The Internet of Things (IoT) is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically, IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework, a Covariance Linear Learning Embedding Selection (CL2ES) methodology is used at first to extract the features highly associated with the IoT intrusions.Then, the Kernel Distributed Bayes Classifier (KDBC) is created to forecast attacks based on the probability distribution value precisely.In addition, a unique Mongolian Gazellas Optimization (MGO) algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyberattack datasets, The obtained results are then compared with current classification methods regarding accuracy (97%), precision (96.5%), and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed, which provides valuable insight into its performance, efficiency, and suitability for securing IoT networks.

Piece selection policy in dynamic P2P networks play crucial role and avoid the last piece problem. BitTorrent uses rarest-first piece selection mechanism to deal with this problem, but its efficacy is limited because each peer only has a local view of piece rareness. The problem of piece section is multiple objectives. A novel fuzzy programming approach is introduced in this article to solve the multiple objectives piece selection problem in P2P network, in which some of the factors are fuzzy in nature. Piece selection problem has been prepared as a fuzzy mixed integer goal programming piece selection problem that includes three primary goals such as minimizing the download cost, time, maximizing speed and useful information transmission subject to realistic constraints regarding peer’s demand, capacity and dynamicity. The proposed approach has the ability to handle practical situations in a fuzzy environment and offers a better decision tool to each peer to select optimal pieces to download from other peers in dynamic P2P network. Extensive simulations are carried out to demonstrate the effectiveness of the proposed model. It is proved that proposed system outperforms existing with respect to download cost, time and meaningful exchange of useful information.

The rapid population growth in urban areas leads to increased waste generation and the need for sustainable waste management solutions to preserve the global environment. In this paper, we present an intelligent garbage collection system for smart city environments, which consists of three key components. First, we propose a novel model to optimize the positioning of garbage bins to prevent both overflow and underutilization. We leverage a fuzzy soft expert set-based solution to identify optimal locations within a selected region. Experimental results demonstrate that our proposed method significantly outperforms manual placement strategies in mitigating issues arising from inappropriate bin placement. Second, we address the problem of improper waste collection from overflowing garbage bins by developing a smart bin system. This system employs a voice-controlled bin that sends automatic alerts to collectors before reaching its full capacity, as well as audio alerts for the public to prevent waste overflow around the bin. Lastly, we integrate a mobile application and cloud-based database to enable real-time monitoring of bin statuses. This comprehensive approach to waste management in urban areas not only enhances the efficiency of garbage collection but also contributes to a cleaner and more sustainable environment.

Internet of things (IoT) comprises many heterogeneous nodes that operate together to accomplish a human friendly or a business task to ease the life. Generally, IoT nodes are connected in wireless media and thus they are prone to jamming attacks. In the present scenario jamming detection (JD) by using machine learning (ML) algorithms grasp the attention of the researchers due to its virtuous outcome. In this research, jamming detection is modelled as a classification problem which uses several features. Using one/two or minimum number of features produces vague results that cannot be explained. Also the relationship between the feature and the class label cannot be efficiently determined, specifically, if the chosen number of features for training is minimum (say 1 or 2). To obtain good results, machine-learning algorithms are trained by large number of data sets. However, collection of large number of datasets to solve jamming detection is not easy and most of the times generation and collection of large data sets become paradigmatic. In this paper, to solve this problem, more number of features with nominal number of data’s is considered that eases the data collection and the classification accuracy. In this research, an efficient technique based on locality sensitive hashing (LSH) for K-nearest neighbor algorithm (K-NN), which takes less time for constructing and querying the hash table that gives good accuracy is proposed and evaluated. From the results, it is clear that the obtained results are validatable and the model is more sensible.

Block scheduling is difficult to implement in P2P network since there is no central coordinator. This problem can be solved by employing network coding technique which allows intermediate nodes to perform the coding operation instead of store and forward the received data. There is a general assumption in this area of research so far that a target download rate is always attainable at every peer as long as coding operation is performed at all the nodes in the network. An interesting study is made that a maximum download rate can be attained by performing the coding operation at relatively small portion of the network. The problem of finding the minimal set of node to perform the coding operation and links to carry the coded data is called as a network code minimization problem (NCMP). It is proved to be an NP hard problem. It can be solved using genetic algorithm (GA) because GA can be used to solve the diverse NP hard problem. A new NCMP model which considers both minimize the resources needed to perform coding operation and dynamic change in network topology due to disconnection is proposed. Based on this new NCMP model, an effective and novel GA is proposed by implementing problem specific GA operators into the evolutionary process. There is an attempt to implement the different compositions and several options of GA elements which worked well in many other problems and pick the one that works best for this resource minimization problem. Our simulation results prove that the proposed system outperforms the random selection and coding at all possible node mechanisms in terms of both download time and system throughput.

Peer-to-peer network is one in which each node in the network can act as a client or server for the other nodes in the network. It allows shared access to various resources such as files, peripherals, and sensors without the need for a central server. Content distribution in the P2P network from server is done by multicasting. Multicasting is the process of sending the data to the multiple designations. This technology is highly efficient for the large scale multimedia content delivery in P2P network where the end peer have identical set of system components. But in reality, the peers have heterogeneous set of requirements for different service levels as well as different service components. The ability to provide differentiated services to each peer with widely varying requirements is becoming important. We need to provide differentiated Services above the existing shared network infrastructure. The solution proposed to solve the above said problem is to provide individualized service to each peer. It focuses on constructing and maintaining an efficient multiple overlay multicast tree structure in the P2P network. The tree maintenance process is governed by two mechanisms called as dynamic reconfiguration driven by peer and less frequent tree maintenance by network status change observation. In this paper new scalable architecture is constructed and analysed based on the above strategies.

This paper presents an Internet of Things (IoT) based system by designing a novel Nitrogen-Phosphorus-Potassium (NPK) sensor with Light Dependent Resistor (LDR) and Light Emitting Diodes (LED). The principle of colorimetric is used to monitor and analyze the nutrients present in the soil. The data sensed by the designed NPK sensor from the selected agricultural fields are sent to Google cloud database to support fast retrieval of data. The concept of fuzzy logic is applied to detect the deficiency of nutrients from the sensed data. The crisp value of each sensed data is discriminated into five fuzzy values namely very low, low, medium, high and very high during fuzzification. A set of If-then rules are framed based on individual chemical solutions of Nitrogen (N), Phosphorous (P) and Potassium (K). Mamdani inference procedure is used to derive the conclusion about the deficiency of N, P and K available in soil chosen for testing and accordingly an alert message is sent to the farmer about the quantity of fertilizer to be used at regular intervals. The proposed hardware prototype and the software embedded in the microcontroller are developed in Raspberry pi 3 using Python. The developed model is tested in three different soil samples like red soil, mountain soil and desert soil. It is observed that the developed system results in linear variation with respect to the concentration of the soil solution. A sensor network scenario is created using Qualnet simulator to analyze the performance of designed NPK sensor in terms of throughput, end to end delay and jitter. From the different variety of experiments conducted, it is noticed that the developed IoT system is found to be helpful to the farmers for high yielding of crops. (C) 2019 Elsevier Inc. All rights reserved.

Most of the existing peer-to-peer (P2P) content distribution schemes carry out a random or rarest piece first content dissemination procedure to avoid duplicate transmission of the same pieces of data and rare pieces of data occurring in the network. This problem is solved using P2P content distribution based on network coding scheme. Network coding scheme uses random linear combination of coded pieces. Hence, the above-stated problem is solved easily and simply. Our proposed mechanism uses network coding mechanism in which several contents having the same message are grouped into different groups and coding operation is performed only within the same group. The interested peers are also divided into several groups with each group having the responsibility to spread one set of contents of messages. The coding system is designed to assure the property that any subset of the messages can be utilized to decode the original content as long as the size of the subset is suitably large. To meet this condition, dynamic smart network coding (DSNC) scheme is defined which assures the preferred property, then peers are connected in the same group to send the corresponding message, and connect peers in different groups to disseminate messages for carrying out decoding operation. Moreover, the proposed system is readily expanded to support topology change to get better system performance further in terms of reliability, link stress and throughput. The simulation results prove that the proposed system can attain 20–25% higher throughput than existing systems, good reliability, link failure and robustness to peer churn.

Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of oil and 25% of protein content. The oil obtained from the groundnut is widely used for cooking and losing body weight, and its fats are widely used for making soaps. The groundnut cultivation is affected by different kinds of diseases such as fungi, viruses, and bacteria. Hence, these diseases affect the leaf, root and stem of the groundnut plant and it leads to heavy loss in yield. Moreover, the enlarger number of diseases affects the leaf and root-like Alternaria, Pestalotiopsis, Bud necrosis, tikka, Phyllosticta, Rust, Pepper spot, Choanephora, early and late leaf spot. To overcome these issues, we introduce an efficient method of deep convolutional neural network (DCNN) because it automatically detects the important features without any human supervision. The DCNN procedure can deeply detect plant disease by using a deep learning process. Moreover, the DCNN training and testing process demonstrate an accurate groundnut disease determination and classification result. The number of groundnut leaf disease images is chosen from the plant village dataset, and it is used for the training and testing process. The stochastic gradient decent momentum method is used for dataset training, and it has shown the better performance of proposed DCNN. From the comparison analysis, the 6th combined layer of proposed DCNN delivers a 95.28% accuracy value. Ultimately, the groundnut disease classification with its overall performance of proposed DCNN provides 99.88% accuracy.

Background: Influenza A/H1N1(pdm09) strain is in circulation across the globe since 2009, with frequent seasonal outbreaks. The disease burden had varied across geographical regions and temporal spheres. Though studies have linked the role of climatic variables with influenza outbreaks in temperate zones, this is limited in tropical countries. We explored the relationship between climatic factors and incidence of influenza A/H1N1 in Puducherry, a southern district in India during 2009–2019. Methods and materials: We retrieved the laboratory confirmed influenza A/H1N1 patients done by standard polymerase chain reaction analysis during June 2009 to April 2019 from the state disease surveillance unit and accredited virology laboratories. The line list was epidemiologically analysed and patterns in the outbreaks were looked into. We collected monthly averages of local weather parameters such as absolute humidity, relative humidity, temperature, rainfall and insolation from the local meteorological department and respective month's incidence of Influenza was estimated using monthly projected population for the period. We conducted an ecological study to determine the relationship between incidence of influenza and weather parameters using negative binomial regression models. Results: There were 680 patients, lab confirmed with influenza A/H1N1 between June 2009 and April 2019 in Puducherry district. Three outbreaks occurred in the last five years and it was observed that mortality was often confined to the initial half of the outbreak period and healthcare personnel were the major patients in the first half. Between 2009 and 2019, the monthly incidence ranged between 0 and 14.5 per 100,000 population. Univariate negative binomial regression analysis of the influenza incidence with the local weather parameters demonstrated that absolute humidity, relative humidity, insolation and rainfall were associated. On multivariate regression analysis, absolute humidity (IRR: 1.37, 95% CI: 1.06–1.76), insolation (IRR: 1.02, 95% CI: 1.001–1.03) and rainfall (IRR: 1.01, 95% CI: 1.004–1.02) were significantly associated. Conclusion: We found the absolute humidity, insolation and rainfall were associated with incidence of influenza A/H1N1. It is suggested to include climatic factors in Disease surveillance system to detect influenza outbreaks at the earliest. Specific protective measures of the influenza could be planned at the earliest among the at risk population.

Software reliability is the important attribute for complex computing systems to provide reliability could cause series issues such as extra cost, development delay and image of the software solution providers. Hence, ensuring the reliability of software before deliver to the customer is essential part for the company. Finding the error in right time with reasonable degree of accuracy helps to prevent the consequences. Several software reliability growth models developed and used to measure the trustworthiness based on development and testing phases with unrealistic assumption over the environment and applied Block box methodologies while constructing model. This paper presents well established statistical time series (S)ARIMA approach for developing a forecasting model that able to provide significantly improved reliability prediction. Using real time publicly available software failure sets, the prediction of proposed model is developed and compared with previously available reliability models.

In vehicular ad hoc networks (VANETs), the frequent change in vehicle mobility creates dynamic changes in communication link and topology of the network. Hence, the key challenge is to address and resolve longer transmission delays and reduced transmission stability. During the establishment of routing path, the focus of entire research is on traffic detection and road selection with high traffic density for increased packet transmission. This reduces the transmission delays and avoids carry‐and‐forward scenarios; however, these techniques fail in obtaining accurate traffic density in real‐time scenario due to rapid change in traffic density. Thus, it is necessary to create a model that efficiently monitors the traffic density and assist VANETs in route selection in an automated way with increased accuracy. In this article, a novel machine learning architecture using deep reinforcement learning (DRL) model is proposed to monitor and estimate the data essential for the routing protocol. In this model, the roadside unit maintains the traffic information on roads using DRL. The DRL predicts the movement of the vehicle and makes a suitable routing path for transmitting the packets with improved transmission capacity. It further uses predicted transmission delays and the destination location to choose the forwarding directions between two road safety units (RSUs). The application of DRL over VANETs yields increased network performance, which provides on‐demand routing information. The simulation results show that the DRL‐based routing is effective in routing the data packets between the source and destination vehicles than other existing method.

In this article, the computational complexity reduction of zero forcing (ZF) and minimum mean square error (MMSE) detection is presented for the uplink multiple input multiple output (MIMO) single carrier frequency division multiple access (SC-FDMA) systems. MIMO SC-FDMA structure with detection of user’s data by the well-known multiuser linear detection approaches such as MMSE and ZF often use single input single output detection systems due to its simple detection and significant performance. Although, these approaches involve inversion of a matrix computation whose matrix dimension depend on number of subcarriers used in the system, mainly, it can be few thousands. In practical detection, the computational complexity of matrix inversion becomes very high. Also, the complexity of the receiver is raised because of the superposition of all the transmitted signals at each antenna received in the systems. The proposed conjugate gradient approach reduces the higher computational overhead of linear detectors, which updates iteratively the ZF and MMSE solution and avoids the direct computation of matrix inverse operation. The analysis of the proposed algorithm reveals the superior performance and the low complexity detection in spatial multiplexing SC-FDMA system. Simulations have investigated that the computational complexity of the proposed method has been greatly reduced and bit-error-rate performance is closer to matched filter bound.

Summary The jamming detection approach based on fuzzy assisted multicriteria decision‐making system (JDA) is proposed to detect the presence of jamming in downstream communication for Cluster based Wireless Sensor Network (CWSN). The proposed approach is deployed in cluster head (CH). The JDA functions in two aspects: First, the CH periodically measures the jamming detection metrics namely Packet Delivery Ratio (PDR) and Received Signal Strength Indicator (RSSI) of every node in the cluster to determine the behavior of the sensor nodes. In order to determine the behavior of members in the cluster, the CH compares the measured PDR with the PDR threshold. If the measured PDR is lesser than the PDR threshold, then CH applies the TOPSIS method on the PDR and RSSI metrics to determine the presence of jamming. These metrics are considered as the criteria and the nodes or the members are considered to be the alternatives. Next, the fuzzy logic is applied on the results obtained from the TOPSIS method to optimize the jamming detection metrics and identify the presence of jamming accurately. The proposed jamming detection approach detects well and arrives at 99.6% jamming detection rate as shown in simulation.

Wireless sensor network (WSN) is employed in variety of applications ranging from agriculture to military. WSN is vulnerable to various security attacks, in which jamming attacks obstruct and disturb the exchange of information between sensor nodes in WSN by transmitting signals to jam legitimate transmission to cause a denial of service. Hence, it is essential to secure the sensor networks from jamming attacks. In this paper, two approaches: fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS)-based jamming detection system are proposed for detecting the presence of jamming by computing two jamming detection metrics, namely, packet delivery ratio and received signal strength indicator. FIS approach is based on Takagi–Sugeno fuzzy logic which optimizes the jamming detection metrics. ANFIS approach combines fuzzy logic and learning ability of the neural network to optimize the metrics for detecting various types of jamming. The proposed approaches are compared with existing system and themselves. The simulation result shows that the proposed ANFIS approach detects the jamming attacks as high as true detection ratio.

The number of cloud users and their aspiration for completion of tasks at less energy consumption and operating cost are rapidly increasing. Hence, the authors of this paper aim to minimize the makespan and operating cost by optimally scheduling the tasks and allocating the resources of cloud service. The optimum task scheduling and resource allocation are obtained for each objective function using the simple genetic algorithm. Further, the non-dominated solutions of the dual objectives are obtained using the non-dominated sorting genetic algorithm-II, the most successful multi-objective optimization technique. A complex cloud service problem consisting of ten tasks, fifteen subtasks and fifteen heterogeneous resources is considered to investigate the proposed method. The numerical results obtained in the single objective and multi objective optimization problems show that the makespan and the operating cost are significantly reduced using the simple genetic algorithm and a wide range of non-dominated solutions are obtained in the multi-objective optimization problem, by which the cloud users shall be benefitted to choose the most appropriate solution based on the other design constraints they have.

In the medical field, many images are acquired using different modalities like X-ray, Computed Tomography and Magnetic Resonance Imaging. Organizing these images and retrieving them is a critical task. In this paper, a novel retinal image retrieval and automatic annotation of ten different retinal diseases is presented and is comprised of three steps namely, feature extraction, automatic annotation and retrieval of similar images. In feature extraction, visual features like texture, color and shape were derived from the images and stored in a database. The retinal images are affected by various diseases and have to be identified easily and quickly for better treatment. For automatic annotation, we have considered ten different retinal diseases and annotated the retinal image with its corresponding disease using Support Vector Machine (SVM) with Active Learning (AL) and discriminative based automatic image annotation. In retrieval of images, Bray Curtis distance measure was used to retrieve the similar images by comparing the feature values of images stored in the database. Our proposed system has achieved a precision of 82% and specificity of 96.3%.