![]() When considering large indoor areas, such as hospitals or industrial scenarios, the mesh must cover a large area, which introduces concerns regarding range and the number of gateways needed and respective wall cabling infrastructure. Wireless mesh networks are a critical component to achieve this ubiquitous connectivity for a wide range of services, and are composed of terminal devices (i.e., nodes), such as sensors of various types, and wall powered gateway devices, which provide further internet connectivity (e.g, via WiFi). In order to achieve the full potential of the Internet-of-Things, connectivity between devices should be ubiquitous and efficient. The proposed method that is HFSA-VANET has been implemented in the MATLAB and NS2 environment. The HFSA-VANET method shows an overall drop in the delay of 33% and a decrease in the energy consumption of 81% and an increase of 8% in the throughput as compared with the CRSM-VANET method at 80 node. Comparative analysis between the proposed method (HFSA-VANET) and (CRSM-VANET was done on different performance parameters like throughput, delay, drop, network lifetime, and energy consumption to assess system performance on two factors Speed and Nodes. As a result, the performance of machine learning algorithms may be studied and used to achieve the best results. The current model's execution is calculated using a variety of Machine Learning techniques, including SVM, Nave Bayes, ANN, and Decision Tree. The suggested approach incorporates an ensemble machine learning and hybrid metaheuristic method to reduce the latency. Based on information collected from the Road Side Unit (RSU) or the Base Station, a hybrid metaheuristic (Seagull optimization and Artificial Fish Swarm Optimization) method is used to estimate (BS). Effective routing based on a hybrid metaheuristic algorithm combined with Ensemble Learning yields significantly improved results. Therefore, an ensemble-based machine-learning technique is used to forecast VANET mobility. As a result, a better routing protocol improves VANET overall performance by permitting frequent service availability. High vehicle mobility, changing vehicle density and dynamic inter-vehicle spacing are all important issues in the VANET environment. Finally, the analytical results are validated with measurements of a custom radio node based on the ubiquitous AD9364 transceiver. This paper provides a detailed analysis of PCO-based BLE mesh networks and explores per-node system-level requirements. The accelerator is a fully digital solution that can be synthesized with only the standard cells available in any silicon technology. This paper presents a lightweight physical (PHY) layer accelerator to the BLE stack that enables scalable synchronization command with a PCO. Pulse-coupled oscillators (PCOs) have been studied extensively and are able to achieve fast and reliable synchronization across a range of applications and network topologies. A major limitation of mesh networks is the inability of the BLE stack to handle network-scalable time synchronization. However, existing BLE mesh implementations cannot simultaneously achieve low-power operation, symmetrical communication, and scalability. The article also suggested ways to improve network resiliency.īluetooth Low Energy (BLE) mesh networks enable diverse communication for the Internet of Things (IoT). At the moment, a comparison has been made between attacks and defense mechanisms that overlap these attacks. The introduction of devices into the network is provided with an encryption key, and the out-of-band (OOB) mechanism is also supported. The network uses sequence numbers for each message to protect against replay attacks. This network provides encryption at two layers: network and upper transport layers, which increases the level of data security. ![]() This paper presents an overview of security mechanisms for the Bluetooth mesh network. This network is built on top of Bluetooth Low-Energy devices, which are widespread in the market and whose radio modules are available from several manufacturers. The Bluetooth mesh was chosen as such a network. Particular attention must be paid to the protection of the transmitted data. Due to the distance between devices, battery power, and the possibility of sudden device failure, the network that connects the devices must be scalable, energy efficient, and flexible. In recent years, a lot of IoT devices, wireless sensors, and smart things contain information that must be transmitted to the server for further processing.
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