Internet of Things (IoT) has gained popularity with the growth and prospects of smart networks. It is intended to exploit the network edges, which enables smart service and assessment for IoT. Moreover, this exploitation not only improves the user experiences but also provides service resiliencies at any catastrophe. The IoT appliances deploy distributed structure and proximity of end-users to offer quicker responses and improved quality of service (QoS). Nevertheless, security is mainly considered to resist the susceptibility of attacks. This work aims to introduce a novel secure routing model by carrying out optimal path selection and encryption. Initially, optimal link-state multipath routing takes place, where optimal paths or nodes are chosen for secure transmission. For optimal path selection destination and source, a Crossover Mutated Marriage in Honey Bee (CM-MH) algorithm is developed and proposed in this work. Next, encryption takes place that ensures secure transmission. In this research, an Improved Blowfish algorithm (IBFA) is proposed for secured authentication. Finally, the updates are monitored. At last, the supremacy of the developed approach is examined via evaluation over numerous existing techniques.

Wireless sensor networks (WSNs) are progressive ad hoc networks which comprise of distributed sensors which are typically and randomly deployed over the target region. The valuable information from the sensor nodes is allowed to access with the help of gateway node by the registered user. To ensure secure communication, a session key is exchanged between the participants over the insecure channel. In this paper, we identified some deficiencies in Jung et al.’s scheme and then devised an enhanced biometric-based anonymous user authentication and key agreement scheme which is also embedded with symmetric cryptosystem for WSNs. The advantage of using biometric login is to ensure the legal user’s efficient login. We conferred about preserving the security of our proposed scheme, we primarily applied formal verification BAN-logic method to check the exactness of mutual authentication. Further, used Automated Validation of Internet Security Protocols and Applications (AVISPA) software which is widely accepted and its results confirm that our scheme is secure against active and passive attacks including forgery, replay, and man-in-the-middle attack. Additionally, an informal analysis proves our scheme can withstand various possible attacks on authentication protocols over the insecure channel. Furthermore, our scheme is more appropriate for WSNs based upon the comparison of computational efficiency and security requirements with recent results.

Wireless sensor networks (WSNs) consist of small sensor nodes with limited energy. Such nodes have the ability to monitor the physical conditions and communicate information among the nodes without the requirement of the physical medium. WSNs are autonomous and are distributed in space. Due to the absence of central authority and random deployment of nodes in the network, WSN is prone to security threats. Well-known attacks in WSN are a malicious attack (such as compromised node imitating as one of the network nodes, misleading other nodes). In the art of work, various methods are developed to overcome these attacks either by cryptographic approaches or by time synchronization. But these methods may fail because of WSN autonomous structure. In this paper, an efficient approach called Hamming residue method (HRM) is presented to mitigate the malicious attacks. The experimental results validate the presented approach.

This paper aims to introduce a novel approach that includes three steps, namely Energy efficient clustering, Disease diagnosis, and an Alert system. Initially, energy-efficient clustering of nodes was conducted, and to render the clustering more optimal, its centroid was optimally selected by a new hybrid algorithm. In addition, this cluster formation was conducted based on constraints such as distance and energy. Further, the disease diagnosis in IoT was performed under two phases namely, “feature extraction and classification”. During feature extraction, the statistical and higher-order features were extracted. These extracted features were then classified via Optimized Deep Convolutional Neural Network (DCNN). To make the classification more precise, the weights of the DCNN were optimally tuned by a new hybrid algorithm referred to as Hybrid Elephant and Moth Flame with Adaptive Learning (HEM-AL). Finally, an alert system was enabled via proposed severity level estimation, which determined the severity of the disease, suggesting patients to visit the hospital. Lastly, the supremacy of the developed approach was examined via evaluation over the other extant techniques. Accordingly, the proposed model attained an accuracy of 0.99 for test case 1, and was 7.41%, 17.34%, and 13.41% better than traditional NN, CNN, and DCNN models.

Precision Agriculture (PA) techniques employing Internet of Things (IoT) can significantly improve crop yields and enhance productivity. Radio frequency identification (RFID) based IoT systems can enhance precision livestock farming by enabling livestock traceability and identification in large farms, but congestion is a major challenge. In such conditions, livestock estimation using RFID is a complex task because of high chances of tag collision due to significant increase in interference at the gates. Several anti-collision algorithms have been explored in recent past to address this issue with the aid of a single antenna. However, the throughput gains from a single antenna are limited particularly for dense scenarios like gates at a small ruminant farm. Therefore, we propose a multi-antenna RFID reader system with a dynamic frame slotted ALOHA (DFSA) anti-collision algorithm to address the tag collision problem by exploiting the spatial diversity of the multiple inputs and multiple outputs (MIMO) backscattering channel. The proposed technique is simulated in MATLAB for several tag population figures both with and without interference actions of various intensities. Results have shown that significant throughput gains can be achieved, in comparison with gains attained by anti-collision algorithms based on a single antenna. The simulation testbed enables us to evaluate the proposed technique for multiple receive antenna configurations.

In this paper, we briefly look at the latest state-ot-the-art in the domain of multi-input multi-output (MIMO) radio frequency identification (RFID) systems while detailing the work done in the domain of anti-collision, range enhancements, bit error rate (BER) improvements and security. Various passive ultra-high frequency (UHF) RFID implementations are considered that employ multiple antennas at the reader and single or multiple antennas at each tag. We look at several recent works those explored MIMO for RFID receivers. When using MIMO at the backscatter channel, significant improvements can be achieved in the BER as well as range extension. With the extra reliability and increased throughput, such systems can be deployed in many important applications like large tag reading scenarios and accurate tracking. Increased throughput is directly dependent on estimation of tag quantity in a bulk reading environment and usually estimators designed for single antenna systems under-perform in such settings causing low signal to noise ratio (SNR) when employed in MIMO systems where tag signal overlapping can happen more often. One of the key challenges is to keep the design of the RFID tag simple, cutting cost and power requirement when employing anti-collision schemes. We provide a brief survey in some of the recent developments related to MIMO RFID systems, the protocols and algorithms used, and improvements achieved.