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

Abstract

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

Data-driven generation of spatio-temporal routines in human mobility

“…Realistic generative algorithms for human mobility are fundamental for testing the efficiency of OppNets protocol, as real data about the functioning of the network is obviously not available during the protocol design (Tomasini et al. 2017). Ditras can be used to instantiate many generative algorithms and then generate realistic mobility routines to test the efficiency of a given network protocol for OppNets.…”

Fog-based Data Offloading in Urban IoT Scenarios

“…The bottom tier comprises IoT sensors, namely, wireless embedded devices with limited battery, computing, and storage resources. Sensors are static, periodically collecting data from the environment through short-or medium-range communication technologies (such as Bluetooth Low Energy [16,17]) and storing it in a local buffer. They forward the data to other sensors or to the intermediate tier based on their buffer occupancy and communication opportunities.…”

“…In contrast, helper sensors encounter mobile gateways more frequently and can temporarily store the data collected The mobility of the gateways is exogenous to the system [12]. Sensors may not all have physical neighbors, however, each group of connected sensors is visited at least once by a mobile gateway during the lifetime of the network [18], which is realistic for densely populated urban scenarios [17].…”

Embedding Internet-of-Things in Large-Scale Socio-technical Systems: A Community-Oriented Design in Future Smart Grids

“…Since IoT is expected to have a massive impact on society and wider cultural milieu, its ultimate status should accordingly be a human-centred STS although how the IoT landscape will look like in the future is yet uncertain [1,20,37,50,55].…”