The main objective of short-term hydrothermal scheduling is the optimal allocation of the hydro and thermal generating units, so that the total cost of thermal plants can be minimized. The time of operation of the functioning units are considered to be 24 h. To achieve this objective, the hybrid algorithm combination of Artificial Bee Colony (ABC) and the BAT algorithm were applied. The swarming behavior of the algorithm searches the food source for which the objective function of the cost is to be considered; here, we have used two search algorithms, one to optimize the cost function, and another to improve the performance of the system. In the present work, the optimum scheduling of hydro and thermal units is proposed, where these units are acting as a relay unit. The short term hydrothermal scheduling problem was tested in a Chilean system, and confirmed by comparison with other hybrid techniques such as Artificial Bee Colony–Quantum Evolutionary and Artificial Bee Colony–Particle Swarm Optimization. The efficiency of the proposed hybrid algorithm is established by comparing it to these two hybrid algorithms.

This paper suggests an innovative approach for the ideal placement and categorization of capacitors in radial distribution networks (RDNs) by applying symmetric fuzzy and improved bacterial foraging optimization algorithm (IBFOA) solutions. The reactive power reimbursement significantly enhances the function of the power system, and capacitor placement is an impressive technique used to reduce loss of the system. The capacitor allocation for distribution system problems involves determining the ideal location and size of the capacitor. In this work, load flow is performed at first to compute actual losses and voltages at different nodes without compensation. In the planned technique, the loss sensitivity factor (VSF) and voltage stability index (VSI) are utilized to determine the optimal location of capacitors in RDNs. Here, the IBFOA is used to determine the proper rating of the capacitor. The suggested scheme is applied on three different types of RDNs.

Cloud computing plays a major role in sharing data and resources to other devices through data outsourcing. During sharing resources, it is a challenging task to provide access control and secure write operations. The main issue is to provide secure read and write operations collaboratively and to reduce computational overload by effective key management. In this paper, a secure and an efficient data collaboration scheme blowfish hybridized weighted attribute-based Encryption (BH-WABE ) for secure data writing and proficient access control has been proposed. Here, weight is assigned to each attribute based on its importance and data are encrypted using access control policies. The cloud service provider stores the outsourced data and an attribute authority revokes or updates the attributes by assigning different attributes based on the weight. The receiver can access the data file corresponding to its weight in order to reduce the computational overload. The proposed BH-WABE provides collusion resistance, multiauthority security and fine-grained access control in terms of security, reliability, and efficiency. The performance is compared with the conventional hybrid attribute-based encryption (HABE) scheme in terms of data confidentiality, flexible access control, data collaboration, full delegation, partial decryption, verification, and partial signing.

The increasing need for observation in seawater or ocean monitoring systems has ignited a considerable amount of interest and the necessity for enabling advancements in technology for underwater wireless tracking and underwater sensor networks for wireless communication. This type of communication can also play an important role in investigating ecological changes in the sea or ocean-like climate change, monitoring of biogeochemical, biological, and evolutionary changes. This can help in controlling and maintaining the production facilities of outer underwater grid blasting by deploying unmanned underwater vehicles (UUVs). Underwater tracking-based wireless networks can also help in maintaining communication between ships and divers, submarines, and between multiple divers. At present, the underwater acoustic communication system is unable to provide the data rate required to monitor and investigate the aquatic environment for various industrial applications like oil facilities or underwater grit blasting. To meet this challenge, an optical and magnetic tracking-based wireless communication system has been proposed as an effective alternative. Either optical or magnetic tracking-based wireless communication can be opted for according to the requirement of the potential application in sea or ocean. However, the hybrid version of optical and wireless tracking-based wireless communication can also be deployed to reduce the latency and improve the data rate for effective communication. It is concluded from the discussion that high data rate optical, magnetic or hybrid mode of wireless communication can be a feasible solution in applications like UUV-to-UUV and networks of aquatic sensors. The range of the proposed wireless communication can be extended using the concept of multihop.

The current and future status of the internet is represented by the upcoming Internet of Things (IoT). The internet can connect the huge amount of data, which contains lot of processing operations and efforts to transfer the pieces of information. The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics, sensors and network connectivity. Nowadays, there are two trending technologies that take the platform i.e., Software Defined Network (SDN) and IoT (SD-IoT). The main aim of the IoT network is to connect and organize different objects with the internet, which is managed with the control panel and data panel in the SD network. The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers. It is more significant for wide area networks, because of the large packet propagation latency and the controller placement problem is more important in every network. In the proposed work, IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization (ESFO) algorithm and Pareto Optimal Controller placement tool (POCO) for the placement problem of the controller. In order to prove the efficiency of the proposed system, it is compared with other existing methods like PASIN, hybrid SD and PSO in terms of load balance, reduced number of controllers and average latency and delay. With 2 controllers, the proposed method obtains 400 miles as average latency, which is 22.2% smaller than PSO, 76.9% lesser than hybrid SD and 91.89% lesser than PASIN.

A growing interest in renewable energy resources has been observed for several years, due to their pollution-free nature, availability all over the world, and continuity. These facts make these energy resources attractive for many applications. In this work, the hybrid combination of a photovoltaic-biomass system is investigated as an energy source. This paper determines optimal sizing and cost reduction of grid-integrated renewable energy resources by using an intelligence optimization technique, the dragonfly algorithm. The efficiency of the proposed methodology is also compared with an existing technique, which uses the artificial bee colony (ABC) algorithm. The scope of this work is to reduce the annual total cost of power with a reduced number of solar panels. The monthly average solar radiation is used to compute the obtained power. The outcome of the proposed technique proves that the grid-connected system with an optimal number of components satisfactorily meets the needs of the village at a reduced price. The simulation results are carried out under the MATLAB environment. The comparison of results clearly demonstrates that the proposed system is much more efficient than the existing one.