Wireless energy harvesting for the 5G generation

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Many of EAI conferences are dealing with the design and functions of the soon-to-be 5G mobile network. International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (QSHINE) is very much a senior conference in this area, as last year we had the pleasure of organizing this event for the eleventh time. High-quality papers were presented, but it was the creation of an international team (China, Singapore, Sweden, Norway) that appealed to the judges the most, and won the Best Paper award. Titled An Optimal Replenishment Strategy in Energy Harvesting Wireless Networks with A Mobile Charger, the paper proposes a different way to look at power supply for sensors in a network.

Building on the recent developments in wireless charging, the paper notes that there have been well-articulated studies focused on mobile wireless charging. In the past, researchers were dealing with advanced problems, such as multiple charging vehicles, efficiency, travel times, and travel speeds. The limitations of the current research are mainly the problems connected to the fixed constants in the models, which do not mirror the real time environment. This paper works with variable data rates of the nodes in the network, variable amounts of replenished energy, variable energy storage of the sensors in the network, and self-discharging of the batteries is considered as well.

Model
Scheme depicting the model proposed in the paper

The energy harvesting sensor nodes will be feeding their activity status and battery status to the base station, which then forwards the data to service station, where the mobile charger is located. Mobile charger will be deployed each time a node needs more power to sustain function, and it will get only as much energy as it needs in order to be more-cost efficient. The amount of needed energy will be calculated based on historic data. Charging tour will also be drafted based on an efficiency algorithm. The paper also presents results from a simulation on a square (1 x 1 km2) with 50 wireless nodes deployed randomly, and base station is the middle of the square. From the numerical findings it can be concluded that the model is very efficient and truly capable of optimization of variables in order to save resources, but not at cost of cutting down any the data traffic from the nodes.

Want to learn more? Grab the full paper on EUDL.

Get more information on this year’s Qshine conference here.

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