Maximizing the network lifetime is one of the major challenges in Low Power and Lossy Networks (LLN). Routing plays a major role in LLN, for minimizing the energy consumption across the network nodes. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for LLN. Though, RPL fulfilled the necessity of LLN, several issues like increasing the energy efficiency, quality of service and the network lifetime are to be focused. In LNN, the inefficient route selection results in increased network traffic, energy depletion and packet loss ratio across the network. In this paper, we propose a fuzzy logic based energy aware routing protocol (FLEA-RPL), which considers the routing metrics load, residual energy (RER) and expected transmission count (ETX) for the best route selection. FLEA-RPL applies fuzzy logic over these metrics, to select the best route to transfer the network data efficiently. The COOJA simulator is used to assess the efficiency of the proposed FLEA-RPL. The FLEA-RPL protocol is compared with similar protocol standard RPL, MRHOF (ETX) based RPL (MRHOF-RPL) and FL-RPL. The simulation result shows that FLEA-RPL improves the network lifetime by 10-12% and packet delivery ratio by 2-5%.

In recent years, the Internet of Things (IoT) has evolved as a research field that transforms human lifestyle from traditional to sophisticated. In IoT, the network plays a crucial role in collecting data from sensors and moving to the sink. Increasing the network lifetime is a challenging task in IoT, which is connected to devices that are limited by resource. Clustering is one of the effective methods of increasing the network lifetime. However, improper cluster head (CH) selection easily drains the energy early in network nodes. With the aim to overcome the issue, this paper proposes the Type-2 Fuzzy Logic-based Particle Swarm Optimization (T2FL-PSO) algorithm to select the optimal CH to extend the network lifetime. The T2FL is highly useful in providing the accurate solution in uncertain network environments. Hence, T2FL is applied on the network parameters, residual energy, and the distance between sensor node and base station to determine the fitness value. Later, virtual clusters are formed on the basis of distance between sensor node and CH and between node and base station. To validate the performance of the proposed T2FL-PSO algorithm, extensive simulations are carried out using MATLAB 2019a. Furthermore, the proposed T2FL-PSO algorithm is compared with Particle Swarm Optimization Clustering (PSO-C) and Particle Swarm Optimization Wang Zhang (PSO-WZ). The result confirms that the proposed T2FL-PSO increases the network lifetime by 10%-15% and the packet transmission ratio by 10%. Compared with similar algorithms, the proposed T2FL-PSO also causes a higher increase of network lifetime.

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Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL’s performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio.

Internet of Things (IoT) is a recent paradigm to improve human lifestyle. Nowadays, number devices are connected to the Internet drastically. Thus, the people can control and monitor the physical things in real-time without delay. The IoT plays a vital role in all kind of fields in our world such as agriculture, livestock, transport, and healthcare, grid system, connected home, elderly people carrying system, cypher physical system, retail, and intelligent systems. In IoT energy conservation is a challenging task, as the devices are made up of low-cost and low-power sensing devices and local processing. IoT networks have significant challenges in two areas: network lifespan and energy usage. Therefore, the clustering is a right choice to prolong the energy in the network. In LEACH clustering protocol, sometimes the same node acts as CH again and again probabilistically. To overcome these issues, this paper proposes the Energy-Aware Cluster-based Routing (EACR-LEACH) protocol in WSN based IoT. The Cluster Head (CH) selection is a crucial task in clustering protocol in WSN based IoT. In EACR-LEACH, the CH is selected by using the routing metrics, Residual Energy (RER), Number of Neighbors (NoN), Distance between Sensor Node and Sink (Distance) and Number of Time Node Act as CH (NTNACH). An extensive simulation is conducted on MATLAB 2019a. The accomplishment of EACR-LEACH is compared to LEACH and SE-LEACH. The proposed EACR-LEACH protocol extends the network's lifetime by 4%-8% and boosts throughput by 16%-24%.