Abstract
Routing is a challenging task in Wireless Sensor Networks (WSNs) due to the limitation in energy and hardware capabilities in WSN nodes. This challenge prompted researchers to develop routing protocols that satisfy WSNs needs. The main design objectives are reliable delivery, low energy consumption, and prolonging network lifetime. In WSNs, routing is based on local information among neighboring nodes. Routing decisions are made locally; each node will select the next hop without any clue about the other nodes on the path. Although a full knowledge about the network yields better routing, that is not feasible in WSNs due to memory limitation and to the high traffic needed to collect the needed data about all the nodes in the network. As an effort to try to overcome this disadvantage, we are proposing in this paper aware diffusion routing protocol. Aware diffusion follows a semi-holistic approach by collecting data about the available paths and uses these data to enforce healthier paths using machine learning. The data gathering is done by adding a new stage called data collection stage. In this stage, the protocol designer can determine which parameters to collect then use these parameters in enforcing the best path according to certain criteria. In our implementation of this paradigm, we are collecting total energy on the path, lowest energy level on the path, and hop count. Again, the data collected is designer and application specific. The collected data will be used to compare available paths using non-incremental learning, and the outcome will be preferring paths that meet the designer criteria. In our case, healthier and shorter paths are preferred, which will result in less power consumption, higher delivery rate, and longer network life since healthier and fewer nodes will be doing the work.
Highlights
Recent advances in technology especially in electronics and communications allowed the emergence of Wireless Sensor Networks (WSNs)
A WSN is a collection of hundreds or thousands of wireless sensor nodes that are often deployed in remote areas as shown in Figure 1, whose job is to collect data wirelessly and deliver it to a base station [3]
Based on the fact that transmission is the main source of energy depletion in WSNs, in our work we decided to use non-incremental learning to avoid the continues data collection required in incremental learning which will result in more data collection packets being sent on regular basis, and as result more energy consumption that overweighs the benefit of improving the learned concept [23]
Summary
Recent advances in technology especially in electronics and communications allowed the emergence of WSNs. The special nature of WSNs, mainly the limited energy source in addition to low computation and memory capabilities, made traditional routing algorithms unsuitable for WSNs [7]. We will be introducing aware diffusion which ensures data is being sent through the best path between the source and the sink according to path length and energy level metrics. This will result in less and healthier nodes being used to transmit the same data which in turn will result in less energy consumption and longer network lifetime.
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