Internet of People
Maozhen Li · Concurrency and Computation Practice and Experience · 2016
This Editorial introduces the articles to be included in the Special Issue on Internet of People. Internet of People (IoP) refers to digital connectivity of people through the Internet infrastructure forming a network of collective intelligence and stimulating interactive communication among people. The purpose of the special issue is to collate a selection of representative articles that were primarily presented at the IEEE International Conference on Internet of People (IoP 2015) on August 10 to 14, 2015, in Beijing, China. The special issue was also made open to public submissions for a wide inclusion. The scope of the special issue is broad and represents a multidisciplinary nature of IoP. It covers topics from the network enabling technologies at the physical layer to services at the application layer. It is reported that around 40% of the world population has an Internet connection today, and the number of Internet users reached 3 billion in 2014. The sheer size of the Internet population has led to big data challenges of volume, velocity, variety, and veracity of the digital data generated. For this purpose, this special issue also includes a few articles on high performance computing techniques that can speed up the computation process in analyzing big volumes of data. Mobile and wireless networks facilitate the communication among people. In the work of Huang et al. 1 they present a multiple-user partner selection algorithm to improve the overall secrecy rate of the network. Zhong et al. 2 focus on key management for multiple groups of the IoP using multicasting. A novel area-based multiple group key management scheme is proposed to facilitate the movement of mobile users in wireless communication networks with minimized communication overhead. On the basis of a self-organizing feature map neural network model, Yao et al. 3 present wireless local area network interference self-optimization method to quickly locate the fault access point and optimize the network performance to smoothen the communication process of people. Neural networks are also used in the work of Jin et al. 4 for online recognition of glass defects. Crowdsourcing enables IoP by soliciting contributions from a large group of people connected by the Internet. Wang et al. 5 conduct a survey on mobile crowdsourcing from the aspects of real-time and location-sensitive crowdsourced tasks. Related research challenges and possible solutions are discussed. Shao et al. 6 use crowdsensing in vehicular networks to predict the traffic condition in intelligent transportation systems. This solves the problem of traditional approaches being inefficiency ineffectiveness in data uploading and usage. Guo et al. 7 extract information from external-related attributes and improve latent Dirichlet allocation to build a topic mining model to facilitate services in telephone call centers. Knowledge diffusion as a component of crowdsourcing also plays an increasing role in modern communication networks and social networks. Zhang et al. 8 study the topological structure and research a new knowledge diffusion model taking into account both learning and forgetting attributes. The results from this research reveal that the social networks with a high degree of heterogeneity well suit for knowledge diffusion. In a mobile environment in IoP, network latency would have a significant impact on the communication of mobile services. For this purpose, Ding et al. 9 target at service composition so that mobile services can be optimized and provisioned to users with low communication latency. In the work of Gopalakrishna et al. 10 they assess relevance in cyber-physical systems. For this purpose, a new metric called relevance score is proposed for evaluation of a number of machine learning techniques. Big data has received a momentum from both academia and industry since the US government announced the big data initiative in 2012. MapReduce11-13 has become a major computing model in support of big data applications especially in dealing with data of a huge volume. Hadoop,14 which is an open source implementation of MapReduce, has been widely used in developing MapReduce applications. However, Hadoop does not have a sophisticated scheme in job scheduling. For this purpose, Liu et al. 15 present a dynamic load balancing algorithm on the basis of sliding windows with an aim to target at heterogeneous Hadoop cluster systems. Hadoop has over 190 parameters for user to configure. It has become a challenging issue for user to optimize the performance of Hadoop through a manual process of tuning these parameters. For this purpose, Khan et al. 16 use gene expression programming to dynamically mine the correlation of Hadoop parameters and further use particle swarm optimization technique to optimize the parameter settings to enhance the performance of Hadoop. Compared with the default Hadoop settings, this work can achieve 50% faster in Hadoop performance. On the basis of the MapReduce model, Cheng et al. 17 develop a MapReduce style parallel and distributed deep convolutional neural network for person re-identification in surveillance videos. Qi18 presents a parallel motion detection algorithm using a cluster of inexpensive computing nodes. Zhao et al. 19 parallelize the work on anomalous subgraph detection using Spark,20 an in-memory fast processing technology that can be deployed on MapReduce. Finally, Ren et al. 21 look at job scheduling problems in virtualized cloud environments from the aspect of power consumption. We hope that the perspectives presented in this special issue would be of a great interest to the readers. We also expect the readers to contribute to this exciting and fast growing research area. We would like to thank Professor Geoffrey Fox, the editor-in-chief of Concurrency and Computation: Practice and Experience for his timely advice on this special issue. A big thanks also goes to Rizza Mostar-Salterio, the production editor of Wiley for her great support in publication of the special issue.