On robust estimation of the location parameter

Chapter 1. Introduction and Overview of Wireless Sensor Networks. Chapter 2. Commercial and Scientific Applications of Wireless Sensor Networks. Chapter 3. Basic Wireless Sensor Technology. Chapter 4. Wireless Sensors Networks Protocols: Physical Layer. Chapter 5. Medium Access Control Protocols for Wireless Sensor Networks. Chapter 6. Sensors Network Protocols: Routing Protocols. Chapter 7. Transport Control Protocols for Wireless Sensors Networks. Chapter 8. Middleware for Sensor Networks. Chapter 9. Network Management for Wireless Sensor Networks. Chapter 10. Operating Systems for Sensor Networks. Chapter 11. Performance and Traffic Management Issues. Appendix A: Analysis. Appendix B: Discussions. Index.

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Wireless Sensor Networks: Technology, Protocols, and Applications

Orthogonal frequency-division multiplexing (OFDM) effectively mitigates intersymbol interference (ISI) caused by the delay spread of wireless channels. Therefore, it has been used in many wireless systems and adopted by various standards. In this paper, we present a comprehensive survey on OFDM for wireless communications. We address basic OFDM and related modulations, as well as techniques to improve the performance of OFDM for wireless communications, including channel estimation and signal detection, time- and frequency-offset estimation and correction, peak-to-average power ratio reduction, and multiple-input-multiple-output (MIMO) techniques. We also describe the applications of OFDM in current systems and standards.

OFDM and Its Wireless Applications: A Survey

A multiple-access MIMO WLAN system that employs MIMO, OFDM, and TDD. The system (1) uses a channel structure with a number of configurable transport channels, (2) supports multiple rates and transmission modes, which are configurable based on channel conditions and user terminal capabilities, (3) employs a pilot structure with several types of pilot (e.g., beacon, MIMO, steered reference, and carrier pilots) for different functions, (4) implements rate, timing, and power control loops for proper system operation, and (5) employs random access for system access by the user terminals, fast acknowledgment, and quick resource assignments. Calibration may be performed to account for differences in the frequency responses of transmit/receive chains at the access point and user terminals. The spatial processing may then be simplified by taking advantage of the reciprocal nature of the downlink and uplink and the calibration.

MIMO WLAN system

Network congestion occurs when offered traffic load exceeds available capacity at any point in a network. In wireless sensor networks, congestion causes overall channel quality to degrade and loss rates to rise, leads to buffer drops and increased delays (as in wired networks), and tends to be grossly unfair toward nodes whose data has to traverse a larger number of radio hops.Congestion control in wired networks is usually done using end-to-end and network-layer mechanisms acting in concert. However, this approach does not solve the problem in wireless networks because concurrent radio transmissions on different "links" interact with and affect each other, and because radio channel quality shows high variability over multiple time-scales. We examine three techniques that span different layers of the traditional protocol stack: hop-by-hop flow control, rate limiting source traffic when transit traffic is present, and a prioritized medium access control (MAC) protocol. We implement these techniques and present experimental results from a 55-node in-building wireless sensor network. We demonstrate that the combination of these techniques, Fusion, can improve network efficiency by a factor of three under realistic workloads.

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Mitigating congestion in wireless sensor networks

Channel estimation techniques for OFDM systems based on a pilot arrangement are investigated. Channel estimation based on a comb type pilot arrangement is studied through different algorithms for both estimating the channel at pilot frequencies and interpolating the channel. Channel estimation at pilot frequencies is based on LS and LMS methods while channel interpolation is done using linear interpolation, second order interpolation, low-pass interpolation, spline cubic interpolation, and time domain interpolation. Time-domain interpolation is obtained by passing to the time domain by means of IDFT (inverse discrete Fourier transform), zero padding and going back to the frequency domain by DFT (discrete Fourier transform). In addition, channel estimation based on a block type pilot arrangement is performed by sending pilots in every sub-channel and using this estimation for a specific number of following symbols. We have also implemented a decision feedback equalizer for all sub-channels followed by periodic block-type pilots. We have compared the performances of all schemes by measuring bit error rates with 16QAM, QPSK, DQPSK and BPSK as modulation schemes, and multipath Rayleigh fading and AR based fading channels as channel models.

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Channel estimation techniques based on pilot arrangement in OFDM systems

A system based on orthogonal frequency division multiple access (OFDMA) has been developed to deliver mobile broadband data service at data rates comparable to those of wired services, such as DSL and cable modems. We consider the resource allocation problem of assigning a set of subcarriers and determining the number of bits to be transmitted for each subcarrier in OFDMA systems. We compare simplicity, fairness and efficiency of our algorithm with the optimal and proposed suboptimal algorithms for varying values of delay spread, number of users and total power constraint. The results show that the performance of our approach is appealing and can be close to optimal. We also consider another resource allocation scheme in which there is no fixed QoS requirements per symbol but capacity is maximized.

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QoS aware adaptive resource allocation techniques for fair scheduling in OFDMA based broadband wireless access systems

We consider a class of sensor networks with two special characteristics. First, the nodes periodically generate data for transfer to a distinguished node called the access point. Second, the nodes are (transmit) power and energy limited, but the access point, which communicates with the 'outside world', is not so limited. Such networks might be used for instance when a geographically distributed physical process, such as traffic on a freeway or at an urban street intersection, is periodically sensed for purposes of process control. We propose a medium access control scheme, called PEDAMACS, for this special class of networks. PEDAMACS uses the high-powered access point to synchronize the nodes and to schedule their transmissions and receptions in a TDMA manner. The protocol first enables the access point to gather topology (connectivity) information. A scheduling algorithm then determines when each node should transmit its data, and the access point announces the transmission schedule to the other nodes. The scheduling algorithm ideally should minimize the delay-the time needed for data from all nodes to reach the access point. However, this optimization problem is NP-complete. PEDAMACS instead uses a polynomial-time scheduling algorithm which guarantees a delay proportional to the number of nodes in the sensor network. Because PEDAMACS schedules node transmissions, its performance is much better than that of protocols designed for more general contention (or random access) networks in terms of power consumption, delay, fairness, and congestion control. The comparison is based on simulations in TOSSIM, a simulation environment for TinyOS, the operating system for the Berkeley sensor nodes. For the traffic application we consider, the PEDAMACS network provides a lifetime of several years compared to several months and days based on random access schemes with and without sleep cycles respectively, making sensor network technology economically viable.

PEDAMACS: Power Efficient and Delay Aware Medium Access Protocol for Sensor Networks

Wireless magnetic sensor networks offer a very attractive, low-cost alternative to inductive loops for traffic measurement in freeways and at intersections. In addition to vehicle count, occupancy and speed, the sensors yield traffic information (such as vehicle classification) that cannot be obtained from loop data. Because such networks can be deployed in a very short time, they can also be used (and reused) for temporary traffic measurement. This paper reports the detection capabilities of magnetic sensors, based on two field experiments. The first experiment collected a two-hour trace of measurements on Hearst Avenue in Berkeley. The vehicle detection rate is better than 99 percent (100 percent for vehicles other than motorcycles); and estimates of vehicle length and speed appear to be better than 90 percent. Moreover, the measurements also give inter-vehicle spacing or headways, which reveal such interesting phenomena as platoon formation downstream of a traffic signal. Results of the second experiment are preliminary. Sensor data from 37 passing vehicles at the same site are processed and classified into 6 types. Sixty percent of the vehicles are classified correctly, when length is not used as a feature. The classification algorithm can be implemented in real time by the sensor node itself, in contrast to other methods based on high scan-rate inductive loop signals, which require extensive offline computation. We believe that when length is used as a feature, 80-90 percent of vehicles will be correctly classified.

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Traffic Measurement and Vehicle Classification with a Single Magnetic Sensor

Wireless magnetic sensor networks offer an attractive, low-cost alternative to inductive loops for traffic measurement in freeways and at intersections. In addition to providing vehicle count, occupancy, and speed, these sensors yield information (such as non-axle-based vehicle classification) that cannot be obtained from standard loop data. Because such networks can be deployed quickly, they can be used (and reused) for temporary traffic measurement. This paper reports the detection capabilities of magnetic sensors on the basis of two field experiments. The first experiment collected a 2-h trace of measurements on Hearst Avenue in Berkeley, California. The vehicle detection rate was better than 99% (100% for vehicles other than motorcycles), and estimates of average vehicle length and speed appear to have been better than 90%. The measurements also yield intervehicle spacing or headways, revealing interesting phenomena such as platoon formation downstream of a traffic signal. Results of the second experiment are preliminary. Sensor data from 37 passing vehicles at the same site are processed and classified into six types. Sixty percent of the vehicles are classified correctly when length is not used as a feature. The classification algorithm can be implemented in real time by the sensor node itself, in contrast to other methods based on high-scan-rate inductive loop signals, which require extensive off-line computation. It is believed that if length were used as a feature, 80% to 90% of vehicles would be correctly classified.

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Sinem Coleri

Papers

Channel estimation techniques based on pilot arrangement in OFDM systems

QoS aware adaptive resource allocation techniques for fair scheduling in OFDMA based broadband wireless access systems

PEDAMACS: Power Efficient and Delay Aware Medium Access Protocol for Sensor Networks

Traffic Measurement and Vehicle Classification with a Single Magnetic Sensor

Traffic Measurement and Vehicle Classification with Single Magnetic Sensor