Modeling and Optimization for Collaborative Business Process Towards IoT Applications

Zhao

et al. 2018

Sensors

Self Cite

The concept of the Internet of Things (IoT) is an important part of the next generation of information. Wireless sensor networks are composed of independent distributed smart sensor nodes and gateways. These discrete sensors constantly gather external physical information, such as temperature, sound, and vibration. Owing to the diversity of sensor devices and the complexity of the sensor sensing environment, the direct modeling of an IoT-aware business process application is particularly difficult. In addition, how to effectively deploy those designed applications to discrete servers in the heterogeneous sensor networks is also a pressing problem. In this paper, we propose a resource-oriented modeling approach and a dynamic consistent hashing (DCH)-based deploying algorithm to solve the above problems. Initially, we extended the graphic and machine-readable model of Business Process Model Notation (BPMN) 2.0 specification, making it able to support the direct modeling of an IoT-aware business process application. Furthermore, we proposed the DCH-based deploying algorithm to solve the problem of dynamic load balancing and access efficiency in the distributed execution environment. Finally, we designed an actual extended BPMN plugin in Eclipse. The approach presented in this paper has been validated to be effective.

Section: Implementation and Discussionmentioning

confidence: 99%

Section: The Resource-oriented Modeling Specificationmentioning

confidence: 99%

Modeling and Deploying IoT-Aware Business Process Applications in Sensor Networks

Zhao

et al. 2018

Sensors

Self Cite

“…They claimed that their model perfectly works for the first two quality of services but put an error against the third quality of service and, hence, they suggested a chance of enhancement on the specification level of the protocol [ 41 ]. The research in [ 42 ] proposes a Petri net-based modeling methodology and optimization algorithm for collaborative business processes in IoT applications and solves the problem of sensing event scheduling. This study does not consider the RPL-based security scheme in IoT.…”

Section: Related Workmentioning

confidence: 99%

Formal modeling and analysis of security schemes of RPL protocol using colored Petri nets

Ahmad,

Chaudhry,

Jamal

et al. 2023

PLoS ONE

In the Internet of things (IoT), data packets are accumulated and disseminated across IoT devices without human intervention, therefore the privacy and security of sensitive data during transmission are crucial. For this purpose, multiple routing techniques exist to ensure security and privacy in IoT Systems. One such technique is the routing protocol for low power and lossy networks (RPL) which is an IPv6 protocol commonly used for routing in IoT systems. Formal modeling of an IoT system can validate the reliability, accuracy, and consistency of the system. This paper presents the formal modeling of RPL protocol and the analysis of its security schemes using colored Petri nets that applies formal validation and verification for both the secure and non-secure modes of RPL protocol. The proposed approach can also be useful for formal modeling-based verification of the security of the other communication protocols.

Computer Systems Science and Engineering

“…Metaheuristic and evolutionary optimization algorithms are currently used to solve a variety of problems in a variety of fields, including machine learning [1], formulas estimation [2,3], integrated decision making [4], transportation [5], mechanics [6], engineering [7], economics [8], business processes [9], and scheduling [10]. Optimization is the process of selecting the best solution from a large number of options for a given problem.…”

Section: Introductionmentioning

confidence: 99%

Al-Biruni Earth Radius (BER) Metaheuristic Search Optimization Algorithm

El-kenawy¹,

Abdelhamid²,

Ibrahim³

et al. 2023

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields. Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention, such as particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), genetic algorithm (GA), and gravitational search algorithm (GSA). According to the literature, no one metaheuristic optimization algorithm can handle all present optimization problems. Hence novel optimization methodologies are still needed. The Al-Biruni earth radius (BER) search optimization algorithm is proposed in this paper. The proposed algorithm was motivated by the behavior of swarm members in achieving their global goals. The search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method. A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER's validation and testing against seven mathematical optimization problems. The results show that BER can both explore and avoid local optima. BER has also been tested on an engineering design optimization problem. The results reveal that, in terms of performance and capability, BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.