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This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed. It balances the load on cloud and cloudlet while minimizing makespan and execution time. However, it may trap in local optimum; levy flight can handle it. Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan, execution time, and Degree of imbalance while balancing the load. Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration (NASA) and Clarknet datasets. Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker (ACOQDM), Distributed Scheduling Optimization Algorithm (DSOA), andUtility-based Firefly Algorithm (UFA) when compared in terms of makespan, Degree of imbalance, and Figure of Merit. [ABSTRACT FROM AUTHOR]

This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed. It balances the load on cloud and cloudlet while minimizing makespan and execution time. However, it may trap in local optimum; levy flight can handle it. Hybridization of fuzzy firefly with levy flight is a novel technique that provides reduced makespan, execution time, and Degree of imbalance while balancing the load. Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration (NASA) and Clarknet datasets. Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker (ACOQDM), Distributed Scheduling Optimization Algorithm (DSOA), and Utility-based Firefly Algorithm (UFA) when compared in terms of makespan, Degree of imbalance, and Figure of Merit. [ABSTRACT FROM AUTHOR]

Summary: Miniaturization of Internet of Things (IoT) enabled devices has led to advancement in Intelligent Transportation System. Some of the challenges for efficient traffic management are connection of vehicles through an IP based infrastructure for transportation, reliable, and flexible traffic control management, maintaining quality of services for video streaming applications in Internet of Vehicles and many more. Today there is a need of an efficient transportation system with improved efficiency of traffic safety and lower traveling costs. An intelligent traffic control system using Ant Colony Optimization (ACO) algorithm has been proposed and analyzed in this paper. Proposed algorithm has been compared with existing state of art algorithms in terms of average waiting time and average traveling time. [ABSTRACT FROM AUTHOR]

In this era, fog computing is a horizontal, physical, or virtual resource emerging paradigm that extends cloud computing to the edge of the network. Now a day, there are many resources hungry applications i.e. face recognition, augmentation reality, video surveillance, speech recognition, virtual reality that requires high computation capabilities and large memory, which are not available at the end devices. Moreover the distance between the cloud and the end devices may also be an issue for delay sensitive applications. This paper present a comprehensive survey of computation offloading in fog computing. It reviews different techniques used to implement computation offloading along with variation parameters. This study also represents that there are various use cases of computation offloading in real world environment. Finally, open research issues related to computation offloading are also investigated to point out future research directions for the efficient computation offloading in the fog computing environment.. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have emerged as a backbone technology for the wireless communication era. The demand for WSN is rapidly increasing due to their major role in various applications with a wider deployment and omnipresent nature. The WSN is rapidly integrated into a large number of applications such as industrial, security, monitoring, tracking, and applications in home automation. The widespread use in many different areas attracts research interest in WSNs. Therefore, researchers are taking initiatives in exploring innovation day by day particularly towards the Internet of Things (IoT). But, WSN is having lots of challenging issues that need to be addressed, and the inherent characteristics of WSN severely affect the performance. Energy constraints are one of the primary issues that require urgent attention from the research community. Optimal energy optimization strategies are needed to counter the issue of energy constraints. Although one of the most appropriate schemes for handling energy constraints issues is the appropriate energy harvesting technique, the optimal energy optimization strategies should be coupled together for effectively utilizing the harvested energy. In this high-level systematic and taxonomical survey, we have organized the energy optimization strategies for EH-WSNs into eleven factors, namely, radio optimization schemes, optimizing the energy harvesting process, data reduction schemes, schemes based on cross-layer optimization, schemes based on cross-layer optimization, sleep/wake-up policies, schemes based on load balancing, schemes based on optimization of power requirement, optimization of communication mechanism, schemes based on optimization of battery operations, mobility-based schemes, and finally energy balancing schemes. We have also prepared the summarized view of various protocols/algorithms with their remarkable details. This systematic and taxonomy survey also provides a progressive detailed overview and classification of various optimization challenges for the EH-WSNs that require attention from the researcher followed by a survey of corresponding solutions for corresponding optimization issues. Further, this systematic and taxonomical survey also provides a deep analysis of various emerging energy harvesting technologies in the last twenty years of the era. [ABSTRACT FROM AUTHOR]

Energy is an astronomical issue in the WSNs environment because the sensor nodes are operated with a limited power battery. Hence, energy harvesting (EH) techniques are needed with the capability of harvesting energy from natural resources. But, lack of central authority, deployment issues, EH-WSNs are prone to security threats. This paper presents an energy-efficient, reliable multi-hop route selection algorithm in which security is based on the newly designed multi-trust model. The multi-trust model is formed by considering the multiple trust metrics. This research article includes the energy-efficient optimal clustering process consisting of cluster formation and dynamic optimal cluster head (CH) selection using hybrid adapted grey wolf sine cosine optimizer (HGWSCO) metaheuristic algorithm and further, the trusted CHs are selected by using the newly designed multi-trust model and later these trusted CHs are used in optimal secure route selection in multi-hop routing based on the novel hybrid adapted whale harris hawks optimizer (W-HHO) metaheuristic algorithm. [ABSTRACT FROM AUTHOR]

New and facile one-pot three component approach for the synthesis of substituted dihydropyrimidinones derivatives (4a-4h) from reaction of equimolar substituted aldehydes (1a-1h), methyl acetoacetate (2a) and urea (3a) in presence of nature derived catalyst viz. Cocos nucifera L. juice, Solanum lycopersicum L. juice and Citrus limetta juice, commonly known as coconut juice, tomato juice and musambi juice respectively, at room temperature has been carried out. All synthesized compounds were evaluated for in vitro herbicidal activity against Raphanus sativus L. (Radish seeds). The compounds (4a-4h) were also screened for their antifungal activity against Rhizoctonia solani and Colletotrichum gloeosporioides by poisoned food techniques method. Antibacterial activity was also studied against Erwinia cartovora and Xanthomonas citri by inhibition zone method. From activity data, it was found that compounds 4g and 4d were most active against Raphanus sativus L. (root) and Raphanus sativus L. (shoot) respectively. Compounds 4f and 4c was found most active against Rhizoctonia solani and Colletotrichum gloeosporioides fungus respectively at highest concentration. Compound 4g has shown maximum inhibition zone i.e. 1.00–5.50 mm against Erwinia cartovora at 2000 μg/mL concentration. Maximum Xanthomonas citrii growth was inhibited by compounds 4f showing inhibition zone 4.00–12.00 mm at highest concentration. Short reaction time, high yields, mild reaction condition and simple work-up are some merits of present methodology. [ABSTRACT FROM AUTHOR]

The energy harvesting methods enable WSNs nodes to last potentially forever with the help of energy harvesting subsystems for continuously providing energy, and storing it for future use. The energy harvesting techniques can use various potential sources of energy, such as solar, wind, mechanical, and variations in temperature. Energy-constrained sensor nodes are small in size. Therefore, some mechanisms are required to reduce energy consumption and consequently to improve the network lifetime. The clustering mechanism is used for energy efficiency in WSNs. In the clustering mechanism, the group of sensor nodes forms the clusters. The performance of the clustering process depends on various factors such as the optimal number of clusters formation and the process of cluster head selection. In this paper, we propose a hybrid whale and grey wolf optimization (WGWO)-based clustering mechanism for energy harvesting wireless sensor networks (EH-WSNs). In the proposed research, we use two meta-heuristic algorithms, namely, whale and grey wolf to increase the effectiveness of the clustering mechanism. The exploitation and exploration capabilities of the proposed hybrid WGWO approach are much higher than the traditional various existing metaheuristic algorithms during the evaluation of the algorithm. This hybrid approach gives the best results. The proposed hybrid whale grey wolf optimization-based clustering mechanism consists of cluster formation and dynamically cluster head (CH) selection. The performance of the proposed scheme is compared with existing state-of-art routing protocols. [ABSTRACT FROM AUTHOR]

In today's era, distributed computing is rising in the field of data processing. It gives a broad measure for the services to be processed for clients through the web. Everyone's data is on cloud. There resides many problems when we process data on cloud like as security, privacy etc. And load balancing is one of the problem in cloud computing. There are many requests generating on a single virtual machine and these requests are to be processed on various machines. This is controlled by load balancer. Load balancing is the technique used for assigning different- different tasks on different- different virtual machines by using various algorithms. Load balancing plays a vital role in the era of cloud computing which is assumed to circulate on various machines. This paper presents the study of various load balancing techniques. [ABSTRACT FROM AUTHOR]

Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic underwater network environments. To this end, this paper presents an adapted whale and wolf optimization-based energy and delay-centric green underwater networking framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics in networking. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting optimal and stable relay nodes for centric underwater communication paths. Thirdly, a complete workflow of the W-GUN framework is presented with an optimization flowchart. The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments. [ABSTRACT FROM AUTHOR]

Minimizing energy consumption is one of the major challenges in wireless sensor networks (WSNs) due to the limited size of batteries and the resource constrained tiny sensor nodes. Energy harvesting in wireless sensor networks (EH-WSNs) is one of the promising solutions to minimize the energy consumption in wireless sensor networks for prolonging the overall network lifetime. However, static energy harvesting in individual sensor nodes is normally limited and unbalanced among the network nodes. In this context, this paper proposes a modified echo state network (MESN) based dynamic duty cycle with optimal opportunistic routing (OOR) for EH-WSNs. The proposed model is used to act as a predictor for finding the expected energy consumption of the next slot in dynamic duty cycle. The model has adapted a whale optimization algorithm (WOA) for optimally selecting the weights of the neurons in the reservoir layer of the echo state network towards minimizing energy consumption at each node as well as at the network level. The adapted WOA enabled energy harvesting model provides stable output from the MESN relying on optimal weight selection in the reservoir layer. The dynamic duty cycle is updated based on energy consumption and optimal threshold energy for transmission and reception at bit level. The proposed OOR scheme uses multiple energy centric parameters for selecting the relay set oriented forwarding paths for each neighbor nodes. The performance analysis of the proposed model in realistic environments attests the benefits in terms of energy centric metrics such as energy consumption, network lifetime, delay, packet delivery ratio and throughput as compared to the state-of-the-art-techniques. [ABSTRACT FROM AUTHOR]