An Improved Energy-Efficient Clustering Protocol to Prolong the Lifetime of the WSN-Based IoT

Abstract

A wireless sensor network (WSN) is an important part of the Internet of Things (IoT). However, sensor nodes of a WSN-based IoT network are constraining with the energy resources. A clustering protocol provides an efficient solution to ensure energy saving of nodes and prolong the network lifetime by organizing nodes into clusters to reduce the transmission distance between the sensor nodes and base station (BS). However, existing clustering protocols suffer from issues concerning the clustering structure that adversely affects the performance of these protocols. In this study, we propose an improved energyefficient clustering protocol (IEECP) to prolong the lifetime of the WSN-based IoT. The proposed IEECP consists of three sequential parts. First, an optimal number of clusters is determined for the overlapping balanced clusters. Then, the balancedstatic clusters are formed on the basis of a modified fuzzy C-means algorithm by combining this algorithm with a mechanism to reduce and balance the energy consumption of the sensor nodes. Lastly, cluster heads (CHs) are selected in optimal locations with rotation of the CH function among members of the cluster based on a new CH selection-rotation algorithm by integrating a back-off timer mechanism for CH selection and rotation mechanism for CH rotation. In particular, the proposed protocol reduces and balances the energy consumption of nodes by improving the clustering structure, where IEECP is suitable for networks that require a long lifetime. The evaluation results prove that the IEECP performs better than existing protocols.

Energy Harvesting Techniques for Internet of Things (IoT)

“…Since the amount of scavenged energy is so low (as Table 1 shows), in most cases the energy harvesting IoT device must operate on amounts of power as low as possible. Several strategies have been proposed for prolonging the lifetime of wireless IoT devices, including duty-cycling [104], sleep scheduling [105], the reduction of the required transmission distance for IoT devices through efficient clustering [106], optimized strategies for adaptively setting the rates of sensor reading and data transmission depending on available energy [107], or the development of scheduling schemes that take into account power consumption when waking up the wireless sensing systems [108]. Some practical techniques for reducing the energy required by an IoT device, also used in the development of low-power embedded systems, include Dynamic Voltage Scaling (DVS) [109], the reduction of the frequency of the processing unit [110], or the appropriate selection of peripherals in the device [111] or of the type of memory involved in data processing and storage (i.e., Flash or RAM -Random access memory) [112], the adaptation of transmission power depending on required communication range and environment [113,114], or logic for deciding the moment and format for sending slow varying data [115 -117].…”

Energy Efficient Clustering-Based Mobile Routing Algorithm on WSNs

“…For an energy-constrained network, the clustering algorithm plays an important role in saving power. Choosing right CH will balance the load on the network, thereby reducing the consumption of energy and prolonging the network lifespan [2], [19].…”

Grid-Based Routing Model for Energy Efficient and Secure Data Transmission in WSN for Smart Building Applications

“…Alghamdi et al [23] attempted to develop a new clustering model with optimal cluster head selection by considering four major criteria: energy, delay, distance, and security. Furthermore, to select the optimal CHs, this paper proposes a new hybrid algorithm that hybridizes the concept of dragon fly and firefly algorithms, termed fire fly re-placed position update in dragonflies.…”

“…The review papers [16][17][18][19][20][21][22][23][24][25][26][27][28][29] are not up to the mark. The proposed method in this paper addresses more about the security issues and solved energy consumption, latency and packet delivery ratio problems.…”