Wireless sensor networks comprise of a large number of low cost sensor nodes that have strictly restricted sensing, computation and communication capabilities. In addition to this, sensor nodes have limited battery life which is not rechargeable in many applications. Due to resource limitations for the sensor nodes, it is important to use energy efficiently for each sensor node. This will result in prolonged network lifetime and functionality. Energy consumption balancing (ECB) property ensures that the average energy dissipation per sensor is equal for all sensors in the network. ECB can be considered as energy efficiency property that optimally manages energy consumption of sensors to prolong network lifetime. This paper investigates the ECB theory and ECB related mechanisms. A classification of ECB mechanism is given by surveying the current and state of the art research in this area. In addition, comparison and main constraints of the mechanisms are presented.

This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.

Trust is an important factor in wireless sensor networks (WSNs) in terms of security enhancement and successful collaboration. Trust management (TM) can ensure that all communicating nodes are trustworthy during authentication, authorisation, or key management, which makes traditional security services more robust and reliable. Moreover, by helping to find reliable nodes, TM improves cooperation among nodes, which is vital for improvement of system performance. Trust estimations and management are highly challenging issues because of the unique features and susceptibility of WSNs to different attacks. These factors prevent direct application of schemes suited to other networks and require careful design in a TM system. Hence, our objective is to discuss and present the concept and design factors of TM in WSNs in detail. Moreover, we explore the current state of research as well as open research issues by reviewing proposed trust computation and management schemes in WSNs. Copyright © 2013 John Wiley & Sons, Ltd.

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A smart grid (SG) is a modernized electric grid that enhances the reliability, efficiency, sustainability, and economics of electricity services. Moreover, it plays a vital role in modern energy infrastructure. The core challenge faced by SGs is how to efficiently utilize different kinds of front-end smart devices, such as smart meters and power assets, and in what manner to process the enormous volume of data received from these devices. Furthermore, cloud and fog computing provide on-demand resources for computation, which is a good solution to overcome SG hurdles. Fog-based cloud computing has numerous good characteristics, such as cost-saving, energy-saving, scalability, flexibility, and agility. Resource management is one of the big issues in SGs. In this paper, we propose a cloud-fog-based model for resource management in SGs. The key idea of the proposed work is to determine a hierarchical structure of cloud-fog computing to provide different types of computing services for SG resource management. Regarding the performance enhancement of cloud computing, different load balancing techniques are used. For load balancing between an SG user's requests and service providers, five algorithms are implemented: round robin, throttled, artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization. Moreover, we propose a hybrid approach of ACO and ABC known as hybrid artificial bee ant colony optimization (HABACO). Simulation results show that our proposed technique HABACO outperformed the other techniques.