The lifetime of a node in wireless sensor networks (WSN) is directly responsible for the longevity of the wireless network. The routing of packets is the most energy-consuming activity for a sensor node. Thus, finding an energy-efficient routing strategy for transmission of packets becomes of utmost importance. The opportunistic routing (OR) protocol is one of the new routing protocol that promises reliability and energy efficiency during transmission of packets in wireless sensor networks (WSN). In this paper, we propose an intelligent opportunistic routing protocol (IOP) using a machine learning technique, to select a relay node from the list of potential forwarder nodes to achieve energy efficiency and reliability in the network. The proposed approach might have applications including e-healthcare services. As the proposed method might achieve reliability in the network because it can connect several healthcare network devices in a better way and good healthcare services might be offered. In addition to this, the proposed method saves energy, therefore, it helps the remote patient to connect with healthcare services for a longer duration with the integration of IoT services.

In Wireless Sensor Network (WSN), the routing protocols have been given attention because most of the routing protocols are application and architecture dependent. This chapter presents routing protocols for wireless sensor networks and also classifies routing in WSN. Chapter gives five main classifications of routing protocols in WSN which are data-centric, hierarchical, location-based, network flow and QoS aware and opportunistic routing protocols. The focus has been given on advancement of routing in WSN in form of opportunistic routing, in which the sensor nodes utilize broadcasting nature of wireless links and the data packets can be transmitted through different paths. The routing protocols for WSN are described and discussed under the appropriate classification. A table of comparison of routing protocols on the basis of power usage, data aggregation, scalability, query basis, overhead, data delivery model and QoS parameters has been presented.

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