An Opportunistic Vehicle Based Computing for IoT Offloading

Abstract

IoT is one of the most prolific origins of data that is collected from sensory inputs. However, IoTs are characterized by low computational power, thus motivating the data-offloading scheme, a promising technique that improves energy performance issues in limited power devices. Instead of offloading to fog or cloud, a new research track is emerging where devices unload their data to vehicles roaming around, and this particular type of offloading is called vehicular cloud offloading. In this thesis, we propose the use of vehicles as Mobile Computing Nodes roaming around the cities to offer computational services to IoT devices. Choosing the most appropriate MCN by an IoT device that wishes to offload specific communication tasks to it will be formulated as an optimization problem. Indeed, we choose the top N MCNs within the range of the fixed IoT according to the SINR and the time of connection, taking into consideration the mobility of the MCN. Another challenge would be delivering the results back to the IoT device or another party, knowing that the vehicles will not be stationary.