Showing papers by "Mohammed Abo-Zahhad published in 2017"

A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions

Abstract: Introducing mobility to Wireless Sensor Networks (WSNs) puts new challenges particularly in designing of routing protocols. Mobility can be applied to the sensor nodes and/or the sink node in the network. Many routing protocols have been developed to support the mobility of WSNs. These protocols are divided depending on the routing structure into hierarchical-based, flat-based, and location-based routing protocols. However, the hierarchical-based routing protocols outperform the other routing types in saving energy, scalability, and extending lifetime of Mobile WSNs (MWSNs). Selecting an appropriate hierarchical routing protocol for specific applications is an important and difficult task. Therefore, this paper focuses on reviewing some of the recently hierarchical-based routing protocols that are developed in the last five years for MWSNs. This survey divides the hierarchical-based routing protocols into two broad groups, namely, classical-based and optimized-based routing protocols. Also, we present a detailed classification of the reviewed protocols according to the routing approach, control manner, mobile element, mobility pattern, network architecture, clustering attributes, protocol operation, path establishment, communication paradigm, energy model, protocol objectives, and applications. Moreover, a comparison between the reviewed protocols is investigated in this survey depending on delay, network size, energy-efficiency, and scalability while mentioning the advantages and drawbacks of each protocol. Finally, we summarize and conclude the paper with future directions.

Eye Blinking EOG Signals as Biometrics

Abstract: In this chapter, the feasibility of using eye blinking Electro-Oculo-Gram (EOG) signal as a new biometric trait for human identity recognition is tested. For this purpose, raw Electro-Encephalo-Gram (EEG) signals were recorded from 40 volunteers while performing the task of eye blinking. These signals were recorded using portable EEG headset, known as Neurosky Mindwave, which has wireless and dry electrodes at Fp1 position above the left eye. This makes it practical for biometric applications and for measuring EOG signals. For pre-processing, Discrete Wavelet Transform (DWT) is adopted to isolate EOG signals from brainwaves. Then, the onset and the offset of the eye blinking waveforms in the EOG signals are detected. After that, features are extracted using time delineation of the eye blinking waveform where important marks like the amplitude, position, and duration of the positive and negative pulses of the eye blinking waveform are employed as features. Finally, Discriminant Analysis (DA) classifier is used for classification. Moreover, a feature selection technique based on differential evolution is added for the proposed system. The best Correct Recognition Rate (CRR) achieved is 93.75 %. In verification mode, the lowest Equal Error Rate (EER) achieved is 7.45 %. Also, the permanence issue is evaluated using training and testing samples with different time separation between them. The optimistic results achieved in this chapter direct the scientific research to study different approaches for human identification using eye blinking to increase system’s performance. Moreover, eye blinking EOG biometric trait can be fused with other traits like EEG signals to build a multi-modal system to improve the performance of the EEG-based biometric authentication systems.

Modeling of Wireless Sensor Networks with Minimum Energy Consumption

Abstract: Energy saving is one of the most important issues of wireless sensor networks that are gaining a lot of attention. In other words, energy modeling plays a greater role in energy optimization that helps designers to produce an economical and practical design of sensor nodes. In this paper, an energy consumption model is proposed considering most of the parameters of both MAC and physical layers, unlike other related works that concern with either MAC or physical layer parameters. The proposed energy consumption model is validated with real measurements and NS-2 simulator. Results show good agreement between proposed model, experimental measurements and NS-2 simulator with mean absolute percentage error less than 6 %. The validated model is used to optimize transmitted power to achieve minimum energy consumption. Finally, a closed-form expression for optimum transmitted power is derived for different modulation schemes.

A New Localization Technique for Wireless Sensor Networks Using Social Network Analysis

Abstract: The application of wireless sensor networks is widely spread through the last years as it is well known in many fields and multiple applications. Localization of nodes within wireless sensor networks is one of the important topics in studying and managing these networks. The presented new localization technique combines the received signal strength indicator method which limits the communications between nodes to the range of accessible radiated signal, with a new trend which is social network analysis that deals with relationships between nodes in any network with metrics and layouts. By using mixed metric between degree and closeness, we will maintain the suitable elected seeds that will be anchors for around nodes inside the network. Trilateration calculations will be applied between optimized elected nodes with higher centrality to localize the target nodes.

Low power HBC PHY baseband transceiver for IEEE 802.15.6 WBAN

Abstract: The monitoring healthcare systems that can be used by patients wherever they are, has become very important for today efficient healthcare. Wireless body area network is one possible realization of these systems. Based on IEEE 802.15.6-2012 standard, this paper proposes a low power architecture of Human Body Communication transceiver for Wireless Body Area Network. A new efficient frame synchronization algorithm based on adaptive threshold is adopted. The proposed design is coded and simulated using MATLAB software. Then, the transceiver is implemented using Verilog and synthesized to 90nm CMOS technology. The implemented architecture meets all the standard requirements, consumes 0.63mW, and operates at a clock frequency of 42MHz.

Optimization of Transmitted Power and Modulation Level for Minimizing Energy Consumption in Wireless Sensor Networks

Abstract: Energy saving is one of the most important issues of wireless sensor networks (WSNs) that are gaining a lot of attention In other words, energy modeling plays a greater role in energy optimization that helps designers to produce an economical and practical design of sensor nodes In this paper, the issue of energy-efficient WSNs design is investigated by focusing on the setting of physical layer parameters This is achieved by deriving an energy consumption model that considers most of the parameters of the physical layers The proposed model is validated with real measurements to measure the accuracy of the proposed model Results show good agreement between proposed model and experimental measurements with mean absolute percentage error less than 6% The validated model is used to optimize transmitted power and modulation level to achieve minimum energy consumption Finally, closed-form expressions for optimum transmitted power and modulation level are derived for different modulation schemes

Detection of primary user signal in wideband cognitive radio networks exploiting DCT as sensing matrix

Abstract: Wideband spectrum sensing is a challenging task in wideband cognitive radio networks. It needs to be efficient, robust and fast. However, there are many challenges facing sensing in wideband spectrum. One of these challenges is Nyquist sampling rate bottleneck which required high speed DSP and large storage spaces. Compressive sensing is a pioneer solution for wideband spectrum sensing which have proved sampling below Nyquist criterion. In this paper, we provide mathematical expression that computes the number of measurements required for compressed detection exploiting DCT as sensing matrix. The simulation results show that the accuracy of detection for certain number measurements is similar to the results obtained by compressed measurements based spectrum sensing for wideband cognitive radio systems with small relative error. The comparison is based on term of probability of detection versus compression ratios.