Mobile ad hoc network (MANETs) is infrastructure-less, self-organizing, fast deployable wireless network, so they truly are exceptionally appropriate for purposes between special outside occasions, communications in locations without a radio infrastructure, crises, and natural disasters, along with military surgeries. Security could be the primary weak spot in manet on account of this flexibility of structures and always changing dynamic topology, that will be very exposed to your selection of strikes like eavesdropping, routing, and alteration of programs. MANET is affected with security issues, surpassing Quality of services (QoS).So, intrusion tracking which modulates your system to recognize some other violation weakness would be that the top approach to guarantee security for MANET. Detecting intrusions has a critical part in supplying protections and functions as beyond layer of defenses against access. Power collapse of the cellular node maybe not merely alter the node alone but its capacity to forwards packets which is based on total system life. This also caused the institution of the routing protocol to its stable optimal choice of this multipath to increase the navigation MANETs. Provision of energy-efficient and secure routing is a challenge given the changing topology and restricted resources of this kind of network. To address the energy efficiency and security we suggest a trust-based secure energy efficient navigation in MANETs employing the hybrid algorithm, cat slap single-player algorithm (C-SSA), that selects the best jumps in advancing the routing. In the beginning, the fuzzy clustering is put on, and the cluster heads (CHs) are picked predicated maximum worth of indirect, direct, and recent trust. Predicated on trust threshold worth nodes additionally discovered. Even the CHs are participated from the multi hop routing, and the assortment of the best route relies upon the projected hybrid protocol, and that selects the best routes determined by the delay, throughput, along with connectivity within this course. The proposed method obtained a minimal energy of 0.11m joules, a negligible delay of 0.005 msec, a maximum throughput of 0.74 bps, a maximum packet delivery ratio of 0.99 percent, and a maximum detection rate of 90%. The proposed method compared with the existing techniques in the presence and absent of the selective packet dropping attack.INDEX TERMS MANET, Selective packet dropping attack, Energy efficiency, Cluster head, Trust values.

Data management is one obstacle in the production sector to be reconfigured and adapted through optimum parameterization in industry cyber-physical systems. This paper presents an intelligent data management framework for a cyber-physical system (IDMF-CPS) with machine-learning methods. A training approach based on two enhanced training procedures, running concurrently to upgrade the processing and communication strategy and the predictive models, is contained in the suggested reasoning modules. The method described spreads computational and analytical engines in several levels and autonomous modules to enhance intelligence and autonomy for controlling and tracking behavior on the work floor. The appropriateness of the suggested solution is supported by rapid reaction time and a suitable establishment of optimal operating variables for the required quality during macro- and micro-operations.

Traditional insurance policy settlement is a manual process that is never hassle-free. There are many issues, such as hidden conditions from the insurer or fraud claims by the insured, making the settlement process rough. This process also consumes a significant amount of time that makes the process very inefficient. This whole scenario can be disrupted by the implementation of blockchain and smart contracts in insurance. Blockchain and innovative contract technology can provide immutable data storage, security, transparency, authenticity, and security while any transaction process is triggered. With the implementation of blockchain, the whole insurance process, from authentication to claim settlement, can be done with more transparency and security. A blockchain is a virtual chain of data blocks that is a decentralized technology. Any transaction or change in the blocks is done after the decentralized validator entity, not a single person. The smart contract is a unique facility stored on the blockchain that gets executed when the predetermined conditions are met. This paper presents a framework where smart contracts are used for insurance contracts and stored on blockchain. In the case of a claim, if all the predetermined conditions are met, the transaction happens; otherwise, it is discarded. The conditions are immutable. That means there is scope for alteration from either side. This blockchain and intelligent contract-based framework are hosted on a private Ethereum network. The Solidity programming language is used to create smart contracts. The framework uses the Proof of Authority (PoA) consensus algorithm to validate the transactions. In the case of any faulty transaction request, the consensus algorithm acts according to and cancels the claim. With blockchain and smart contract implementation, this framework can solve all the trust and security issues that rely on a standard insurance policy.

Bangladesh should have owned a decentralized medical record server. We face a lot of issues, such as doctor’s appointments, report organization in one spot, and report follow-ups. People now bring a large number of papers to the doctor’s chamber. They carry prescriptions, reports, and X-ray files, among other things. It complicates everyone’s life as a result. All of the reports must be reviewed by doctors on a regular basis. It is difficult to read old reports on a regular basis, and patients do not receive the correct medications or treatment. Doctors also find it extremely difficult to comprehend handwritten prescriptions. Data security, authenticity, time management, and other areas of data administration are dramatically improved when blockchain (smart contract) technology is linked with standard database management solutions. Blockchain is a groundbreaking, decentralized technology that protects data from unauthorized access. After smart contracts are implemented, the management will be satisfied with the patients. As a result, maintaining data privacy and accountability in the system is tough. It signifies that the information is only accessible to those who have been authenticated. This study focuses on limiting third-party engagement in medical health data and improving data security. Throughout the process, this will improve accessibility and time efficiency. People will feel safer during the payment procedure, which is the most significant benefit. A smart contract and a peer-to-peer encrypted technology were used. The hacker will not be able to gain access to this system since this document uses an immutable ledger. They will not be able to change any of the data if they gain access to the system. If the items are found to be defective, the transaction will be halted. Transaction security will be a viable option for recasting these problems using cryptographic methodologies. We developed a website where patients and doctors will both benefit because of the use of blockchain technology to ensure the security of medical data. We have different profiles for doctors and patients. In the patient profile, they can create their own account by using a unique address, name, and age. This unique address will be created from the genesis block. The unique address is completely private to the owner, who will remain fully secure in our network. After creating an account, the patient can view the doctors’ list and they can upload their medical reports such as prescriptions and X-rays. All the records uploaded by the patient will be stored on our local server (Ganache). The records are stored as hashed strings of the data. Those files will also have a unique address, and it will be shown in the patient profile. After granting access, the doctors will be able to view their records in the respective doctor’s profile. For accessing the options such as uploading, viewing, or editing the data, Ethereum currency (a fee) will have to be paid in order to complete the request. On the other hand, doctors can enter their profile using their name and unique address. After logging in, they can view their name, unique address, and the list of patients that have granted access to the doctor to view their files. On our website, the front end is handled by JavaScript, ReactJS, HTML, and CSS. The backend is handled by Solidity. Storage is handled by Ganache as the local host. Finally, this paper will show how to ensure that the procedure is as safe as feasible. We are also maintaining transparency and efficiency here.

Human locomotion is an imperative topic to be conversed among researchers. Predicting the human motion using multiple techniques and algorithms has always been a motivating subject matter. For this, different methods have shown the ability of recognizing simple motion patterns. However, predicting the dynamics for complex locomotion patterns is still immature. Therefore, this article proposes unique methods including the calibration-based filter algorithm and kinematic-static patterns identification for predicting those complex activities from fused signals. Different types of signals are extracted from benchmarked datasets and pre-processed using a novel calibration-based filter for inertial signals along with a Bessel filter for physiological signals. Next, sliding overlapped windows are utilized to get motion patterns defined over time. Then, polynomial probability distribution is suggested to decide the motion patterns natures. For features extraction based kinematic-static patterns, time and probability domain features are extracted over physical action dataset (PAD) and growing old together validation (GOTOV) dataset. Further, the features are optimized using quadratic discriminant analysis and orthogonal fuzzy neighborhood discriminant analysis techniques. Manifold regularization algorithms have also been applied to assess the performance of proposed prediction system. For the physical action dataset, we achieved an accuracy rate of 82.50% for patterned signals. While, the GOTOV dataset, we achieved an accuracy rate of 81.90%. As a result, the proposed system outdid when compared to the other state-of-the-art models in literature.