Biosensors, as an application for animal health management, are an emerging market that is quickly gaining recognition in the global market. Globally, a number of sensors being produced for animal health management are at various stages of commercialization. Some technologies for producing an accurate health status and disease diagnosis are applicable only for humans, with few modifications or testing in animal models. Now, these innovative technologies are being considered for their future use in livestock development and welfare. Precision livestock farming techniques, which include a wide span of technologies, are being applied, along with advanced technologies like microfluidics, sound analyzers, image-detection techniques, sweat and salivary sensing, serodiagnosis, and others. However, there is a need to integrate all the available sensors and create an efficient online monitoring system so that animal health status can be monitored in real time, without delay. This review paper discusses the scope of different wearable technologies for animals, nano biosensors and advanced molecular biology diagnostic techniques for the detection of various infectious diseases of cattle, along with the efforts to enlist and compare these technologies with respect to their drawbacks and advantages in the domain of animal health management. The paper considers all recent developments in the field of biosensors and their applications for animal health to provide insight regarding the appropriate approach to be used in the future of enhanced animal welfare.

Recent advances in wearable sensors for animal health management

In recent years, smart cities have emerged with energy conservation systems for managing energy in cities as well as buildings. Although many studies on energy conservation systems of smart homes have already been conducted, energy management at the city level is still a challenge due to the various building types and complex infrastructure. Therefore, this paper investigated the research themes on smart homes and cities through a quantitative review and identified barriers to the progression of smart homes to sustainable smart cities through a qualitative review. Based on the results of the holistic framework of each domain (smart home and city) and the techno-functional barriers, this study suggests that the following innovative solutions be suitably applied to advanced energy conservation systems in sustainable smart cities: (i) construction of infrastructure for advanced energy conservation systems, and (ii) adoption of a new strategy for energy trading in distributed energy systems. Especially, to reflect consumer behavior and energy in sustainable smart cities, the following responses to future research challenges according to the “bottom-up approach (smart home level to smart city level)” are proposed: (i) development of real-time energy monitoring, diagnostics and controlling technologies; (ii) application of intelligent energy management technologies; and (iii) implementation of integrated energy network technologies at the city level. This paper is expected to play a leading role as a knowledge-based systematic guide for future research on the implementation of energy conservation systems in sustainable smart cities.

A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities

In recent years, the need for high security with reliability in the wireless network has tremendously been increased. To provide high security in reliable networks, mobile ad hoc networks (MANETs) play a top role, like open network boundary, distributed network, and fast and quick implementation. By expanding the technology, the MANET faces a number of security challenges due to self-configuration and maintenance capabilities. Besides, traditional security solutions for wired networks are ineffective and inefficient because of the nature of highly dynamic and resource-constrained MANETs. In this paper, the researchers focus on improving reliable data transmission with high security in the MANET using an optimization technique. In the proposed MANET system, the nodes are clustered by utilizing an energy-efficient routing protocol. Then, the modified discrete particle swarm optimization is used to select the optimal cluster head. A secured routing protocol and a signcryption model can be used to improve the transmission security of the reliable MANET. The signcryption algorithm encrypts the digital signature, which can enhance the overall efficiency and confidentiality. The security-based analysis is performed on the basis of packet delivery ratio, energy consumption, network lifetime, and throughput. Finally, the result demonstrates that the MANET with optimization techniques achieves a high transmission rate and improves the reliable data security.

Reliable Data Transmission Model for Mobile Ad Hoc Network Using Signcryption Technique

Deep learning (DL) is an efficient method for botnet attack detection. However, the volume of network traffic data and memory space required is usually large. It is, therefore, almost impossible to implement the DL method in memory-constrained Internet-of-Things (IoT) devices. In this article, we reduce the feature dimensionality of large-scale IoT network traffic data using the encoding phase of long short-term memory autoencoder (LAE). In order to classify network traffic samples correctly, we analyze the long-term inter-related changes in the low-dimensional feature set produced by LAE using deep bidirectional long short-term memory (BLSTM). Extensive experiments are performed with the BoT-IoT data set to validate the effectiveness of the proposed hybrid DL method. Results show that LAE significantly reduced the memory space required for large-scale network traffic data storage by 91.89%, and it outperformed state-of-the-art feature dimensionality reduction methods by 18.92–27.03%. Despite the significant reduction in feature size, the deep BLSTM model demonstrates robustness against model underfitting and overfitting. It also achieves good generalisation ability in binary and multiclass classification scenarios.

/pdf/hybrid-deep-learning-for-botnet-attack-detection-in-the-1ygf68htdv.pdf

Hybrid Deep Learning for Botnet Attack Detection in the Internet-of-Things Networks

Supervised learning and pattern recognition is a crucial area of research in information retrieval, knowledge engineering, image processing, medical imaging, and intrusion detection. Numerous algorithms have been designed to address such complex application domains. Despite an enormous array of supervised classifiers, researchers are yet to recognize a robust classification mechanism that accurately and quickly classifies the target dataset, especially in the field of intrusion detection systems (IDSs). Most of the existing literature considers the accuracy and false-positive rate for assessing the performance of classification algorithms. The absence of other performance measures, such as model build time, misclassification rate, and precision, should be considered the main limitation for classifier performance evaluation. This paper’s main contribution is to analyze the current literature status in the field of network intrusion detection, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps. Therefore, fifty-four state-of-the-art classifiers of various different groups, i.e., Bayes, functions, lazy, rule-based, and decision tree, have been analyzed and explored in detail, considering the sixteen most popular performance measures. This research work aims to recognize a robust classifier, which is suitable for consideration as the base learner, while designing a host-based or network-based intrusion detection system. The NSLKDD, ISCXIDS2012, and CICIDS2017 datasets have been used for training and testing purposes. Furthermore, a widespread decision-making algorithm, referred to as Techniques for Order Preference by Similarity to the Ideal Solution (TOPSIS), allocated ranks to the classifiers based on observed performance reading on the concern datasets. The J48Consolidated provided the highest accuracy of 99.868%, a misclassification rate of 0.1319%, and a Kappa value of 0.998. Therefore, this classifier has been proposed as the ideal classifier for designing IDSs.

https://www.mdpi.com/2227-7390/9/6/690/pdf

Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research

The public transportation system (PTS) in a metropolitan area is a nonlinear, dynamic, and complex system. Managing and providing suitable public transportation services are difficult. In this article, we propose an Internet of Things-based intelligent PTS (IoT-IPTS) in a metropolitan area. An IoT is used to interconnect transportation entities, such as vehicles, commuters (mobile phones), routes (sensors), roadside units (RSUs), etc., in a metropolitan area. The IoT provides the seamless connectivity between different networking technologies whenever the commuters or vehicles move from one location to another location. Hence, IoT provides the suitable seamless public transportation services in the metropolitan area. In addition, we have used context information of transportation entities, such as routes condition, traffic density, number of routes available, traffic congestion, vehicles’ movement, and their mobility, which are stored in the cloud. The stored context information in cloud along with the IoTs are used to find the relevant routes, alternative modes, departure times, and many more for providing public transportation services in a metropolitan area. The proposed IoT-IPTS makes use of static and mobile agents with the emergent intelligence technique (EIT) for collecting, analyzing, and sharing context information. The analyzed context information is used to form the policies to provide the best available public transportation services to the commuters in a metropolitan area. The software-defined network is used to enable the cloud computing and EI network to manage the public transportation services to the commuters.

IoT-Based Context-Aware Intelligent Public Transport System in a Metropolitan Area

In recent years, modern metropolitan areas are the main indicators of economic growth of nation. In metropolitan areas, number and frequency of vehicles have increased tremendously, and they create issues, like traffic congestion, accidents, environmental pollution, economical losses and unnecessary waste of fuel. In this paper, we propose traffic management system based on the prediction information to reduce the above mentioned issues in a metropolitan area. The proposed traffic management system makes use of static and mobile agents, where the static agent available at region creates and dispatches mobile agents to zones in a metropolitan area. The migrated mobile agents use emergent intelligence technique to collect and share traffic flow parameters (speed and density), historical data, resource information, spatio-temporal data and so on, and are analyzes the static agent. The emergent intelligence technique at static agent uses analyzed, historical and spatio-temporal data for monitoring and predicting the expected patterns of traffic density (commuters and vehicles) and travel times in each zone and region. The static agent optimizes predicted and analyzed data for choosing optimal routes to divert the traffic, in order to ensure smooth traffic flow and reduce frequency of occurrence of traffic congestion, reduce traffic density and travel time. The performance analysis is performed in realistic scenario by integrating NS2, SUMO, OpenStreatMap (OSM) and MOVE tool. The effectiveness of the proposed approach has been compared with the existing approach.

Prediction based traffic management in a metropolitan area

Emergent Intelligence Based QoS Routing in MANET

A comprehensive review on IoT-based infrastructure for smart grid applications

The continuous urbanization with extensive dynamic situations on evolving cities, urban, and suburban areas, it is not feasible to categorize the navigation as fastest route, toll-free, and other variants. Metropolitan areas are more prone to traffic congestion, lane blocking, accidents, etc., due to the overcrowding and dynamic change of commuters’ arrival rates. In the metropolitan areas, most of the commuters’ use Google map to reach their desired destinations. It is quite often that route specified by navigation will not be reliable because sometimes due to the inability to update the sudden occurrence of incidents on the routes. Currently, Google map and GPS provide the time required to cover the distance and shortest route to reach the destination. The main issues with the existing Google map are it does not considers the impact of sudden occurrence of incidents, does not show the type of incidents that occurred, clearance time, and optimal routes. These issues are solved by designing an efficient context-aware vehicle incidents route service management for an intelligent transport system. The proposed system takes the context information of incidents, vehicles, weather conditions, roadside units, roads, and so on. This context information will be collected and shared with the nearby vehicles and roadside units using both mobile agents and dedicated short-range communication protocols. The proposed system suggests alternative routes with minimal delay and traffic clearance time and severity of incidents to the commuters. Also, it provides the incident information to the neighborhood vehicles, roadside units, nearby hospitals, ambulance, and members of the victims. The proposed system is exhaustively simulated in objective modular network testbed in C++, simulation of urban mobility, and Veins with different simulation parameters. The proposed system’s simulation results reduce the travel time (7 min) compared to the without the context information system (25 min), least collision rate (0.785%) compared to the existing system, minimizes the traffic clearance time in the incident zone, and uniform distribution of vehicle traffic on the estimated routes.

Suresh Chavhan

Papers

IoT-Based Context-Aware Intelligent Public Transport System in a Metropolitan Area

Prediction based traffic management in a metropolitan area

Emergent Intelligence Based QoS Routing in MANET

A comprehensive review on IoT-based infrastructure for smart grid applications

An Efficient Context-Aware Vehicle Incidents Route Service Management for Intelligent Transport System