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

Tomasini, Marcello; Zambonelli, Franco; Menezes, Ronaldo

doi:10.1109/greencom-ithings-cpscom.2013.125

Cited by 7 publications

(7 citation statements)

References 28 publications

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“…In CPSS, the physical sensors are generally fixed and static. The social sensors are affected by individual affective and physical state [25,26], which is a dynamic process. That is, these social sensors are carried by people and each social sensor moves among different locations based on the social dynamics.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…That is, these social sensors are carried by people and each social sensor moves among different locations based on the social dynamics. It should be noted that the concept of “location” has different definitions in the literature [25,26]. The authors of [25] utilized Voronoi diagram to divide the plane into cells and each Voronoi cell is referred to as a location.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

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Transmission Optimization of Social and Physical Sensor Nodes via Collaborative Beamforming in Cyber-Physical-Social Systems

Bao

Liang

Han

2018

Sensors

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The recently emerging cyber-physical-social system (CPSS) can enable efficient interactions between the social world and cyber-physical system (CPS). The wireless sensor network (WSN) with physical and social sensor nodes plays an important role in CPSS. The integration of the social sensors and physical sensors in CPSS provides an advantage for smart services in different application areas. However, the dynamics of social mobility for social sensors pose new challenges for implementing the coordination of transmission. Furthermore, the integration of social and physical sensors also faces the challenges in term of improving energy efficiency and increasing transmission range. To solve these problems, we integrate the model of social dynamics with collaborative beamforming (CB) technique to formulate the transmission optimization problem as a dynamic game. A novel transmission scheme based on reinforcement learning is proposed to solve the formulated problem. The corresponding implementation of the proposed transmission scheme in CPSS is presented by the design of message exchange processes. The extensive simulation results demonstrate that the proposed transmission scheme presents lower interference to noise ratio (INR) and better signal to noise ratio (SNR) performance in comparison with the existing schemes. The results also indicate that the proposed method has effective adaptation to the dynamic mobility of social sensor nodes in CPSS.

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Section: System Model and Problem Formulationmentioning

confidence: 99%

Section: System Model and Problem Formulationmentioning

confidence: 99%

Transmission Optimization of Social and Physical Sensor Nodes via Collaborative Beamforming in Cyber-Physical-Social Systems

Bao

Liang

Han

2018

Sensors

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“…We start with a representation of the environment. We envision a mix of mobile and fixed sensors in a metropolitan environment where people carry sensors forming a SNoS, but fixed sensors are attached to the city infrastructure [55]; Figure 1 depicts a city with fixed sensors (light, temperature and sound) coupled with people distributed in the city who assumed to carry smart devices with sensor capabilities. The city model consists of a square lattice of side( representing the metropolitan area) divided in square patches of one unit area (representing blocks); we have used this approach because it avoids us worrying about side-e↵ects to our results due to the geography of the city.…”

Section: The Modelmentioning

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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“…Where γ and v are the minimum and maximum distances, respectively. This model is based on two mechanisms which describe the human mobility [10,12]:…”

Section: (3)mentioning

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

Cited by 7 publications

References 28 publications

Transmission Optimization of Social and Physical Sensor Nodes via Collaborative Beamforming in Cyber-Physical-Social Systems

Transmission Optimization of Social and Physical Sensor Nodes via Collaborative Beamforming in Cyber-Physical-Social Systems

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

Effects of Mobility Models and Nodes Distribution on Wireless Sensors Networks

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