This paper presents a study of free space path loss of WIFI network using Cell Info Lite App and WIFI analyzer tool. This paper focuses on experimental tests by using WIFI router, mobile phones to measure and analyze the loss of signal strength over distance from 0 meter to 9 meter. The result of Free Space Path Loss (FSPL) for frequency 2457 MHz shows that the longer the distance, the worse the signal get. In addition, the experiment test indoor and outdoor was done analyse. The results of the comparison conclude that the signal strength becomes weak can affect by distance and obstacles such as door, wall. Signal strength also can say from colors which is when the measurement result shows green color is strong signal while orange color is weak signal and red color is bad signal. When the signal strength become weak Wi-Fi analyzer was displayed green zone to red zone. The paper purpose showed that where the area have a stronger/weaker connection in a house environment.

A Wireless Network is an assortment of organization hubs that can work with framework or Adhoc mode. Because of the absence of incorporated control in Adhoc-based remote organizations, planning a steering convention to give a proficient and appropriate technique to course the information with fewer clogs, bundle drop has turned into a difficult issue in remote organizations. The significant goal of the present review is to foster a Load Aware Congestion Control. The further developed rendition of directing conventions is proposed Fuzzy Enhanced Load Aware Routing in Ad hoc (FE-LARA). Here, WNs climate is recreated in MATLAB for different situations to figure LARA and FE-LARA. Outcomes got from this extensive investigation discover that FE-LARA offers a strong improvement in opposition to winning methodology with LARA and it largely concentrates for corresponding flow of load of data amidst various multi-paths which are exposed, lower queue length for various nodes. A network would consequently be resilient to node catastrophes and activate through diminutive or no mainstay substructure

One of the business activities in the company is Inventory management. Stocktaking is an important process to ensure stable operation. A conventional method for item identification is through an attached human-readable label. Employees use an inventory sheet of paper to keep track of items. We propose new system inventory management based on an android application to increase time efficiency for Obtain data tracking through NFC (Near Field Communication) tag-based system. This approach involved 11 participants who performed stocktaking to compare performance between the new and old systems. We collect 54 of the Sample data for time evaluation using a t-test to analyze the data. The result was significantly shorter time in stocktaking process of new system than conventional method (df = 26, t = -7.075, p < .001). Furthermore, the proposed system could be a viable solution for reduce the time needed to complete the stocktaking process in the company.

DNS is responsible for the hostname to IP address translation. It is an open resolver that's why vulnerable to different kinds of attacks such as cache poisoning, man-in-the-middle, DOS and DDOS, etc. DNS is responsible for the hostname to IP address translation. To protect DNS IETF added a layer of security to it known as Domain Name System Security Extensions (DNSSEC). DNSSEC is also vulnerable to phishing, spoofing, and MITM attacks. To protect DNS, along with DNSSEC we require certifying authorities to authenticate the communicating parties. DNSSEC combined with an SSL certificate issued by Certifying Authorities (CA's) can protect the DNS from various attacks. The main weakness of this system is there are too many CA's and It is not feasible to trust all of them. Any breached CA can issue a certificate for any domain name. A certificate issued from a compromised CA's is valid. In this scenario, it is necessary for the organization to limit the number of CAs and to check whether the server is signed by a trusted CA's or not. DNS Based Authentication of Named Entities (DANE) permits a domain possessor to stipulate specific CA's issue certificates for a specific resource. DANE will not allow any CA to issue certificates for any domain. It limits the number of CA's used by the client. As there were still some security issues left in it that can be resolved using a mechanism called D-TS. It is a DANE-based trusted server that acts as a third party and validates the certificates of all the entities of the network. D-TS will be a proof-of-concept for enhancing the security in communications between Internet applications by using information available in DNS. The system attempts to solve the shortcomings of DANE by establishing a trust zone between the clients and the services. By adding multiple levels of validations, it aims to provide improved authenticity of services to clients, thereby mitigating attacks like phishing, Spoofing, Dos, and man-in-the-middle attack. In this paper, we will discuss the detailed working of our proposed solution D-TS.

Internet of Things (IoT) consists of group of devices which communicates information over private networks. One of the key challenges faced by IoT networks is the security breaches.  With the objective of automating the detection of possible security breaches in five categories, IoT traffic created with Message Queue Telemetry Transport (MQTT) protocol is analyzed. The five categories of cyber-attacks considered are brute force, denial of service (DoS), flooding, malformed data, and SlowITe attacks along with legitimate traffic.  The popular five machine learning (ML) models, LightGBM, Random Forest, MLP, AdaBoost, and Decision Tree Classifiers are trained to predict cyber-attacks. In traditional traffic analysis all the available features of MQTT traffic were utilized for the ML modeling and in this work, we challenge the practice by showing that automated feature selection improves the performance of the overall ML models. The average accuracy, precision, recall and the F1 score are used as performance evaluation metrics. It is observed that all models in average are able to achieve 90% of accuracy in classification, while MLP model is trained 10 times faster than the other models.  Further the optimal number of features for correct classification is identified as 10 features through Monte Carlo analysis. With the reduced features, it is possible to detect DoS, flooding, and  SlowITe attacks with more than 90% accuracy and precision. Yet, it is difficult to tell apart brute force and malformed data attacks.