A new multi-hop clustering algorithm based on iterative delay to enhance QoS for Internet of Things

In general, Internet of Things (IoT) as a new technology refers to a network of physical things in which objects have a unique identity and are able to communicate with each other or with the end user via the Internet.  The Internet of Things refers to a collection of sensor-embedded devices, processing ability, software, and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks. Due to the limited radio range of objects and also the reduction of energy consumption, information transmission is carried out through the intermediate objects, which highlights the necessity for routing. Routing algorithms can be classified into static and dynamic techniques as well as source initiated and destination initiated approaches. In general, routing algorithms can be classified into data centric, hierarchical, geographical, and quality of service-based mechanisms. A routing algorithm directly affects reliability, transmission latency, power consumption, network throughput, bandwidth utilization, and network lifetime. This paper proposes a new routing method based on distributed clustering and iterative latency to improve the Quality of Service (QoS) of IoT, which divides network things into a number of separate clusters. The proposed method consists of four stages, i.e. network clustering, steady state, multi-hop transmission based on delay estimation, and investigation of adjacent headers. Clustering is performed based on the different states of neighbors, and the iterative delay mechanism is used between the cluster heads. The simulation results conducted through Cooja tool indicate that the proposed method outperforms the LEACH, LEACH-E, NCACM, and distributed clustering techniques in terms of energy consumption and packet delivery ratio by 33% and 9%. Furthermore, simulation results illustrate that the proposed method outperforms in terms of the first node death time and the number of dead objects in scattered and dense networks by 14% and 12%, respectively.