Obstructed Nearest Neighbor Query under Uncertainty in the Internet of Things Environment

Physical security systems are applied to alert in advance a well-known vector of attacks. This paper presents an analysis of the research and assessment of physical security systems applying the PSMECA technique (analysis of modes, efforts, and criticality of physical security). The object of research and analysis is the physical security system of the Ministry of Education and Science of Iraq (as the infrastructure of the region's objects), as well as the area of the compact living of students and co-workers (campus). This paper discusses the organization of physical security systems, which are based on devices with low power consumption and function in the Internet of things environment. The main aim is to describe and develop a physical security system that functions in the Internet of things environment, as well as the development of a scheme for the research and development of models and methods for risk analysis, models of functions and components, models of failures and conducting research and analysis of occurrence failures of PSS. The generalized structural and hierarchical scheme of the physical security system of the infrastructure of the region is presented, as well as the applied application of the scheme is illustrated by the example of the physical security system of a student campus of one of the universities of Baghdad. The functional modeling scheme of the object is provided and is based on the use of the Raspberry Pi microcomputer and the Arduino microcontroller. The set-theoretical models of functions, components, and failures of the system under study, as well as the projection of a hierarchical failure structure in the table of the basic structural elements of the system, are presented. The IDEF0 diagram, showing a power outage scenario (accidental or intentional) in connection with lighting and video subsystems, is presented. The scheme of research and development of models and methods of analysis of risks of PSS is carried out in the paper. A PSMECA table for the CCTV system has been created, which allows you to more precisely determine the cause of the failure in the physical security system and the importance of failure criticality

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A bio-inspired adaptive model for search and selection in the Internet of Things environment

PeerJ Computer Science ◽

10.7717/peerj-cs.762 ◽

2021 ◽

Vol 7 ◽

pp. e762

Author(s):

Soukaina Bouarourou ◽

Abdelhak Boulaalam ◽

El Habib Nfaoui

Keyword(s):

Internet Of Things ◽

Real Data ◽

Distributed Model ◽

The Internet ◽

Data Sets ◽

Semantic Categories ◽

Internet Of Things Environment ◽

Search And Selection ◽

Sensor Clustering ◽

The Internet Of Things

The Internet of Things (IoT) is a paradigm that can connect an enormous number of intelligent objects, share large amounts of data, and produce new services. However, it is a challenge to select the proper sensors for a given request due to the number of devices in use, the available resources, the restrictions on resource utilization, the nature of IoT networks, and the number of similar services. Previous studies have suggested how to best address this challenge, but suffer from low accuracy and high execution times. We propose a new distributed model to efficiently deal with heterogeneous sensors and select accurate ones in a dynamic IoT environment. The model’s server uses and manages multiple gateways to respond to the request requirements. First, sensors were grouped into three semantic categories and several semantic sensor network types in order to define the space of interest. Second, each type’s sensors were clustered using the Whale-based Sensor Clustering (WhaleCLUST) algorithm according to the context properties. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was improved to search and select the most adequate sensor matching users’ requirements. Experimental results from real data sets demonstrate that our proposal outperforms state-of-the-art approaches in terms of accuracy (96%), execution time, quality of clustering, and scalability of clustering.

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