Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection.

Underwater communication is one of the important research areas which involves design and development of communication systems that can demonstrate high data rate and low Bit Error Rate (BER). In this work three different modulation schemes are compared for their performances in terms of BER and Peak to Average Power Ratio (PAPR). The realistic channel model called WATERMARK is used as a benchmark to evaluate channel performances. The mathematical model is developed in MATLAB and channel environments such as Norway Oslo fjord (NOF1), Norway Continental Shelf (NCS1), Brest Commercial Harbour (BCH1), Kauai (KAU1, KAU2) are considered for modelling different underwater channels. The data symbols are modulated using Dual Tree Complex Wavelet Transform (DTCWT) Orthogonal Frequency Division Multiplexing (OFDM) model to generate multi subcarriers and are demodulated at the receiver considering underwater channel environments. The BER results are evaluated for channel depth less than 10m and 10-50m. An improvement of 2x10-2 in terms of BER is observed when compared with Fast Fourier Transform (FFT) based OFDM model.

A mobile agent is a small piece of software which works on direction of its source platform on a regular basis. Because mobile agents roam around wide area networks autonomously, the protection of the agents and platforms is a serious worry. The number of mobile agents-based software applications has increased dramatically over the past year. It has also enhanced the security risks associated with such applications. Most of the security mechanisms in the mobile agent architecture focus solely on platform security, leaving mobile agent safety to be a significant challenge. An efficient authentication scheme is proposed in this article to address the situation of protection and authentication of mobile agent at the hour of migration of across multiple platforms in malicious environment. An authentication mechanism for the mobile agent based on the Hopfield neural network proposed. The mobile agent’s identity and password are authenticate using the specified mechanism at the moment of execution of assigned operation. An evaluative assessment has been offered, along with their complex character, in comparison to numerous agent authentication approaches. The proposed method has been put into practice, and its different aspects have been put to the test. In contrasted to typical client-server and code-on-demand approaches, the analysis shows that computation here is often more safe and simpler.

Cloud computing's popularity and success are directly related to improvements in the use of Information and Communication Technologies (ICT). The adoption of cloud implementation and services has become crucial due to security and privacy concerns raised by outsourcing data and business applications to the cloud or a third party. To protect the confidentiality and security of cloud networks, a variety of Intrusion Detection System (IDS) frameworks have been developed in the conventional works. However, the main issues with the current works are their lengthy nature, difficulty in intrusion detection, over-fitting, high error rate, and false alarm rates. As a result, the proposed study attempts to create a compact IDS architecture based on cryptography for cloud security. Here, the balanced and normalized dataset is produced using the z-score preprocessing procedure. The best attributes for enhancing intrusion detection accuracy are then selected using an Intelligent Adorn Dragonfly Optimization (IADO). In addition, the trained features are used to classify the normal and attacking data using an Intermittent Deep Neural Network (IDNN) classification model. Finally, the Searchable Encryption (SE) mechanism is applied to ensure the security of cloud data against intruders. In this study, a thorough analysis has been conducted utilizing various parameters to validate the intrusion detection performance of the proposed I2ADO-DNN model.

In Mobile Adhoc Networks (MANETs), nodes are mobile and interact through wireless links. Mobility is a significant advantage of MANETs. However, due to the unpredictable nature of mobility, the link may fail frequently, degrading the Quality of Service (QoS) of MANETs applications. This paper outlines a novel Ad hoc On-Demand Distance Vector with Proactive Alternate Route Discovery (AODV-PARD) routing protocol that uses signal strength-based link failure time estimation. The node predicts the link failure time and warns the upstream node through a warning message about the failure. On the basis of this information, a mechanism for identifying alternate routes is started in order to reroute traffic to the alternate route prior to the link failure. It significantly reduces packet loss and improves all the QoS parameters. The suggested protocol is compared to the traditional Ad hoc On-Demand Distance Vector (AODV) routing protocol and shows that the outlined protocol results in an improvement in QoS.

The increased number of cellular network subscribers is giving rise to the network densification in next generation networks further increasing the greenhouse gas emission and the operational cost of network. Such issues have ignited a keen interest in the deployment of energy-efficient communication technologies rather than modifying the infrastructure of cellular networks. In cellular network largest portion of the power is consumed at the Base stations (BSs). Hence application of energy saving techniques at the BS will help reduce the power consumption of the cellular network further enhancing the energy efficiency (EE) of the network. As a result, BS sleep/wake-up techniques may significantly enhance cellular networks' energy efficiency. In the proposed work traffic and interference aware BS sleeping technique is proposed with an aim of reducing the power consumption of network while offering the desired Quality of Service (QoS) to the users. To implement the BS sleep modes in an efficient manner the prediction of network traffic load is carried out for future time slots. The Long Short term Memory model is used for prediction of network traffic load. Simulation results show that the proposed system provides significant reduction in power consumption as compared with the existing techniques while assuring the QoS requirements. With the proposed system the power saving is enhanced by approximately 2% when compared with the existing techniques. His proposed system will help in establishing green communication networks with reduced energy and power consumption.

The broadband access networks require suitable differential modulation techniques that can provide better performance in real-time fading channels. A heterogeneous optical access network adopting spectrally efficient DAPSK – Orthogonal Frequency Division Multiple (OFDMA) - Passive Optical Network (PON) is proposed and simulated. The performance of the proposed heterogeneous network is analyzed in terms of received Bit Error Rate (BER) and spectral efficiency. The results show that 64 DAPSK – OFDMA over the proposed architecture achieves a better spectral efficiency of about 1.062 bps/Hz than 64 QAM – OFDMA with less degradation in error performance.

Energy–aware routing in wireless ad hoc networks is one of the demanding fields of research. Nodes of the network are battery operated that are difficult to recharge and replace, that's why while developing a routing protocol energy consumption metric should always be at high priority. Nodes of mobile ad hoc networks are distributed in different directions forming arbitrary topology instantly. To propose an energy-efficient routing protocol for such a dynamic, self-organized, self-configured, and self-controlled network is certainly a challenge and an open research problem. Energy constraints and mobility leading to link breakage are the motivating factors behind the development of the proposed Optimized Priority-based Ad Hoc on Demand Multi-path Distance Vector Energy Efficient Routing Protocol (OPAOMDV-EE). The routing protocol added three fields (CE, MAX_E, MIN_E) to the traditional AOMDV RREQ and RREP packets, which are further used for calculating total priority field value. This value is used by the source node for selecting an optimal prioritized energy-efficient route. The proposed OPAOMDV-EE protocol has been simulated on Network Simulator-2 (NS-2) for two different scenarios that prove the effectiveness of OPAOMDV-EE in terms of various performance metrics with reduced energy consumption.

The healthcare industry makes rampant strides in sharing electronic health records with upgraded efficiency and delivery. Electronic health records comprise personal and sensitive information of patients that are confidential. The current security mechanism in cloud computing to store and share electronic health records results in data breaches. In the recent era, blockchain is introduced in storing and accessing electronic health records. Blockchain is utilized for numerous applications in the healthcare industry, such as remote patient tracking, biomedical research, collaborative decision making and patient-centric data sharing with multiple healthcare providers. In all circumstances, blockchain guarantees immutability, data privacy, data integrity, transparency, interoperability, and user privacy that are strictly required to access electronic health records. This review paper provides a systematic study of the security of blockchain-based electronic health records. Moreover, based on thematic content analysis of various research literature, this paper provides open challenges in the blockchain-based electronic health record.