Evaluating the Performance of Social Networks of Sensors under Different Mobility Models

Tomasini, Marcello; Zambonelli, Franco; Brayner, Ângelo; Menezes, Ronaldo

doi:10.1109/socialcom.2013.62

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Random walks are a powerful tool to model individual mobility; in our work, we show that these models can be useful to better analyze the impact of mobility in SNoS [34]. One of the first patterns of movements studied by scientists was the model that is generally referred to as Brownian motion, because it was observed in 1827 by the botanist Robert Brown, and later explained by Albert Einstein [14].…”

Section: Mobility Dynamicsmentioning

confidence: 93%

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: Mobility Dynamicsmentioning

confidence: 93%

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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“…For each mobile network that we consider, given a uniform node distribution at each time slot, we can view the mobile network at a single time slot as an instance of the corresponding static network. A future work is to consider other mobility models [39]- [42].…”

Section: B Mobilitymentioning

confidence: 99%

Probabilistic Key Predistribution in Mobile Networks Resilient to Node-Capture Attacks

Zhao

2017

IEEE Trans. Inform. Theory

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We present a comprehensive analysis on connectivity and resilience of secure sensor networks under the widely studied q-composite key predistribution scheme. For network connectivity which ensures that any two sensors can find a path in between for secure communication, we derive the conditions to guarantee connectivity in consideration of (i) node-capture attacks, where the adversary may capture a set of sensors and compromise keys in their memory; (ii) sensor mobility, meaning that sensors can move around so that the network topology may change over time; (iii) physical transmission constraints, under which two sensors have to be within each other's transmission range for communication; (iv) the boundary effect of network fields; and (v) link unreliability, meaning that links are allowed to be unreliable. In contrast, many prior connectivity analyses of secure sensor networks often ignore the above issues. For resilience, although limited studies have presented formal analysis, it is often assumed that the adversary captures a random set of sensors, whereas this paper allows the adversary to capture an arbitrary set of sensors. We present conditions to ensure unassailability and unsplittability in secure sensor networks under the q-composite scheme. Unassailability ensures that an adversary capturing any set consisting of a negligible fraction of sensors can compromise only a negligible fraction of communication links although the adversary may compromise communications between noncaptured nodes which happen to use keys that are shared by captured nodes. Unsplittability means that when a negligible fraction of sensors are captured, almost all of the remaining nodes are still securely connected. Based on the results of connectivity, unassailability and unsplittability, we provide useful guidelines for the design of secure sensor networks.

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“…Integration WSN with (Social Network of Sensors) where this aspect comes from the movement of nodes with sensing capability such as phones, navigation systems, and other smart objects brought around by humans, vehicles, or even animals. A mobility model in a dynamic network describes the movement of mobile nodes and how their positions, directions, and velocity variation by time [4]. In the context of social movement of humans, we need models that have the ability to describe most of human mobility characteristics or any other models because most of the future sensors will been carried by cars, human, animal and vehicle.…”

Section: Introductionmentioning

confidence: 99%

Effects of Mobility Models and Nodes Distribution on Wireless Sensors Networks

Al-Sabbagh¹,

Alsabah²,

Y.Mjhool³

2014

IJASUC

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Wireless sensor networks (WSN) is an important future technology, in several applications in military, health, environment and industries. Currently the integration of social and sensor is very important by considering the characteristics of social networks in designing wireless sensor networks WSN for improvement such as (number of messages from source to destination, radius of coverage, connectivity, and spreading). This area has not received much attention and few researches focus on the performance evaluation. In this paper we have studied the impact of different mobility and distribution models which is a variable one should define which model is best for the infrastructure given their differences, also study include the exact effect of nodes distribution and analyzed by calculation the number of messages of 12 cases to get a real performance evaluation under different conditions and same routing techniques. This work provides us a greater understanding and clear an idea of the effect of mobility plus distribution.

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Evaluating the Performance of Social Networks of Sensors under Different Mobility Models

Cited by 5 publications

References 20 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

Probabilistic Key Predistribution in Mobile Networks Resilient to Node-Capture Attacks

Effects of Mobility Models and Nodes Distribution on Wireless Sensors Networks

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