Wenhao Fan

Optimal resource allocation for transmission diversity in multi-radio access networks: a coevolutionary genetic algorithm approach

The next generation wireless communication systems aim at supporting enhanced diversified network access and data transmission abilities via the cooperative integration and unified management of various radio access technologies (RATs). The resource allocation is the core component leading the network system and mobile terminals to the service robustness and performance maximization. In this paper, a numeric optimization model for optimizing terminals’ transmission power and allocated RAT bandwidth for maximizing system capacity is proposed with the focal consideration of the multi-radio transmission diversity for parallel transmission through multiple links from different RATs, and different terminal characteristics on RAT supports. Also, we design a centralized and periodic scheduling algorithm including an improved coevolutionary genetic algorithm for efficiently solving the optimization problem. Simulation results demonstrate that our propose algorithm can distinctly enhance the system performance and improve the computational efficiency.

Graph-Based Data Gathering Scheme in WSNs With a Mobility-Constrained Mobile Sink

Sink mobility can improve the energy efficiency and data gathering performance in wireless sensor networks. However, mobility of the sink station may be constrained in some applications and it becomes a challenge to optimize the network performance for random distribution and hierarchical topology. We focus on prolonging the network lifetime and keep the data gathering performance at an acceptable level. To address this issue, we propose a comprehensive data gathering scheme based on graphing technique, called double optimization of energy efficiency (DOEE) to optimize the energy consumption of all sensor nodes hierarchically. The objective problem of DOEE is solved with a heuristic algorithm. Along with the scheme, we also specialize a route discovery protocol and its corresponding dynamic gateway protocol to balance the energy consumption among sensor nodes. The proposed algorithm and protocols are evaluated in the simulation platform Network Simulation-3 and the experiment results show our technique has superior performance.

TerminalBooster: Collaborative Computation Offloading and Data Caching via Smart Basestations

A novel scheme, called TerminalBooster, which aims at efficiently enhancing the performance of mobile terminals through computation offloading-enabled and data caching-enabled basestations, is proposed in this letter. Considering the intercommunications between mobile applications and their clouds, we design a collaborative offloading and caching model, formulate an optimization problem including two independent sub-problems, and develop a resource management algorithm which guides the basestation to jointly schedule computation offloading and allocate data caching. The total delays in app-cloud intercommunications can be minimized via optimal offloading and caching decisions. Simulation results demonstrate TerminalBooster can reach optimality efficiently and it outperforms traditional schemes with either offloading or caching.

Multisite computation offloading in dynamic mobile cloud environments

Dear editor, With the development of wireless communication and computer technology, using mobile devices (MDs) has become more and more popular. However, due to a series of constraints such as heat dissipation, volume and battery capacity, MDs take much time and energy to run some applications and even cannot run heavy applications. As a result, these constraints hinder the further development of MDs. To solve the problem that MDs’ performance is restricted by their own limited resources, mobile cloud computing (MCC) has been proposed to augment MDs’ capabilities [1]. Compared with the client-server architecture which always executes components in the server, MCC needs to make offloading decisions that determine whether components should be offloaded or not according to the optimization objective [2].

Toward high efficiency for content-based multi-attribute event matching via hybrid methods

Event matching is a core in decoupled end-to-end communications, which are extensively applied to various areas. Event matching seeks the subscriptions that match a given event from a subscription set, however, this work becomes increasingly complicated in content-based multi-attribute scenarios, where events and subscriptions are formed in content, and described by multiple attributes. In addition, large-scale systems are easier to suffer from severe degradation in event matching performance. To this end, this paper presents a high-efficiency content-based multi-attribute event matching algorithm, called HEM (hybrid event matching), which is hybridized by 2 different methods. In HEM, the matching on each single attribute (called single-attribute matching) is processed by a triangle-based matching method or a direct matching method dynamically. All single-attribute matchings are sorted via a fast near-optimal algorithm, and each of them is carried out sequentially. In this manner, the searching space of event matching shrinks gradually, so that the searching performance is boosted along with the process of event matching. Experiments are conducted to evaluate HEM comprehensively, where it is observed that HEM outperforms 3 state-of-the-art counterparts (TAMA, H-TREE and REIN) in main criteria, such as event matching time, insertion time and deletion time. Moreover, the gap of performance between HEM and the counterparts enlarges with the increase of system scale.创新点在数据分发领域中,事件匹配是其核心环节,它负责查找与某事件匹配的所有接收者。在基于内容的多属性发布/订阅系统中,由于属性维度的上升,内容规模的扩大,使得传统的事件匹配算法难以有效支撑大规模、高维度的系统级应用。基于此,本文提出了一种联合利用两种匹配方法的事件匹配机制,包含一种新颖的利用解析几何理论的方法和一种直接匹配方法。两种方法通过优化算法动态使用,使得搜索空间在事件匹配过程中不断收缩,从而有效提升了事件匹配的速度。同时,该机制的检索结构能够高效支持快速的订阅插入和删除操作,因此,在高动态发布/订阅系统中具有优异的性能。我们将本文所提出机制和多个相关领域的最新算法进行了详细的实验比较,结果表明,与这些参考算法相比,该机制具有最短的事件匹配时间,以及最短的订阅插入和删除时间。

Computation Offloading Based on Cooperations of Mobile Edge Computing-Enabled Base Stations

Mobile edge computing (MEC) can augment the computation capabilities of mobile terminals (MTs) through offloading the computational tasks from the MTs to the MEC-enabled base station (MEC-BS) covering them. However, the load of MEC-BS will rise as the increase of the scale of tasks. Existing schemes try to alleviate the load of MEC-BS through refusing, postponing, or queuing the offloading requests of the MTs; thus, the users’ QoS will largely deteriorate due to service interruption and prolonged waiting and execution time. In this paper, we investigate the cooperations of multiple MEC-BSs and propose a novel scheme to enhance the computation offloading service of an MEC-BS through further offloading the extra tasks to other MEC-BSs connected to it. An optimization algorithm is proposed to efficiently solve the optimization problem which maximizes the total benefits of time and energy consumptions gained by all the MTs covered by the MEC-BS. A balance factor is used to flexibly adjust the bias of optimization between minimizations of time and energy consumption. Extensive simulations are carried out in eight different scenarios, and the results demonstrate that our scheme can largely enhance the system performance, and it outperforms the reference scheme in all scenarios.

DEXIN: A fast content-based multi-attribute event matching algorithm using dynamic exclusive and inclusive methods

Abstract Event matching acts as a key role in publish/subscribe services, which takes charge of finding all subscriptions that match an event from a subscription set. In large content-based multi-attribute publish/subscribe systems, the performance of event matching is severely challenged due to the increased system scale and search complexity. Most existing event matching algorithms cannot effectively sustain in such a scenario. In this paper, we propose DEXIN (Dynamic EXclusive and INclusive), a fast content-based multi-attribute event matching algorithm using dynamic exclusive and inclusive methods. DEXIN supports highly efficient event matching for subscriptions with limited range-based, unlimited range-based and point-based constraints. DEXIN prepares a pipelined event matching process optimized for each incoming event. The exclusive and inclusive methods, which have different matching costs, are dynamically chosen for event matching over a single attribute. Thus, the search space shrinks very fast since non-matched subscriptions can be filtered out by DEXIN at very early stages in the pipeline. The experiment results demonstrate that DEXIN is superior to several state-of-the-art reference algorithms. Especially, the leading advantage of DEXIN is more significant on matching time and stability for large number of subscriptions and attributes in multiple scenarios.

GEM: An analytic geometrical approach to fast event matching for multi-dimensional content-based publish/subscribe services

Event matching is vital in multi-dimensional content-based publish/subscribe services, which are widely employed for data dissemination in various scenarios. Existing mechanisms suffer from performance degradation in high-dynamic large-scale systems. To this end, we present GEM (Geometrical Event Matching), an analytic geometrical approach to fast event matching. GEM offers a very high event matching speed, and it also has low costs for subscription insertion/deletion operations and memory usage. In GEM, subscriptions are organized efficiently by a triangle-like index structure. A graph partitioning matching method and a selection matching method are jointly used for single-dimensional matching (SDM). Optimized by a decision algorithm for each incoming event, the event matching process is carried out in a pipeline consisting the SDM for each dimension. The search space shrinks continuously as the process goes, so that the event matching performance is promoted adaptively. A cache method is also designed to boost the first SDM in the pipeline. We implement extensive experiments to evaluate the performance of GEM in comparison with 3 state-of-the-art reference algorithms (TAMA, H-TREE and REIN). The results show that, the event matching time, subscription insertion/deletion time and memory consumption of GEM is on average 53.9%, 42.3%/49.5% and 31.8% lower than the best in other 3 algorithms, respectively. The event matching time of GEM is reduced efficiently via the cache method. The superiority of GEM appears more significantly as system scale and dynamic grow, and its performance also maintains in a high stability.

Power constrained optimal traffic allocation for multi-access utilization of multi-mode terminals

In heterogeneous wireless networks, data transmission performance can be enhanced through the multi-access utilization of multi-mode terminals, it is carried out by the simultaneous employment of multiple parallel links over terminals multiple radio access technologies. Promoting the multi-mode terminals transmission performance while ensuring its energy efficiency is focused in this paper, we design a multiaccess transmission model and formulate the transmission delay minimization problem under the total data transmission rate demand and total transmission power constraint. An optimal traffic allocation algorithm using a modified particle swarm method is conducted to dynamically allocate traffic arrival rate for terminals each radio access technology. Compared with the traditional algorithm, simulation results show that the proposed algorithm is efficient, and can significantly reduce the transmission delay while guaranteeing the total transmission power constraint.