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For mobile clients, sufficient resources with the assurance of efficient performance and energy efficiency are the core concerns. This article mainly considers this need and proposes a resourceful architecture, called mRARSA that addresses the critical need in a mobile cloud environment. This architecture consists of cloud resources, mobile devices, and a set of functional components. The performance efficiency evaluates implementing the proposed context-aware multi-criteria decision offloading algorithm. This algorithm considers both device context (network parameters) and application content (task size) at run time when offloading an executable code to allocate the cloud resources. The appropriate resources select based on offloading decisions and via the wireless communication channels. The architecture's remarkable component is the signal strength analyzer that determines the signal quality (e.g.-60 dBm) and contributes to performance efficiency. The proposed prototype model has implemented several times to monitor the performance efficiency, mobility, performance at communication barriers, and the outcomes of resource-demanding application's execution. Results indicate performance improvement, such as the algorithm appropriately decides the cloud resources based on device network context, application content, mobility, and the signal strength quality and range. Moreover, the results also show significant improvement in achieving performance and energy efficiency. Sufficient resources and performance efficiency are the most significant features that distinguish this framework from the other existing frameworks.

Continuous-monitoring applications in sensor network applications require periodic data transmissions to the base-station (BS), which may lead to unnecessary energy depletion. The energy-efficient data aggregation solutions in sensor networks have evolved as one of the favorable fields for such applications. Former research works have recommended many spatial-temporal designs and prototypes for successfully minimizing the data-gathering overheads, but these are constrained to their relevance. This work has proposed a data aggregation technique for homogeneous application set-ups in sensor networks. For this, the authors have employed two ways of model generation for reducing correlated spatial-temporal data in cluster-based sensor networks: one at the Sensor nodes (SNs) and the other at the Cluster heads (CHs). Building on this idea, the authors propose two types of data filtration, first at the SNs for determining temporal redundancies (TRs) in data readings by both relative deviation (RD) and adaptive frame method (AFM) and second at the CHs for determining spatial redundancies (SRs) by both RD and AFM.

We study the time varying co-movement patterns of the crypto-currency prices with the help of wavelet-based methods; employing daily bilateral exchange rate of four major crypto-currencies namely Bitcoin, Ethereum, Lite and Dashcoin. First, we identify Bitcoin as potential market leader using Wavelet multiple correlation and Cross correlation. Further, Wavelet Local Multiple Correlation for the given cryptocurrency prices are estimated across different timescales. From the results, it is found that that the correlation follows an aperiodic cyclical nature, and the crypto-currency prices are driven by Bitcoin price movements. Based on the results obtained, we suggest that constructing a portfolio based on crypto-currencies may be risky at this point of time as the other crypto-currency prices are mainly driven by Bitcoin prices, and any shocks in the latter is immediately transformed to the former.

We test for herding in crypto-currency markets using the CSAD method of Chang et al. (2000). Daily returns of 6 major crypto-currencies and market index CCI30 for the period 07-08-2015 t0 18-01-2018 is used. Possibility of herding under up and down market and high and low volatility is tested. Herding is found under up and down market activity, indicating over-enthusiasm and over-reaction. Market volatility is found not to have any significant impact on herding behavior. Herding is found to be dependent upon the market activity rather than market volatility.

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We test the suitability of Gold and Bitcoin as safe-haven instruments in the backdrop of the Covid-19 related equity market meltdown by implementing the newly proposed Wavelet Quantile Correlation. We employ daily returns of Bitcoin, Gold, DJIA, CAC40, NSE50, S&P 500, NASDAQ, and EUROSTOXX from 05–01–2015 to 31–12–2020 . Our results show that Gold consistently exhibits safe haven properties for all the markets except NSE in the long and short run, while Bitcoin provided mixed results. We find that Gold can act as an effective hedge and diversifier as well.

Energy consumption is a prerequisite for economic growth especially in developing countries, and its demand is expected to increases as population increases. The quality of institutions is also germane for tourism development, as weak institutions may discourage the arrival of tourists into a country. This study examines the asymmetric impact of energy consumption (EC) and institutional quality (IQ) on tourist arrival (TA) in Pakistan for the period 1984–2017 by employing the non‐linear autoregressive distributive lag model. The bounds test result reveals a long‐run relationship among the variables used in the model. The findings of the study suggest an asymmetric relationship among EC, IQ and TA. The results imply that an improvement in IQ will increase TA. From the results, it can be said that reducing environmental pollution by switching into clean sources of energy may have an indirect positive effect on the tourism industry. Therefore, policies for enhancing TA, quality institutions and curtailing environmental degradation were suggested.