Aduwati Sali

A Review on Hierarchical Routing Protocols for Wireless Sensor Networks

The routing protocol for Wireless Sensor Networks (WSNs) is defined as the manner of data dissemination from the network field (source) to the base station (destination). Based on the network topology, there are two types of routing protocols in WSNs, they are namely flat routing protocols and hierarchical routing protocols. Hierarchical routing protocols (HRPs) are more energy efficient and scalable compared to flat routing protocols. This paper discusses how topology management and network application influence the performance of cluster-based and chain-based hierarchical networks. It reviews the basic features of sensor connectivity issues such as power control in topology set-up, sleep/idle pairing and data transmission control that are used in five common HRPs, and it also examines their impact on the protocol performance. A good picture of their respective performances give an indication how network applications, i.e whether reactive or proactive, and topology management i.e. whether centralized or distributed would determine the network performance. Finally, from the ensuring discussion, it is shown that the chain-based HRPs guarantee a longer network lifetime compared to cluster-based HRPs by three to five times.

Reduced resolution depth coding for stereoscopic 3D video

In this paper, Reduced Resolution Depth Compression (RRDC) is proposed for Scalable Video Coding (SVC) to improve the 3D video rate distortion performance. RRDC is applied by using Down-Sampling and Up-Sampling (DSUS) of the depth data of the stereoscopic 3D video. The depth data is down-sampled before SVC encoding and up-sampled after SVC decoding operation. The proposed DSUS method reduces the overall bit rates and consequently: 1) improves SVC rate distortion for 3D video, particularly at lower bit rates in error free channels; and 2) improves 3D SVC performance for 3D transmission in error prone channels. The objective quality evaluation of the stereoscopic 3D video yields higher PSNR values at low bit rates for SVCDSUS compared to the original SVC (SVC-Org), which makes it advantageous in terms of reduced storage and bandwidth requirements. Moreover, the subjective quality evaluation of the stereoscopic 3D video further confirmed that the perceived stereoscopic 3D video quality of the SVC-DSUS is very similar to the stereoscopic 3D video of the SVC-Org by up to 98.2%.

Radio Resource Management for Green 3GPP Long Term Evolution Cellular Networks: Review and Trade-offs

Abstract Conventional design of cellular systems aims to maximize the system capacity and spectral efficiency due to sustainable growth of data rate requirements. As the energy consumption becomes relatively high, energy-efficient design for cellular systems is highly required to save energy as well as reducing the undesirable carbon dioxide emitted by these systems. However, reducing the energy consumption will degrade other system performances such as the data rate and quality of service. Therefore, joint optimization for overall system performances should be achieved. In this paper, the energy-efficient radio resource management (RRM) for Long Term Evolution (LTE) systems is addressed. After a brief introduction to LTE radio resource block and LTE frame, different types of energy efficiency metrics are defined to give a better understanding to the energy efficiency perspectives. The energy-efficient approaches related to link adaptation and RRM are explained. The state-of-the-art energy-efficient schedulers are also discussed, and a comprehensive comparison between them is adopted in this paper. Moreover, many trade-offs, challenges, and open issues are addressed to optimize the system performances.

Image skin segmentation based on multi-agent learning Bayesian and neural network

Skin colour is considered to be a useful and discriminating spatial feature for many skin detection-related applications, but it is not sufficiently robust to address complex image environments because of light-changing conditions, skin-like colours and reflective glass or water. These factors can create major difficulties in face pixel-based skin detectors when the colour feature is used. Thus, this paper proposes a multi-agent learning method that combines the Bayesian method with a grouping histogram (GH) technique and the back-propagation neural network with a segment adjacent-nested (SAN) technique based on the YCbCr and RGB colour spaces, respectively, to improve skin detection performance. The findings from this study have shown that the proposed multi-agent learning for skin detector has produced significant true positive (TP) and true negative (TN) average rates (i.e. 98.44% and 99.86% respectively). In addition, it has achieved a significantly lower average rate for the false negative (FN) and false positive (FP) (i.e. only 1.56% and 0.14% respectively). The experimental results show that multi-agent learning in the skin detector is more efficient than other approaches.

On the multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: An automated anti-pornography system

The main objective on this study proposed anti-pornography system works on four machine learning methods in two different stages namely skin detector stage and pornography classifier stage. A multi-agent learning is used twice. In the first stage, we propose a multi-agent learning method that combines the Bayesian method with a grouping histogram (GH) technique and the back-propagation neural network with a segment adjacent-nested (SAN) technique based on the YCbCr and RGB colour spaces respectively, to extract skin regions from the image accurately with taking into consideration the problems of the light-changing conditions, skin-like colour and reflection from glass and water. In the second stage, the features from the skin are extracted to classify the images into either pornographic or non-pornographic. Inaccurate classification occurs when different image sizes are used in the existing anti-pornography systems. Thus, this paper proposes a multi-agent learning that combines the Bayesian method with a grouping histogram technique again to extract the features from the skin detection based on YCbCr colour space and the back propagation neural network method using shape features extracted again from skin detection. The classification of the pornographic images becomes more robust to the variation in images sizes. The findings from this study have shown that the proposed multi-agent learning system for skin detection has produced a significant rate of true positives (TP) (i.e., 98.44%). In addition, it has achieved a significant low average rate for the false positives (FP) (i.e., only 0.14%) while the proposed multi-agent learning for pornography classifier has produced significant rates of TP (i.e., 96%). Moreover, it has achieved a significant low average rate of FP (i.e., only 2.67%). The experimental results show that multi-agent learning in the skin detector and pornography classifier are more efficient than other approaches.

A New Super Wideband Fractal Monopole-Dielectric Resonator Antenna

The small physical size and multiband capability are significant in the design of ultrawideband (UWB) antennas. Fractal geometry provides a good method for achieving the desired miniaturization and multiband performances. Furthermore, using a dielectric resonator improves bandwidth and radiation characteristics. A combination of these methods in the UWB antenna design is presented. The proposed design is a new hybrid dielectric resonator antenna (DRA) excited by a new fractal monopole antenna. The simulation and optimization have been carried out using Ansoft HFSS. The simulation and measurement results show that the proposed structure provides a huge bandwidth ranging from 2 to 40 GHz. Radiation patterns and gains show a good agreement over the bandwidth.

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

Detecting sybil attacks in clustered wireless sensor networks based on energy trust system (ETS)

Wireless sensor networks (WSNs) are an emerging technology used in many applications in both the civilian and military domains. Typically, these networks are deployed in remote and hostile environments. They are vulnerable to various kinds of security attacks, of which sybil attacks are some of the most harmful. Thus, it is necessary to solve the problems related to sensor node constraints and the need for high WSN security. This paper proposes an energy trust system (ETS) for WSNs to effectively detect sybil attacks. It employs multi-level detection based on identity and position verification. Then, a trust algorithm is applied based on the energy of each sensor node. Data aggregation is also utilized to reduce communication overhead and save energy. We analyze the performance of the proposed system in terms of security and resource consumption using theoretical and simulation-based approaches. The simulation results show that the proposed ETS is effective and robust in detecting sybil attacks in terms of the true and false positive rates. By virtue of the application of multi-level detection, the proposed system achieves more than 70% detection at the first level, which significantly increases to 100% detection at the second level. Furthermore, this system reduces communication overhead, memory overhead, and energy consumption by eliminating the exchange of feedback and recommendation messages among sensor nodes.

A self-configured link adaptation for green LTE downlink transmission

Green deployment for cellular eNodeBs has been proposed recently to save power and reduce the huge amount of carbon dioxide CO 2 emitted by traditional power-hungry base stations. Green eNodeBs should also be subjected to restrictions on high data rate and quality of service QoS, which both entail a high level of power consumption. In this regard, this paper addresses the trade-off between energy efficiency EE and spectral efficiency SE in both traditional and green long-term evolution eNodeBs without sacrificing the QoS. EE is proved to monotonically increase with SE in traditional macrocells and quasi-concave in green macrocells. Accordingly, a new mapping between channel quality indicator and modulation and coding scheme is proposed to address EE-SE trade-off with the use of a multi-criteria decision-making technique. Then, a self-configured link adaptation SCLA algorithm is developed to ensure that the priority weights related to EE and SE are adapted according to network load with the use of real-time cross-layer optimization. Simulation results show that the proposed SCLA provides a significant gain in EE and 52% reduction of CO 2 while maintaining SE close to the optimal value. Current and next-generation cellular networks require such interactive techniques in order to be self-optimised without complex modifications. Copyright

A FOUR-PHASES METHODOLOGY TO PROPOSE ANTI-PORNOGRAPHY SYSTEM BASED ON NEURAL AND BAYESIAN METHODS OF ARTIFICIAL INTELLIGENCE

Pornographic images are disturbing and malicious contents that are easily available through Internet technology. It has a negative and lasting effect on children who use the Internet; thus, pornography has become a serious threat not only to Internet users but also to society at large. Therefore, developing efficient and reliable tools to automatically filter pornographic contents is imperative. However, the effective interception of pornography remains a challenging issue. In this paper, a four-phase anti-pornography system based on the neural and Bayesian methods of artificial intelligence is proposed. Primitive information on pornography is examined and then used to determine if a given image falls under the pornography category. First, we present a detailed description of preliminary study phase followed by the modeling phase for the proposed skin detector. An anti-pornography system is created in the development phase, which also includes the proposed pornography classifier based on skin detection. Finally, the performance assessment method for the proposed anti-pornography system is discussed in the evaluation phase.