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Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other CHs are the major problems leading to higher energy consumption in IoT networks. In this paper, using fuzzy logic, bio-inspired chicken swarm optimization (CSO) and a genetic algorithm, an optimal cluster formation is presented as a Hybrid Intelligent Optimization Algorithm (HIOA) to minimize overall energy consumption in an IoT network. In HIOA, the key idea for formation of IoT nodes as clusters depends on finding chromosomes having a minimum value fitness function with relevant network parameters. The fitness function includes minimization of inter- and intra-cluster distance to reduce the interface and minimum energy consumption over communication per round. The hierarchical order classification of CSO utilizes the crossover and mutation operation of the genetic approach to increase the population diversity that ultimately solves the uneven distribution of CHs and turnout to be balanced network load. The proposed HIOA algorithm is simulated over MATLAB2019A and its performance over CSO parameters is analyzed, and it is found that the best fitness value of the proposed algorithm HIOA is obtained though setting up the parameters, number of rooster, number of hen’s and swarm updating frequency. Further, comparative results proved that HIOA is more effective than traditional bio-inspired algorithms in terms of node death percentage, average residual energy and network lifetime by 12%, 19% and 23%.

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Mobile edge computing (MEC) has been proposed as a promising solution, which enables the content processing at the edges of the network helping to significantly improve the quality of experience (QoE) of end users. In this article, we aim to utilize the MEC facilities integrated with time-varying renewable energy resources for charging/discharging scheduling known as green scheduling of on-move electric vehicles (EVs) in a geographical wide area comprising of multiple charging stations (CSs). In the proposed system, the charging/discharging demands and the contextual information of EVs are first transmitted to nearby edge servers. With instantaneous electricity load/pricing and the availability of renewable energy at nearby CSs collected by aggregators, a weighted social-welfare maximization problem is then solved at the edges using greedy-based algorithms to choose the best CS for the EV's service. From the system point of view, our results reveal that compared to cloud-based scheme, the proposed MEC-assisted EVs scheduling system significantly improves the complexity burden, boosts the satisfaction (QoE) of EVs' drivers by localizing the traffic at nearby CSs, and further helps to efficiently utilize the renewable energy across CSs. Furthermore, our greedybased algorithm, which utilizes the internal updating heuristics, outperforms some baseline solutions in terms of social welfare and power grid ancillary services. Index Terms-Ancillary services, electric vehicles (EVs), greedy-based algorithms, mixed integer nonlinear programming (MINLP), mobile edge computing (MEC), renewable energy. Abbas Mehrabi (Member, IEEE) received the B.Sc. degree in computer engineering from Azad University, Tehran, Iran in 2008, the M.Sc. degree in computer engineering from the Shahid Bahonar University of Kerman, Kerman, Iran in 2010, and the Ph.D. degree from the School of Electrical Engineering and Computer Science,

Content-based Image Retrieval (CBIR) involvesretrieving images similar to an example query image in terms of some features extracted from the image. However, inherent subjectivity in user perception of an image results in retrieved images that are largely irrelevant to the user. We propose a novel methodology for efficient understanding of user perception from the query image itself. Our system automatically generates a set of modified images, after the user selects object(s) of interest from the segmented query image. Our goal is to learn the retrieval parameters by modifying the segment-level description of the query image. Segment-level description includes individual segment properties as well as the inter-segment relationships. The user perception is then learnt on the basis of user feedback on this set of modified images. We demonstrate the feasibility and advantages of the proposed approach with examples. The proposed methodology of intra-query learning saves the cost of repeated database search incurred in existing relevance feedback based approaches.