Muhammad Alam

Predicted and Corrected Location Estimation of Mobile Nodes Based on the Combination of Kalman Filter and the Bayesian Decision Theory

The main objective of this research is to apply statistical location estimation techniques in cellular networks in order to calculate the precise location of the mobile node. Current research is focusing on the combination of Kalman filter and the Bayesian decision theory based location estimation. In this research basic four steps of Kalman filter are followed which are Estimation, Filtering, Prediction and Fusion. Estimation is done by using Receive Signal Strength (RSS), Available Signal Strength (ASS) and the Angle of Arrival (AOA). Filtering is done by calculating the average location and variation in values of location. Prediction is done by using the Bayesian decision theory. Fusion is done by combining the variances calculated in filtering step. Finally by combining the prediction and fusion results PCLEA (Predicted and Corrected Location Estimation Algorithm) is established. Timestamp is used for recursive step in kalman filter. The aim of this research is to minimize the dependence on the satellite based location estimation and increase its accuracy, efficiency and reliability.

Cloud Service Ranking Using Checkpoint-Based Load Balancing in Real-Time Scheduling of Cloud Computing

Cloud computing has been gaining popularity in the recent years. Several studies are being proceeded to build cloud applications with exquisite quality based on user’s demands. In achieving the same, one of the applied criteria is checkpoint-based load balancing in real-time scheduling through which suitable cloud service is chosen from a group of cloud services candidates. Valuable information can be collected to rank the services within this checkpoint-based load balancing. In order to attain ranking, different services are needed to be invoked in the cloud, which is time-consuming and wastage of services invocation. To avoid the same, this chapter proposes an algorithm for predicting the ranks of different cloud services by using the values from previously offered services.

Modeling Interpolated Distance Error for Clutter Based Location Estimation of Wireless Nodes

This research work is focusing on the location estimation by using interpolated distance calculation and its error in wireless nodes based on different clutters/terrains. This research is based on our previous findings in which we divided the clutter/terrain into thirteen different groups. As radio waves behave differently in different terrains therefore we recorded data points in all different terrains. C# program was used with WiFi (IEEE 802.11) prototype to record the behavioral changes in radio signals because of atmospheric attenuation. We took readings in all different clutters at a regular interval of 10 meters (from 10m to 150m). In current research, we are using linear interpolation to calculate the distances other than regular interval with minimal error rate. First, we calculate actual distance based on receive radio signals at random and then compare it with our interpolated distance and calculate the error. We take a sample size of four in each terrain and divide the interpolated data in six zones for accuracy. This comparison table helps to estimate the location of wireless node in different terrains/clutters with the error rate of as minimum as 0.5 meter.

Checkpoints and Requirements Based Cloud Service Ranking

Cloud computing and the services offered by cloud computing in the field of Information and Communication Technology has impacted the enterprises and is stimulating at a more prominent pace in the recent years. Various studies have been conducted in the field to meet the client’s requirements and raise the quality of services offered. Based on the client’s requests and integrating it with load balancing as one of the challenges of cloud computing to be addressed the cloud services are ranked. In order to offer better services to the clients and to maintain the trust, load balancing as one of the criteria in real time scheduling is adopted. In order to attain ranking, different services are required to be invoked in the cloud, the requirement factors and the ranking criteria for each factor has been considered to rank them using entropy analysis of Shannon Furthermore, a framework has been proposed to rank them based on the weights attained by the requirements and the ranking criteria.

Recognizing Foreign Object Debris (FOD): False Alarm Reduction Implementation

Received Dec 04, 2017 Revised Jan 11, 2018 Accepted Apr 15, 2018 Wireless Body Area Networks (WBANs) are fundamental technology in health care that permits the information of a patient’s essential body parameters to be gathered by the sensors. However, the safety and concealment defense of the gathered information is a key uncertain problem. A Hybrid Key Management (HKM) scheme [13] is worked based on Public Key Cryptography (PKC)-authentication scheme. This scheme uses a oneway hash function to construct a Merkle Tree. The PKC method increase the computational complexity and lacking scalability. Additionally, it increases expensive computation, communication costs and delay. To overcome this problem, Robust Security for Protected Health Information by ECC with signature Hash Function in WBAN (RSP) is proposed. The system employs hash-chain based key signature technique to achieve efficient, secure transmission from sensor to user in WBAN. Moreover, Elliptical Curve Cryptography algorithm is used to verifies the authenticate sensor. In addition, it describes the experimental results of the proposed system demonstrate the efficient data communication in a network.A Weblogs contains the history of User Navigation Pattern while user accessing the websites. The user navigation pattern can be analyzed based on the previous user navigation that is stored in weblog. The weblog comprises of various entries like IP address, status code and number of bytes transferred, categories and time stamp. The user interest can be classified based on categories and attributes and it is helpful in identifying user behavior. The aim of the research is to identifying the interested user behavior and not interested user behavior based on classification. The process of identifying user interest, it consists of Modified Span Algorithm and Personalization Algorithm based on the classification algorithm user prediction can be analyzed. The research work explores to analyze user prediction behavior based on user personalization that is captured from weblogs.Wireless Body Area Networks (WBANs) are fundamental technology in health care that permits the information of a patient’s essential body parameters to be gathered by the sensors. However, the safety and concealment defense of the gathered information is a key uncertain problem. A Hybrid Key Management (HKM) scheme [13] is worked based on Public Key Cryptography (PKC)-authentication scheme. This scheme uses a oneway hash function to construct a Merkle Tree. The PKC method increase the computational complexity and lacking scalability. Additionally, it increases expensive computation, communication costs and delay. To overcome this problem, Robust Security for Protected Health Information by ECC with signature Hash Function in WBAN (RSP) is proposed. The system employs hash-chain based key signature technique to achieve efficient, secure transmission from sensor to user in WBAN. Moreover, Elliptical Curve Cryptography algorithm is used to verifies the authenticate sensor. In addition, it describes the experimental results of the proposed system demonstrate the efficient data communication in a network.