Bihua Tang

SNMS： an intelligent transportation system network architecture based on WSN and P2P network

With the development of city road networks, the question of how to obtain information about the roads is becoming more and more important. In this article, sensor network with mobile station (SNMS), a novel two-tiered intelligent transportation system (ITS) network architecture based on wireless sensor network (WSN) and peer-to-peer (P2P) network, is proposed to provide significant traffic information about the road and thereby, assist travelers to take optimum decisions when they are driving. A detailed explanation with regard to the strategy of each level as well as the design of two main components in the network, sensor unit (SU) and mobile station (MS), is presented. Finally, a representative scenario is described to display the operation of the system.

A Novel Multiuser Detection Algorithm for CDMA-Based MIMO-OFDM System

This paper investigates QR matrix decomposition based successive interference cancellation multiuser detection algorithms in synchronous uplink for code division multiple access based multiple input multiple output orthogonal frequency division multiplexing system. The Symbol Error Rate (SER) performance of the optimal order and the suboptimal order QR-SIC MUD algorithms are compared with conventional zero forcing and minimum mean square error multiuser detection algorithms by Monte Carlo simulations. Complexity analysis is presented at the end of the paper. Both our simulation results and complexity analysis show that SER performance of QR-Successive Interference Cancellation (QR-SIC) algorithms is superior to those of zero forcing (ZF) and minimum mean square error algorithms, and the suboptimal order QR-SIC algorithm has a good trade-off between SER performance and computation complexity.

Low-Overhead and High-Precision Prediction Model for Content-Based Sensor Search in the Internet of Things

A growing number of Internet-connected sensors have already promoted the advance of sensor search service. Accessing all available objects to find the sought sensor results in huge communication overhead, thus a low-overhead and high-precision prediction model (LHPM) is proposed to improve the sensor search efficiency. We design the approximation method to lower the reporting energy cost. Then a multistep prediction method is proposed to accurately estimate the sensor state. Furthermore, a sensor ranking method is presented to assess the matching probabilities of sensors, so as to effectively reduce the communication overhead of the search process. Simulation results demonstrate the validity of the proposed prediction model in the area of content-based sensor search.

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.

Throughput oriented forwarders selection analysis for opportunistic routing in wireless mesh network

Abstract Opportunistic routing explicitly takes advantage of the broadcast nature of wireless communications by using a set of forwarders to opportunistically perform packet forwarding. A key issue in the design of opportunistic routing protocols is the forwarder list selection problem. This paper proposes a novel routing metric which shows the end-to-end throughput and a corresponding throughput oriented opportunistic routing forwarder-selecting algorithm throughput oriented forwarders selection (TOFS) through analyzing forwarding characteristics of forwarders. The algorithm puts forward a constraint mechanism that controls the number of forwarders by constraint of throughput for forwarders selection, achieving a better balance between number of forwarders and effective link stability by introducing the factor of transmission time. Simulation results show that the algorithm can improve the network end-to-end throughput effectively over existing methods.

Capacity analysis of based-regular-topologies cognitive wireless mesh networks with power control

Abstract Capacity analysis is a fundamental and essential work for evaluating the performance of cognitive wireless mesh network (CWMN) which is considered a promising option for the future network. Power control is an efficient way to avoid interference and improve capacity of wireless mesh networks. In this paper, a quantitative result of the per-node average throughput capacity of CWMN with power control is deduced for the first time, which is much helpful for understanding the limitations of CWMN. Firstly, under the large-scale channel fading model and protocol interference model, a closed-form expression for the maximum channel capacity of each node with power control is presented, under the constraint that the interference tolerated by the primary users (PUs) does not exceed a threshold. And then, with the deduced channel capacity result, the per-node average throughput capacity of CWMN is derived based on two regular topologies, i.e. square topology and triangle topology. The simulation results indicate that the capacity is effectively improved with power control, and affected by topology, tolerated interference threshold, the number of cognitive users (CUs) and primary users (PUs).

Error exponents for two-hop Gaussian multiple source-destination relay channels

We investigate the two-hop multiple source-destination relay channels using the rate splitting transmission scheme where each source can split its message into private and public parts. We determine the system error probability via integrated exponent function under amplify-and-forward (AF) and decode-and-forward (DF) relay strategies. The most significant difference between AF and DF system error probability evaluations lies in a minimized cut-set bound of transmission rate under the DF strategy. There are many cases among transmission rate intervals for different system parameters (e.g. transmit power) and it is extremely complex to derive the system error probability for the DF strategy. We obtain a relatively simple result, which unifies various cases by a few expressions. Numerical results show that the system error probability decreases with the increase of the public message. Moreover, in order to draw deep insight into the reliability requirement of each source node in this network, we provide the error exponent region (EER) for different source nodes to show the trade-off of error probability among source nodes.

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。