An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks

[EN] Green energy management is an economical solution for better energy usage, but the employed literature lacks focusing on the potentials of edge intelligence in controllable Internet of Things (IoT). Therefore, in this article, we focus on the requirements of todays' smart grids, homes, and industries to propose a deep-learning-based framework for intelligent energy management. We predict future energy consumption for short intervals of time as well as provide an efficient way of communication between energy distributors and consumers. The key contributions include edge devices-based real-time energy management via common cloud-based data supervising server, optimal normalization technique selection, and a novel sequence learning-based energy forecasting mechanism with reduced time complexity and lowest error rates. In the proposed framework, edge devices relate to a common cloud server in an IoT network that communicates with the associated smart grids to effectively continue the energy demand and response phenomenon. We apply several preprocessing techniques to deal with the diverse nature of electricity data, followed by an efficient decision-making algorithm for short-term forecasting and implement it over resource-constrained devices. We perform extensive experiments and witness 0.15 and 3.77 units reduced mean-square error (MSE) and root MSE (RMSE) for residential and commercial datasets, respectively.This work was supported in part by the National Research Foundation of Korea Grant Funded by the Korea Government (MSIT) under Grant 2019M3F2A1073179; in part by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" Within the Project under Grant TIN2017-84802-C2-1-P; and in part by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET Joint Activities and Beyond) Project ERANETMED3-227 SMARTWATIR.Han, T.; Muhammad, K.; Hussain, T.; Lloret, J.; Baik, SW. (2021). An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks. IEEE Internet of Things. 8(5):3170-3179. https://doi.org/10.1109/JIOT.2020.3013306S317031798

Intelligent and Energy-Efficient Data Prioritization in Green Smart Cities: Current Challenges and Future Directions

[EN] The excessive use of digital devices such as cameras and smartphones in smart cities has produced huge data repositories that require automatic tools for efficient browsing, searching, and management. Data prioritization (DP) is a technique that produces a condensed form of the original data by analyzing its contents. Current DP studies are either concerned with data collected through stable capturing devices or focused on prioritization of data of a certain type such as surveillance, sports, or industry. This necessitates the need for DP tools that intelligently and cost-effectively prioritize a large variety of data for detecting abnormal events and hence effectively manage them, thereby making the current smart cities greener. In this article, we first carry out an in-depth investigation of the recent approaches and trends of DP for data of different natures, genres, and domains of two decades in green smart cities. Next, we propose an energy-efficient DP framework by intelligent integration of the Internet of Things, artificial intelligence, and big data analytics. Experimental evaluation on real-world surveillance data verifies the energy efficiency and applicability of this framework in green smart cities. Finally, this article highlights the key challenges of DP, its future requirements, and propositions for integration into green smart citiesThis work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (no. 2016R-1A2B4011712).Muhammad, K.; Lloret, J.; Baik, SW. (2019). Intelligent and Energy-Efficient Data Prioritization in Green Smart Cities: Current Challenges and Future Directions. IEEE Communications Magazine. 57(2):60-65. https://doi.org/10.1109/MCOM.2018.1800371S606557

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

SEM-ACSIT:Secure and Efficient Multiauthority Access Control for IoT Cloud Storage

Data access control in a cloud storage system is regarded as a promising technique for enhanced efficiency and security utilizing a ciphertext-policy attribute-based encryption (CP-ABE) approach. However, due to a large number of data users as well as limited resources and heterogeneity of data devices in Internet of Things (IoT), existing access control schemes for the cloud storage are not effectively applicable to IoT applications. In this article, we construct a new CP-ABE-based storage model for data storing and secure access in a cloud for IoT applications. Our new framework introduces an attribute authority management (AAM) module in the cloud storage system functioned as an agent that provides a user-friendly access control and highly reduces the storage overhead of public keys. Then, we propose a novel secure and efficient multiauthority access control scheme of the cloud storage system for IoT, namely, SEM-ACSIT, which obtains both backward security and forward security when an attribute of a user is revoked. By exploiting encryption outsourcing, simplified key structuring and the AAM module, the computational overhead of a user is immensely decreased. Moreover, a user access control list (UACL) in the cloud server is constructed newly to support authorization access for a specific user. The analysis and simulation results demonstrate that our SEM-ACSIT scheme achieves powerful security with less computational overhead and lower storage costs than the existing schemes