Abdelhamid Helali

Adaptive image compression technique for wireless sensor networks

When using wireless sensor networks for real-time image transmission, some critical points should be considered. These points are limited computational power, storage capability, narrow bandwidth and required energy. Therefore, efficient compression and transmission of images in wireless sensor network is considered. To address the above mentioned concerns, an efficient adaptive compression scheme that ensures a significant computational and energy reduction as well as communication with minimal degradation of the image quality is proposed. This scheme is based on wavelet image transform and distributed image compression by sharing the processing of tasks to extend the overall lifetime of the network. Simulation results are presented and they show that the proposed scheme optimizes the network lifetime, reduces significantly the amount of the required memory and minimizes the computation energy by reducing the number of arithmetic operations and memory accesses.

Adaptive image transfer for wireless sensor networks (WSNs)

When using wireless sensor networks for real-time data transmission, some critical points should be considered. Restricted computational power, memory limitations, narrow bandwidth and energy supplied present strong limits in sensor nodes. Therefore, maximizing network lifetime and minimizing energy consumption are always optimization goals. To reduce the energy consumption of the sensor network during image transmission, an energy efficient image compression scheme is proposed. The image compression scheme reduces the required memory. To address the abovementioned concerns, in this paper we describe an approach of image transmission in WSNs, taking advantage of JPEG2000 still image compression standard and using MATLAB and C from Jasper. JPEG2000 provides a practical set of features, not necessarily available in the previous standards. These features were achieved using techniques: the Discrete Wavelet Transform (DWT), and Embedded Block Coding with Optimized Truncation (EBCOT). Performance of the proposed image compression scheme is investigated with respect to image quality and energy consumption. Simulation results are presented and show that the proposed scheme optimizes network lifetime and reduces significantly the amount of required memory by analyzing the functional influence of each parameter of this distributed image compression algorithm.

Energy-efficient wavelet image compression in Wireless Sensor Network

In the recent years, the wireless technology would have known an exponential growth, which has an impact on developing and improving the field of telecommunications beyond the means of transmission wire to the radio frequency communication. The Wireless Sensor Network (WSN) is enrolled in this context. Its a collection of component (nodes) organized into a cooperative network. The main components of this network are tiny battery powered cameras with wireless communication capability. Therefore, image transfer in WSNs presents major challenge which raises issues related to its representation, its storage and its transmission. However, communication of image content has several bottlenecks, including limited bandwidth of cellular networks, restricted computational power, limited storage capability, and battery constraints of the appliances. In this paper, we address the energy, system lifetime and bandwidth bottlenecks of image communication. We present an efficient adaptive compression scheme that can significantly minimize the energy required for wireless image communication while meeting bandwidth constraints of wireless network and image quality. Based on Discrete Wavelet Transform, we propose an efficient image compression scheme, enabling significant reduction in computation energy needed with minimal degradation of image quality. Simulation results are done with C++ and show that the proposed scheme optimizes network lifetime, reduces significantly the amount of required memory and minimizes both (i) computation energy, by reducing the computation needed to compress an image and (ii) communication energy, consumed by the RF component which is proportional to the number of transmitted bits.

Energy conservation for image transmission over wireless sensor networks

Images processing is one of the most important applications for wireless sensor networks. One of the major challenges in enabling images transmission in wireless sensor network will be the need to process and wirelessly transmit very large amounts of data. Therefore, optimized computation and energy dissipation are critical requirements to maximize the lifetime of the sensor network. To address this problem, the energy efficient image compression over a resource constrained multi-hop wireless network is considered. Adaptive Wavelet Image Compression and Organization (AWICOA) is an algorithm that ensures efficient energy dissipation and low computation overhead on the sensor nodes. The proposed algorithm is investigated with respect to energy consumption and image quality. Performance studies indicate that the proposed algorithm enabling significant reductions in computation as well as communication energy needed.

Improved feature extraction method based on Histogram of Oriented Gradients for pedestrian detection

In recent years, pedestrian detection for Automobile Driver Assistance System (ADAS) is a primordial task in the smart vehicle. Histogram of oriented gradients (HoG) is one of the most effective pedestrian feature extraction approaches to the study. In this paper, an optimization of pedestrian detection based on HOG method is presented and investigated to achieve an accurate human detection system. The study of different computation steps of the standard algorithm shows the possibility of improving the system performance, specifically in the build histograms step. The main idea is to customize each bin weight according to its contribution in the pedestrian extracted features. Actually, the different bins of a HoG improved vector that encodes a single cell will not have the same weight. Indeed, after the histograms computation, we will distribute an amplification factor for each bin in order to increase the weight bins that describe the relevant pedestrian features from a side. Top of that, we were interested to decrease the bins weight that affect the irrelevant features such as, other obstacle or the image background. The classification system is performed using a linear SVM classifier which is simple and easy to implement in ADAS applications. The performance studies using MATLAB simulation, proves the effectiveness of our approach.

Priority image transmission in wireless sensor networks

The emerging technology during the last years allowed the development of new sensors equipped with wireless communication which can be organized into a cooperative autonomous network. Some application areas for wireless sensor networks (WSNs) are home automations, health care services, military domain, and environment monitoring. The required constraints are limited capacity of processing, limited storage capability, and especially these nodes are limited in energy. In addition, such networks are tiny battery powered which their lifetime is very limited. During image processing and transmission to the destination, the lifetime of sensor network is decreased quickly due to battery and processing power constraints. Therefore, digital image transmissions are a significant challenge for image sensor based Wireless Sensor Networks (WSNs). Based on a wavelet image compression, we propose a novel, robust and energy-efficient scheme, called Priority Image Transmission (PIT) in WSN by providing various priority levels during image transmissions. Different priorities in the compressed image are considered. The information for the significant wavelet coefficients are transmitted with higher quality assurance, whereas relatively less important coefficients are transmitted with lower overhead. Simulation results show that the proposed scheme prolongs the system lifetime and achieves higher energy efficiency in WSN with an acceptable compromise on the image quality.

Distributed fuzzy logic based routing protocol for wireless sensor networks

A Wireless Sensor Network is a special network that requires adaptive methods and techniques to meet the application requirements. Optimizing the energy consumption and enhancing the network lifetime while routing data from sensor nodes to the base station is the subject of extensive research works. This paper presents and evaluates an energy efficient cluster-based routing protocol that uses a fuzzy logic module during the cluster-head election process in order to increase the network lifetime. The objective of this work is not to minimize the whole network consumption, but to balance the consumption over nodes to increase the network lifetime. The proposed protocol is based on the well-known cluster-based routing protocol, the Low Energy Adaptive Clustering Hierarchy protocol. The performance of the proposed fuzzy-logic-based algorithm is evaluated using MATLAB simulation.

Ultra low power low noise amplifier design for 2.4 GHz applications

Requirements of Wireless Sensor Network application which are limited power, required energy and moderate performance has been forced the Wireless Sensor Network transceiver designers toward an adaptive transceiver structures. To address the above mentioned concerns, a low-noise amplifier integrated in 2.4 GHz, in 0.18 μm CMOS technology is developed. The proposed LNA can operate at 1.8 V supply voltage. The LNA provides a good gain of 17 dB, a noise figure of 0.01 dB, reverse isolation (S12) of -3 dB, input return loss (S11) of -15 dB and output return loss (S22) of -7 dB, while consuming only 7.2 Mw DC power.

Image encryption with the AES algorithm in wireless sensor network

The growth of multimedia applications in wireless sensor network requires more efficient data storage and transmission techniques. In addition, when it comes to wireless sensor network, we mainly focus several constraints. Among which the materials resources where a wireless sensor network consists of several small size devices called nodes sensors. Thus, the second challenge we face is the hostile deployment environment where safety intervention is required to ensure the data transmission. In this paper, we propose a symmetric cryptographic algorithm contains simple mathematical operations that agrees with the sensor node nature and increase its lifespan. Our algorithm used to encrypt the image in real time while reducing the execution time.

Images compression techniques for wireless sensor network applications

Adaptive compression for images transmission in resource-constrained multi-hop wireless network applications is considered. In this strategy, development of an energy efficient image compression scheme is proposed as a means to overcome the computation and/or energy limitation of individual nodes. It has the additional benefit of extending the “life” of individual node by saving its energy power. Two methods for energy efficient image compression are proposed and investigated with respect to energy consumption and image quality. Simulation results show that the proposed scheme prolongs the system lifetime and minimizes the computation energy by reducing the number of arithmetic operations and memory accesses.