Analysis of Bluetooth Versions (4.0, 4.2, 5, 5.1, and 5.2) for IoT Applications

IoT includes many sensors that have to collect the data and send it to the superior nodes; for such interaction between the IoT devices, various wireless technologies are available, like infrared, Li-Fi, WI-Fi, Zigbee, Bluetooth, etc. Among all the available, Bluetooth proved the most promising short-range wireless communication technology due to various factors. To fulfil the increasing demand for wireless connectivity, the Bluetooth SIG must continuously perform up-gradation. Here, analysis of Bluetooth versions are discussed based on the characteristics such as speed, bandwidth, range, power, message capacity, beacon provision, compatibility, reliability, errors detection, correction capability, advertisement packets, duty cycle, slot availability masks, and many more. This analysis concluded that all the versions have their own set of merits and limitations. For the basic IoT applications (limited functionalities), Bluetooth 4.0/4.2 is a good choice, while for the complex IoT applications (advance functionalities), Bluetooth 5/ 5.1/ 5.2 is better.

A heap strategy for UAV deployment issues under mobile terrestrial wireless communication networks

Unmanned on-board mobile base stations (MBSs) can more effectively solve wireless connectivity problems in terrestrial communication networks without fixed infrastructure. The purpose of this article is to minimize the number of MBS required to provide wireless coverage for a set of distributed ground terminals (GTs). Traditional clustering algorithms are no longer applicable because each drone has a different coverage area size and the traditional K-Means clustering algorithm has no limit on the number of heaps that can exceed the maximum coverage area of a single drone, making it impossible for a drone to provide services. In response to this problem, the traditional K-Means clustering algorithm is optimized, and the results of the optimized K-Means clustering algorithm are stacked to ensure that each pile has the corresponding drone capability to serve it.

Multi-Objective UAV Positioning Mechanism for Sustainable Wireless Connectivity in Environments with Forbidden Flying Zones

A communication system based on unmanned aerial vehicles (UAVs) is a viable alternative for meeting the coverage and capacity needs of future wireless networks. However, because of the limitations of UAV-enabled communications in terms of coverage, energy consumption, and flying laws, the number of studies focused on the sustainability element of UAV-assisted networking in the literature was limited thus far. We present a solution to this problem in this study; specifically, we design a Q-learning-based UAV placement strategy for long-term wireless connectivity while taking into account major constraints such as altitude regulations, nonflight zones, and transmit power. The goal is to determine the best location for the UAV base station (BS) while reducing energy consumption and increasing the number of users covered. Furthermore, a weighting method is devised, allowing energy usage and the number of users served to be prioritized based on network/battery circumstances. The suggested Q-learning-based solution is contrasted to the standard k-means clustering method, in which the UAV BS is positioned at the centroid location with the shortest cumulative distance between it and the users. The results demonstrate that the proposed solution outperforms the baseline k-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption.

Multi-objective UAV Positioning Mechanism for Sustainable Wireless Connectivity in Environments with Forbidden Flying Zones

Unmanned aerial vehicles (UAVs)-based communication system is a promising solution to meet coverage and capacity requirements of future wireless networks. However, UAV-enabled communications is constrained with its coverage, energy consumption, and flying regulations, and the number of works focusing on the sustainability aspect of UAV-assisted networking has been limited in the literature so far. In this paper, we propose a solution to this limitation; particularly, we design a $Q$-learning-based UAV positioning scheme for sustainable wireless connectivity considering key constraints, that are, altitude regulations, non-flight zones, and transmit power. The objective is to find the optimal position of the UAV base station (BS) and minimize the energy consumption while maximizing the number of users covered. Moreover, a weighting mechanism is developed, where the energy consumption and number of users covered can be prioritized according to network/battery conditions. The proposed Q-learning-based solution is compared to the baseline k-means clustering method, where the UAV BS is positioned at the centroid location that minimizes the cumulative distance between the UAV BS and the users. The results demonstrate that the proposed solution outperforms the baseline k-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption.

Treatment of Repeated Letdowns in Coordinated Consistent Recovery Line Compilation for Mobile Distributed Systems

We put forward a least_int_method (least interactive method) orchestrated CRL-compilation (consistent recovery line compilation) etiquette for non- deterministic Mob_DS (Mobile Distributed Systems); where no inoperable reinstatement-points are recorded. Recurrent terminations of CRL-compilation procedure may happen in Mobile_DS due to exhausted battery, non-voluntary disconnections of Mob_Nodes, or poor wireless connectivity. Therefore, we put forward that in the first stage, all pertinent Mob_Nodes will capture transient reinstatement-point only. Transient reinstatement-point is stored on the memory of Mob_Node only. In this case, if some method fails to capture its reinstatement-point in the first stage, then Mob_Nodes need to abandon their transient reinstatement-points only. In this way, we try to abate the loss of CRL-compilation effort when any method fails to capture its reinstatement-point in harmonization with others. We also try to reduce the CRL- compilation time and intrusion time of methods by limiting CRL-compilation tree which may be formed in other etiquettes [2, 9, 10]. We captured the transitive dependencies during the normal execution by piggybacking causal-dependency-vectors onto computation communications.