Journal of Ambient Intelligence and Humanized Computing

Journal Information
ISSN / EISSN : 1868-5137 / 1868-5145
Published by: Springer Nature (10.1007)
Total articles ≅ 3,441
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Journal of Ambient Intelligence and Humanized Computing pp 1-12; https://doi.org/10.1007/s12652-021-03566-2

Abstract:
In wireless sensor networks (WSNs), the energy-hole or hotspot problem is an important, challenging issue because it isolates some nodes from the sink. The hotspot problem is addressed by introducing a mobile sink, where the mobile sink traverse in the WSN, collects the data from rendezvous points (RPs) instead of visiting each sensor node. But, selecting the best set of RPs and mobile sink trajectories is challenging in the WSNs. In this context, this paper proposes an optimal RP and trajectory construction (ORPSTC) for the mobile sink in WSNs for data collection. Initially, we apply the minimum spanning tree-based clustering approach for RP selection. In this stage, an RP is identified from each partition, whereas other nodes can transmit the data to RP. Next, we construct a trajectory for mobile sink among all the RPs, including the sink node using a computational geometric method. It results in a near-optimal route with minimal computational resources. Further, we also apply the RP re-selection and virtual RP selection strategy to balance the energy among the SNs. We simulate and evaluate the proposed ORPSTC and existing approaches, and the proposed work outperforms among them.
Journal of Ambient Intelligence and Humanized Computing pp 1-19; https://doi.org/10.1007/s12652-021-03548-4

Abstract:
Saving energy is primary challenge in wireless sensor network (WSN) to prolong network lifetime within coverage area is key to attain it. Previously different methods have been proposed for this energy efficiency purpose, namely centralized immune-Voronoi deployment algorithm (CIVA) and fixed parameter tractable (FPT) approximation algorithm. These methods showed drawback of creating energy hole problem with increased network coverage and routing problem. In order to overcome these issues, this paper presented an Energy Efficient Cluster Based Routing (EECBR) model. This proposed model utilized energy and distance as parameters and made an optimized Cluster Head (CH) selection using Grey Wolf Optimization algorithm. EECBR performs advanced Multihop Dijkstras algorithm for intra cluster routing and it replaced Base Station (BS) by linking clusters using router node, using Advanced Multi-hop Dijkstras algorithm and Tree based Remote Vector approach. This model was evaluated and compared with previous protocols; simulation results show that EECBR model outperforms previous ones. It improved network lifetime by 13% with the help of optimal CH selection based clustering and combined routing techniques. Thus, proposed EECBR model outperforms in the field of energy efficient routing protocol design.
Journal of Ambient Intelligence and Humanized Computing pp 1-28; https://doi.org/10.1007/s12652-021-03550-w

Abstract:
Bonferroni mean (BM) operators have been established as a powerful tool for handling the interrelationship between the input arguments under various decision-making information. However, the existing BM operators do not take into account the overall interaction among decision makers or criteria. To overcome this limitation, this study considers the Shapley fuzzy measure (SFM) with the normalized weighted BM (NWBM) operator under a neutrosophic environment. In addition, the current research ignores the bipolarity and hesitancy during decision elicitations, resulting in the imprecise decision results. In this paper, the hesitant bipolar-valued neutrosophic set (HBNS) which is the extension of hesitant fuzzy set and bipolar neutrosophic set is employed. The main focus of this paper is in the development of an aggregation operator for HBNS. Based on the literature review, we would like to fill in the gaps by developing a hesitant bipolar-valued neutrosophic Shapley NWBM (HBN-SNWBM) operator where the overall interaction among decision makers can be considered. Besides that, a three-phase decision making framework is also proposed to show the applicability of the proposed aggregation operator to the real-world decision problems. The HBN-SNWBM operator and the decision making framework are applied to two examples of investment selection where evaluations are implemented using the proposed aggregations that based upon hesitant bipolar-valued neutrosophic sets. In the first example, it is found that a weapon company is the best alternative for investment followed by a food company. Sensitivity of parameters of the aggregation operator is also analysed and it is found that the ranking results are consistent despite of different parameter values used. This verifies the insensitivity of p,q parameters in the developed aggregation operator. The proposed decision making framework and hesitant bipolar-valued neutrosophic sets would be a great significance for the practical implementation of the aggregation operators.
Journal of Ambient Intelligence and Humanized Computing pp 1-17; https://doi.org/10.1007/s12652-021-03527-9

Abstract:
Bitcoin is the most popular cryptocurrency and it uses proof of work protocol for consensus of all transactions in a block. The blocks are to be appended to the digital ledger, the blockchain. The miners compete for the mining of blocks in the main canonical blockchain. A miner can participate in block mining either individually with his computational power or join a mining pool. Here, the classification of crypto address, whether it belongs to a mining pool or an individual miner, is done with a deep learning Keras framework. The classification accuracy of 99.47% is obtained with 100,000 addresses which is higher than the machine learning random forest classification obtained by Kaggle with 22,000 addresses. The miners in mining pools deploy selfish mining or honest mining to mine a block and get the reward accordingly. In block mining, both honest and selfish miners expose the blocks produced by them. The default protocol of the main canonical blockchain leads to the selection of the longest branch of blocks of the selfish miner, discarding the honest miner’s block. To alleviate this, we deploy a reinforcement learning algorithm to choose the block with high upper confidence bound value. This selection explores the branch exposed by honest miners. The algorithm is deployed after the first difficulty adjustment algorithm, where there is more selfish mining activity. Our promising results show that the main blockchain exhibits less regret by selecting the honest miner’s branch.
Journal of Ambient Intelligence and Humanized Computing pp 1-10; https://doi.org/10.1007/s12652-021-03561-7

Abstract:
In recent years, mobile applications have emerged as a conceivable solution to facilitate daily activities in various aspects of human life. Due to the resource-limited of mobile devices, they are inadequate to execute mobile applications. To deal with this issue, edge clouds have emerged to extend resource capabilities at the network edge near mobile devices. Therefore, transferring and outsourcing compute-intensive tasks from mobile devices to edge servers is one of the challenging issues to be investigated. This paper considers the task offloading issue as an NP-hard problem and proposes a metaheuristic-based task offloading mechanism using the non-dominated sorting genetic algorithm (NSGA-II) technique named iNSGA-II for serving mobile applications in the edge/cloud networks. Besides, we improve the crossover and mutation operators, making the proposed solution converge faster than other evolutionary algorithms. The obtained numerical results under synthetic workloads indicate that the proposed mechanism is a cost-effective solution, and it increases the average edge server utilization and reduces the energy consumption and the execution time than metaheuristic-based task offloading mechanisms.
Journal of Ambient Intelligence and Humanized Computing pp 1-16; https://doi.org/10.1007/s12652-021-03536-8

Abstract:
Air pollution has become a major environmental risk of the new civilized world due to its severe influence on public health and the environment. Eventually, understanding the spatiotemporal variability of air pollution at high granularity is necessary to make relevant public policies. To explore spatiotemporal variability of air pollution at high granularity we have utilized the power of IoT based participatory sensing and data science. In this paper, we propose a predictive model for spatiotemporal air pollution estimation technique called Multiview data Fusion model (MVDF) that can consider spatial as well as temporal dependencies of air pollutants. The proposed technique is evaluated based on real-world air pollution dataset collected by participants over a period of 1 year in an urban area of city Kolkata. The results show that MVDF dominates over some baselines like Simple Kriging (SK), Modified Shepard’s Method (MSM) and Nearest Neighbor (NN). Besides, in this paper, we attempt to perform visual analysis that consists of state-of-the-art visualization techniques to explore spatiotemporal variability at different granularities on the estimated pollution levels of MVDF.
Prateek,
Journal of Ambient Intelligence and Humanized Computing pp 1-15; https://doi.org/10.1007/s12652-021-03559-1

Abstract:
Triangulation uncertainty is the uncertainty associated when we try to locate an unknown target with the help of three anchor nodes resulting in formation of a triangulated region. Efficient triangulation leads to superior accuracy and lower rate of errors in sensor networks. Although sufficient work has been done to compute localization uncertainties, there is dearth of work pertaining to triangulation uncertainty. The existing problems are: first, localization incurs large computation cost, necessitating some hierarchy or clustering techniques. Second, linear, non-linear and optimization-based solvers invariably simplify the occurrence of errors during estimation of localization. To solve these problems, the present work proposes a range free assistive approach in detecting symmetric triangulations. This approach combined with semidefinite programming of the cost function is shown to exhibit improved localization performance. Numerical results show that the RMS errors is reduced by using triangulation assisted node deployment. The results are compared with the standard weighted least square method for different number of anchor nodes.
, Arindam Sarkar, Sunil Karforma, Bappaditya Chowdhury
Journal of Ambient Intelligence and Humanized Computing pp 1-22; https://doi.org/10.1007/s12652-021-03531-z

Abstract:
The outbreak of novel corona virus had led the entire world to make severe changes. A secured healthcare data transmission has been proposed through Telecare Medical Information System (TMIS) based on metaheuristic salp swarm. Patients need proper medical remote treatments in this Post-COVID-19 time from their quarantines. Secured transmission of medical data is a significant challenge of digitally overwhelmed environment. The objective is to impart the patients’ data by encryption with confidentiality and integrity. Eavesdroppers can carry sniffing and spoofing in order to deluge the data. In this paper, a novel scheme on metaheuristic salp swarm based intelligence has been sculptured to encrypt electrocardiograms (ECG) for data privacy. Metaheuristic approach has been blended in cryptographic engineering to address the TMIS security issues. Session key has been derived from the weight vector of the fittest salp from the salp population. The exploration and exploitation control the movements of the salps. The proposed technique baffles the eavesdroppers by the key strength and other robustness factors. The results, thus obtained, were compared with some existing classical techniques with benchmark results. The proposed MSE and RMSE were 28,967.85, and 81.17 respectively. The time needed to decode 128 bits proposed session key was 8.66 × 1052 years. The proposed cryptographic time was 8.8 s.
Journal of Ambient Intelligence and Humanized Computing pp 1-19; https://doi.org/10.1007/s12652-021-03534-w

Abstract:
In wireless sensor networks (WSNs), clustering is one of the most effective routing protocols. Most clustering algorithms include two stages of operations: cluster head selection and cluster formation. Cluster heads are selected from the sensor nodes based on several key parameters like residual energy of the cluster heads candidates, the distance of cluster heads from their cluster members, and the distance between cluster head and the base station. Cluster formation deals with the association of sensor nodes with one of the selected cluster heads. This paper presents an energy-efficient clustering algorithm with linearly decreasing inertia weight particle swarm optimization (PSO) and improved weight factor. The interia weight PSO is based on cluster head selection and the improved weight factor-based cluster formation. The merit of the proposed approach is the robust formulation of a linear weight factor that leads to efficient cluster formation. The efficacy of the proposed algorithm is verified via different scenarios with varying numbers of nodes and different positions of base stations. Results are compared with some of the existing algorithms, and it is found that the proposed approach outperforms other approaches in terms of various evaluation parameters.
Journal of Ambient Intelligence and Humanized Computing pp 1-15; https://doi.org/10.1007/s12652-021-03538-6

Abstract:
Internet of Things (IoT) has emerged as a novel paradigm that focuses on connecting a large number of devices with the Internet infrastructure. To address the performance requirements of IoT devices, the Content-Centric Networking (CCN) becomes an encouraging future Internet architecture that emphasizes name-based content access, instead of searching for the host-location in the network. The in-network content caching is an essential characteristic for rapid information dissemination and efficient content delivery in the CCN. To this end, a novel content caching scheme has been proposed for comprehensive utilization of the available caching resources. The proposed scheme partitions the CCN-enabled IoT networks hierarchically to reduce content redundancy and excessive cache replacement operations. For content caching decisions, the proposed caching strategy considers normalized distance-based metrics along with dynamic threshold heuristics to improve content retrieval delay by placing the contents near the IoT devices. Extensive simulation analysis on realistic network configurations demonstrates that the proposed caching scheme outperforms the existing competing content placement strategies on performance parameters such as network cache hit-ratio, hop-count, delay, and average network traffic. Thus, the proposed caching scheme becomes more promising for CCN based IoT applications.
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