The Internet of Things paradigm is creating an environment where the big data originators will be located at the edge of the Internet. Accordingly, data analytic infrastructure is also being relocated to the network edges, to fulfill the philosophy of data gravity, under the umbrella of Fog computing. The extreme edge of the hierarchical infrastructure consists of sensor devices that constitute the wireless sensor networks. The role of these devices has evolved tremendously over the past few years owing to significant improvements in their design and computational capabilities. Sensor devices, today, are not only capable of performing sense and send tasks but also certain kinds of in-network processing. As such, triple optimization of sensing, computing and communication tasks is required to facilitate the implementation of data analytics on the sensor devices. A sensor node may optimally partition a computation task, for instance, and offload sub-tasks to cooperative neighbouring nodes for parallel execution to, in turn, optimize the network resources. This approach is crucial, especially, for energy harvesting sensor devices where the energy profile and, therefore, the computation capability of each device differs depending on the node location and time of day. Accordingly, future in-network computing must capture the energy harvesting information of sensor nodes to jointly optimize the computation and communication within the network. In this paper, we present a theoretical model for computation offloading in micro-solar powered energy harvesting sensor devices. Optimum data partitioning to minimize the total energy consumption has been discussed based on the energy harvesting status of sensor nodes for different scenarios. The simulation results show that our model reduced both energy losses and waste due to energy conversion and overflows respectively compared to a data partitioning algorithm that offloads computation tasks without taking the energy harvesting status of nodes into consideration. Our approach also improves energy balance of a WSN which is an important factor for its long-term autonomous operation.
Kulatunga, C., Bhargava, K., Vimalajeewa, D., & Ivanov, S. (2017). Cooperative in-network computation in energy harvesting device clouds. Sustainable Computing: Informatics and Systems, 16, 106–116. https://doi.org/10.1016/j.suscom.2017.10.006