Smart handoff technique for internet of vehicles communication using dynamic edge-backup node

© 2020 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/electronics9030524A vehicular adhoc network (VANET) recently emerged in the the Internet of Vehicles (IoV); it involves the computational processing of moving vehicles. Nowadays, IoV has turned into an interesting field of research as vehicles can be equipped with processors, sensors, and communication devices. IoV gives rise to handoff, which involves changing the connection points during the online communication session. This presents a major challenge for which many standardized solutions are recommended. Although there are various proposed techniques and methods to support seamless handover procedure in IoV, there are still some open research issues, such as unavoidable packet loss rate and latency. On the other hand, the emerged concept of edge mobile computing has gained crucial attention by researchers that could help in reducing computational complexities and decreasing communication delay. Hence, this paper specifically studies the handoff challenges in cluster based handoff using new concept of dynamic edge-backup node. The outcomes are evaluated and contrasted with the network mobility method, our proposed technique, and other cluster-based technologies. The results show that coherence in communication during the handoff method can be upgraded, enhanced, and improved utilizing the proposed technique.Published onlin

Mitigation measures for significant factors instigating cost overrun in highway projects

Construction industry has created numerous employment opportunities and playing a role model in economic growth of Pakistan. This industry is facing serious and critical problem of cost overrun especially in highway sector in country Pakistan particularly in Sindh Province. The purpose of this study is to identify mitigation measures for significant factors of cost overrun in highway projects of Sindh Province. In this study, mixed-mode research approach has been used. Quantitatively, a structured questionnaire based on 64 common factors of cost overrun from in-depth literature review was developed and distributed to30 selected respondents among the client, contractor and consultant having more than 15 years of experience in handling highway projects in Sindh Province. The collected data was statistically analyzed using SPSS where 8 most significant factors of cost overrun were identified. Qualitatively, the identified eight most significant factors were then incorporated in open ended questionnaire and distributed to 30 selected experts for them to write possible mitigation measures for each of the significant factors. The data was then analyzed through content analysis technique to rank the mitigation measures according to their substantiality. The results of this study would be helpful for construction practitioners to be used as reference in taking up appropriate measures in controlling cost overrun in highways projects in Pakista

Motivating and De-Motivating Factors towards Marketing of Rice for the Rice Marketing Channel Members in Pakistan

From the production of rice at the rice fields to the final consumers, the rice moves from different marketing channels. This study aims to identify those factors which motivate and de-motivate the rice marketing channels members towards rice marketing. For this study the data were collected from 120 rice farmers, 45 rice commission agents, 45 rice millers and 45 rice traders from three districts of Punjab province in Pakistan. A structured questionnaire was used to collect the data from the respondents. OLS regression was applied using SPSS. The results of the study revealed different motivating and de-motivating factors for different marketing channel members. For the rice growing farmers, the motivating factors towards rice marketing were found to be cash payment at the spot, selling paddy at the farm gate, high demand due to exportable item, better results as compare to other crops and rice as the status symbol crop. The significant de-motivating factors for rice growers were found to be difficulties due to transportation issues, shortage of water for rice cultivation and low yield per acre. The significant motivating factors for commission agents were found to be less risk because of the working on rice mills payroll, profitable business despite the lack of education and financing by the rice millers. The significant de-motivating factors for commission agents were found to be issues in storage facilities, rising transportation cost and delay in payments by the rice millers. The significant motivating factors for rice millers were found to be growing demand of branded rice and satisfactory profit margins while significant de-motivating factors were found to be seasonal nature of business, high fixed cost, increasing cost due to alternative power usage and labor cost and mixing of different verities. The significant motivating factors for rice traders were found to be whole year running business, good profit margin and growing demand of branded rice while high taxes and increasing transportation cost were found to be significant de-motivating factors for rice traders. Keywords: Rice, Marketing, Marketing channels, motivating factors, De-motivating factor

A blockchain-based deep-learning-driven architecture for quality routing in wireless sensor networks

Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities. © 2013 IEEE

Malicious node detection using machine learning and distributed data storage using blockchain in WSNs

In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage. © 2013 IEEE

Domestic Rice Marketing Structure and Marketing Margins in Pakistan

Agriculture plays a key role in the economic development of Pakistan. Its contribution to GDP in Pakistan accounts 25% and it is a major source of raw material for different industries in Pakistan. Punjab is one of the four provinces of Pakistan and is a major producer of agricultural commodities. Rice is one of the most important agricultural commodities in Pakistan. The Punjab province produces 56% of the total rice in Pakistan as well as this province is solely producer of basmati rice variety which is a type of fragrant rice and is very famous for its aroma. For the development of agricultural sector, the importance of agricultural marketing cannot be ignored. In developing countries like Pakistan where the population is growing rapidly, an efficient internal agricultural marketing system for agricultural commodities can be very useful for not only to meet the domestic food needs but also for the development of rural economy. The aim of this study was to analyze the current status, structure and operations of rice marketing in Pakistan as well as to explore the marketing margins of different marketing intermediaries and to identify the respective marketing problems faced by those marketing intermediaries. Three districts famous for rice production were purposely selected for this study and a sample of 120 small, 45 medium and 45 large farmers was obtained from the study area. Along with the rice growing farmers, 45 commission agents, 45 rice millers, 45 rice traders, 45 whole sellers and 45 retailers in the study area were also contacted for the purpose of data collection. Three majorly grown rice varieties of basmati and non basmati rice were found being cultivated in the study area. The results revealed that majority of the rice producers were involved in selling their produce (paddy) to commission agents at their farm gate. The absolute cash margins for different marketing channel members were estimated and it was found that the rice producer was earning the maximum share for both varieties in the marketing chain i.e. 62.57% and 47.71% respectively for basmati and non basmati rice varieties. Along with the absolute cash margins, the net marketing margins were also calculated by deducting the marketing cost of respective marketing chain members. The overall domestic rice marketing structure was found to be efficient yet there is need for further improvement in order to enhance the rice production and exports from Pakistan. Keywords: Agricultural marketing, Rice, Rice marketing, marketing margins, Rice marketing problems

Balancer genetic algorithm-a novel task scheduling optimization approach in cloud computing

Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing

Heartbeat classification and arrhythmia detection using a multi-model deep-learning technique

Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost

Methyl 2-butyl-4-hy­droxy-1,1-dioxo-2H-1,2-benzothia­zine-3-carboxyl­ate

In the title compound, C14H17NO5S, the thia­zine ring adopts a half-chair conformation. The mol­ecule exhibits an intra­molecular O—H⋯O hydrogen bond, which forms a six-membered S(6) ring motif. The planes of the benzene and thia­zine rings are inclined at a dihedral angle of 15.30 (12)°

Dicarbonyl­dichlorido(N,N,N′,N′-tetra­methyl­ethylenediamine)­ruthenium(II)

In the title compound, [RuCl2(C6H16N2)(CO)2], the geometry around the RuII atom is a distorted RuC2N2Cl2 octa­hedron, with pairs of C and Cl atoms trans to each other and the N atoms of the bidentate ligand in a cis conformation. The five-membered chelate ring is puckered on the C—C bond