This page has only limited features, please log in for full access.
Sustainable energy development consists of design, planning, and control optimization problems that are typically complex and computationally challenging for traditional optimization approaches. However, with developments in artificial intelligence, bio-inspired algorithms mimicking the concepts of biological evolution in nature and collective behaviors in societies of agents have recently become popular and shown potential success for these issues. Therefore, we investigate the latest research on bio-inspired approaches for smart energy management systems in smart homes, smart buildings, and smart grids in this paper. In particular, we give an overview of the well-known and emerging bio-inspired algorithms, including evolutionary-based and swarm-based optimization methods. Then, state-of-the-art studies using bio-inspired techniques for smart energy management systems are presented. Lastly, open challenges and future directions are also addressed to improve research in this field.
Tri-Hai Nguyen; Luong Nguyen; Jason Jung; Israel Agbehadji; Samuel Frimpong; Richard Millham. Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges. Sustainability 2020, 12, 8495 .
AMA StyleTri-Hai Nguyen, Luong Nguyen, Jason Jung, Israel Agbehadji, Samuel Frimpong, Richard Millham. Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges. Sustainability. 2020; 12 (20):8495.
Chicago/Turabian StyleTri-Hai Nguyen; Luong Nguyen; Jason Jung; Israel Agbehadji; Samuel Frimpong; Richard Millham. 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges." Sustainability 12, no. 20: 8495.
Social media sites, namely Facebook and Twitter, have gained popularity in current times. These sites generate huge data that expresses thoughts and opinions of its users on issues. In this context, private individuals become the sources of information through online sharing of opinions and thoughts. These thoughts and opinions can be extracted to show patterns on sentiments of its users. Sentiment analysis, also referred as opinion mining, studies the “opinions, attitudes and emotions” of peoples. Sentiment analysis is a text classification problem, and identifying and extracting the right sentiment from huge data on social media sites are challenge. In this chapter, we presented different approaches to sentiment analysis on social media sites and social media business communication.
Israel Edem Agbehadji; Abosede Ijabadeniyi. Approach to Sentiment Analysis and Business Communication on Social Media. Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing 2020, 169 -193.
AMA StyleIsrael Edem Agbehadji, Abosede Ijabadeniyi. Approach to Sentiment Analysis and Business Communication on Social Media. Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing. 2020; ():169-193.
Chicago/Turabian StyleIsrael Edem Agbehadji; Abosede Ijabadeniyi. 2020. "Approach to Sentiment Analysis and Business Communication on Social Media." Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing , no. : 169-193.
Data visualization plays an important role in gaining insight from data. Generally, traditional methods are used to systematically create graphical formats of data attributes of either numeric or textual data. However, these traditional methods are very time-consuming computationally when they must display data points of big data sources. It is significant to explore new methods and algorithms that require less computational time while taking into consideration the volume of data attributes involved. In this chapter, the behavior of animals is explored to help create a method and an algorithm for data visualization suited for big data visualization.
Israel Edem Agbehadji; Hongji Yang. Data Visualization Techniques and Algorithms. Springer Tracts in Nature-Inspired Computing 2020, 195 -205.
AMA StyleIsrael Edem Agbehadji, Hongji Yang. Data Visualization Techniques and Algorithms. Springer Tracts in Nature-Inspired Computing. 2020; ():195-205.
Chicago/Turabian StyleIsrael Edem Agbehadji; Hongji Yang. 2020. "Data Visualization Techniques and Algorithms." Springer Tracts in Nature-Inspired Computing , no. : 195-205.
The current dispensation of big data analytics requires innovative ways of data capturing and transmission. One of the innovative approaches is the use of a sensor device. However, the challenge with a sensor network is how to balance the energy load of wireless sensor networks, which can be achieved by selecting sensor nodes with an adequate amount of energy from a cluster. The clustering technique is one of the approaches to solve this challenge because it optimizes energy in order to increase the lifetime of the sensor network. In this article, a novel bio-inspired clustering algorithm was proposed for a heterogeneous energy environment. The proposed algorithm (referred to as DEEC-KSA) was integrated with a distributed energy-efficient clustering algorithm to ensure efficient energy optimization and was evaluated through simulation and compared with benchmarked clustering algorithms. During the simulation, the dynamic nature of the proposed DEEC-KSA was observed using different parameters, which were expressed in percentages as 0.1%, 4.5%, 11.3%, and 34% while the percentage of the parameter for comparative algorithms was 10%. The simulation result showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime, and network throughput. In addition, the proposed DEEC-KSA has the optimal time (in seconds) to send a higher number of packets to the base station successfully. The advantage of the proposed bio-inspired technique is that it utilizes random encircling and half-life period to quickly adapt to different rounds of iteration and jumps out of any local optimum that might not lead to an ideal cluster formation and better network performance.
Israel Edem Agbehadji; Samuel Ofori Frimpong; Richard C Millham; Simon James Fong; Jason J Jung. Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks. International Journal of Distributed Sensor Networks 2020, 16, 1 .
AMA StyleIsrael Edem Agbehadji, Samuel Ofori Frimpong, Richard C Millham, Simon James Fong, Jason J Jung. Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks. International Journal of Distributed Sensor Networks. 2020; 16 (7):1.
Chicago/Turabian StyleIsrael Edem Agbehadji; Samuel Ofori Frimpong; Richard C Millham; Simon James Fong; Jason J Jung. 2020. "Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks." International Journal of Distributed Sensor Networks 16, no. 7: 1.
Water allocation planning in an equitable and sustainable way is intrinsically complex. This study proposes a water resource allocation system using an integrated Soil and Water Assessment Tool and Water Evaluation and Planning tool (SWAT-WEAP) model for hydrological simulation and prognostic scenarios sustainability prediction. The study explores the use of Digital Elevation Model (DEM), soil and land raster image in deriving physiographic information for land degradation impact assessment, quantification of optimal water allocation and generation of minimum ecosystem water requirement. Consequently, the SWAT quantifies the catchment water yield before been allocated optimally based on percentage dependable flow rates of 70% and 85% reliability flow regime at Makurdi, Nigeria discharge station. The WEAP model assesses the water resources utilization following scenarios adaptation by riparian users. Both models performed satisfactorily for streamflow and water yield prediction and resource sharing both in the calibration and validation phases with a correlation coefficient (R2) of 0.57–0.74 and root squared error (RSR) of 0.66–0.82. The results show how drainage network, channel length, drainage boundary, slope, and sub-catchment geometric properties demonstrate Geographic Information Systems (GIS) utility in morphoclimatic impacts assessment as a data management, scenario analysis, and decision support tool in water management for the Lower Benue River Basin, Nigeria. Planners and decision-makers need to consider several integrated plans as alternatives to adapting to climate change impacts and anthropogenic human activities in resolving the unmet demands.
Zainab Abdulmalik; Adebayo Wahab Salami; Solomon Olakunle Bilewu; Ayanniyi Mufutau Ayanshola; Oseni Taiwo Amoo; Abayomi Abdultaofeek; Israel Edem Agbehadji. Geoinformatics Approach to Water Allocation Planning and Prognostic Scenarios Sustainability: Case Study of Lower Benue River Basin, Nigeria. Innovations in Smart Cities Applications Edition 3 2020, 1249 -1261.
AMA StyleZainab Abdulmalik, Adebayo Wahab Salami, Solomon Olakunle Bilewu, Ayanniyi Mufutau Ayanshola, Oseni Taiwo Amoo, Abayomi Abdultaofeek, Israel Edem Agbehadji. Geoinformatics Approach to Water Allocation Planning and Prognostic Scenarios Sustainability: Case Study of Lower Benue River Basin, Nigeria. Innovations in Smart Cities Applications Edition 3. 2020; ():1249-1261.
Chicago/Turabian StyleZainab Abdulmalik; Adebayo Wahab Salami; Solomon Olakunle Bilewu; Ayanniyi Mufutau Ayanshola; Oseni Taiwo Amoo; Abayomi Abdultaofeek; Israel Edem Agbehadji. 2020. "Geoinformatics Approach to Water Allocation Planning and Prognostic Scenarios Sustainability: Case Study of Lower Benue River Basin, Nigeria." Innovations in Smart Cities Applications Edition 3 , no. : 1249-1261.