# A Survey of Using Swarm Intelligence Algorithms in IoT
## 5 Application of Swarm Intelligence Algorithms in Other IoTs  
The VANET is a special form of MANET (mobile ad-hoc network). Multicast routing under QoS constraint is proven to be NP-complete. Therefore, reference [51] transformed the QoS constrained multicast routing problem of VANETs into an SMT (Steiner minimum tree) search problem, and designed a micro-ABC (MABC) [52] method based on binary representation to solve the SMT problem effectively. The method consists of a tiny colony, saving computation time per cycle compared to using a normal swarm size. Simulation results showed that the MABC can find the optimal solution under four destiny nodes, and the increase of destiny nodes has little effect on run time. Reference [52] proposed an efficient ant colony based routing algorithm for the IoT. The algorithm is more efficient, scalable, and adaptable to network parameter changes, taking energy into account. In this paper, considering the IoT systems as a graph G and turning the IoT routing problem into a TSP-like problem, they use ACO to solve the problem. Simulation results showed that the proposed method can reduce energy consumption, end-to-end delay, and extend node life. Reference [53] presented a multi-objective PSO (MOPSO) cloud agent scheme. This scheme optimizes resource selection in cloud computing by optimizing the cloud agent (the middle layer between the client and the cloud service provider in cloud computing), using multi-objective PSO to find Pareto optimal solution of cloud agent problem. Compared with GA and a random search algorithm, MOPSO reduces response time, and energy consumption and improves profits. However, as iteration increases, the profit of MOPSO is almost the same as the random search algorithm. Reference [54] showed an optimization model for virtual machine selection in cloud IoT health service applications to effectively manage and integrate a large amount of data in industry 4.0. The GA, PSO, and PPSO are both considered for optimization of virtual machine selection. The proposed model can optimize data storage requirements by reducing the stakeholders' request execution time, turnaround time, and waiting time of medical requests (tasks). The simulation result showed the proposed model demonstrated its superior to the existing model in terms of overall execution time and real-time data retrieval efficiency. When the IoT devices transmit and exchange data, attackers can easily get detailed information about shared data. Based on the MOPSO [54], literature [55] proposed a security processing method adopting hierarchical clustering and PSO. Its purpose is to find the optimal particle as Pareto solution and hide the secret information innocently. The main idea is that each particle is given a probability, thus increasing the diversity of global optima and preventing it from falling into local optima. Compared with other multi-objective algorithms and GA--based algorithms, the HCMPSO can have the lowest hiding failure in most cases. Reference [56] proposed a PSO-based non-invasive load scheduling method for the residential user center. It is characterized by edge computing and drives the computing power of the Internet edge and IoT data sources to optimize cloud computing technology. The model is based on the historical power consumption trend, and user satisfaction data in the smart home environment and the constraint PSO is used to load the data. It not only considers the load dispatching of the state grid but also considers the local renewable resources, which is beneficial to the safe development of the power grid. An important problem in the IoT is to reduce the number of hops from the sensor to the sink. Reference [57] studied the modeling problem of small-world networks with multiple convergence points, and proposed a PSO-based novel shortcut addition algorithm for multi-sink IoT (SAPS). Each cluster is treated as a single aggregate network. The fitness function is composed of the average path length and load to sink.