M-ATTEMPT: A New Energy-Efficient Routing Protocol for Wireless Body Area Sensor Networks

In this paper, we propose a new routing protocol for heterogeneous Wireless Body Area Sensor Networks (WBASNs); Mobility-supporting Adaptive Threshold-based Thermal-aware Energy-efficientMulti-hop ProTocol (M-ATTEMPT). A prototype is defined for employing heterogeneous sensors on human body. Direct communication is used for real-time traffic (critical data) or on-demand data while Multi-hop communication is used for normal data delivery. One of the prime challenges in WBASNs is sensing of the heat generated by the implanted sensor nodes. The proposed routing algorithm is thermal-aware which senses the link Hot-spot and routes the data away from these links. Continuous mobility of human body causes disconnection between previous established links. So, mobility support and energy-management is introduced to overcome the problem. Linear Programming (LP) model for maximum information extraction and minimum energy consumption is presented in this study. MATLAB simulations of proposed routing algorithm are performed for lifetime and successful packet delivery in comparison with Multi-hop communication. The results show that the proposed routing algorithm has less energy consumption and more reliable as compared to Multi-hop communication.Comment: arXiv admin note: substantial text overlap with arXiv:1208.609

Computational alignment of goals and scenarios for complex systems

The purpose of requirements validation is to determine whether a large requirements set will lead to the achievement of system-related goals under different conditions - a task that needs automation if it is to be performed quickly and accurately. One reason for the current lack of software tools to undertake such validation is the absence of the computational mechanisms needed to associate scenario, system specification and goal analysis tools. Therefore, in this paper, we report first research experiments in developing these new capabilities, and demonstrate them with a non-trivial example associated with a Rolls Royce aircraft engine software component

Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks

[EN] Intrusion detection system (IDS) is regarded as the second line of defense against network anomalies and threats. IDS plays an important role in network security. There are many techniques which are used to design IDSs for specific scenario and applications. Artificial intelligence techniques are widely used for threats detection. This paper presents a critical study on genetic algorithm, artificial immune, and artificial neural network (ANN) based IDSs techniques used in wireless sensor network (WSN)The authors extend their appreciation to the Distinguished Scientist Fellowship Program(DSFP) at King Saud University for funding this research.Alrajeh, NA.; Lloret, J. (2013). Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks. International Journal of Distributed Sensor Networks. 2013(351047):1-6. https://doi.org/10.1155/2013/351047S16201335104

On Adaptive Energy-Efficient Transmission in WSNs

One of the major challenges in design of Wireless Sensor Networks (WSNs) is to reduce energy consumption of sensor nodes to prolong lifetime of finite-capacity batteries. In this paper, we propose Energy-efficient Adaptive Scheme for Transmission (EAST) in WSNs. EAST is an IEEE 802.15.4 standard compliant. In this scheme, open-loop is used for temperature-aware link quality estimation and compensation. Whereas, closed-loop feedback process helps to divide network into three logical regions to minimize overhead of control packets. Threshold on transmitter power loss (RSSIloss) and current number of nodes (nc(t)) in each region help to adapt transmit power level (Plevel) according to link quality changes due to temperature variation. Evaluation of propose scheme is done by considering mobile sensor nodes and reference node both static and mobile. Simulation results show that propose scheme effectively adapts transmission Plevel to changing link quality with less control packets overhead and energy consumption as compared to classical approach with single region in which maximum transmitter Plevel assigned to compensate temperature variation.Comment: arXiv admin note: text overlap with arXiv:1303.624

Towards Sustainable Energy Efficiency with Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks

In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset’s dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models’ performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD