Using predictable mobility patterns to support scalable and secure MANETs of handheld devices

Bild, David R.; Liu, Yue; Dick, Robert P.; Mao, Z. Morley; Wallach, Dan S.

doi:10.1145/1999916.1999921

Cited by 5 publications

(6 citation statements)

References 17 publications

(22 reference statements)

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“…Understanding the impact of mobility patterns is also of relevance in the design of forwarding protocols. Bild et al [27] exploited the predictability of mobility patterns of people to improve the reliability and scalability of routing on Mobile Ad-Hoc Networks (MANETs). Scalability is important because most routing protocols do not scale to very large networks without traditional fixed infrastructures (obviously hard to be made available in hostile environments).…”

Section: Sensor Networkmentioning

confidence: 99%

On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors

Tomasini

Mahmood

Zambonelli

et al. 2017

Pervasive and Mobile Computing

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We live in a world where demand for monitoring natural and artificial phenomena is growing. The practical importance of Sensor Networks is continuously increasing in our society due to their broad applicability to tasks such as traffic and air-pollution monitoring, forest-fire detection, agriculture, and battlefield communication. Furthermore, we have seen the emergence of sensor technology being integrated in everyday objects such as cars, traffic lights, bicycles, phones, and even being attached to living beings such as dolphins, trees, and humans. The consequence of this widespread use of sensors is that new sensor network infrastructures may be built out of static (e.g., traffic lights) and mobile nodes (e.g., mobile phones, cars). The use of smart devices carried by people in sensor network infrastructures creates a new paradigm we refer to as Social Networks of Sensors (SNoS). This kind of opportunistic network may be fruitful and economically advantageous where the connectivity, the performance, of the scalability provided by cellular networks fail to provide an adequate quality of service. This paper delves into the issue of understanding the impact of human mobility patterns to the performance of sensor network infrastructures with respect to four different metrics, namely: detection time, report time, data delivery rate, and network coverage area ratio. Moreover, we evaluate the impact of several other mobility patterns (in addition to human mobility) to the performance of these sensor networks on the four metrics above. Finally, we propose possible improvements to the design of sensor network infrastructures

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Section: Sensor Networkmentioning

confidence: 99%

On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors

Tomasini

Mahmood

Zambonelli

et al. 2017

Pervasive and Mobile Computing

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“…Mobility patterns and sensing. Previous studies have shown the benefits controlled (or predictable) mobility can have on sensing coverage and routing efficiency [21], [2], [3], [6], [28]. Reddy et al put forward a closely related idea leveraging participant's previous location traces to guide scheduling in participatory sensing campaigns [33].…”

Section: Related Workmentioning

confidence: 99%

“…(2) We describe the design ( § III) and implementation of a full CSC prototype and its integration in different example applications ( § IV). (3) We report real-world evaluation results of our ideas with 2 different applications, and example crowdsensing tasks to demonstrate the lowoverhead and high effectiveness of Crowd Soft Control ( § V).…”

Section: Introductionmentioning

confidence: 99%

Crowdsensing Under (Soft) Control

Rula

Bustamante

2015

2015 IEEE Conference on Computer Communications (INFOCOM)

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Crowdsensing leverages the pervasiveness and power of mobile devices, such as smartphones and tablets, to enable ordinary citizens to collect, transport and verify data. Application domains range from environment monitoring, to infrastructure management and social computing. Crowdsensing services' effectiveness is a direct result of their coverage, which is driven by the recruitment and mobility patterns of participants. Due to the typically uneven population distributions of most areas, and the regular mobility patterns of participants, less popular or populated areas suffer from poor coverage.In this paper, we present Crowd Soft Control (CSC), an approach to exert limited control over the actions of participants by leveraging the built-in incentives of location-based gaming and social applications. By pairing crowdsensing with location-based applications, CSC allows sensing services to reuse the incentives of location-based apps to steer the actions of participating users and increase the effectiveness of sensing campaigns. While there are several domains where this intentional movement is useful such as data muling, this paper presents the design, implementation and evaluation of CSC applied to crowdsensing. We built a prototype of CSC and integrated it with two locationbased applications, and crowdsensing services. Our experimental results demonstrate the low-cost of integration and minimal overhead of CSC.

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“…Bild et al [13] exploited the predictability of mobility patterns of people to improve the reliability and scalability of routing on Mobile AdHoc Networks (MANETs). This is quite important because most routing protocols do not scale to very large networks without traditional fixed infrastructures which are hard to be made available in certain environments (battlefields for instance).…”

Section: A Sensor Networkmentioning

confidence: 99%

Using Patterns of Social Dynamics in the Design of Social Networks of Sensors

Tomasini

Zambonelli

Menezes

2013

2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical A

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Sensor networks are gaining in importance in today's society; from air-pollution monitoring to forest-fire detection, from agriculture to battlefield communication, sensor networks are ubiquitous in many parts of the world. Furthermore, this is a world where sensors are increasingly integrated in smart phones and tablets. The use of this collection sensors as sensornetwork infrastructures (towards what we call Social Networks of Sensors", or SNoS) may be very fruitful and economically advantageous. However, it also introduces some new research challenges, one of which relates to understanding the potential impact of social mobility patterns to the performances of sensor infrastructures. This paper delves into such issue and analyses how the presence of mobile sensors moving according to social mobility patterns can impact the performances of fixed sensor network infrastructures and their design choices. Simulation results show that such integration can lead to increased efficiency of the integrated SNoS infrastructure for both sensing coverage and data delivery.

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Using predictable mobility patterns to support scalable and secure MANETs of handheld devices

Cited by 5 publications

References 17 publications

On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors

On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors

Crowdsensing Under (Soft) Control

Using Patterns of Social Dynamics in the Design of Social Networks of Sensors

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