(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 11, 2021 481 | Page www.ijacsa.thesai.org A New Energy-efficient Multi-hop Routing Protocol for Heterogeneous Wireless Sensor Networks Rowayda A. Sadek 1 , Doha M. Abd-alazeem 2 , Mohamed M. Abbassy 3 Information Technology Department, Faculty of Computers & Artificial Intelligence, Helwan University, Cairo, Egypt 1 Information Technology Department, Faculty of Computers & Artificial Intelligence, Beni-Suef University, Cairo, Egypt 2, 3 Abstract—Energy use of sensor nodes efficiently and extending the lifetime of heterogeneous wireless sensor networks (HWSNs) is a main goal of HWSNs routing optimization methods, and therefore building an energy-efficient routing protocol becomes critical for HWSN performance improvement. They present an energy-efficient routing protocol based on the grey wolf optimizer (GWO) and the Tabu search algorithm (TSA) in this paper. Proposed routing system with primary objectives include clustering and the selection of cluster heads (CH) utilizing GWO with a fitness function based on the residual energy of sensor nodes and the average distance between CH and sink nodes base station (BS ) due to the mobility of sensors, the quality of service (QoS) parameters such as reliability and energy consumption can be improved by discovering multiple optimized paths for data transmission from CH to BS and by TSA selecting the optimal route from CH to BS based on the forwarding of reliable route packets (FRRPs). The experimental results indicate that the proposed grey wolf optimizer with tabu Search Algorithm (GWO-TSA) can reduce HWSNs energy consumption by 10% and 20%, increase lifetime by 13% and 18%, and finally increase throughput by 6% and 14% when compared to the genetic algorithm with tabu search algorithm (GA-TSA) and grey wolf optimizer with crow search algorithm (GWO-CSA). When compared to GA-TSA & GWO-CSA, simulation reveals that the proposed GWO-TSA protocol improves HWSNs performance by minimizing energy consumption, maximizing network lifetime, and boosting network throughput. Keywords—Heterogeneous wireless sensor networks (HWSNs); forwarding of reliable route packets (FRRPs); grey wolf optimizer (GWO); routing optimization; tabu search algorithm (TSA); quality of service (QoS) I. INTRODUCTION Wireless sensor networks (WSNs) connect the physical and digital worlds via many sensor nodes. Military surveillance, environmental monitoring, medical and healthcare applications all make use of WSNs. Because nodes are mobile, WSNs have a dynamic topology. This frequently results in the battery being unable to be changed or replaced, reducing the network's life, and it is critical to conserve energy and reduce the energy consumption of sensor nodes, as they are critical for communication in Wireless sensor networks. In the event of battery exhaustion, node and link failures may occur, necessitating the immediate suggestion of an alternate route to continue data transmission from the source to the destination, which requires additional energy. Thus, by establishing multiple paths for data communication, the overall efficiency, reliability, and integrity of the wireless sensor network can be increased, and the network's traffic load can be distributed evenly [1]. Sensor nodes in WSNs are made up of wireless transceivers that can collect data from sensors and communicating with one another as in Fig. 1. A self-contained sensor node is a tiny device composed of four major components: sensing, computation, communication, and power. The sensor node's battery capacity is restricted, charging is difficult, and charging may be impossible [2]. There are two types of wireless sensor networks: heterogeneous and homogeneous as in Fig. 2. Heterogeneous wireless sensor networks are composed of sensor nodes with varying capabilities, including varying computational capabilities and sensing ranges, as well as certain sophisticated nodes [3]. The sensor node's primary function is to detect data from the environment and transmit it to the heterogeneous high-level node CH, which has high-level energy or communication capabilities as in Fig. 3. Advanced nodes may be equipped with greater memory and more powerful microprocessors/microcontrollers than cluster member nodes. The primary benefit of HWSNs is that it extends the life of the network, increases data transmission reliability, and reduces latency [4]. By reducing the transmission distance between the CH and the BS and optimizing the energy consumption of the sensor node, the clustering-based hierarchical routing protocol improves energy efficiency and network lifespan [5]. Clustering splits the region of WSNs sensing into many clusters. Each cluster's CH is responsible for connecting to other cluster members (CMs), collecting data from them, aggregating it, and transmitting it to the BS as in Fig. 4. As a consequence, how do you optimize your website? The most critical element of clustering-based routing protocols is the process of clustering and choosing CHs [6]. Fig. 1. Sensor Node Structure [16].