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.

In this work, we extend and adapt the binary consensus algorithm to operate using clusters in a wireless sensor network. Binary consensus is a decision making algorithm used to cause distributed entities to agree on the majority opinion held by the group when posed with a true or false question. Clustering is a common technique used in WSNs to increase the overall lifetime of the network by reducing energy consumed due to communication. Our clustered binary consensus implementation is tested using a large WSN testbed, and the experimental results show that incorporating clustering into the binary consensus algorithm reduces the time required for motes to reach consensus and lowers the number of packets sent in the network by approximately 94%

The paper cogitates about a comparative simulation for various distillation, reconciliation, and privacy amplification techniques that are used to generate secure symmetric physical layer keys. Elementary wireless model of two mobile nodes in the presence of a passive eavesdropper is used to perform the comparison process. Important modifications are proposed to some phases’ techniques in order to increase the performance of the generation process as a whole. Different metrics were used for comparison in each phase, in the distillation phase, we use the Bit Mismatch Rate (BMR) for different SNR values to compare various extracted random strings of the two intended nodes. On the other hand, the messaging rate and process complexity is exploited to estimate the performance of the compared techniques in both reconciliation and privacy amplification phases. The randomness and entropy properties of the keys are verified using the NIST suite, all the generated keys are 128 bits, it is shown that the success rate of the keys passing the randomness tests depends strongly on the techniques that are used through the three generation phases.

We consider the design and implementation of the binary consensus algorithm in wireless sensor networks (WSN) under real life environment. This algorithm is applied for the evaluation of a consensus of a measured values in presence of a faulty/attacked node. As this algorithm has been tested theoretically, we deploy it for real-life environment including distributed and routing features. In this paper, we propose the development and the implementation of the distributed binary consensus algorithm in WSN under Tinyos environment. The implementation was tested on sensor nodes using the TinyOSSimulator for a WSN with a large number of nodes and a testbed with limited number of nodes. We evaluate the performances related to the average convergence time of nodes states to a consensus value. As in analytical results, in the simulations, we applied the distributed algorithm for fully connected, ring, cycle, Erdos Reny random, and star-shaped topologies.

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.

Mobile-health (m-health) systems leverage wireless and mobile communication technologies to promote new ways to acquire, process, transport, and secure the raw and processed medical data to provide the scalability needed to cope with the increasing number of elderly and chronic disease patients requiring constant monitoring. However, the design and operation of such health monitoring systems with Body Sensor Networks (BASNs) is challenging in twofold. First, limited power source of the sensor nodes. Second, Quality of Service (QoS) guarantee for the delivery of medical data. Therefore, we propose a cross-layer framework that integrates network particularities and application requirements and constraints to provide a sustainable and high-quality service for health monitoring systems. This framework focuses on energy minimization and EEG signal distortion trade-off for delay sensitive transmission of medical data over heterogeneous wireless environment. Simulation results show that the proposed scheme achieves the optimal tradeoff between energy efficiency and QoS requirements for health monitoring systems.

Estimating the number of sources received by an antenna array have been well known and investigated since the starting of array signal processing. Accurate estimation of such parameter is critical in many applications that involve prior knowledge of the number of received signals. Information theoretic approaches such as Akaikes information criterion (AIC) and minimum description length (MDL) have been used extensively even though they are complex and show bad performance at some stages. In this paper, a new algorithm for estimating the number of sources is presented. This algorithm exploits the estimated eigenvalues of the auto correlation coefficient matrix rather than the auto covariance matrix, which is conventionally used, to estimate the number of sources. We propose to use either of a two simply estimated decision statistics, which are the moving increment and moving standard deviation as metric to estimate the number of sources. Then process a simple calculation of the increment or standard deviation of eigenvalues to find the number of sources at the location of the maximum value. Results showed that our proposed algorithms have a better performance in comparison to the popular and more computationally expensive AIC and MDL at low SNR values and low number of collected samples.

This paper studies the optimal placement problem in structural health monitoring (SHM) using wireless sensor networks (WSN). This problem is formulated as a refined multi-objective optimization problem with various constraints of connectivity and flow conservation conditions, while minimizing the energy consumption and maximizing the information quality are the objectives. To perform the multi-objective optimization, we modify the heuristic single objective power-aware sensor placement using the effective independence model (p-SPEM) algorithm introduced in [1]. In this paper, we design and analyze a multi-objective p-SPEM (mop-SPEM) algorithm for sensor node placement. The multi-objective formulation adds more flexibility because the energy consumption and the information quality can be easily traded-off. The mop-SPEM is simulated and compared with p-SPEM for a nine-storey building. Results show that mop-SPEM is able to trade-off the energy with the information quality by adjusting the relative weights of the objective functions.

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

The link delivery probability in a wireless mesh network is most accurately determined only by experiment, as it depends on many factors, including environment characteristics, transmission power, distance between transmitter and receiver, channel fading, and background noise. This paper provides an insightful framework for characterizing link performance results produced at Qatar University (QU) wireless mesh test-bed under harsh environmental conditions, including heavy buildings structure, mixed indoor/outdoor architecture, weather conditions, and coexistence with high interfering wireless transmission. Indeed, we are interested in looking at the link quality behavior for indoor and outdoor environments. In the outdoor environment, the effect of different weather conditions such as high humidity and sandstorms, which are typical in the Qatari weather, will be analyzed. Regarding the indoor environment, we investigate the effect of unique cell structure, and heavy lab machinery of the engineering building on the link performance. Furthermore, we study different wireless mesh node configurations and equipments’ effect on performance and network connectivity. The causes behind link instability and performance shortcoming of the QU wireless mesh network (QUMESH) are identified. Thus, the content of this paper provides an input for developing the criteria for the test-bed and performing the evaluation of new proposed adaptive mechanisms.

Wireless sensor networks (WSNs) have shown to be an efficient solution for physical parameters monitoring and activity detection. WSNs are usually designed using single optimization by optimizing one objective function such as energy consumption taking some constraints into consideration. In multi-objective optimization (MOPT) multiple and often conflicting objectives need to be optimized while satisfying some constraints. This paper proposes MOPT-based routing and flow rate assignment methodologies for WSNs. The first methodology employs Karush-Kuhn-Tucker (KKT) conditions to find the optimal solution, while the second one is based on genetic algorithms (GA). A new formulation is introduced jointly minimize the energy and the delay. Performance metrics such as the average end-to-end delay and energy consumption are evaluated and compared with those of previous work, namely the hybrid geographical routing (HGR) algorithm. The proposed work helps network designers to address design procedures efficiently and increase the flexibility in the energy delay tradeoff for communications in WSNs.

Within the paradigm of physical layer secrecy, typically a physical layer specific characteristic is used as key generator to guarantee information hiding from eavesdroppers. In this paper, we propose a novel secret key generation algorithm based on two reciprocal physical layer parameters; the channel measurements and the distances between the two communicating nodes. The two parameters are estimated experimentally using implementations of our algorithm on three FPGA-based WARP kits emulating the two communicating nodes and the eavesdropper. The parameters are used as common sources of randomness to generate the secret key. We evaluate the performance of our algorithm through extensive iterations. We compare the bit mismatch rate as well as the entropy of the generated secret key of our algorithm versus classical channel only and distance only based algorithms. Our results reveal that even in the worst case scenarios, our algorithm outperforms the two other algorithms and overcomes their vulnerabilities.

Within the paradigm of physical layer secrecy, typically a physical layer specific characteristic is used as key generator to guarantee information hiding from eavesdroppers. In this paper, we propose a novel secret key generation algorithm based on two reciprocal physical layer parameters; the channel measurements and the distances between the two communicating nodes. The two parameters are estimated experimentally using implementations of our algorithm on three FPGA-based WARP kits emulating the two communicating nodes and the eavesdropper. The parameters are used as common sources of randomness to generate the secret key. We evaluate the performance of our algorithm through extensive iterations. We compare the bit mismatch rate as well as the entropy of the generated secret key of our algorithm versus classical channel only and distance only based algorithms. Our results reveal that even in the worst case scenarios, our algorithm outperforms the two other algorithms and overcomes their vulnerabilities.

In this paper, we introduce the hardware implementation of video acquisition in a sensor node of wireless sensor network with the help of USB 2.0 interface. The USB 2.0 video acquisition is based on the CY7C67300 controller and the DCC1545M image sensor. In this paper, we detail the hardware architecture and the application program design in a sensor node using field-programmable gate array (FPGA) board and USB 2.0 interface. The CY7C67300 controller is the suitable choice for FPGA Virtex 4 and 5 based USB peripherals. A simple interface module capable of transferring data rates above 400 Mbits/s is implemented to communicate with the CY7C67300 controller. In order to use the developed module, in Xilinx embedded design, we provide a custom peripheral which includes the CY767300 interface as its core and additional logic for the connection to the external peripheral controller (EPC) and then the processor local bus (PLB).