An energy‐aware method for task allocation in the Internet of things using a hybrid optimization algorithm

2020 ◽  
Author(s):  
Xiaojun Ren ◽  
Zhijun Zhang ◽  
Shaochun Chen ◽  
Karlo Abnoosian
Keyword(s):  
Internet Of Things ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Hybrid Optimization ◽  
The Internet ◽  
Energy Aware ◽  
Hybrid Optimization Algorithm ◽  
The Internet Of Things

scholarly journals A Novel Bioinspired Multiobjective Optimization Algorithm for Designing Wireless Sensor Networks in the Internet of Things

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Jun Huang ◽  
Liqian Xu ◽  
Cong-cong Xing ◽  
Qiang Duan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Chaotic Behavior ◽  
Ant Colony ◽  
Wireless Sensor ◽  
The Internet ◽  
The Internet Of Things

The design of wireless sensor networks (WSNs) in the Internet of Things (IoT) faces many new challenges that must be addressed through an optimization of multiple design objectives. Therefore, multiobjective optimization is an important research topic in this field. In this paper, we develop a new efficient multiobjective optimization algorithm based on the chaotic ant swarm (CAS). Unlike the ant colony optimization (ACO) algorithm, CAS takes advantage of both the chaotic behavior of a single ant and the self-organization behavior of the ant colony. We first describe the CAS and its nonlinear dynamic model and then extend it to a multiobjective optimizer. Specifically, we first adopt the concepts of “nondominated sorting” and “crowding distance” to allow the algorithm to obtain the true or near optimum. Next, we redefine the rule of “neighbor” selection for each individual (ant) to enable the algorithm to converge and to distribute the solutions evenly. Also, we collect the current best individuals within each generation and employ the “archive-based” approach to expedite the convergence of the algorithm. The numerical experiments show that the proposed algorithm outperforms two leading algorithms on most well-known test instances in terms of Generational Distance, Error Ratio, and Spacing.

scholarly journals A Novel Task Allocation Algorithm in Mobile Crowdsensing with Spatial Privacy Preservation

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Wenyi Tang ◽  
Qi Jin ◽  
Xu Zheng ◽  
Guangchun Luo ◽  
Guiduo Duan ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Task Allocation ◽  
Privacy Preservation ◽  
The Internet ◽  
Fundamental Issue ◽  
Sufficient Data ◽  
Mobile Crowdsensing ◽  
Real World Applications ◽  
Step Algorithm ◽  
The Internet Of Things

The Internet of Things (IoT) has attracted the interests of both academia and industry and enables various real-world applications. The acquirement of large amounts of sensing data is a fundamental issue in IoT. An efficient way is obtaining sufficient data by the mobile crowdsensing. It is a promising paradigm which leverages the sensing capacity of portable mobile devices. The crowdsensing platform is the key entity who allocates tasks to participants in a mobile crowdsensing system. The strategy of task allocating is crucial for the crowdsensing platform, since it affects the data requester’s confidence, the participant’s confidence, and its own benefit. Traditional allocating algorithms regard the privacy preservation, which may lose the confidence of participants. In this paper, we propose a novel three-step algorithm which allocates tasks to participants with privacy consideration. It maximizes the benefit of the crowdsensing platform and meanwhile preserves the privacy of participants. Evaluation results on both benefit and privacy aspects show the effectiveness of our proposed algorithm.

Evolutionary multi-objective set cover problem for task allocation in the Internet of Things

2021 ◽  
Vol 102 ◽  
pp. 107097
Author(s):  
Hussein M. Burhan ◽  
Bara’a A. Attea ◽  
Amenah D. Abbood ◽  
Mustafa N. Abbas ◽  
Mayyadah Al-Ani
Keyword(s):  
Internet Of Things ◽  
Task Allocation ◽  
Set Cover ◽  
The Internet ◽  
Multi Objective ◽  
Set Cover Problem ◽  
Cover Problem ◽  
The Internet Of Things

scholarly journals Application Research of Internet of Things Technology in the Causes of Dragon Boat Sports Injury

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuai Wang ◽  
Xia Zhao
Keyword(s):  
Internet Of Things ◽  
Optimization Algorithm ◽  
Detection System ◽  
Sports Injury ◽  
The Internet ◽  
Maintenance Optimization ◽  
Dragon Boat ◽  
Internet Of Things Technology ◽  
The Internet Of Things ◽  
Cluster Maintenance

In recent years, the Internet of Things technology can effectively innovate applications and services. The Internet of Things technology has become more and more popular. It provides an effective and direct bridge between the physical world and virtual objects in cyberspace. With the increase in the intensity of dragon boat training and the increasingly fierce competition, the possibility of injury is increasing. Dragon boat racing is a noncontact team sport based on strength and technology. The purpose of this paper is to solve the problem of people's lack of understanding of the sports injuries and causes of dragon boat athletes. We used the data fusion algorithm and cluster maintenance optimization algorithm to study the application of Internet of Things technology in the cause of dragon boat sports injury. In order to save energy, extend the network life cycle, shorten service interruption time, and increase data packet transmission, the cluster maintenance optimization algorithm in this paper mainly improves and optimizes the startup time of cluster maintenance, which depends on the maintenance cost. The experiment result shows that the etiological detection system proposed in this paper matches the actual sports injury results well. The experiment result shows that the research on the cause of injury in dragon boat sports based on Internet of Things technology can detect the damage law well and can have a more comprehensive understanding for the cause of injury, which helps to prevent injuries better and take effective treatments. In the analysis part, it can be concluded that the detection system is very accurate in detecting the cause, and the accuracy rate is basically 100%.

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