Energy efficient routing in IOT based UWSN using bald eagle search algorithm

Abstract

The underwater wireless sensor network (UWSN) faces many problems such as limited bandwidth, large path loss, high attenuation, and limited battery power. In recent times, different routing protocols have been proposed in the Internet of Things (IoT) enabled UWSN (IoT-UWSNs) to explore the underwater environment for different purposes, that is, scientific and military purposes.However, high energy consumption (EC), end to end (E2E) delay, low packet delivery ratio (PDR) and minimum network lifetime make the energy efficient communication a challenging task in UWSN. The high E2E delay, EC, and reliable data delivery are the critical issues, which play an important role to enhance the network throughput. For effective achievement of EC, proposed a new routing algorithm called bald eagle search (BES). It is a nature-inspired optimization algorithm that copied bald eagle hunting behavior. The proposed routing protocol includes three stages: initialization stage, construction stage, and the data transmission phase. The nodes identify the location and the sector during the initialization process. The construction phase includes two steps, neighbor node selection, and identification of energy efficient path. Then the last phase includes the transmission, in this the broadcasting node chooses the nodes with high residual energy (RE) in comparison to the average RE of the entire network. The proposed method is implemented in MATLAB. The outcomes of simulation confirm that the BES algorithm has better performance on EC, average RE, and network lifetime than the three existing algorithm in UWSN.

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

“…However, this method consumes many iterations and easily falls into local optima. Kapileswar et al 31 consider the improved UWSN using IoT and analysis of SNR. Orthogonal signal division multiplexing (OSDM) was introduced as the modulation method.…”

Three-Dimensional Iterative Enhancement for Coverage Hole Recovery in Underwater Wireless Sensor Networks

“…The length L, width W, and height H of the monitoring area are 500 m; the sensing radius R s of the node is 100 m; the radius R c of node communication is 200 m; the maximum number K of iterations is 100; the number n of particles swarm is 50; the number N of nodes is 25-50; the maximum inertia weight w max is 0.9, the minimum inertia weight w min is 0.4; acceleration factors c 1min = 0.25, c 1max = 2.75, c 2min = 1.25, c 2max = 2.5, c 3min = 0.25, and c 3max = 2.75; the maximum step length of sensor node movement under the action of grid point is max_step = 2.5/2; the maximum step length of sensor node movement under the action of sensor node is max_sensor = 3.5/2; node initial energy is 100 J; and the transmitting power, receiving power, and idle power of the node are 2 w, 0.75 w, and 0.001 w, respectively. To verify the effectiveness of the algorithm, the proposed algorithm was compared with the BES [20], 3D-IVFA [22], and DABVF [24] algorithms under the same parameter conditions. The simulation performance of the proposed algorithm was compared with those of BES-DBR, 3D-IVFA-DBR, and DABVF-DBR on the NS-3 software platform.…”

“…However, it is difficult for the UWSNs to achieve a balance in coverage, node energy consumption, and execution time. Kapileswar et al adopted a bald eagle search (BES) to optimize the entire UWSN performance [20]. Node battery replacement is challenging and demanding in changing underwater environments.…”

Advancements in Neighboring-Based Energy-Efficient Routing Protocol (NBEER) for Underwater Wireless Sensor Networks

“…Additionally, most applications involve nodes that move with water flow. GPS systems do not function underwater [7]. This analysis concludes that routing protocols designed for TWSNs are inappropriate for underwater applications.…”