Detection and estimation in Wireless Sensor Networks

WSNs spatially deployed over a field can be designed to collect information and monitor many phenomena of interest. Important role in several daily application scenarios such as health-care monitoring, home applications, smart farming, environment monitoring, and military. Low Power Hardware: Clearly, the biggest design constraint in WSNs still remains the power consumption. Even-though the SNs are being designed using low-power micro controllers, their power dissipation is still orders of magnitude too high. Resource Constraints: Battery operated devices with limited on-board energy, both the system lifetime and communication bandwidth (BW) are restricted. Both the signal processing and communication should be carefully designed to consume minimal energy in order to extend the lifetime and improve the overall reliability of the WSN. Network Security:Usually unattended (geographically dispersed) and this makes them vulnerable to attacks. The overall detection and estimation strongly depends on the reliability of these SNs

LDPC coded OFDM and its application to DVB-T2, DVB-S2 and IEEE 80216e

Since the invention of Information Theory by Shannon in 1948, coding theorists have been trying to come up with coding schemes that will achieve capacity dictated by Shannon’s Theorem. The most successful two coding schemes among many are the LDPCs and Turbo codes. In this thesis, we focus on LDPC codes and in particular their usage by the second generation terrestrial digital video broadcasting (DVB-T2), second generation satellite digital video broadcasting (DVB-S2) and IEEE 802.16e mobile WiMAX standards. Low Density Parity Check (LDPC) block codes were invented by Gallager in 1962 and they can achieve near Shannon limit performance on a wide variety of fading channels. LDPC codes are included in the DVB-T2 and DVB-S2 standards because of their excellent error-correcting capabilities. LDPC coding has also been adopted as an optional error correcting scheme in IEEE 802.16e mobile WiMAX. This thesis focuses on the bit error rate (BER) and PSNR performance analysis of DVB-T2, DVB-S2 and IEEE 802.16e transmission using LDPC coding under additive white Gaussian noise (AWGN) and Rayleigh Fading channel scenarios

Distributed Detection and Estimation in Wireless Sensor Networks: Resource Allocation, Fusion Rules, and Network Security

This thesis addresses the problem of detection of an unknown binary event. In particular, we consider centralized detection, distributed detection, and network security in wireless sensor networks (WSNs). The communication links among SNs are subject to limited SN transmit power, limited bandwidth (BW), and are modeled as orthogonal channels with path loss, flat fading and additive white Gaussian noise (AWGN). We propose algorithms for resource allocations, fusion rules, and network security. In the first part of this thesis, we consider the centralized detection and calculate the optimal transmit power allocation and the optimal number of quantization bits for each SN. The resource allocation is performed at the fusion center (FC) and it is referred as a $centralized$ approach. We also propose a novel fully $distributed$ algorithm to address this resource allocation problem. What makes this scheme attractive is that the SNs share with their neighbors just their individual transmit power at the current states. Finally, the optimal soft fusion rule at the FC is derived. But as this rule requires a-priori knowledge that is difficult to attain in practice, suboptimal fusion rules are proposed that are realizable in practice. The second part considers a fully distributed detection framework and we propose a two-step distributed quantized fusion rule algorithm where in the first step the SNs collaborate with their neighbors through error-free, orthogonal channels. In the second step, local 1-bit decisions generated in the first step are shared among neighbors to yield a consensus. A binary hypothesis testing is performed at any arbitrary SN to optimally declare the global decision. Simulations show that our proposed quantized two-step distributed detection algorithm approaches the performance of the unquantized centralized (with a FC) detector and its power consumption is shown to be 50% less than the existing (unquantized) conventional algorithm. Finally, we analyze the detection performance of under-attack WSNs and derive attacking and defense strategies from both the Attacker and the FC perspective. We re-cast the problem as a minimax game between the FC and Attacker and show that the Nash Equilibrium (NE) exists. We also propose a new non-complex and efficient reputation-based scheme to identify these compromised SNs. Based on this reputation metric, we propose a novel FC weight computation strategy ensuring that the weights for the identified compromised SNs are likely to be decreased. In this way, the FC decides how much a SN should contribute to its final decision. We show that this strategy outperforms the existing schemes

Image transmission over Gilbert-Elliot and ITU fading channels using DVB-T2 channel coding and QPSK-OFDM

In this work, a concatenated forward error correction (FEC) scheme together with Orthogonal Frequency Division Multiplexing (OFDM) have been used for effective transmission of data/images over additive and fading channels. With a Bose Chaudhuri Hocquenghem (BCH) code as the outer code and a Low Density Parity Check (LDPC) code as the inner code, the transmission has been simulated over both the Gilbert-Elliot and ITU Rayleigh fading channels. The FEC parameters assumed throughout the simulations were obtained from the DVB-T2 standard and the Base Band (BB) frames were created by making use of shortening and zero-padding concepts. The results which have been presented in terms of BER and psycho-visual performances show the resilience of the FEC schemes and OFDM to channel impairments. The BER performances attained over the Gilbert-Elliot Channel (a channel that introduces burst errors when in the bad state) using LDPC only and BCH-LDPC concatenated coding indicated that the outer BCH coding will start to achieve a much lower BER after an SNR of 5 dB. Over the ITU-A Rayleigh fading channel it was observed that the performance increment due to the outer BCH encoder only become apparent after 6 dB when compared to the rate ¼ LDPC only coded system BER performance. Over the Gilbert-Elliot channel a BCH-LDPC coded QPSK-OFDM system would provide a BER of 3×10-4 at 6 dB while the same BER for the ITU Vehicular-A channel was possible at 6.6 dB

Distributed Detection and Estimation in Wireless Sensor Networks: Resource Allocation, Fusion Rules, and Network Security

This thesis addresses the problem of detection of an unknown binary event. In particular, we consider centralized detection, distributed detection, and network security in wireless sensor networks (WSNs). The communication links among SNs are subject to limited SN transmit power, limited bandwidth (BW), and are modeled as orthogonal channels with path loss, flat fading and additive white Gaussian noise (AWGN). We propose algorithms for resource allocations, fusion rules, and network security. In the first part of this thesis, we consider the centralized detection and calculate the optimal transmit power allocation and the optimal number of quantization bits for each SN. The resource allocation is performed at the fusion center (FC) and it is referred as a $centralized$ approach. We also propose a novel fully $distributed$ algorithm to address this resource allocation problem. What makes this scheme attractive is that the SNs share with their neighbors just their individual transmit power at the current states. Finally, the optimal soft fusion rule at the FC is derived. But as this rule requires a-priori knowledge that is difficult to attain in practice, suboptimal fusion rules are proposed that are realizable in practice. The second part considers a fully distributed detection framework and we propose a two-step distributed quantized fusion rule algorithm where in the first step the SNs collaborate with their neighbors through error-free, orthogonal channels. In the second step, local 1-bit decisions generated in the first step are shared among neighbors to yield a consensus. A binary hypothesis testing is performed at any arbitrary SN to optimally declare the global decision. Simulations show that our proposed quantized two-step distributed detection algorithm approaches the performance of the unquantized centralized (with a FC) detector and its power consumption is shown to be 50% less than the existing (unquantized) conventional algorithm. Finally, we analyze the detection performance of under-attack WSNs and derive attacking and defense strategies from both the Attacker and the FC perspective. We re-cast the problem as a minimax game between the FC and Attacker and show that the Nash Equilibrium (NE) exists. We also propose a new non-complex and efficient reputation-based scheme to identify these compromised SNs. Based on this reputation metric, we propose a novel FC weight computation strategy ensuring that the weights for the identified compromised SNs are likely to be decreased. In this way, the FC decides how much a SN should contribute to its final decision. We show that this strategy outperforms the existing schemes

Distributed detection and estimation in wireless sensor networks: resource allocation, fusion rules, and network security

This thesis addresses the problem of detection of an unknown binary event. In particular, we consider centralized detection, distributed detection, and network security in wireless sensor networks (WSNs). The communication links among SNs are subject to limited SN transmit power, limited bandwidth (BW), and are modeled as orthogonal channels with path loss, flat fading and additive white Gaussian noise (AWGN). We propose algorithms for resource allocations, fusion rules, and network security. In the first part of this thesis, we consider the centralized detection and calculate the optimal transmit power allocation and the optimal number of quantization bits for each SN. The resource allocation is performed at the fusion center (FC) and it is referred as a centralized approach. We also propose a novel fully $distributed$ algorithm to address this resource allocation problem. What makes this scheme attractive is that the SNs share with their neighbors just their individual transmit power at the current states. Finally, the optimal soft fusion rule at the FC is derived. But as this rule requires a-priori knowledge that is difficult to attain in practice, suboptimal fusion rules are proposed that are realizable in practice. The second part considers a fully distributed detection framework and we propose a two-step distributed quantized fusion rule algorithm where in the first step the SNs collaborate with their neighbors through error-free, orthogonal channels. In the second step, local 1-bit decisions generated in the first step are shared among neighbors to yield a consensus. A binary hypothesis testing is performed at any arbitrary SN to optimally declare the global decision. Simulations show that our proposed quantized two-step distributed detection algorithm approaches the performance of the unquantized centralized (with a FC) detector and its power consumption is shown to be 50% less than the existing (unquantized) conventional algorithm. Finally, we analyze the detection performance of under-attack WSNs and derive attacking and defense strategies from both the Attacker and the FC perspective. We re-cast the problem as a minimax game between the FC and Attacker and show that the Nash Equilibrium (NE) exists. We also propose a new non-complex and efficient reputation-based scheme to identify these compromised SNs. Based on this reputation metric, we propose a novel FC weight computation strategy ensuring that the weights for the identified compromised SNs are likely to be decreased. In this way, the FC decides how much a SN should contribute to its final decision. We show that this strategy outperforms the existing schemes

A Practical Implementation of an Agriculture Field Monitoring using Wireless Sensor Networks and IoT Enabled

In this work, we consider the problem of designing a state of the art energy-efficient wireless sensor network (WSN) practically deployed in a large field. The sensor nodes (SNs) are tasked to monitor a large region of interest (ROI) and report their test statistics to the fusion center (FC) over a wireless fading channel. To maximize the lifetime of the WSN and enable long range communication with minimal transmit power, the long range wide area network (LoRaWAN) communication protocol is adopted. Each of the SN is designed and enabled with several state of the art sensors in order to estimate different and diverse parameters of interest (e.g., soil moisture, soil temperature, and salinity at different soil depth; barometric pressure, ambient humidity, leaf wetness, and etc.). The core feature of the proposed solution is that the SNs learn and adopt over the sensing time. This is very important in extending the operational lifetime of the WSN. The proposed system is validated through the infield experiments using few concept devices. Experimental results show that the proposed WSN features an effective large ROI monitoring with minimal number of SNs, a significantly reduced SN transmission power required and thus an extended WSN operational lifetime

Image transmission over fading channels using RS-CC versus LDPC coding

In this paper we present effective means of digital image transmission by means of Forward Error Correcting (FEC) schemes and Orthogonal Frequency Division Multiplexing (OFDM). The transmission was simulated over the AWGN and a Rayleigh fading channel whose power delay profile was adopted from the ITU channel model. The FEC and OFDM parameters were adopted from the DVB-T, WiMAX, and DVB-T2 standards. The results presented herein are in terms of BER, PSNR and visual performances. It is evident from the presented results that effective FEC schemes are necessary for reliable transmission of digital media in a mobile wireless scenario. Image transmission over fading channels using RS-CC versus LDPC coding

Distributed binary event detection under data-falsification and energy-bandwidth limitation

We address the problem of centralized detection of a binary event in the presence of falsifiable sensor nodes (SNs) (i.e., controlled by an attacker) for a bandwidth-constrained under-attack spatially uncorrelated distributed wireless sensor network (WSN). The SNs send their quantized test statistics over orthogonal channels to the fusion center (FC), which linearly combines them to reach a final decision. First (considering that the FC and the attacker do not act strategically), we derive (i) the FC optimal weight combining; (ii) the optimal SN to FC transmit power, and (iii) the test statistic quantization bits that maximize the probability of detection (Pd). We also derive an expression for the attacker strategy that causes the maximum possible FC degradation. But in these expressions, both the optimum FC strategy and the attacker strategy require

Optimal quantization and power allocation for energy-based distributed sensor detection

We consider the decentralized detection of an unknown deterministic signal in a spatially uncorrelated distributed wireless sensor network. N samples from the signal of interest are gathered by each of the M spatially distributed sensors, and the energy is estimated by each sensor. The sensors send their quantized information over orthogonal channels to the fusion center (FC) which linearly combines them and makes a final decision. We show how by maximizing the modified deflection coefficient we can calculate the optimal transmit power allocation for each sensor and the optimal number of quantization bits to match the channel capacity