Tarek Elfouly

Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey

Structural health monitoring (SHM) using wireless sensor networks (WSNs) has gained research interest due to its ability to reduce the costs associated with the installation and maintenance of SHM systems. SHM systems have been used to monitor critical infrastructure such as bridges, high-rise buildings, and stadiums and has the potential to improve structure lifespan and improve public safety. The high data collection rate of WSNs for SHM pose unique network design challenges. This paper presents a comprehensive survey of SHM using WSNs outlining the algorithms used in damage detection and localization, outlining network design challenges, and future research directions. Solutions to network design problems such as scalability, time synchronization, sensor placement, and data processing are compared and discussed. This survey also provides an overview of testbeds and real-world deployments of WSNs for SH.

Joint Optimal Placement, Routing, and Flow Assignment in Wireless Sensor Networks for Structural Health Monitoring

Sensor node placement optimization has a significant role in wireless sensor networks, especially in structural health monitoring. Since sensor node placement affects the routing, optimization should be Jointly done for the node placement and routing. The existing work separately optimizes the node placement and routing (by performing routing after carrying out the node placement). However, this approach does not guarantee the optimality of the overall solution. In this paper, joint optimization of sensor placement, routing, and flow assignment is introduced and solved using mixed integer programming modeling. Finding an optimal solution for this joint problem is too complex. Hence, a near-optimal solution is obtained using genetic algorithms with reduced complexity. In addition, a heuristic algorithm for joint routing and flow assignment with placement is proposed using the effective independence model, which optimizes the information quality and energy consumption for efficient communication. Lastly, results are presented in a nine-floor building to compare the three proposed algorithms with the heuristic algorithm by Li et al. The numerical results show the efficiency of the proposed algorithms and the tradeoff between the efficiency and the complexity.

Multimodal Deep Learning Approach for Joint EEG-EMG Data Compression and Classification

In this paper, we present a joint compression and classification approach of EEG and EMG signals using a deep learning approach. Specifically, we build our system based on the deep autoencoder architecture which is designed not only to extract discriminant features in the multimodal data representation but also to reconstruct the data from the latent representation using encoder-decoder layers. Since autoencoder can be seen as a compression approach, we extend it to handle multimodal data at the encoder layer, reconstructed and retrieved at the decoder layer. We show through experimental results, that exploiting both multimodal data intercorellation and intracorellation 1) Significantly reduces signal distortion particularly for high compression levels 2) Achieves better accuracy in classifying EEG and EMG signals recorded and labeled according to the sentiments of the volunteer.

Maximizing Lifetime in Wireless Sensor Network for Structural Health Monitoring With and Without Energy Harvesting

This paper presents an optimization framework to maximize the lifetime of wireless sensor networks for structural health monitoring with and without energy harvesting. We develop a mathematical model and formulate the problem as a large-scale mixed integer non-linear programming problem. We also propose a solution based on the Branch-and-Bound algorithm augmented with reducing the search space. The proposed strategy builds up the optimal route from each source to the sink node by providing the best set of hops in each route and the optimal power allocation of each sensor node. To reduce the computational complexity, we propose heuristic routing algorithms. In this heuristic algorithm, the power levels are selected from the optimal predefined values, the problem is formulated by an integer non-linear programming, and the Branch-and-Bound reduced space algorithm is used to solve the problem. Moreover, we propose two sub-optimal algorithms to reduce the computation complexity. In the first algorithm, after selecting the optimal transmission power levels from a predefined value, a genetic algorithm is used to solve the integer non-linear problem. In the second sub-optimal algorithm, we solve the problem by decoupling the optimal power allocation scheme from the optimal route selection. Therefore, the problem is formulated by an integer non-linear programming, which is solved using the Branch-and-Bound space-reduced method with reduced binary variables (i.e., reduced complexity), and after the optimum route selection, the optimal power is allocated for each node. The numerical results reveal that the presented algorithm can prolong the network lifetime significantly compared with the existing schemes. Moreover, we mathematically formulate the adaptive energy harvesting period to increase the network lifetime with the possibility to approach infinity. Finally, the minimum harvesting period to have infinite lifetime is obtained.

Non-data-aided SNR Estimation for QPSK Modulation in AWGN Channel

Signal-to-noise ratio (SNR) estimation is an important parameter that is required in any receiver or communication systems. It can be computed either by a pilot signal data-aided approach in which the transmitted signal would be known to the receiver, or without any knowledge of the transmitted signal, which is a non-data-aided (NDA) estimation approach. In this paper, a NDA SNR estimation algorithm for QPSK signal is proposed. The proposed algorithm modifies the existing Signal-to-Variation Ratio (SVR) SNR estimation algorithm in the aim to reduce its bias and mean square error in case of negative SNR values at low number of samples of it. We first present the existing SVR algorithm and then show the mathematical derivation of the new NDA algorithm. In addition, we compare our algorithm to two baselines estimation methods, namely the M2M4 and SVR algorithms, using different test cases. Those test cases include low SNR values, extremely high SNR values and low number of samples. Results showed that our algorithm had a better performance compared to second and fourth moment estimation (M2M4) and original SVR algorithms in terms of normalized mean square error (NMSE) and bias estimation while keeping almost the same complexity as the original algorithms.

Binary consensus in sensor motes

In this work, we adapt the binary consensus algorithm for use in wireless sensor networks. Binary consensus is used to allow a collection of distributed entities to reach consensus regarding the answer to a binary question and the final decision is based on the majority opinion. Binary consensus can play a basic role in increasing the accuracy of detecting event occurrence. Existing work on the algorithm focuses on simulation of the algorithm in a purely theoretic sense. In this work, we modify the algorithm to function in wireless sensor networks by adding a method for nodes to determine who to communicate with as well as adding a heuristic for nodes to know when the algorithm has completed. We implement and test our algorithm in real wireless sensor motes and further support our results with a wireless mote simulator.

Unleashing the secure potential of the wireless physical layer

Within the paradigm of physical layer security, a physical layer characteristic is used as a common source of randomness to generate the secret key. This key is then used to encrypt the data to hide information from eavesdroppers. In this paper, we survey the most recent common sources of randomness used to generate the secret key. We present the steps used to extract the secret key from the estimated common source of randomness. We describe the metrics used to evaluate the strength of the generated key. We follow that with a qualitative comparison between different common sources of randomness along with a proposed new direction which capitalizes on hybridization of sources of randomness. We conclude by a discussion about current open research problems in secret key generation.

Distributed algorithms in wireless sensor networks

In this work, we realize the binary consensus algorithm for use in wireless sensor networks. Binary consensus is used to allow a collection of distributed entities to reach consensus regarding the answer to a binary question and the final decision is based on the majority opinion. Binary consensus can play a basic role in increasing the accuracy of detecting event occurrence. Existing work on the binary consensus algorithm focuses on simulation of the algorithm in a purely theoretical sense. We fill the gap between the theoretical work and real hardware implementation by modifying the algorithm to function in wireless sensor networks. This is achieved by adding a method for nodes to determine who to communicate with as well as adding a heuristic for nodes to know when the algorithm has completed. Our implementation is asynchronous and based on random communication. In this work, we expand our previous implementation to test it on 139 hardware testbed. Moreover, we are able to minimize the convergence time achieving ultimate results. Our implementation show successful results and all the motes are able to converge to the expected value in very short time.

Distributed binary consensus algorithm in wireless sensor networks with faulty nodes

In sensor networks, consensus is a procedure to enhance the local measurements of the sensors with those of the surrounding nodes, and leads to a final agreement about a common value. The question here is how we can achieve the the consensus in a large network containing some faulty nodes. In this paper, we present distributed binary consensus algorithm (BCA) over the wireless sensor networks (WSN) in presence of faulty nodes. With binary consensus, each sensor node, observes one of two states TRUE and FALSE and the aim is to decide which one of the two states was held by the majority of the nodes. We details the implementation of the distributed BCA in WSN when the network contains P faulty nodes. The implementation was tested on sensor nodes using the TinyOS Simulator (TOSSIM) for a WSN with a large number of nodes. Here, TOSSIM guarantees that the code performs correctly when deployed on the physical nodes. In performance evaluation, we consider the analysis of the average convergence time over the simulated environment and considering the presence of P malicious nodes. These results are presented for a WSN with different topologies such as fully connected, path, ring, Erdos Reny random, and star-shaped.

Secure multiple-users transmission using multi-path directional modulation

This work introduces a physical-layer secure multiple-users communication scheme. Our scheme employs the multi-path nature of the wireless channel to provide a different secure communication link for each of the legitimate users. We show that the proposed scheme highly degrades the eavesdroppers channel even for the worst case scenarios. We also provide the secrecy capacity and secrecy outage probability for the proposed scheme. We analyze the effect of the number of users, channel paths, and antenna elements on the secrecy performance of the scheme.