On using peer-to-peer communication in cellular wireless data networks

A recent class of approaches for enhancing the performance of cellular wireless data networks has focused on improving the underlying network model. It has been shown that using the peer-to-peer network model, a mode of communication typically seen in ad hoc wireless networks, can result in performance improvements such as increased data rate, reduced transmission power, better load balancing, and enhanced network coverage. However, the true impact of adopting the peer-to-peer network model in such an environment is yet to be fully understood. In this paper, we investigate the performance benefits and drawbacks of using the peer-to-peer network model for Internet access in cellular wireless data networks. We find that, although the peer-to-peer network model has significantly better spatial reuse characteristics, the improved spatial reuse does not translate into better throughput performance. Instead, we observe that using the peer-to-peer network model as-is might actually degrade the throughput performance of the network. We identify and discuss the reasons behind these observations. Using the insights gained through the performance evaluations, we then propose two categories of approaches to improve the performance of the peer-to-peer network model: approaches that leverage assistance from the base station and approaches that leverage the relaying capability of multihomed hosts. Through simulation results, we show that using the peer-to-peer network model in cellular wireless data networks is a promising approach when the network model is complemented with appropriate mechanisms.     

Energy optimized micro genetic algorithm based LEACH protocol for WSN

This article presents the design, analyses and implementation of the novel routing protocol for energy optimization based on LEACH for WSN. Network Lifetime is the major problem in various routing protocols used in WSN. In order to overcome that problem, our proposed routing protocol is developed, which is a combination of Micro Genetic algorithm with LEACH protocol. Our proposed µGA-LEACH protocol, strengthen the cluster head (CH) selection and also reduce the energy consumption of the network when compared to existing protocols. This paper shows the improvement of network lifetime and energy consumption with the optimal CH selection based on a micro genetic algorithm and also compared the results with an existing hierarchical routing protocol like LEACH, LEACH-C, LEACH GA and GADA LEACH routing protocol with various packet sizes, and initial energy.

     

A hybrid elephant herding optimization and cultural algorithm for energy‐balanced cluster head selection scheme to extend the lifetime in WSNs

Clustering‐based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO‐CA‐OCHS) scheme is proposed to extend the lifetime. This proposed HEHO‐CA‐OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO‐CA‐OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO‐CA‐OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes.     

Deep Convolutional Neural Network (Falcon) and transfer learning‐based approach to detect malarial parasite

Deep learning models have already benchmarked its demonstration in the applications of Medical Sciences. Present day medical industries suffer due to deadly disease such as malaria etc. As per the report from World Health Organization (WHO), it is noted that the amount of caution and care taken per patient by a human doctor to cure malaria is decreasing. To address this issue, this paper proposes an automated solution for the detection of malaria from the real-time image. The key idea of the proposed solution is to use a Deep Convolutional Neural Network (DCNN) called “Falcon” to detect the parasitic cells from blood smeared slide images of Malaria Screener. Furthermore, the class accuracy of the given dataset samples is maintained in order to model not only the normal case but to accurately predict the presence of malaria as well. Experimental results confirms that the model does not possess overfitting, class imbalance, and provides a reasonable classification report and trustworthy accuracy with 95.2?% when compared to the state-of-the-art Convolutional Neural Network (CNN) models.

     

Design and development of microstrip patch antenna with circular and rectangular slot for structural health monitoring

Structural health monitoring (SHM) is the technique to identify the damage in the building or physical structures. Several structural health monitoring has been proposed in recent years. Increase in number of sensors and its wiring mechanism causes issue in signal processing demand, efficiency, and unreliability. In current state, already present sensors cannot be used for Structural Health Monitoring because of its high cost and low battery capacity, although none of these techniques are capable of monitoring the condition of complex physical structures. The measurement of strain can be done by using patch antenna as strain sensors. In currently available sensors only the wireless range. The main objective of this paper is to investigate the feasibility of using circular and rectangular slotted microstrip patch antenna as a strain sensor for structural health monitoring without these afore mentioned problems. These antenna sensors’ (slotted rectangular and circular microstrip patch antennas) simulation results are confirmed. From the simulation results, the sensor provides information about crack orientation.     

English to Tamil machine translation system using universal networking language

This paper proposes English to Tamil machine translation system, using the universal networking language (UNL) as the intermediate representation. The UNL approach is a hybrid approach of the rule and knowledge-based approaches to machine translation. UNL is a declarative formal language, specifically designed to represent semantic data extracted from a natural language text. The input English sentence is converted to UNL (enconversion), which is then converted to a Tamil sentence (deconversion) by ensuring that the meaning of the input sentence is preserved. The representation of UNL was modified to suit the translation process. A new sentence formation algorithm was also proposed to rearrange the translated Tamil words to sentences. The translation system was evaluated using bilingual evaluation understudy (BLEU) score. A BLEU score of 0.581 was achieved, which is an indication that most of the information in the input sentence is retained in the translated sentence. The scores obtained using the UNL based approach were compared with existing approaches to translation, and it can be concluded that the UNL is a more suited approach to machine translation.     

Does family life cycle stage influence e-shopping acceptance by Indian women? An examination using the technology acceptance model

This study contributes to the literature that advocates the use of consumer segmentation by family life cycle (FLC) stages, which are found to have a greater influence on changes occurring in an individual’s life than age. It addresses the youth bias of most research on e-shopping adoption and the emergence of older e-shoppers as an attractive market segment by covering women of all age groups. It evaluated the effect of perceptions and attitudes towards e-shopping acceptance, as well as their variations across FLC stages, by using the technology acceptance model. The 10 FLC stages were selected by modifying the Duvall FLC classification system to reflect the Indian context. The results show that the different FLC stages have a significant impact on the e-shopping behaviour of women. While deals and discounts were found to influence all groups, time-saving and better decision-making through online shopping sites also significantly influenced e-shopping behaviour. Furthermore, the perceived usefulness and/or attitude were important factors that directly affected e-shopping technology adoption and usage patterns across a majority of the FLC stages. The study proved that FLC stages had greater power than age in explaining e-shopping adoption and e-retailers should not ignore them in designing e-marketing applications.     

Refined trust energy-ad hoc on demand distance vector (ReTE-AODV) routing algorithm for secured routing in MANET

The characteristics of MANET such as decentralization, dynamic topology and openness are susceptible for security threats. To overcome the security threats and to provide a reliable network to transmit packets, a need for trust based routing arises. Moreover, the trust along with energy requirement on ad hoc on demand distance vector have paved way for the development of the newly proposed algorithm named as refined trust and energy based ad hoc on demand distance vector algorithm which is the refined form of the existing trust and energy based ad hoc on demand distance vector algorithms and the classical AODV. In this paper, the refinement parameter is the trust. Moreover, Bayesian probability is introduced in this paper for trust management due to its ability to handle uncertainty for obtaining the refined form of Trust calculation. The proposed algorithm routes the packets from the source to destination not through the shortest route but by selecting a reliable route which consumes low energy and trustful for sending the packets. The simulation results obtained from this work show that the proposed algorithm performs better than the existing algorithms in terms of Trust based routing and energy efficiency.     

New methods for the one-pot processing of polysaccharide components (cellulose and hemicelluloses) of lignocellulose biomass into valuable products. Part 3: Products synthesized via the biotechnological conversion of poly- and monosaccharides of biomass

Part 3 of the review discusses the modern aspects in the biotechnological synthesis of the valuable chemicals derived from the lignocellulosic biomass, including ethanol, n-butanol, isobutanol, 2,3-butanediol, and lactic and succinic acids. A comparative characteristic of different approaches (including SHF, SSF, SSCF and CBP) toward biosynthesis of valuable products is given. It is shown that the consolidated processing of lignocellulose into the valuable chemicals is a promising approach toward their direct synthesis by fermentation, but remains less efficient than other processing methods. Development of genetic engineering tools and the application of synthetic biology will allow to develop more efficient strains and advanced biotechnological processes for lignocellulose processing.     

New methods for the one-pot processing of polysaccharide components (cellulose and hemicelluloses) of lignocellulose biomass into valuable products. Part 2: Biotechnological approaches to the conversion of polysaccharides and monosaccharides into the valuable industrial chemicals

Part 2 of the review discusses modern processes for biotechnological conversion of lignocellulosic biomass into the valuable chemicals. It also recognizes the new approaches toward the development of more efficient enzymes for the depolymerization of biomass and the properties of the microorganisms employed in the fermentation of the biomass-derived sugars. Various biotechnological approaches to the fermentation of the depolymerized biomass products are described, including SHF, SSF, NSSF, SSFF, SSCF, and CBP. It is demonstrated that that the main tendencies for development of the new technologies for biotechnological biomass processing are the application of genetic engineering, synthetic biology and reduction of the number of processing steps. Application of one-pot processing of lignocellulosic biomass is promising for development of the new and efficient manufacturing technologies for production of valuable chemicals.