Incorporation of Energy Efficient Computational Strategies for Clustering and Routing in Heterogeneous Networks of Smart City

Abstract

For decision-making and governance, smart cities depend on tracking data collected via a substantial percentage of wireless sensing nodes. However, several limitations affect Wireless Sensor Network (WSN)-based Internet of Things (IoT) services, such as low battery life, recurrent connectivity problems due to multi-hop connections, and a limited channel capacity. Furthermore, in many systems, clustering and routing are handled independently, which prevents the adaptation of effective strategies for optimal energy usage and prolonged network lifespan. This research gathers data from heterogeneous IoT nodes linked via WSN and distributed across a smart infrastructure. There are two interrelated problems to be addressed with respect to energy efficiency computations: clustering and routing. We provide a new clustering strategy through which efficient routing of critical and regular data is handled. As a result, both clustering and routing have been significantly strengthened, which balances the communication load across different sectors of the smart infrastructure network. Minkowski distance and ranking strategy are used for routing and selecting cluster heads, respectively. Deterministic distributed–time division multiple access (DD-TDMA) scheduling is employed to balance the communication load across the network. The experimental results show that the proposed work outperforms some of the popular cluster-based routing strategies.

Investigating and Analyzing Simulation Tools of Wireless Sensor Networks: A Comprehensive Survey

“…By adding additional modules for designing, developing, and analyzing various WSN applications, MANNASIM expands NS-2 to be specific for WSN simulation. This framework offers a method for configuring the environments by setting different variables, such as the data distribution method, routing hardware capabilities, and sensor nodes' initial power sources [120]. Additional features of this simulator are outlined below:…”

“…▪ Routing [120][189] ▪ Energy Consumption [120] ▪ Security [189] ▪ Fault Tolerance [190] ▪ Creating models in Ptolemy might need extensive programming abilities, particularly in Java or AML. ▪ The simulation accuracy depends on the network model used in the simulation.…”

Heterogeneous sensing for target tracking: architecture, techniques, applications and challenges

“…• There are four ways to extend network life in a heterogeneous sensing node-scheduling method. (i) Design a better sleep strategy [145][146][147][148][149][150]. An efficient sleep strategy can ensure network coverage and connectivity while reducing network energy consumption.…”

“…(iv) Provide additional energy replenishment to the network. Due to the development of wireless charging technology, wireless rechargeable sensing systems have become a hot topic of research for scholars at present; such methods can prolong the life of a sensor system simply and effectively, with a wide range of recent literature addressing this topic [145,147,149,150]. • Future energy-saving strategies for heterogeneous sensing systems can be divided into two routes [154].…”

Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

“…Game theory is proposed in and as a decentralized method for controlling energy usage and conservation and coordinating the recharging of Pluggable Hybridized E-Vehicles (PHEVs) at end-user locations. Their results show that through Nash equilibriums, they've reached guaranteed optimum performance concerning the cost reduction in generating power for the grid and maintaining a steady frequency [26,31].…”