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Wireless sensor networks (WSNs) have demonstrated research and developmental interests in numerous fields, like communication, agriculture, industry, smart health, monitoring, and surveillance. In the area of agriculture production, IoT-based WSN has been used to observe the yields condition and automate agriculture precision using various sensors. These sensors are deployed in the agricultural environment to improve production yields through intelligent farming decisions and obtain information regarding crops, plants, temperature measurement, humidity, and irrigation systems. However, sensors have limited resources concerning processing, energy, transmitting, and memory capabilities that can negatively impact agriculture production. Besides efficiency, the protection and security of these IoT-based agricultural sensors are also important from malicious adversaries. In this article, we proposed an IoT-based WSN framework as an application to smart agriculture comprising different design levels. Firstly, agricultural sensors capture relevant data and determine a set of cluster heads based on multi-criteria decision function. Additionally, the strength of the signals on the transmission links is measured while using signal to noise ratio (SNR) to achieve consistent and efficient data transmissions. Secondly, security is provided for data transmission from agricultural sensors towards base stations (BS) while using the recurrence of the linear congruential generator. The simulated results proved that the proposed framework significantly enhanced the communication performance as an average of 13.5% in the network throughput, 38.5% in the packets drop ratio, 13.5% in the network latency, 16% in the energy consumption, and 26% in the routing overheads for smart agriculture, as compared to other solutions.

The infrastructure of wireless sensor networks (WSN) is structured in an ad-hoc manner and organized nodes reporting the events to the Base Station (BS). A WSN is integrated with smart technologies to develop fast Internet of Things (IoT) communications among different applications. Recently, many researchers proposed their solutions to optimize IoT data transmissions in an energy efficient manner with cost effective support. However, most of the solutions have focused on the design and development of static topologies and overlooked the dynamic structure of mobile sensor nodes. Furthermore, due to limited constraints of sensor nodes with open accessibility of wireless communications medium, data protection against malicious activities need to be redesign with the least network overheads. Therefore, the contribution of this article is to propose an intrusion prevention framework for mobile IoT devices with its integration to WSN so that to provide data security with improved network delivery ratio. The proposed framework is composed of two sub-components. Firstly, non-overlapping and autonomously organized clusters are generated and maintained the clusters' stability based on the uncertainty principle. Secondly, end-to-end secure and multi-hop routing paths are developed based on the blockchain architecture. The simulation results demonstrate a significant improvement when compared to existing solutions in terms of different network metrics.

The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise.

Due to the advancement of information and communication technologies, the use of Internet of Things (IoT) devices has increased exponentially. In the development of IoT, wireless sensor networks (WSNs) perform a vital part and comprises of low-cost smart devices for information gathering. However, such smart devices have constraints in terms of computation, processing, memory and energy resources. Along with such constraints, one of the fundamental challenges for WSN is to achieve reliability with the security of transmitted data in a vulnerable environment against malicious nodes. This paper aims to develop an energy-efficient and secure routing protocol (ESR) for intrusion avoidance in IoT based on WSN to increase the network period and data trustworthiness. Firstly, the proposed protocol creates different energy-efficient clusters based on the intrinsic qualities of nodes. Secondly, based on the (k,n) threshold-based Shamir secret sharing scheme, the reliability and security of the sensory information among the base station (BS) and cluster head are achieved. The proposed security scheme presents a light-weight solution to cope with intrusions generated by malicious nodes. The experimental results using the network simulator (NS-2) demonstrate that the proposed routing protocol achieved improvement in terms of network lifetime as 37%, average end-to-end delay as 24%, packet delivery ratio as 30%, average communication cost as 29%, network overhead as 28% and the frequency of route re-discoveries as 38% when compared with the existing work under dynamic network topologies.

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Internet of Things (IoT) enables modern improvements in smart sensors, RFID, Internet technologies, and communication protocols. Sensor nodes are treated as smart devices and widely used to gather and forward sensed information. However, besides intrinsic constraints on sensor nodes, they are vulnerable to a variety of security threats. This paper presents an energy-aware and secure multi-hop routing (ESMR) protocol by using a secret sharing scheme to increase the performance of energy efficiency with multi-hop data security against malicious actions. The proposed protocol comprises three main aspects. First, the network field is segmented into inner and outer zones based on the node location. Furthermore, in each zone, numerous clusters are generated on the basis of node neighborhood vicinity. Second, the data transmission from cluster heads in each zone towards the sink node is secured using the proposed efficient secret sharing scheme. In the end, the proposed solution evaluates the quantitative analysis of data links to minimize the routing disturbance. The presented work provides a lightweight solution with secure data routing in multi-hop approach for the IoT-based constrained wireless sensor networks (WSNs). The experimental results demonstrate the efficacy of proposed energy-aware and secure multi-hop routing protocol in terms of network lifetime by 38%, network throughput by 34%, energy consumption by 34%, average end-to-end delay by 28%, and routing overhead by 36% in comparison with the existing work.

The advancement of computer‐ and internet‐based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient–doctor diagnostics is extended to a more robust advanced concept of E‐health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud‐based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape‐based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross‐validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.