通信基本原理,主流的通信技术,短波、微波、卫星 ATM、TCPIP、AAA、路由和MPLS

1、Application-driven, energy-efficient communication in wireless sensor networksSeveral 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 efficient c

2、ommunication 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 data path

3、. 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 result

4、s 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 energy

5、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 the appr

6、oach 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 the s

7、trategies fix, lin, and mix8. Simulations9. ConclusionsThe changing usage of a mature campus-wide wireless network无线局域网络在数字化校园/社区/办公区的创新应用 校园无线通信网络及其产品市场开发Wireless Local Area Networks (WLANs) are now commonplace on many academic and corporate campuses. As “Wi-Fi” technology becomes ubiquitous, it is

8、 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 syslog messages, telep

9、hone 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 changed dramatically,

10、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 on the wired netwo

11、rk. 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 characteristics than laptops,

12、 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. SNMP3.3. Ethernet sn

13、iffers3.4. 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 workAcknowledgementsReferencesCollaborative da

14、ta gathering in wireless sensor networks using measurement co-occurrence并发性事件的衡量/确认和信息协同化收集 无线传感器网络技术与建设Wireless ad hoc networks of battery-powered microsensors (WSNs) are proliferating rapidly and transforming how information is gathered and processed, and how we affect our environment. The limited

15、 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 base station. Often there is redu

16、ndancy 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 redundancy and a novel collabora

17、tive 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 measurements alternate in transmitt

18、ing 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 as a simple and efficient prot

19、ocol 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 with a small number of measuremen

20、t 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 projection2.2.3. Mis-identification errors2.2.

21、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. Experimental results real dataset5. Related

22、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 networksIEEE协议下无线网络的动态端到端访问能力/容量The IEEE 802.16 standard defines mesh mode as one of its two operational modes in medium access c

23、ontrol (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 dynamic link capacity and end-to-end capacity of a

24、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 broadcast nature of radio propagation. In this paper

25、, 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 of networks are also considered in the capacity estim

26、ation. 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 in IEEE 802.16 mesh networks 3.1. Transmission sched

27、uling 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 optimally deployed networks with IEEE 802.16 mesh c

28、onfiguration4.4. 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 and future workAcknowledgementsAppendix A. Proof of

29、Theorem 1Appendix B. Proof of Theorem 2Appendix C. Proof of Theorem 4ReferencesVehicular telematics over heterogeneous wireless networks: A surveyThis article presents a survey on vehicular telematics over heterogeneous wireless networks. An advanced heterogeneous vehicular network (AHVN) architectu

30、re 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, congestion control, security and privacy, and application de

31、velopment) 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 Outline1. Introduction2. Vehicular telematic applications and re

32、quirements3. 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: challenges and approaches 4.1. Selection of access network4.2.

33、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 5.2.1. MAC Protocols for V2R Networks5.2.2. MAC protocols

34、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-layer protocol design in vehicular networks5.8. Security

35、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 directionsAcknowledgementsOptimized network management for energy savings of w

36、ireless access networksThe 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 of mobile operators. The new wireless technologies with a growth

37、 of data rates by a factor of roughly 10 every 5years and the increase in the number of users result in a doubling of the power consumption of cellular networks infrastructure every 45 years to 60TWh in 2007. In this paper we consider possible energy savings through optimized management of on/off st

38、ate 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 while ensuring area coverage and enough capacity for guaranteeing quality of s

39、ervice. 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 the optimum or with a gap to the optimum of less than 2.7% on a set of synthe

40、tic 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. Power consumption model 3.1. AP power consumption3.2. Transmitted power and co

41、verage 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. Formulation of optimization models5.2. Basic energy optimization model5.3.

42、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 results 7.1. Results on small instances7.2. Results on realistic instance7.3

43、. Energy savings7.4. Further extensions of the models8. ConclusionAcknowledgementsReferences无线网络安装部署的优化管理与规划设计 基于节能和可访问性的角度Wireless communications deployment in industry: a review of issues, options and technologiesComputers in IndustryPresent basis of knowledge management is the efficient share of

44、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 commercial interest, wireless networks like GSM or IEEE 802.11 are now the focus of indu

45、strial 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, flexibility, high availability, immunity to interference, security and many others that are

46、 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 existing wireless standards.1. Introduction2. Related work3. Communication systems in

47、 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. Regulation issues 4.4.1. Spectrum regulation issues4.4.2. Industrial and security

48、 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. Networks Taxonomy and Technological description 4.7.1. Historical preview4.7.2. Cellula

49、r 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. Trunking 4.7.4.1. TETRA4.7.4.2. Industrial applications of TETRA4.7.5. Indoor wire

50、less 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 IrDA4.8. Complementary technologies 4.8.1. RF Tags systems4.8.2. Positioning systems

51、5. 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. ConclusionsAcknowledgementsReferences无线通信网在工业、生产、物流、过控中的应用调查：相关技术动态 设备选型 注意事项等Capacity bounds of d

52、eployment concepts for Wireless Mesh NetworksPerformance EvaluationLocal area wireless networks are like cellular systems: Stations associate to one out of several access points (APs), which connect to a wired backbone. Due to signal attenuation and transmission power limitations, radio connectivity

53、 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 multiple t