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The success of next-generation Internet of Things (IoT) applications could be boosted with state-of-the-art communication technologies, including the operation of millimeter-wave (mmWave) bands and the implementation of three-dimensional (3D) networks. With some access points (APs) mounted on unmanned aerial vehicles (UAVs), the probability of line-of-sight (LoS) connectivity to IoT nodes could be augmented to address the high path loss at mmWave bands. Nevertheless, system optimization is essential to maintaining reliable communication in 3D IoT networks, particularly in dense urban areas with elevated buildings. This research adopts the implementation of a geometry-based stochastic channel model. The model customizes the standard clustered delay line (CDL) channel profile based on the environmental geometry of the site to obtain realistic performance and optimize system design. Simulation validation is conducted based on the actual maps of highly dense urban areas to demonstrate that the proposed approach is comprehensive. The results reveal that the use of standard channel models in the analysis introduces errors in the channel quality indicator (CQI) that can exceed 50% due to the effect of the environmental geometry on the channel profile. The results also quantify accuracy improvements in the wireless channel and network performance in terms of the CQI and downlink (DL) throughput.

In recent years, multi-axis robots are indispensable in automated factories due to the rapid development of Industry 4.0. Many related processes were required to have the increasing demand for accuracy, reproducibility, and abnormal detection. The monitoring function and immediate feedback for correction is more and more important. This present study integrated a highly sensitive lithium niobate (LiNbO3) vibration sensor as a sensor node (SN) and architecture of wireless mesh network (WMN) to develop a monitoring system (MS) for the robotic arm. The advantages of the thin-film LiNbO3 piezoelectric sensor were low-cost, high-sensitivity and good electrical compatibility. The experimental results obtained from the vibration platform show that the sensitivity achieved 50 mV/g and the reaction time within 1 ms. The results of on-site testing indicated that the SN could be configured on the relevant equipment quickly and detect the abnormal vibration in specific equipment effectively. Each SN could be used more than 10 h at the 80 Hz transmission rate under WMN architecture and the loss rate of transmission was less than 0.01% within 20 m.

In the era of latest technologies, wireless sensor network (WSN) is becoming more popular in many applications: e-health, e-commerce, banking, farming and many others. However, WSNs have limitations in the sense of processing power, life time and data gathering. Among the above said issues, energy efficiency is the main hindrance of WSN deployment. Different data gathering schemes such as low energy adaptive clustering hierarchy (LEACH) and power efficient gathering in sensor information systems (PEGASIS) have been proposed. However, LEACH and PEGASIS do not provide optimal results for the energy consumption problem and are not feasible to implement. In this research paper, energy efficiency in terms of data gathering in WSN is presented. In this research work, a combined flavor of particle swarm optimization (PSO) with simulated annealing (SA) is given. A novel algorithm is proposed in which best chain formation procedure is adopted. Using the proposed algorithm, a balance energy utilization occurred between nodes, which results in increasing the network performance. Simulation results are obtained and compared with other schemes, which shows better performance as compared to LEACH and PEGASIS.

The wireless sensor network (WSN) is a foundational technology for the Internet of Things (IoT), and the application of WSN has experienced significant growth in recent years. The MQTT-SN (Message Queuing Telemetry Transport for Wireless Sensor Networks) protocol is widely used to meet the communication needs of low-power, resource-constrained sensor nodes in WSN. These sensor nodes are often exposed to security risks in wireless communication. Therefore, it is important to verify the security of MQTT-SN to ensure the confidentiality and reliability of the network and data. In this paper, we first propose an MQTT-SN application model that combines the MQTT-SN protocol with an efficient and lightweight cryptographic authentication algorithm called ChaCha20-Poly1305. Then, we formalize the proposed model using the process algebra CSP (Communicating Sequential Process). Afterward, we verify whether the model satisfies the five basic properties with the help of the model checker PAT (Process Analysis Toolkit). We further utilize C# to implement the complex functions and data structures in our model. Finally, we introduce four kinds of attacks and incorporate these attacks into the original model. And we verify the corresponding security properties again with PAT to assess the performance of our model under security threats. According to the verification results, our proposed model of the MQTT-SN protocol combined with the ChaCha20-Poly1305 algorithm satisfies all the basic and security properties. It can be concluded that our model demonstrates a high level of security.

An efficient networks’ energy consumption and Quality of Services (QoS) are considered the most important issues, to evaluate the route quality of the designed routing protocol in Wireless Sensor Networks (WSNs). This study is presented an evaluation performance technique to evaluate two routing protocols: Secure for Mobile Sink Node location using Dynamic Routing Protocol (SMSNDRP) and routing protocol that used K-means algorithm to form Data Gathered Path (KM-DGP), on small and large network with Group of Mobile Sinks (GMSs). The propose technique is based on QoS and sensor nodes’ energy consumption parameters to assess route quality and networks’ energy usage. The evaluation technique is conducted on two routing protocols in two phases: The first phase is used to evaluate the route quality and networks’ energy consumption on small WSN with one mobile Sink Node (SN) and GMSs. The second phase, is used to evaluate the route quality and networks’ energy consumption on large network (four WSNs) with GMSs. The two phases are implementated by creating five sceneries via using NS2.3 simulator software. The implementation results of the proposed performance evaluation technique have demonstrated that SMSNDRP gives better networks’ energy consumption on small single network in comparison with KM-DGP. Also, it gives high quality route in large network that used four mobile SN, in contrast to KM-DGP that used sixteen mobile SNs. While in large network, it found that KM-DGP with sixteen mobile SNs gives better networks’ energy consumption in comparison with SMSNDRP with four mobile SNs.