Wireless Sensor Networks for Smart Communications

(First paragraph) In the first edition of the special issue titled “Wireless Sensor Networks for Smart Communications”, a total of 22 manuscripts were received and 6 of these were accepted. This issue demonstrated that network congestion, user mobility, and adjacent spectrum interference are the main reasons for the degradation ofcommunication quality inWireless Sensor Networks (WSNs)

Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters

Abstract-This paper presents a new kind of adaptive filter: type-2 fuzzy adaptive filter (FAF); one that is realized using an unnormalized type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic system (FLS). We apply this filter to equalization of a nonlinear time-varying channel and demonstrate that it can implement the Bayesian equalizer for such a channel, has a simple structure, and provides fast inference. A clustering method is used to adaptively design the parameters of the FAF. Two structures are used for the equalizer: transversal equalizer (TE) and decision feedback equalizer (DFE). A new decision tree structure is used to implement the decision feedback equalizer, in which each leaf of the tree is a type-2 FAF. This DFE vastly reduces computational complexity as compared to a TE. Simulation results show that equalizers based on type-2 FAFs perform much better than nearest neighbor classifiers (NNC) or equalizers based on type-1 FAFs. Index Terms-Channel equalization, decision feedback equalizer, decision tree, interval type-2 TSK fuzzy logic systems, timevarying channels, type-2 fuzzy adaptive filters

Radar Sensor Networks: Algorithms for Waveform Design and Diversity with Application to ATR with Delay-Doppler Uncertainty

Automatic target recognition (ATR) in target search phase is very challenging because the target range and mobility are not yet perfectly known, which results in delay-Doppler uncertainty. In this paper, we firstly perform some theoretical studies on radar sensor network (RSN) design based on linear frequency modulation (LFM) waveform: (1) the conditions for waveform coexistence, (2) interferences among waveforms in RSN, (3) waveform diversity in RSN. Then we apply RSN to ATR with delay-Doppler uncertainty and propose maximum-likeihood (ML) ATR algorithms for fluctuating targets and nonfluctuating targets. Simulation results show that our RSN vastly reduces the ATR error compared to a single radar system in ATR with delay-Doppler uncertainty. The proposed waveform design and diversity algorithms can also be applied to active RFID sensor networks and underwater acoustic sensor networks

Radar Sensor Networks: Algorithms for Waveform Design and Diversity with Application to ATR with Delay-Doppler Uncertainty

Automatic target recognition (ATR) in target search phase is very challenging because the target range and mobility are not yet perfectly known, which results in delay-Doppler uncertainty. In this paper, we firstly perform some theoretical studies on radar sensor network (RSN) design based on linear frequency modulation (LFM) waveform: (1) the conditions for waveform coexistence, (2) interferences among waveforms in RSN, (3) waveform diversity in RSN. Then we apply RSN to ATR with delay-Doppler uncertainty and propose maximum-likeihood (ML) ATR algorithms for fluctuating targets and nonfluctuating targets. Simulation results show that our RSN vastly reduces the ATR error compared to a single radar system in ATR with delay-Doppler uncertainty. The proposed waveform design and diversity algorithms can also be applied to active RFID sensor networks and underwater acoustic sensor networks.</p

Hop-Distance Estimation in Wireless Sensor Networks with Applications to Resources Allocation

We address a fundamental problem in wireless sensor networks, how many hops does it take a packet to be relayed for a given distance? For a deterministic topology, this hop-distance estimation reduces to a simple geometry problem. However, a statistical study is needed for randomly deployed WSNs. We propose a maximum-likelihood decision based on the conditional pdf of f (r | Hi). Due to the computational complexity of f (r | Hi), we also propose an attenuated Gaussian approximation for the conditional pdf. We show that the approximation visibly simplifies the decision process and the error analysis. The latency and energy consumption estimation are also included as application examples. Simulations show that our approximation model can predict the latency and energy consumption with less than half RMSE, compared to the linear models

Biologically Inspired Target Recognition in Radar Sensor Networks

<p/> <p>One of the great mysteries of the brain is cognitive control. How can the interactions between millions of neurons result in behavior that is coordinated and appears willful and voluntary? There is consensus that it depends on the prefrontal cortex (PFC). Many PFC areas receive converging inputs from at least two sensory modalities. Inspired by human's innate ability to process and integrate information from disparate, network-based sources, we apply human-inspired information integration mechanisms to target detection in cognitive radar sensor network. Humans' information integration mechanisms have been modelled using maximum-likelihood estimation (MLE) or soft-max approaches. In this paper, we apply these two algorithms to cognitive radar sensor networks target detection. Discrete-cosine-transform (DCT) is used to process the integrated data from MLE or soft-max. We apply fuzzy logic system (FLS) to automatic target detection based on the AC power values from DCT. Simulation results show that our MLE-DCT-FLS and soft-max-DCT-FLS approaches perform very well in the radar sensor network target detection, whereas the existing 2D construction algorithm does not work in this study.</p