## 2 Literature Review
### Smart Environment
#### 2.3.1 Cyber Threats or Attacks  
WSNs are networks of autonomous sensing devices that monitor physical or environmental factors such as temperature, pressure, sound, vibration, motion, or pollution at several places. WSNs are multi-hop ad hoc self-organizing networks in which all nodes interact wirelessly and use multiple routing protocols [67]. It works under circumstances of limited bandwidth and performance. It is scalable and may accept more nodes or devices  
Figure 3: Smart environment monitoring application [64]. at any moment. It is also adaptable, allowing for physical divisions, and all nodes may be accessible through a centralized monitoring system [68]. Because it is wireless, it may be used on a big scale and in various environmental applications or sectors. Furthermore, it employs multiple security methods based on the underlying wireless technology, resulting in a dependable network for specific users [69]. Smart cities can save energy and reduce carbon issues using WSNs. Specifically, it can collect environmental factors through many wireless sensors and then return the information to the backend monitoring server. This study [70] presents a WSN using ecological sensors and controllers to adjust the energy consumption of electrical appliances. Because a plethora of wireless nodes are exposed to physical or logical access in remote urban areas, the authors performed a massive simulation of DoS attacks or external damage by human manipulations. The authors used a sophisticated analysis of various packet loss patterns to identify the potential damage and examine the abnormality. After identifying damage by the logical or physical attack, each node is used by the different queue management models to collect environmental data. It is difficult to approach or work at the WSN since it operates in a complicated setting. Because nodes are open, they are susceptible to numerous assaults. Traditional security systems will also mistake nodes deployed in a complex environment with poor-quality connections or poorer conditions (less energy or a higher workload) for malicious nodes. In WSN, the trust and reputation model may be employed to mitigate the harm caused by malicious nodes. However, trust and reputation models have a sizeable false-positive rate since a node with less reputation is evaluated as undesirable owing to the communication context. This study [71] provides trust and reputation-based harmful node detection techniques with environmental factors to prevent malicious nodes from interfering with or selectively forwarding attack nodes. Machine learning's linear regression and combining node energy, data volume, number of nearby nodes, node sparsity, and other deterministic characteristics can solve environmental parameters. Using environmental parameters, benchmark trust is estimated. The Gaussian radial basis function is simplified to compare the benchmark and cycle reputation sequences. Environmental settings provide three reputation intervals and an adoption threshold span to detect malicious nodes based on work environment and node statuses. The simulations show that factors increase malicious node detection by 1% and reduce false positives by 1%. To communicate outside of the wireless communication zone, WSN employs mobile nodes. Wireless ad hoc network routing protocol attacks degrade network performance and dependability. Malicious nodes advertise the shortest route between source and destination in active black hole attacks on wireless networks, resulting in routing table alterations and packet loss. Self-security management is a hot topic in WSN-related environmental applications. For example, this paper [72] provides the grouped black hole attack security model (GBHASM), which prevents grouped hostile nodes from advertising the shortest route between source and destination, preventing routing table alterations and packet loss. The GBHASM proposed is separated into two components. The first module describes how a new node will join the network, while the second handles communication.