (UAVs) as aerial base stations. Not only is coverage maximization, but also better connectivity, a fundamental design challenge that must be solved. The number of applications for unmanned aerial vehicles (UAVs) operating in unlicensed bands is fast expanding as the Internet of Things (IoT) develops. Those bands, however, have become overcrowded as the number of systems that use them grows. Cognitive Radio (CR) and spectrum allocation approaches have emerged as a potential approach for resolving spectrum scarcity in wireless networks, and hence as technological solutions for future generations, from this perspective. As a result, combining CR with UAVs has the potential to give significant benefits for large-scale UAV deployment. The paper examines existing research on the subject of UAV covering and proposes a multi-UAV cognitive-based error-free model for energy-efficient communication. Coverage maximization, power control, and enhanced connection quality are the three steps of the proposed model. To satisfy the desired signal-to-noise ratio, the covering zone efficacy is investigated as a function of the distance among UAVs stationed in a specific geographic region depending on multiple deployment configurations like as rural, suburban, and urban macro deployment scenarios of the ITU-R M.2135 standard (SNR).     

移动智能网用户的通话时长模型分析

根据从移动运营商的移动智能网中采集到的真实数据,分析了"神州行"预付费业务用户通话时长的分布规律.分析结果表明,指数分布与样本数据的经验分布之间存在较大差异,不适合用来描述通话时长规律;经验分布可以用一个混合分布描述,它由两个单点分布和两个对数正态分布的加权和分布组成.类似的结论对于移动虚拟专用网(VPN)业务的通话时长也成立.它对移动智能网的性能分析具有重要意义.     

面向云制造服务的制造资源优化配置研究

为了降低云端制造服务成本,解决云制造环境下无需求偏好的制造资源优化配置的难题,充分考虑制造资源需求企业和云平台运营方的利益以及双方在制造资源配置服务过程中涉及到的服务质量（quality of sevice,QoS）因素和柔性因素,构建了云环境下代表制造资源需求企业和云平台运营方利益的多目标优化资源配置模型,并基于改进NSGA-Ⅱ算法对模型算例进行了求解,计算结果表明了该模型和算法的可行性、有效性和稳定性。