Fractional chaotic maps based short signature scheme under human-centered IoT environments.

Introduction: The Internet of Things (IoT) comprises of various smart devices for the sharing of sensed data through online services. People will be directly contacted to check their health parameters and the reports will be collected centrally through smart devices. The requirement is protection of messages during the exchange of data between sender and receiver in order to tackle human malicious attacks. Various signature-based schemes are discussed in the literature to provide secure communication. Smart devices however require lightweight tasks by ensuring critical safety strengths. An important problem in the signature based method is that it incurs more computational expenses for signing and verification process in large numbers. Objectives: In this study, we introduced an efficient Short Signature Scheme (SSS) that uses Fractional Chaotic Map (FCM) for secure communication in IoT based smart devices, the security of which is closely related to a random oracle based on FCM assumption. Methods: In this study, we have designed new short signature scheme using FCM. The presented scheme consist of four sub-algorithm as follows: setup, key generation, signing and verification. We have used less rigorous operations based on the FCM to carry out signing and verification procedures, similar to human signing on valid documents and then verifying them as per witness. Results: The proposed SSS offers a better security assurance than currently established signature schemes. The key advantage of the SSS over the DSA schemes is that at the verification stage and signing period it takes less computation; it retains the degree of protection. The presented SSS takes less bandwidth for storage, communication, and computing resources; particularly applicable to wireless devices and smart cards. Conclusion: We concluded that the uses of fractional chaotic maps is more effective for secure communication in human-centered IoT to present a provably secure short signature technique.

Impact of roof rain water harvesting of runoff capture and household consumption.

In recent years, the occurrence of floods is one of the most important challenges facing in Hamadan city. In the absence/inefficiency of urban drainage systems, rainwater harvesting (RWH) systems as low-impact development (LID) methods can be considered as a measure to reduce the floods. In this study, three scenarios concerning the RWH from the roof surfaces are studied to evaluate the type of the harvested water on reducing flooding. In the first scenario, which indicates the current situation in the studied area, it is indicated that there is no harvest of the roof surfaces in the studied area. The second scenario is about the use of water harvested from the roof surfaces for household purposes. The third scenario also refers to the use of harvested water for irrigation of gardens. The simulation results of these three scenarios using the Soil Conservation Service (SCS) method in the Hydrologic Modeling System (HEC-HMS) model reveal that if the second scenario is implemented, the runoff volume decreases from 28 to 12% for the return period from 2 to 100 years. However, in the third scenario, this reduction in runoff volume will be 48 and 27% for return periods of 2 to 100 years, respectively. Therefore, the results of this study indicate that the use of harvested water can also affect the reduction on runoff volume.

Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction.

Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end, estimation models are needed to compute suspended sediment load in rivers. The application of artificial intelligence (AI) techniques has become popular in water resources engineering for solving complex problems such as sediment transport modeling. In this study, novel integrative intelligence models coupled with iterative classifier optimizer (ICO) are proposed to compute suspended sediment load in Simga station in Seonath river basin, Chhattisgarh State, India. The proposed models are hybridization of the random forest (RF) and pace regression (PR) models with the iterative classifier optimizer (ICO) algorithm to develop ICO-RF and ICO-PR hybrid models. The recommended models are established using the discharge and sediment daily data spanning a 35-year period (1980-2015). The accuracy of the developed models is examined in terms of error; by root mean square error (RMSE) and mean absolute error (MAE); and based on a correlation index of determination coefficient (R2). The proposed novel hybrid models of ICO-RF and ICO-PR have been found to be more precise than their stand-alone counterparts of RF and PR. Overall, ICO-RF models delivered better accuracy than their alternatives. The results of this analysis tend to claim the appropriateness of the implemented methodology for precise modeling of the suspended sediment load in rivers.

An Efficient Mobile-Healthcare Emergency Framework.

Mobile technologies are capable of offering individual level health care services to users. Mobile Healthcare (m-Healthcare) frameworks, which feature smartphone (SP) utilizations of ubiquitous computing made possible by applying wireless Body Sensor Networks (BSNs), have been introduced recently to provide SP clients with health condition monitoring and access to medical attention when necessary. However, in a vulnerable m-Healthcare framework, clients' personal info and sensitive data can easily be poached by intruders or any malicious party, causing serious security problems and confidentiality issues. In 2013, Lu et al. proposed a mobile-Healthcare emergency framework based on privacy-preserving opportunistic computing (SPOC), claiming that their splendid SPOC construction can opportunistically gather SP resources such as computing power and energy to handle computing-intensive Personal Health Information (PHI) with minimal privacy disclosure during an emergency. To balance between the risk of personal health information exposure and the essential PHI processing and transmission, Lu et al. presented a patient-centric privacy ingress control framework based on an attribute-based ingress control mechanism and a Privacy-Preserving Scalar Product Computation (PPSPC) technique. In spite of the ingenious design, however, Lu et al.'s framework still has some security flaws in such aspects as client anonymity and mutual authentication. In this article, we shall offer an improved version of Lu et al.'s framework with the security weaknesses mended and the computation efficiency further boosted. In addition, we shall also present an enhanced mobile-Healthcare emergency framework using Partial Discrete Logarithm (PDL) which does not only achieve flawless mutual authentication as well as client anonymity but also reduce the computation cost.