Cooperative in-network computation in energy harvesting device clouds

Abstract

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.

An Energy Conserving and Transmission Radius Adaptive Scheme to Optimize Performance of Energy Harvesting Sensor Networks

“…(3) Energy Harvesting Wireless Sensor Networks (EHWSNs). EHWSNs bring profound changes to WSNs whose energy is tense [ 39 , 40 , 41 , 42 , 43 , 44 ]. Since the sensor in EHWSNs increases hardware that can absorb energy, it can obtain energy from the surrounding environment.…”

“…For example, for an EHWSN based on solar energy, its sensor nodes add solar panels that can absorb solar energy, in order to absorb solar energy in time and to charge nodes. [ 42 , 43 ]. At the same time, some sensor devices with a wind wheel can replenish energy through wind energy, while other sensor devices can replenish electric energy by heat energy and vibrational energy [ 44 ].…”

“…In traditional sensor networks that cannot replenish energy from the surrounding environment, the principle of its energy use is to minimize energy consumption and extend network lifetime. Instead, in an energy harvesting network, the principle of its energy use is to maximize energy consumption, which is called the principle of energy neutral [ 39 , 40 , 41 , 42 , 43 , 44 ]. For example, in solar energy harvesting wireless sensor networks, instead of reducing energy consumption, the key is how to utilize the absorbed energy as much as possible to improve network performance during the daytime, especially at noon when solar energy is sufficient.…”

“…The node cannot conserve all the energy absorbed from solar energy when the battery is fully charged, for the battery volume is limited. Therefore, it is an effective way to utilize solar energy as much as possible [ 39 , 43 ].…”

“…For example, in a solar sensor network, when the solar panel is fixed, if the absorbed solar energy is too little to fully charge the battery during a continuously cloudy day, then the energy of the hotspot region nodes may be exhausted [ 39 , 40 ]. (b) Although increasing the solar panel to make the nodes absorb enough solar energy on a continuously cloudy day can support large energy consumption, and ensure the sensor node does not undergo outage in order to maintain the normal network running [ 43 , 44 ], in a homogeneous network, the hotspot area is a relatively small area for the entire network. In other words, when the energy absorbed by the solar collector can meet the energy consumption of the high energy consumption hotspot nodes, the energy of the majority of non-hotspot nodes must have a large surplus.…”