Collecting sensed data with opportunistic networks: The case of contact information overhead

被引:1
|
作者
Amah T.E. [1 ]
Kamat M. [1 ]
Abu Bakar K. [1 ]
Rahman S.O.A. [1 ]
Mohammed M.H. [1 ]
Abali A.M. [1 ]
Moreira W. [2 ,3 ]
Oliveira A., Jr. [4 ]
机构
[1] Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia (UTM), Skudai, Johor
[2] Fraunhofer-AICOS, Porto
[3] PPGMO, Federal University of Goiás, Catalão, GO
[4] INF, Federal University of Goiás, Goiânia, GO
来源
Amah, Tekenate E. (amatek008@gmail.com) | 1600年 / MDPI AG卷 / 08期
关键词
Delay tolerant data; Internet of things; Opportunistic networks; Routing; Sensed data collection; Smart City; Smartphones; Wireless sensors;
D O I
10.3390/info8030108
中图分类号
学科分类号
摘要
The rising human population in urban environments drives the mission towards smart cities, which envisions a wide deployment of sensors in order to improve the quality of living. In this regard, opportunistic networks (OppNets) present an economical means of collecting delay tolerant data from sensors to their respective gateways for providing various Smart City services. Due to the distributed nature of the network, encounter-based routing protocols achieve acceptable throughput by requiring nodes to exchange and update contact information on an encounter basis. Unfortunately, sufficient insight into the associated overhead is lacking in the literature. Hence, we contribute by modelling contact information overhead and investigating its impact on OppNet routing, particularly in terms of data exchange success and energy consumption on portable handheld devices. Our findings reveal that the expected contact information overhead in Smart City scenarios significantly reduces data exchange success and increases energy consumption on portable handheld devices, thereby threatening the feasibility of the technology. We address this issue by proposing an algorithm that can be incorporated into encounter-based routing protocols to reduce contact information overhead without compromising throughput. Simulation results show that our proposed algorithm reduces the average contact information overhead, increases throughput and reduces average energy consumption. © 2017 by the authors.
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