Transient electroluminescence determination of carrier mobility and charge trapping effects in heavily doped phosphorescent organic light-emitting diodes

Transient electroluminescence (EL) was used to measure the delay between the excitation pulse and onset of emission in OLEDs based on phosphorescent bis[3,5-bis(2-pyridyl)-1,2,4-triazolato] platinum(ΙΙ) doped into 4,4′-bis(carbazol-9-yl) triphenylamine (CBP), from which an electron mobility of 3.2 × 10⁻⁶ cm²/V s was approximated. Delayed recombination was observed after the drive pulse had been removed and based on its dependence on frequency and duty cycle, ascribed to trapping and de-trapping processes associated with disorder-induced carrier localization at the interface between the emissive layer and electron blocker. The data suggests that the exciton recombination zone is at, or close to the interface between the emissive layer and electron blocker. Despite the charge trapping effects, a peak power efficiency of 24 lm/W and peak external quantum efficiency of 10.64% were obtained. Mechanisms for the electroluminescence and delayed recombination are proposed.     

Comparative study of two CPU router time management algorithms in cellular IP networks

Providing good quality of service (QoS) in cellular IP networks is an important requirement for performance improvement of the cellular IP network. Resource reservation is one of the methods used in achieving this goal and is proven to be effective. The main resources to be reserved in a cellular IP network are bandwidth, buffer and central processing unit (CPU) cycles. Router CPU cycle is the time taken by the router to process the packet of the flow before forwarding it to the next router (hop). This paper proposes a model for CPU cycle optimization of routers for real‐time flows in a cellular IP network. The model applies both genetic algorithm (GA) and particle swarm optimization (PSO) as soft computing tools to optimize the CPU cycles and reduces the flow processing time at each router in the route taken by a flow. Simulation experiments illustrate a comparative study of the model.     

A new learning automata based approach for increasing utility of service providers

Utility is an important factor for serviceproviders, and they try to increase their utilities through adopting different policies and strategies. Because of unpredictable failures in systems, there are many scenarios in which the failures may cause random losses for service providers. Loss sharing can decrease negative effects of unexpected random losses. Because of capabilities of learning automata in random and stochastic environments, in this paper, a new learning automaton based method is presented for loss sharing purpose. It is illustrated that the loss sharing can be useful for service providers and helps them to decrease negative effect of the random losses. The presented method can be used especially in collaborative environments such as federated clouds. Results of the conducted experiments show the usefulness of the presented approach to improve utility of service providers.     

Grid computing: a STOPE view

Grid computing is emerging as the foundation upon which virtual organizations can be built. Such organizations are becoming of increasing importance for tackling various projects, both in academic and in business fields. This paper is concerned with presenting an integrated view of the grid to readers interested in understanding it, or perhaps in developing it further or making use of it in the future. The target view is based on the STOPE (strategy/technology/organization/people/environment) framework that has previously been used to integrate the issues of various information technology problems over its well‐defined domains. For strategy, the view considers the reasons associated with the need for the grid, including grid services and grid benefits. For technology, the technical components of the grid and their functions are taken into account. For organization, the organizations concerned with the development, technology production, service provisioning and use of the grid are considered. For people, those associated with the grid in the related organizations are taken into account. For the environment, rules, practices and support associated with the grid are discussed. Finally, the paper emphasizes questions that need to be answered in order to contribute to the future development of the grid. Copyright © 2006 John Wiley & Sons, Ltd.     

An Anti-Gray Hole Attack Scheme in Mobile Ad Hoc Network

International Journal of Wireless Information Networks - Dynamic variation of network topology in mobile ad hoc networks (MANET) forces network nodes to work together and rely on each other for...     

An improved cluster formation process in wireless sensor network to decrease energy consumption

Wireless sensor network has special features and many applications, which have attracted attention of many scientists. High energy consumption of these networks, as a drawback, can be reduced by a hierarchical routing algorithm. The proposed algorithm is based on the Low Energy Adaptive Clustering Hierarchy (LEACH) and Quadrant Cluster based LEACH (Q-LEACH) protocols. To reduce energy consumption and provide a more appropriate coverage, the network was divided into several regions and clusters were formed within each region. In selecting the cluster head (CH) in each round, the amount of residual energy and the distance from the center of each node were calculated by the base station (including the location and residual energy of each node) for all living nodes in each region. In this regard, the node with the largest value had the highest priority to be selected as the CH in each network region. The base station calculates the CH due to the lack of energy constraints and is also responsible for informing it throughout the network, which reduces the load consumption and tasks of nodes in the network. The information transfer steps in this protocol are similar to the LEACH protocol stages. To better evaluate the results, the proposed method was implemented with LEACH LEACH-SWDN, and Q-LEACH protocols using MATLAB software. The results showed better performance of the proposed method in network lifetime, first node death time, and the last node death time.

     

A Convex Programming for Range Assignment to Optimize Lifetime in Network-Coding-Based-Wireless-Sensor Networks

Wireless sensor networks (WSNs) are known to be highly energy-constrained and  consequently lifetime is a critical metric in their design and implementation. Range assignment by adjusting the transmission powers of nodes create a energy-efficient topology for such networks while preserving other network issues, however, it may effect on the performance of other techniques such as network coding. This paper addresses the problem of lifetime optimization for WSNs where the network employs both range assignment and network-coding-based multicast. We formulate the problem and then reformulated it as convex optimization that offer a numerous theoretical or conceptual advantages. The proposed programming leads to efficient or distributed algorithms for solving the problem. Simulation results show that the proposed optimized mechanism decreases end-to-end delay and improve lifetime as compared by other conventional ones.     

RTDGPS Accuracy Improvement Using PSO-LSWSVM and Low-Cost GPS Receivers

Wireless Personal Communications - The position of mobile devices is determined by Real Time Differential Global Positioning System (RTDGPS). This system is composed of fixed and mobile station....     

Deep learning methods for fingerprint-based indoor positioning: a review

ABSTRACT

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends.