# Routing Algorithm for Sensor Network Monitoring the Condition of Mining Equipment
## 3 Simulation Analysis of Routing Algorithms  
Both reactive and proactive algorithms are used in current sensor networks [20; 21; 22]. Proactive algorithms maintain an up-to-date routing table at each node in the network. When the network evolves or the topology changes, the nodes exchange information to update the routing table. Proactive algorithms are used when the network has a fixed or frequently changing topology. Reactive algorithms, on the other hand, do not maintain a fixed routing table, but search for a route to a destination only when necessary, for example, to forward data to another node. Reactive algorithms are more suitable for networks with low traffic or a changing topology, as they do not require constant routing table updates. When analysing the literature on the simulation tests of routing algorithms in sensor networks, a deterioration in parameters of networks where both reactive and proactivealgorithms were used was noted (Figure 1). Within the scope of this article, a few of the cases are selected and described. In Figure 1, we can see that as the number of nodes increases, the network throughput decreases for the proactive AODV (ad hoc on-demand distance vector) algorithm, the proactive OLSR (optimised link state routing) algorithm and the reactive DSR (dynamic source routing) algorithm. Figure 2 shows simulations of the reactive AODV (Figure 2a) and particle swarm optimisation (PSO)-based algorithms (Figure 2b). A network of 500 nodes was simulated. It can be clearly seen from the graphs that after the network stabilisation process, the PSO algorithm is able to maintain a higher and stable network throughput compared to the reactive algorithm. Figure 3 shows the energy consumption of each node of proactive and reactive algorithms in relation to the beehive algorithm. Use of the Beehive algorithm minimises the energy consumption of the network node during data transmission (the energy required to transmit a certain amount of data through a node is the lowest for the beehive algorithm in comparison to reactive and proactive algorithms). Figure 1: Reactive and proactive algorithms [21]. Figure 2: _Cont._  
The last simulation shown (Figure 4) is an analysis of the dependence of the routing algorithms on the packet delivery time to destination. The shortest packet delivery time was obtained for the network using the SICROA algorithm (based on the ACO ant algorithm). For the other algorithms, i.e., reactive AODV and proactive DSR, the packet delivery time was longer. In each case, the packet delivery time increased with the number of nodes in the network. Analysis of the network performance graphs as a function of the number of nodes shows that both proactive and reactive algorithms often experience performance degradation as the number of nodes increases. This is due to the increased network load, increased control traffic and data latency. Figure 3: Energy consumption—Beehive, DSR, AODV, DSDV algorithms [23]. Figure 2: AODV (**a**) and SSKIR (**b**) protocols [21]. However, graph analysis of metaheuristics-based algorithms [25; 26; 27; 28; 29; 30; 31; 32; 33; 34] such as particle swarm optimisation (PSO), ant colony optimisation (ACO) and bees algorithm (BEE) suggests that these algorithms have the ability to adapt communication routes more flexibly. The PSO algorithm, inspired by the behaviour of a swarm of birds or fish, can dynamically adjust communication routes in the network based on current conditions, while the ant colony algorithm (ACO), modelled on the way ants find the shortest routes, can find optimal communication routes between nodes even under changing conditions. In contrast, the bees algorithm (BEE), inspired by the foraging behaviour of bees, also has the ability to dynamically adjust communication routes in the network based on local information and interactions in the swarm. As a result, the BEE algorithm can efficiently find optimal communication routes in the sensory network, even under variable and dynamic environmental conditions.