通信基本原理主流的通信技術(shù)短波微波衛(wèi)星 ATMTCPIPAAA路由和MPLS

1、Application-driven, energy-efficient communication in wireless sensor networks Several sensor network applications based on data diffusion and data management can determine the communication transfer rate between two sensors beforehand. In this framework, we consider the problem of energy effic

2、ient communication among nodes of a wireless sensor network and propose an application-driven approach that minimizes radio activity intervals and prolongs network lifetime. On the basis of possible communication delays we estimate packet arrival intervals at any intermediate hop of a fixed-rate dat

3、a path. We study a generic strategy of radio activity minimization wherein each node maintains the radio switched on just in the expected packet arrival intervals and guarantees low communication latency. We define a probabilistic model that allows the evaluation of the packet loss probability that

4、results from the reduced radio activity. The model can be used to optimally choose the radio activity intervals that achieve a certain probability of successful packet delivery for a specific radio activity strategy. Relying on the probabilistic model we also define a cost model that estimates the e

5、nergy consumption of the proposed strategies, under specific settings. We propose three specific strategies and numerically evaluate the associated costs. We finally validate our work with a simulation made with TOSSIM (the Berkeley motes simulator). The simulation results confirm the validity of th

6、e approach and the accuracy of the analytic models.Article Outline1. Introduction2. Related work3. Scenario4. Communication paradigm5. Probabilistic model 5.1. Success and failure probabilities5.2. Cost estimation6. Optimization of the cost function7. Alternative strategies for w(i) 7.1. Analysis of

7、 the strategies fix, lin, and mix8. Simulations9. ConclusionsThe changing usage of a mature campus-wide wireless network  無(wú)線(xiàn)局域網(wǎng)絡(luò)在數(shù)字化校園/社區(qū)/辦公區(qū)的創(chuàng)新應(yīng)用 校園無(wú)線(xiàn)通信網(wǎng)絡(luò)及其產(chǎn)品市場(chǎng)開(kāi)發(fā)Wireless Local Area Networks (WLANs) are now commonplace on many academic and corporate campuses. As “Wi-Fi” technology becomes

8、 ubiquitous, it is increasingly important to understand trends in the usage of these networks. This paper analyzes an extensive network trace from a mature 802.11 WLAN, including more than 550 access points and 7000 users over seventeen weeks. We employ several measurement techniques, including sysl

9、og messages, telephone records, SNMP polling and tcpdump packet captures. This is the largest WLAN study to date, and the first to look at a mature WLAN. We compare this trace to a trace taken after the networks initial deployment two years prior. We found that the applications used on the WLAN chan

10、ged dramatically, with significant increases in peer-to-peer and streaming multimedia traffic. Despite the introduction of a Voice over IP (VoIP) system that includes wireless handsets, our study indicates that VoIP has been used little on the wireless network thus far, and most VoIP calls are made

11、on the wired network. We saw greater heterogeneity in the types of clients used, with more embedded wireless devices such as PDAs and mobile VoIP clients. We define a new metric for mobility, the “session diameter”. We use this metric to show that embedded devices have different mobility characteris

12、tics than laptops, and travel further and roam to more access points. Overall, users were surprisingly non-mobile, with half remaining close to home about 98% of the time.Article Outline1. Introduction2. The test environment 2.1. Voice over IP2.2. Client devices3. Trace collection 3.1. Syslog3.2. SN

13、MP3.3. Ethernet sniffers3.4. VoIP CDR data3.5. Definitions3.6. Defining mobility4. Changes 4.1. Clients4.2. Traffic5. Specific applications 5.1. VoIP5.2. Peer-to-peer applications5.3. Streaming media6. Mobility7. Related work8. Conclusions and recommendations 8.1. Future workAcknowledgementsReferenc

14、esCollaborative data gathering in wireless sensor networks using measurement co-occurrence  并發(fā)性事件的衡量/確認(rèn)和信息協(xié)同化收集 無(wú)線(xiàn)傳感器網(wǎng)絡(luò)技術(shù)與建設(shè)Wireless ad hoc networks of battery-powered microsensors (WSNs) are proliferating rapidly and transforming how information is gathered and processed, and how we affec

15、t our environment. The limited energy of those sensors poses the challenge of using such systems in an energy efficient manner to perform various activities. A common activity of many applications of WSNs is that of data gathering: for each time step, gather the measurement from each sensor to a bas

16、e station. Often there is redundancy and/or dependency among the sensor measurements. How to identify the data redundancy/dependency and utilize them on improving energy efficiency of data gathering has been one of the attractive topics. We propose using measurement co-occurrence to identify data re

17、dundancy and a novel collaborative data gathering approach utilizing co-occurrence that offers a trade-off between the communication cost of data gathering versus errors at estimating the sensor measurements at the base station. A key tenant of our approach is to have sensors with co-occurring measu

18、rements alternate in transmitting such co-occurring measurements to the base station, and having the base station make inferences about the sensor measurements utilizing only the data transmitted to it. We present two effective in-network methods for detecting co-occurrence of measurements, as well

19、as a simple and efficient protocol for scheduling the transmission of the sensor measurements to the base station. We provide experimental results on synthetic and real datasets showing that the proposed system offers substantial (up to 65%) reduction of the communication costs of data gathering wit

20、h a small number of measurement inference errors (<6%) at the base station.Article Outline1. Introduction2. Estimating co-occurrence of sensor measurements 2.1. Measurement co-occurrence2.2. Estimating the resemblance of occurrence sets 2.2.1. Positional min-wise hashing2.2.2. Random projection2.

21、2.3. Mis-identification errors2.2.4. Element signatures3. Collaborative data gathering protocol exploiting measurements co-occurrence 3.1. Analysis of the costs of the protocol4. Experimental evaluation 4.1. Data sets and performance metrics4.2. Experimental results synthetic datasets4.3. Experiment

22、al results real dataset5. Related work 5.1. Set resemblance estimation5.2. Collaborative data gathering6. ConclusionAppendix AAppendix BAppendix C.  Dynamic end-to-end capacity in IEEE 802.16 wireless mesh networks  IEEE協(xié)議下無(wú)線(xiàn)網(wǎng)絡(luò)的動(dòng)態(tài)端到端訪(fǎng)問(wèn)能力/容量The IEEE 802.16 standard defines mesh mode as

23、 one of its two operational modes in medium access control (MAC). In the mesh mode, peer-to-peer communication between subscriber stations (SSs) is allowed, and transmissions can be routed via other SSs across multiple hops. In such an IEEE 802.16 mesh network, accurate and reliable determination of

24、 dynamic link capacity and end-to-end capacity of a given multi-hop route is crucial for robust network control and management. The dynamic capacities are difficult to determine in a distributed system due to decentralized packet scheduling and interference between communicating nodes caused by the

25、broadcast nature of radio propagation. In this paper, we first propose a method for computing the dynamic link capacity between two mesh nodes, and extend that to determine the dynamic end-to-end capacity bounds of a multi-hop route based on the concept of Bottleneck Zone. The physical deployments o

26、f networks are also considered in the capacity estimation. We demonstrate the effectiveness and accuracy of our methods for computing dynamic link capacity and end-to-end capacity bounds through extensive simulations.Article Outline1. Introduction2. Overview of IEEE 802.16 mesh mode3. Link capacity

27、in IEEE 802.16 mesh networks 3.1. Transmission scheduling in wireless mesh networks3.2. Link capacity computation4. End-to-end capacity in IEEE 802.16 wireless mesh networks 4.1. Concurrent transmissions in generic wireless networks4.2. Definitions4.3. End-to-end capacity bounds in dense networks or

28、 optimally deployed networks with IEEE 802.16 mesh configuration4.4. End-to-end capacity bounds in random networks with IEEE 802.16 mesh configuration5. Simulation results 5.1. Optimal deployment 5.1.1. String Topologies5.1.2. Regular mesh topology5.2. Random deployment6. Related work7. Conclusions

29、and future workAcknowledgementsAppendix A. Proof of Theorem 1Appendix B. Proof of Theorem 2Appendix C. Proof of Theorem 4ReferencesVehicular telematics over heterogeneous wireless networks: A survey This article presents a survey on vehicular telematics over heterogeneous wireless networks. An

30、advanced heterogeneous vehicular network (AHVN) architecture is outlined which uses multiple access technologies and multiple radios in a collaborative manner. The challenges in designing the essential functional components of AHVN and the corresponding protocols (for radio link control, routing, co

31、ngestion control, security and privacy, and application development) are discussed and the related work in the literature are reviewed. The open research challenges and several avenues for future research on vehicular telematics over heterogeneous wireless access networks are outlined.Article Outlin

32、e1. Introduction2. Vehicular telematic applications and requirements3. Advanced Heterogeneous Vehicular Network (AHVN) architecture for vehicular telematics 3.1. The access technology options3.2. The essential functional components and their logical relations4. Designing the AHVN architecture: chall

33、enges and approaches 4.1. Selection of access network4.2. Network selection Vs. link selection Vs. inter-system handoff4.3. Hierarchical design4.4. Operating system and application management5. Designing the AHVN protocols: challenges and approaches 5.1. Wireless access strategies5.2. MAC protocols

34、5.2.1. MAC Protocols for V2R Networks5.2.2. MAC protocols for V2V networks5.3. Data dissemination protocols5.4. Data aggregation protocols5.5. Routing protocols5.6. Congestion control protocols 5.6.1. Window-based congestion control algorithms5.6.2. Rate-based congestion control algorithms5.7. Cross

35、-layer protocol design in vehicular networks5.8. Security protocols 5.8.1. PKI-based architectures5.8.2. Hybrid security architectures for vehicular networks5.8.3. Enhancing security by data aggregation, validation, and correction5.9. Privacy protocols6. Open issues and research directionsAcknowledg

36、ementsOptimized network management for energy savings of wireless access networks The energy consumption of wireless access networks is rapidly increasing and in some countries it amounts for more than 55% of the whole communication sector and for a non negligible part of the operational costs

37、of mobile operators. The new wireless technologies with a growth of data rates by a factor of roughly 10 every 5 years and the increase in the number of users result in a doubling of the power consumption of cellular networks infrastructure every 45 years to 60 TWh in 2007. In this paper w

38、e consider possible energy savings through optimized management of on/off state and transmitted power of access stations according to traffic estimates in different hours of the day or days of the week. We propose an optimization approach based on some ILP models that minimizes energy consumption wh

39、ile ensuring area coverage and enough capacity for guaranteeing quality of service. Proposed models capture system characteristics considering different management constraints that can be considered based on traffic requirements and application scenarios. Energy minimization problems are solved to t

40、he optimum or with a gap to the optimum of less than 2.7% on a set of synthetic instances that are randomly generated. Obtained results show that remarkable energy savings, up to more than 50%, can be obtained with the proposed management strategies.Article Outline1. Introduction2. Related work3. Po

41、wer consumption model 3.1. AP power consumption3.2. Transmitted power and coverage range4. Network and traffic model 4.1. Structure of the service area4.2. Capacity load estimation4.3. Traffic pattern for different time periods4.4. Modeling traffic distribution5. Energy consumption minimization 5.1.

42、 Formulation of optimization models5.2. Basic energy optimization model5.3. Modeling complete coverage5.4. Limiting configuration variations5.5. Guaranteed powering of network devices6. Instance generator and reference models 6.1. Generator of input data6.2. Models for energy comparison7. Numerical

43、results 7.1. Results on small instances7.2. Results on realistic instance7.3. Energy savings7.4. Further extensions of the models8. ConclusionAcknowledgementsReferences無(wú)線(xiàn)網(wǎng)絡(luò)安裝部署的優(yōu)化管理與規(guī)劃設(shè)計(jì) 基于節(jié)能和可訪(fǎng)問(wèn)性的角度Wireless communications deployment in industry: a review of issues, options and technologies Com

44、puters in IndustryPresent basis of knowledge management is the efficient share of information. The challenges that modern industrial processes have to face are multimedia information gathering and system integration, through large investments and adopting new technologies. Driven by a notable commer

45、cial interest, wireless networks like GSM or IEEE 802.11 are now the focus of industrial attention, because they provide numerous benefits, such as low cost, fast deployment and the ability to develop new applications. However, wireless nets must satisfy industrial requisites: scalability, flexibili

46、ty, high availability, immunity to interference, security and many others that are crucial in hazardous and noisy environments. This paper presents a thorough survey of all this requirements, reviews the existing wireless solutions, and explores possible matching between industry and the current exi

47、sting wireless standards.1. Introduction2. Related work3. Communication systems in industry 3.1. Field level3.2. Industrial environment requirements3.3. Wireless in industry4. Wireless technology survey 4.1. General overview4.2. Common benefits of wireless networks4.3. Problems and disadvantages4.4.

48、 Regulation issues 4.4.1. Spectrum regulation issues4.4.2. Industrial and security regulation issues4.4.3. Radio frequency safety regulation issues4.5. Security issues4.6. Radio emissions issues 4.6.1. Noise and media effects on communications4.6.2. Environmental impact4.6.3. Health issues4.7. Netwo

49、rks Taxonomy and Technological description 4.7.1. Historical preview4.7.2. Cellular telephony systems 4.7.2.1. GSM4.7.2.2. GPRS and EDGE4.7.2.3. UMTS4.7.2.4. Industrial applications of cellular networks4.7.3. Local loop substitutes 4.7.3.1. LMDS and MMDS4.7.3.2. Industrial applications of WLL4.7.4.

50、Trunking 4.7.4.1. TETRA4.7.4.2. Industrial applications of TETRA4.7.5. Indoor wireless communications 4.7.5.1. DECT4.7.5.2. Industrial application of DECT4.7.6. Wireless local area networks 4.7.6.1. IEEE 802.11 and HIPERLAN4.7.7. Wireless Personal Area Networks 4.7.7.1. Bluetooth, IEEE 802.15 and Ir

51、DA4.8. Complementary technologies 4.8.1. RF Tags systems4.8.2. Positioning systems5. Applications of wireless systems in industry 5.1. Application scenarios 5.1.1. Examples of management processes5.1.2. Examples of production processes 5.1.2.1. New application scenario: a shipyard6. ConclusionsAckno

52、wledgementsReferences無(wú)線(xiàn)通信網(wǎng)在工業(yè)、生產(chǎn)、物流、過(guò)控中的應(yīng)用調(diào)查：相關(guān)技術(shù)動(dòng)態(tài) 設(shè)備選型 注意事項(xiàng)等Capacity bounds of deployment concepts for Wireless Mesh Networks Performance EvaluationLocal area wireless networks are like cellular systems: Stations associate to one out of several access points (APs), which connect to a wired ba

53、ckbone. Due to signal attenuation and transmission power limitations, radio connectivity is available only sufficiently close to an AP. In scenarios with a dense deployment of APs the wired backbone causes unprofitably high costs. A Wireless Mesh Network (WMN) serves to extend the coverage of APs by means of Mesh Points (MPs) that forward data between a station and an AP. This concept reduces deployment costs, but reduces also network capacity, owing to multi