Abstract
In this article, an Arrival and Departure Time Predictor (ADTP) for scheduling communication in opportunistic Internet of Things (IoT) is presented. The proposed algorithm learns about temporal patterns of encounters between IoT devices and predicts future arrival and departure times, therefore future contact durations. By relying on such predictions, a neighbour discovery scheduler is proposed, capable of jointly optimizing discovery latency and power consumption in order to maximize communication time when contacts are expected with high probability and, at the same time, saving power when contacts are expected with low probability. A comprehensive performance evaluation with different sets of synthetic and real world traces shows that ADTP performs favourably with respect to previous state of the art. This prediction framework opens opportunities for transmission planners and schedulers optimizing not only neighbour discovery, but the entire communication process.
Highlights
The Internet of Things (IoT) [1] is an innovative paradigm gaining an increasing traction in the research community and in the real world due to the pervasive diffusion of cheap and smallIoT devices, estimated to generate an economic impact of up to $11.1 trillion per year by 2025 for IoT applications [2]
The results show that ADTP outperforms previous state of the art in all scenarios taking into account both energy spent and discovery latency
A discovery approach for scheduling communication based on such predictions has been introduced and evaluated against previous state-of-the-art protocols showing jointly improvements in terms of discovery latency and power consumption
Summary
The Internet of Things (IoT) [1] is an innovative paradigm gaining an increasing traction in the research community and in the real world due to the pervasive diffusion of cheap and small. Just to name a few, water distribution, pollution control, public transportation and traffic management, street lighting and many more [5] In such a scenario, the introduction of IoT devices and the definition of new services allows for an even higher number of possible smart applications [6] exploiting multiple sensing and actuation capabilities, as well as involving people in the process, e.g., fostering interaction between people and the government. Smartphones or more resource constrained wearables devices of people travelling on public transportation means (i.e., a bus) can encounter many devices such as sensors and actuators and forward data across Bluetooth, Wi-Fi and cellular networks via device-to-device (D2D) communications In this scenario for opportunistic IoT, devices might not always be mobile and static and battery-operated (i.e., roadside sensors/actuators) as well as lacking of readily available sources of power supply, requiring power management techniques in order to maximize their lifetime.
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