IPv6 for Future Wireless Networks

The functionalities of the next generation Internet protocol, IPv6, havebecome increasingly interesting due to the current merging of the traditionalcellular mobile communications and the traditional data-communications intothe future wireless systems, as e.g., UMTS. IPv6 provides several enhancedfunctionalities requested for the future mobile systems. In this article thelargest advantages in relation to mobile systems are presented. Variousaspects of introducing IP throughout the entire mobile network (core, accessand termainals) are also presented along with an illustration of the abilitiesof the future wireless network.     

Advanced Spectrum Functionalities for Future Radio Networks

The IST WINNER (WINNER is part of the European Union research programme within the Sixth Framework Programme (FP6) of the European Information Society Technology (IST) programme. Accepted for the Wireless World Research Forum (WWRF) 2006 Industrial Forum (Kermoal, J.-P. et al. (2006). Spectrum deployment scenarios and advanced functionalities development in WINNER.) project is a major European research activity to develop a flexible and scalable radio access system for 4G, which is addressing the challenging requirements for future systems such as those of the International Telecommunication Union (ITU) in terms of overall performance, flexibility, scalability and optimized spectrum usage. In this article, the Radio Resource Management (RRM) spectrum functionalities are defined and subsequently analyzed. The proposed RRM spectrum architecture developed will result in reducing the time required to tailor new services to network operators. Furthermore, the flexible spectrum access and usage leads to more capable and faster services with high Quality-of-Service (QoS) giving more user satisfaction than conventional networks. The inherent centralization and decentralization degree for the functionalities is outlined and discussed since flexible spectrum techniques on both fast and slow basis will lead to a better utilization of spectrum.

RSS-Based Self-Localization Framework for Future Wireless Networks

The localization is an important asset in all existing and emerging wireless networking solutions since it can extend the radio environmental awareness and assist in providing better network operation. The received signal strength (RSS) based non-Bayesian transmitter localization is especially interesting due to the inherent presence of the RSS observations in all commercial radio devices and the instantaneous estimations without the need for extensive training/learning phases. The existing RSS based localization solutions neglect the problems arising from the inherent sensing network topology uncertainty. The sensors, usually assumed to have a-priori known positions, are often a subject of a previous estimation which propagates errors in the transmitter localization procedure, and, hence, results in significant transmitter localization performance degradation. This paper presents a recently developed generic RSS based joint transmitter/sensors localization framework, founded on the assumption of uncertain topology information. The derived joint maximum likelihood (JML) algorithm simultaneously estimates the transmitter and uncertain sensor positions providing twofold gains: improving the transmitter localization and reducing the network topology uncertainty. The paper broadly evaluates the JML algorithm, emphasizing the substantial localization gains originating from the joint transmitter/sensor position estimation. The results prove up to 85 % sensor position uncertainty reduction with the general system model with multiple transmitters locations and multiple previous estimations of the sensors positions. The paper also derives the theoretical lower bounds of the joint estimation framework, and proves the convergence of the JML algorithm. The presented joint estimation framework is applicable to a variety of wireless networking applications. It can provide self-awareness in future wireless networks and cope with the environment and topology dynamism in wireless ad-hoc networks.     

Maximizing Lifetime for Data Aggregation in Wireless Sensor Networks

This paper studies energy efficient routing for data aggregation in wireless sensor networks. Our goal is to maximize the lifetime of the network, given the energy constraint on each sensor node. Using linear programming (LP) formulation, we model this problem as a multicommodity flow problem, where a commodity represents the data generated from a sensor node and delivered to a base station. A fast approximate algorithm is presented, which is able to compute (1−ε)-approximation to the optimal lifetime for any ε > 0. Then along this baseline, we further study several advanced topics. First, we design an algorithm, which utilizes the unique characteristic of data aggregation, and is proved to reduce the running time of the fastest existing algorithm by a factor of K, K being the number of commodities. Second, we extend our algorithm to accommodate the same problem in the setting of multiple base stations, and study its impact on network lifetime improvement. All algorithms are evaluated through both solid theoretical analysis and extensive simulation results. Yuan Xue received her B.S. in Computer Science from Harbin Institute of Technology, China in 1994 and her M.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2002, and 2005. Currently she is an assistant professor at the Department of Electrical Engineering and Computer Science of Vanderbilt University. Her research interests include wireless and sensor networks, mobile systems, and network security. Yi Cui received his B.S. and M.S. degrees in 1997 and 1999, from Department of Computer Science, Tsinghua University, China, and his Ph.D. degree in 2005 from the Department of Computer Science, University of Illinois at Urbana-Champaign. Since then, he has been with the Department of Electrical Engineering and Computer Science at Vanderbilt University, where he is currently an assistant professor. His research interests include overlay network, peer-to-peer system, multimedia system, and wireless sensor network. Klara Nahrstedt (M ' 94) received her A.B., M.Sc degrees in mathematics from the Humboldt University, Berlin, Germany, and Ph.D in computer science from the University of Pennsylvania. She is an associate professor at the University of Illinois at Urbana-Champaign, Computer Science Department where she does research on Quality of Service(QoS)-aware systems with emphasis on end-to-end resource management, routing and middleware issues for distributed multimedia systems. She is the coauthor of the widely used multimedia book ‘Multimedia:Computing, Communications and Applications’ published by Prentice Hall, and the recipient of the Early NSF Career Award, the Junior Xerox Award and the IEEE Communication Society Leonard Abraham Award for Research Achievements, and the Ralph and Catherine Fisher Professorship Chair. Since June 2001 she serves as the editor-in-chief of the ACM/Springer Multimedia System Journal. An erratum to this article is available at .     

SCT Based Adaptive Data Aggregation for Wireless Sensor Networks

Communication overhead is a major concern in wireless sensor networks because of inherent behavior of resource constrained sensors. To degrade the communication overhead, a technique called data aggregation is employed. The data aggregation results are used to make crucial decisions. Certain applications apply approximate data aggregation in order to reduce communication overhead and energy levels. Specifically, we propose a technique called semantic correlation tree, which divides a sensor network into ring-like structure. Each ring in sensor network is divided into sectors, and each sector consists of collection of sensor nodes. For each sector, there will be a sector head that is aggregator node, the aggregation will be performed at sector head and determines data association on each sector head to approximate data on sink node. We propose a doorway algorithm to approximate the sensor node readings in sector head instead of sending all sensed data. The main idea of doorway algorithm is to reduce the congestion and also the communication cost among sensor nodes and sector head. This novel approach will avoid congestion by controlling the size of the queue and marking packets. Specifically, we propose a local estimation model to generate a new sensor reading from historic data. The sensor node sends each one of its parameter to sector head, instead of raw data. The doorway algorithm is utilized to approximate data with minimum and maximum bound value. This novel approach, aggregate the data approximately and efficiently with limited energy. The results demonstrate accuracy and efficiency improvement in data aggregation.     

Bandwidth Reallocation for Bandwidth Asymmetry Wireless Networks Based on Distributed Multiservice Admission Control

This paper addresses when and how to adjust bandwidth allocation on uplink and downlink in a multi-service mobile wireless network under dynamic traffic load conditions. Our design objective is to improve system bandwidth utilization while satisfying call level QoS requirements of various call classes. We first develop a new threshold-based multi-service admission control scheme (DMS-AC) as the study base for bandwidth re-allocation. When the traffic load brought by some specific classes under dynamic traffic conditions in a system exceeds the control range of DMS-AC, the QoS of some call classes may not be guaranteed. In such a situation, bandwidth re-allocation process is activated and the admission control scheme will try to meet the QoS requirements under the adjusted bandwidth allocation. We explore the relationship between admission thresholds and bandwidth allocation by identifying certain constraints for verifying the feasibility of the adjusted bandwidth allocation. We conduct extensive simulation experiments to validate the effectiveness of the proposed bandwidth re-allocation scheme. Numerical results show that when traffic pattern with certain bandwidth asymmetry between uplink and downlink changes, the system can re-allocate the bandwidth on uplink and downlink adaptively and at the same time improve the system performance significantly.     

认知无线网络中一种新的频谱共享方法

频谱共享是认知无线网络关键技术之一。为消除认知无线网络中频率选择性信道下授权主用户与认知用户间的相互干扰,本文提出了一种新的频谱共享方法。该方法充分利用了无线通信系统中由信道的频率选择性衰落导致的不同用户信道的不相关性,通过求解矩阵方程获得预处理矩阵的通解,并在主用户和认知用户发射端分别进行预处理。从而实现认知系统中主用户与认知用户之间的相互零干扰,并使每个用户都可有效地传输数据。理论推导及系统仿真均表明,新方法可以有效地消除授权主用户与认知用户之间的双向干扰,实现不同用户平等地共享无线频谱资源。新方法可以提高频谱的利用率,一定程度上缓解无线频谱资源在当前及未来无线通信领域日益紧缺的矛盾。而且新方法也同样适用于不同认知用户之间共享频谱。      

Channel Selection for Spectrum Sharing in Wireless Networks

In this paper, we study a spectrum sharing network (SSN) where a spectrum sharing device (SSD) coexists with multiple wireless communication systems (WCSs) in the same channel. The SSD can operate with either a duty cycle (DC) channel access mechanism or a listen‐before‐talk (LBT) channel access mechanism, whereas WCSs operate with an LBT mechanism. An opportunistic channel selection scheme for the SSD in the SSN is first proposed to minimize the outage probability. The optimal data transmission time for the DC‐based SSD is derived to further improve the outage probability. We also derive the exact and closed‐form outage probability of the proposed channel selection in the SSN by assuming that the number of WCSs operating in each channel is uniformly distributed. The simulation results show that the proposed channel selection scheme outperforms other channel selection schemes. It was also observed that a DC‐based SSD with an optimal data transmission time provides a better outage performance than an LBT‐based SSD. As the number of available channels increases, the channel selection scheme plays an important role in minimizing the outage probability of the SSNs.     

Data Aggregation in Wireless Sensor Networks: Previous Research,Current Status and Future Directions

Wireless sensor networks (WSNs) consist of large number of small sized sensor nodes, whose main task is to sense the desired phenomena in a particular region of interest. These networks have large number of applications such as habitat monitoring, disaster management, security and military etc. Sensor nodes are very small in size and have limited processing capability as these nodes have very low battery power. WSNs are also prone to failure, due to low battery power constraint. Data aggregation is an energy efficient technique in WSNs. Due to high node density in sensor networks same data is sensed by many nodes, which results in redundancy. This redundancy can be eliminated by using data aggregation approach while routing packets from source nodes to base station. Researchers still face trouble to select an efficient and appropriate data aggregation technique from the existing literature of WSNs. This research work depicts a broad methodical literature analysis of data aggregation in the area of WSNs in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 123 research papers out of large collection of 932 research papers published in 20 foremost workshops, symposiums, conferences and 17 prominent journals. The current status of data aggregation in WSNs is distributed into various categories. Methodical analysis of data aggregation in WSNs is presented which includes techniques, tools, methodology and challenges in data aggregation. The literature covered fifteen types of data aggregation techniques in WSNs. Detailed analysis of this research work will help researchers to find the important characteristics of data aggregation techniques and will also help to select the most suitable technique for data aggregation. Research issues and future research directions have also been suggested in this research literature.     

基于分簇的无线多媒体传感器网络数据聚合方案研究

该文提出了一种基于分簇的无线多媒体传感器网络(WMSNs)数据聚合方案(Cluster-based Data Aggregation Algorithm, CDAA)。利用新的分簇方法和数据聚合策略,CDAA可以有效延长网络生命期。根据多媒体节点数据采集的方向性和节点剩余能耗,该文提出新的无线多媒体传感器网络的分簇方法,并基于该分簇方法进行网内多媒体数据聚合。仿真结果表明,该方法能够有效减少冗余数据的传送,与LEACH, PEGASIS等传统WSNs路由协议和针对WMSNs的AntSensNet协议相比,在能耗均衡和节能方面表现出更好的性能。     

无线传感器网络中有效的安全数据融合机制

在无线传感器网络中,数据融合是实现有效传输和节省能源的一个重要途径,许多应用都需要可靠并且可信的数据来进行融合.针对上述要求,提出了一个新的安全数据融合算法来保证融合数据的机密性和完整性.算法使用端到端加密和逐跳加密相结合的方式进行数据传输,通过认证过程进行恶意节点及伪造数据的检测.仿真表明,提出的算法能够有效地检测出恶意节点,并保证融合结果的准确性.     

Multi-Source Temporal Data Aggregation in Wireless Sensor Networks

Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes.This paper proposes an energy-efficient multi-source temporal data aggregation model called MSTDA in WSNs. In MSTDA model, a feature selection algorithm using particle swarm optimization (PSO) is presented to simplify the historical data source firstly. And then a data prediction algorithm based on improved BP neural network with PSO (PSO-BPNN) is proposed. This MSTDA model, which helps to find out potential laws according to historical data sets, is deployed at both the base station (BS) and the node. Only when the deviation between the actual and the predicted value at the node exceeds a certain threshold, the sampling value and new model are sent to BS. The experiments on the dataset which comes from the actual data collected from 54 sensors deployed in the Intel Berkeley Research lab made a satisfied performance. When the error threshold greater than 0.15, it can decrease more than 80% data transmissions.     

A Self-Adaptive Spectrum Management Middleware for Wireless Sensor Networks

The vision of the Internet of Things, wherein everyday objects are embedded with smart wireless sensor devices, is making these sensor devices increasingly pervasive. As the density of their deployment in overlapping or adjacent areas increases, the contention for the unlicensed 2.4GHz ISM band will also increase. To deal with the crowded spectrum, nodes must use the channels more judiciously and be able to adapt by detecting and switching to the most available channel. The SAS middleware that we have developed, is a self-adaptive spectrum management middleware for wireless sensor networks that enhances single-frequency MAC protocols with multi-frequency capability, without any change in hardware. It allows a single-frequency MAC protocol, like B-MAC, to automatically adapt to the least congested physical channel at runtime. SAS supports a combination of receiver-initiated and sender-initiated schemes to decide when to switch the channel and which channel to switch to. We have implemented the B-MAC protocol integrated with SAS in TinyOS 2.1 on TelosB sensor devices and evaluated its performance on the conditions of varied data flows and the interference produced by a jammer. The results demonstrate that the integrated B-MAC protocol outperforms B-MAC in terms of packet reception ratio, system throughput, average packet delay, and energy consumption.     

Data Aggregation Routing for Rechargeable Wireless Sensor Networks in Forest Monitoring

In rechargeable wireless sensor networks (r-WSNs), higher data transmitted efficiency is required because sensor have to operate in a very low duty cycle owing to sporadic availability of energy. In r-WSNs, Data collected by many sensors is based on common phenomena, and hence there is a high probability that this data has some redundancy. In this work, we address the problem of jointly optimizing data aggregation and routing so that the network workload can be maximized. Establish the relationship model between data aggregation rate and throughput, so that the balanced was set up between the data aggregation rate and maximum network data traffic. Through the use of optimal candidate sample allocation, the algorithm can coverage efficiently and can make the maximum data aggregation rate flow to the network while maximizing network workload. Simulations are carried out to show that the proposed algorithm can significantly improve workload.     

Distributed Algorithm for En Route Aggregation Decision in Wireless Sensor Networks

In sensor networks, enroute aggregation decision regarding where and when aggregation shall be performed along the routes has been explicitly or implicitly studied extensively. However, existing solutions have omitted one key dimension in the optimization space, namely, the aggregation cost. In this paper, focusing on optimizing over both transmission and aggregation costs, we develop an online algorithm capable of dynamically adjusting the route structure when sensor nodes join or leave the network. Furthermore, by only performing such reconstructions locally and maximally preserving existing routing structure, we show that the online algorithm can be readily implemented in real networks in a distributed manner requiring only localized information. Analytically and experimentally, we show that the online algorithm promises extremely small performance deviation from the offline version, which has already been shown to outperform other routing schemes with static aggregation decision.     

A Path-Based Approach for Data Aggregation in Grid-Based Wireless Sensor Networks

Sensor nodes in a wireless sensor network(WSN) are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme(PBDAS) for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station(BS), we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.     

Optimal Stochastic Policies for Distributed Data Aggregation in Wireless Sensor Networks

The scenario of distributed data aggregation in wireless sensor networks is considered, where sensors can obtain and estimate the information of the whole sensing field through local data exchange and aggregation. An intrinsic tradeoff between energy and aggregation delay is identified, where nodes must decide optimal instants for forwarding samples. The samples could be from a node's own sensor readings or an aggregation with samples forwarded from neighboring nodes. By considering the randomness of the sample arrival instants and the uncertainty of the availability of the multiaccess communication channel, a sequential decision process model is proposed to analyze this problem and determine optimal decision policies with local information. It is shown that, once the statistics of the sample arrival and the availability of the channel satisfy certain conditions, there exist optimal control-limit-type policies that are easy to implement in practice. In the case that the required conditions are not satisfied, the performance loss of using the proposed control-limit-type policies is characterized. In general cases, a finite-state approximation is proposed and two on-line algorithms are provided to solve it. Practical distributed data aggregation simulations demonstrate the effectiveness of the developed policies, which also achieve a desired energy-delay tradeoff.     

An Enhanced A-MSDU Frame Aggregation Scheme for 802.11n Wireless Networks

The main goal of the IEEE 802.11n standard is to achieve a minimum throughput of 100 Mbps at the MAC service access point. This high throughput has been achieved via many enhancements in both the physical and MAC layers. A key enhancement at the MAC layer is frame aggregation in which the timing and headers overheads of the legacy MAC are reduced by aggregating multiple frames into a single large frame before being transmitted. Two aggregation schemes have been defined by the 802.11n standard, aggregate MAC service data unit (A-MSDU) and aggregate MAC protocol data unit (A-MPDU). As a consequence of the aggregation, new aggregation headers are introduced and become parts of the transmitted frame. Even though these headers are small compared to the legacy headers they still have a negative impact on the network performance, especially when aggregating frames of small payload. Moreover, the A-MSDU is highly influenced by the channel condition due mainly to lack of subframes sequence control and retransmission. In this paper, we have proposed an aggregation scheme (mA-MSDU) that reduces the aggregation headers and implements a retransmission control over the individual subframes at the MSDU level. The analysis and simulations results show the significance of the proposed scheme, specifically for applications that have a small frame size such as VoIP.     

一种同时保障隐私性与完整性的无线传感器网络可恢复数据聚合方案

该文针对无线传感器网络(WSNs)数据聚合与安全目标之间的矛盾,基于隐私同态和聚合消息验证码技术提出一种同时保障数据隐私性与完整性的可恢复数据聚合方案。该方案支持由聚合结果恢复出各感知数据,从而一方面能够验证感知数据和聚合数据的完整性,另一方面能够对原始数据进行任意所需的处理,不受聚合函数类型的限制。安全分析表明该方案不仅支持数据隐私性、完整性,还能够抵抗未授权聚合攻击,聚合节点俘获攻击,且能够在一定范围内检测及定位恶意节点。性能分析表明,该方案相比其他算法在通信和计算开销方面具有显著优势。为了评估方案性能和可行性,基于TinyOS给出了算法的原型实现。实验结果表明,该方案开销较低,对于资源受限的WSNs是高效可行的。