Abstract The open communication medium of the Internet of Things (IoT) is more vulnerable to security attacks. As the IoT environment consists of distributed power limited units, the routing protocol used for distributed routing should be light‐weighted compared to other centralized networks. In this situation, complex security algorithms and routing mechanisms affect the generic data communications in IoT platforms. To handle this problem, this proposed system develops a cooperative and feedback‐based trustable energy‐efficient routing protocol (CFTEERP). This protocol calculates local trust value (LTV) and global trust value (GTV) of each node using node attributes and K‐means‐based feedback evaluation procedures. The K‐means clustering algorithm leaves out the distorted node routing metrics and misbehaving node metrics for all channels. This proposed CFTEERP uses the nearest secure node costs to increase the network lifetime without selecting the nearest nodes for routing the data. In this work, secure routing is initiated using multipath routing strategy that analyses LTV, GTV, next trustable node, average throughput, energy consumption, average packet delivery ratio (PDR) and traffic various metrics of entire IoT communication. The technical aspects of proposed system are implemented to solve different existing techniques' limitations. In the comparative experiment, the proposed method provides 90% of PDR and a minimal energy consumption rate of 25% lesser than the existing systems against different malicious attacks.

Summary X‐ray images are widely used to identify fractures in human bones. Radiographic diagnosis models take more time and manual procedures for establishing physical analysis of bone fractures. Furthermore, the lack of clinical resources and the medical support systems lead to inaccurate bone data extractions. In this case, the need for detail is required to understand the scientific issues in x‐ray and magnetic resonance imaging (MRI) based bone fracture diagnosis solutions. Particularly, the current bone fracture detection models are emerging with computerized frameworks and health informatics consoles. In this regard, this article lists five phases. First, data preparation and data collection tasks are initiated. Second, bone fracture diagnosis models are comparatively analyzed to find optimal observations. Consequently, the third phase of the article examines the treatments against bone fracture observations with traditional and deep learning (DL) techniques. The fourth phase consists of relative diagnosis solutions and the treatment benefits on various types of technical frameworks. Finally, this work concludes the comparison with recent DL‐based bone fracture identification models.

Abstract The production rate of crops is significantly declining due to natural disasters, animal interventions and plant diseases. Internet of things (IoT) and wireless sensor networks are widely applied in crop field monitoring systems to observe the quality of each plant and the field. This work proposes IoT based crop field protection system (ICFPS) that monitors and protects the crop fields from animal intrusions. This proposed system uses ultrasonic sensors, hyperspectral cameras, voice recorded buzzers and other agriculture sensors to protect the entire crop field. This system uses numerous sensor nodes and cameras for gathering field objects (images and environmental objects). The proposed ICFPS creates deep learning techniques such as recurrent convolutional neural networks (RCNN) and recurrent generative adversarial neural networks (RGAN) for feature extraction, disease detection and field data monitoring practices. This proposed work develops a smart city‐based agriculture system using cognitive learning approaches. This proposed system analyses crop field data and provide automatic alerts regarding animal interferences and crop diseases. Moreover, the cognitive smart crop field system observes various field conditions which support for good production rate. In this system, sensors and camera‐enabled agriculture drones are coordinated with each other to collect the field data regularly. At the same time, the proposed work trains the RCNN and RGAN units using effective crop field datasets to attain realistic decisions within minimal time intervals. The experiment details and results show the proposed ICFPS works with 8%–10% of more classification accuracy than existing systems.

Selecting secure and dynamic watchdogs for detecting attacks using a type of intrusion detection system (IDS). The selection procedure of watchdogs in the random ad‐hoc wireless sensor network is a load creation job in the absence of a centralised controller. In this type of network, the data processing transmission for the routing process and secure watchdog selection process create overhead in each node. It drains the energy of an individual node easily. Founded on these issues, this work concentrates on the secure selection of concealed watchdogs and maintenance of optimal watchdog availability ratio. In the random ad‐hoc wireless sensor network, the secure and authorised watchdogs are selected from the neighbour list of each node on‐demand basis to provide security for the network. In addition to this work concentrates on dynamic uncertain conditions to build a secure and authenticated multi‐watchdog system in the distributed scenario. The proposed system uses the combination of both customised layer masking techniques and secure routing and monitoring techniques for the protection of random ad‐hoc wireless sensor networks.