iZone: A Location-Based Mobile Social Networking System

Cheng, Rui; Yang, Zaiyou; Xia, Feng

doi:10.1109/paap.2010.67

Cited by 9 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rui Cheng, Zhuo Yang, Feng Xia [7] presented the design and a prototype implementation of the iZone system. A combination of wireless technology, J2ME, LBS and GIS are equipped into a system therefore it is able to locate users and send information based on their locations.…”

Section: Related Workmentioning

confidence: 99%

“…ABCA & VP start with anant-based clustering algorithm presented by Lumer and Faieta [7]. It uses a pick-up or drop strategy to move and sort data [13].…”

Section: An Ant-based Clustering Algorithm With Vanishing Problemmentioning

confidence: 99%

“…Each ant will pick up data and try to drop it into a new cluster that has a similar characteristics (similar distance). The probability of dropping an item is increased if ants are surrounded with similar distance in the8 neighborhood data [7]. In the same token, each ant will pick up data when it feels dissimilarity among its neighborhood and try to drop into a new area.…”

Section: An Ant-based Clustering Algorithm With Vanishing Problemmentioning

confidence: 99%

See 2 more Smart Citations

Location-Based Mobile Community Using Ants-Based Cluster Algorithm

Srisa-An

2021

Int. J. Appl.Info. M.

View full text Add to dashboard Cite

A location based service (LBS) is widely used on modern smartphone around the world asits built-in features. Each smartphone can access a google API or map. People can therefore share their location (latitude and longitude) among friends. Many LBS spots can easily form “location based mobile community (LBMC).” Since the nodes are mobile, the community group changes dynamically and is unstructured. Ant-based clustering algorithm is a special kind of optimization technique which is highly suitable for finding the adaptive clustering for volatile networks. This Paper Aims To form a location based mobile community (LBMC) by using Ant-based clustering algorithm. Due to the mobile type community, a vanishing community problem is also stated in this paper. Instead of redo a whole algorithm again, we modify an original algorithm by applying a pheromone concept to handle a change. Our algorithm is named as ABCA & VP which stands for Ant-Based Clustering Algorithm with Vanishing problem. More than 5,000 samples from their latitude and longitude coordinates in Thailand. From an experiment, K-means clustering work well in small data size and low number of clusters. In Small size of data between 50 and 1000, our algorithm runs battery when a number of clusters reach 15 clusters. In a big data size (between 1,000 and 5,000 samples), our algorithm outperforms K-means clustering when a number of clusters reach 20 clusters.

show abstract

Section: Related Workmentioning

confidence: 99%

“…ABCA & VP start with anant-based clustering algorithm presented by Lumer and Faieta [7]. It uses a pick-up or drop strategy to move and sort data [13].…”

Section: An Ant-based Clustering Algorithm With Vanishing Problemmentioning

confidence: 99%

Section: An Ant-based Clustering Algorithm With Vanishing Problemmentioning

confidence: 99%

See 1 more Smart Citation

Location-Based Mobile Community Using Ants-Based Cluster Algorithm

Srisa-An

2021

Int. J. Appl.Info. M.

View full text Add to dashboard Cite

show abstract

“…At present, there are many studies on exploring the behavior attributes of users in mobile social networks. In [ [26], a mobile social networking system based on the analysis of general requirements of MSN and location based services (LBS). The ultimate goal is developing and establishing an integrated framework for providing social network based healthcare information services targeting patient safety, empowerment, and guidance.…”

Section: Related Workmentioning

confidence: 99%

ODMBP: Behavior Forwarding for Multiple Property Destinations in Mobile Social Networks

Xiang

Chen

et al. 2016

Mobile Information Systems

View full text Add to dashboard Cite

The smartphones are widely available in recent years. Wireless networks and personalized mobile devices are deeply integrated and embedded in our lives. The behavior based forwarding has become a new transmission paradigm for supporting many novel applications. However, the commodities, services, and individuals usually have multiple properties of their interests and behaviors. In this paper, we profile these multiple properties and propose an Opportunistic Dissemination Protocol based on Multiple Behavior Profile, ODMBP, in mobile social networks. We first map the interest space to the behavior space and extract the multiple behavior profiles from the behavior space. Then, we propose the correlation computing model based on the principle of BM25 to calculate the correlation metric of multiple behavior profiles. The correlation metric is used to forward the message to the users who are more similar to the target in our protocol. ODMBP consists of three stages: user initialization, gradient ascent, and group spread. Through extensive simulations, we demonstrate that the proposed multiple behavior profile and correlation computing model are correct and efficient. Compared to other classical routing protocols, ODMBP can significantly improve the performance in the aspect of delivery ratio, delay, and overhead ratio.

show abstract

“…In the area of tracking, the study of power consumption in smart phones was studied [1][2], GPS information was extracted in Nokia N95 [3], social networking applications were studied in [4][5][6][7], features of Android for location-based function was reported in [8], [9] investigated the human behavior for location-based services, using location-based services in city maps was published [10], and middleware development for location-aware was published in [11].…”

mentioning

confidence: 99%

Enterprise Mobile Tracking and Reminder System: MAE

Yoong

Swe²,

Xian³

et al. 2012

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

Abstract-Mobile phones have made significant improvements from providing voice communications to advanced features such as camera, GPS, Wi-Fi, SMS, voice recognition, Internet surfing, and touch screen. This paper presents an enterprise mobile tracking and reminder system (MAE) that enables the elderly to have a better elder-care experience. The high-level architecture and major software algorithms especially the tracking in Android phones and SMS functions in server are described. The analysis of data captured and performance study of the server are discussed. In order to show the effectiveness of MAE, a pilot test was carried out with a retirement village in Singapore and the feedback from the elderly was evaluated. Generally, most comments received from the elderly were positive.

show abstract

iZone: A Location-Based Mobile Social Networking System

Cited by 9 publications

References 22 publications

Location-Based Mobile Community Using Ants-Based Cluster Algorithm

Location-Based Mobile Community Using Ants-Based Cluster Algorithm

ODMBP: Behavior Forwarding for Multiple Property Destinations in Mobile Social Networks

Enterprise Mobile Tracking and Reminder System: MAE

Contact Info

Product

Resources

About