Dempster-Shaffer Theory Research Articles

Towards a Light Weight Routing Security in IoT Using Non-cooperative Game Models and Dempster–Shaffer Theory

The internet of things (IoT) has become an emerging technology owing to the rapidly increasing number of devices and their connectivity to the internet. Routing protocol for low-power and lossy networks (RPL) is a predominant routing protocol for the IoT. The RPL routing embraces the distance to the gateway node as a rank for parent selection and builds the destination oriented directed acyclic graph (DODAG). The malicious nodes utilize the advantage of low rank based parent selection to involve in data forwarding. However, the malicious node behavior and the harsh channel conditions due to the collision are equally considered as malicious in the existing detection mechanisms. In addition, most of the routing solutions exploit the traditional schemes for IoT security, but not considering the adaptability to IoT routing specifications. Thus, this work attempts to integrate the DODAG-specific contextual trust model, and RPL-specific rank variance factor, named as Secure RPL using non-cooperative game MOdels and DEmpster Shaffer Theory in IoT (S-MODEST), such that it can detect the attackers accurately and significantly reduces the resource consumption. The non-cooperative game model consists of two interrelated formulations in the proposed solution. Firstly, non-zero sum game constructs the trust model and extracts the contextual information from DODAG structure to strengthen the trustworthiness. Secondly, the evolutionary game is utilized for trusted router selection. The non-zero sum game theory formulates the interaction of nodes and assists the trust measurement by applying direct and restricted Dempster–Shaffer theory. The DODAG-specific contextual trust evaluation differentiates the collision dropping from the undesirable scenarios and supports the nodes in building a highly trusted DODAG structure to the gateway using multiplicative trust factor. To enforce the routing cooperation of selfish nodes and differentiate malicious nodes, the evolutionary game model embraces the node trust, which is measured using non-zero sum game model. As a result, the non-cooperative game models enable the S-MODEST to precisely select a secure route as well as to maintain the routing performance effectively. The simulation results demonstrate that the detection accuracy and throughput of the proposed context-aware noncooperative game theory based trust model are substantially higher and outperform the existing SecTrust.

Different soft computing algorithms used in fire sensor node of wireless sensor network integrated with IoT

One of the most interesting real world phenomena that can be monitored by wireless sensor network integrated with internet of things is fire. The soft computing technologies like fuzzy logic, DS algorithm using Dempster-Shaffer theory, comparison algorithm and threshold algorithm are incorporated in sensor nodes in order to gain the manageable sensor network and control of the environment. Selecting the best soft computing technique for the sensor node is useful in the maximum extension of components life cycle and best design of sensor node. Many aspects, such as energy efficiency, coverage, cost of the system, prediction of fire and quality of services have been analysed. The node uses the digital temperature - humidity sensor, light sensor, smoke detector for measurement and MSP430 microcontroller for processing and implementing for fire detection. The detected information is transmitted to the base station and it is connected throughout the world through internet.

Different soft computing algorithms used in fire sensor node of wireless sensor network integrated with IoT

One of the most interesting real world phenomena that can be monitored by wireless sensor network integrated with internet of things is fire. The soft computing technologies like fuzzy logic, DS algorithm using Dempster-Shaffer theory, comparison algorithm and threshold algorithm are incorporated in sensor nodes in order to gain the manageable sensor network and control of the environment. Selecting the best soft computing technique for the sensor node is useful in the maximum extension of components life cycle and best design of sensor node. Many aspects, such as energy efficiency, coverage, cost of the system, prediction of fire and quality of services have been analysed. The node uses the digital temperature - humidity sensor, light sensor, smoke detector for measurement and MSP430 microcontroller for processing and implementing for fire detection. The detected information is transmitted to the base station and it is connected throughout the world through internet.

Real Time Monitoring of Wireless Fire Detection Node

This work presents the design of low rate and low power sensor node in wireless based sensor network for early detection and monitoring of fire in environment. It uses the DHT11 digital temperature – humidity sensor for measurement and MSP430 microcontroller for processing and implementing best algorithm from the comparison algorithm and DST algorithm using Dempster-Shaffer Theory for fire detection. The detected information is transmitted to the computer system through XBeepro for short distance wirelessly. The performance of algorithms are analyzed and tested with the metric un_detect fire. The information is shared in real time to the owner/ fire station/ police and all other responsible persons by the computer system using teamviewer software through Internet. The DST has more software design complexity than comparison algorithm. But it gives better performance than comparison.

Risk Analysis Techniques in Construction Engineering Projects

There is a great deal of risk analysis techniques and tools available for the management of risks. In principle, each risk analysis technique has its strengths and weaknesses. Techniques such as Probability Theory, Certainty Factors, Dempster-Shaffer Theory of Evidence and Fuzzy Logic are discussed with regard to their application to risk analysis in construction engineering projects. Suggestions on the most appropriate tools associated with the techniques are also presented. The strengths and weaknesses of each technique are highlighted and discussed. It has been concluded that the nature of risks under consideration is determinant in the selection of modeling and analysis techniques.

Characterization of foods by inexact rules: The SEXIA expert system

SEXIA is an expert system that uses a new methodological approach to identify foods, particularly olive oils according to varieties, olive zones and denominations of origin. The methodological approach provides identification tools, associating a confidence degree or a belief interval to the final hypotheses. The certainty factor and the Dempster–Shaffer theory, with some modifications, have been implemented in SEXIA. The computer can work with 50 chemical parameters whose data have previously been acquired by the food analyst via a dialogue in the Spanish language. The system has been verified with 144 olive oil samples. In this paper some results obtained for distinguishing the Arbequina variety from other varieties using SEXIA and the BMDP stepwise discriminant analysis program are presented. Finally, promising directions for future research are suggested.