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.

Wireless Sensor Networks (WSN) has gained attention as it has been useful in warning about disasters. Predicting natural disasters like hailstorm, fire, rainfall etc. by WSN are infrequent and stochastic. This is an important topic of research. Detection of these disasters should be fast and accurate as they may cause damage and destruction at a large scale. In this paper, comparison of various machine learning techniques such as SVM, regression, decision trees, neural networks etc. has been done for prediction of forest fires. The proposed approach in this paper presents how regression works best for detection of forest fires with high accuracy by dividing the dataset. Fast detection of forest fires is done in this paper by taking less time as compared to other machine learning techniques.

Wireless sensor network (WSN) is made up of tiny sensor nodes. The application of WSN in diverse fields has seen a tremendous escalation in recent years. WSN applications are constrained by the limited set of computing resources possessed by the sensor nodes and the security aspects of data communication in the WSN. Many algorithms based on nature‐inspired optimization (NIO) have been proposed in the past to optimize the issue of energy efficiency and security in WSN. In the proposed work, two opportunistic routing algorithms, i.e., intelligent opportunistic routing protocol (IOP) and trust‐based secure intelligent opportunistic routing protocol (TBSIOP), are compared against two NIO algorithms developed for achieving energy efficiency and security in WSN for performance analysis. The performance is evaluated by simulating the algorithms on MATLAB and comparing the obtained results with existing ACO‐based and PSO‐based routing algorithms. It is observed that the TBSIOP outperforms the NIO‐based algorithms in terms of energy efficiency, network lifetime, packet delivery ratio, end‐to‐end delay, and average risk level. All the parameters under consideration are recorded in the presence of a maximum of 50% malicious nodes for 25, 50, and 100 nodes’ test cases. The increasing size of the network has a significant effect on the performance of TBSIOP, as the packet delivery ratio is close to 100%. Also, TBSIOP can easily avoid malicious nodes during the routing process as reflected from the results. This will improve the network lifetime of TBSIOP compared to other protocols. As far as the application of the work is concerned, it would be beneficial for smart healthcare services. It can also help in better communication during the sharing of data by providing energy‐efficient services and keeping the network alive for a longer period.

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.