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Data traffic through wireless communication is significantly increasing, resulting in the frequency of streaming applications as various formats and the evolution of the Internet of Things (IoT), such as virtual reality, edge device based transportation and surveillance systems. Growth in kind of applications resulted in increasing the scope of wireless communication and allocating a spectrum, as well as methods to decrease the intervention between nearby-located wireless links functioning on the same spectrum bands and hence to proliferation for the spectral efficiency. Recent advancement in drone technology has evolved quickly leading on board sensors with increased energy, storage, communication and processing capabilities. In future, the drone sensor networks will be more common and energy utilization will play a crucial role to maintain a fully functional network for the longest period of time. Envisioning the aerial drone network, this study proposes a robust high level design of algorithms for the drones (group coordination). The proposed design is validated with two algorithms using multiple drones consisting of various on-board sensors. In addition, this paper also discusses the challenges involved in designing solutions. The result obtained through proposed method outperforms the traditional techniques with the transfer rate of more than 3 MB for data transfer in the drone with coordination

Fair Scheduling Algorithm (FSA) using a queue is a distributed slot assignment algorithm. The FSA executes in rounds. The duration of each round is dynamic based upon the delay in the network. FSA prevents the collision by ensuring that none of the neighboring node gets the same slot. Nodes (Arivudainambi et al., 2019) which are separated by two or more hopes can get assigned in the same slot, thereby preventing the collision. To achieve fairness at the scheduling level, the FSA maintains four different states for each node as IDLE, REQUEST, GRANT and RELEASE.

A multi-unmanned aerial vehicle (UAV) system can operate in both centralized and decentralized manner. In a centralized system, the ground control system will take care of drone data collection, decisions on navigation, task updation, etc. In a decentralized system, the UAVs are unambiguously collaborating on various levels as mentioned in the centralized system to achieve the goal which is represented in Figure 2.

However, the multi-UAVs are context aware in situations such as environmental observation, UAV–UAV communication and decision-making. Independent of whether operation is centralized or decentralized, this study relates the goals of the multi-UAVs are sensing, communication and coordination among other UAVs, etc. Figure 3 shows overall system architecture.

The individual events attempts in the UAV’s execution are required to complete the mission in superlative manner which affects in every multi UAV system. This multi UAV systems need to take a steady resolute on what way UAV has to travel and what they need to complete to face the critical situations in changing of environments with the uncertain information. This coordination algorithm has certain dimensions including events that they needs to resolute on, the information that they used to make a resolution, the resolute making algorithm, the degree of decentralization. In multi UAV systems, the coordinated events ranges from lower motion level.

This study has proposed a novel self-organizing coordination algorithm for multi-UAV systems. Further, the experimental results also confirm that is robust to form network at ease. The testbed for this simulation to sensing, communication, evaluation and networking. The algorithm coordination has to testbed with multi UAVs systems. The two scheduling techniques has been used to transfer the packets using done network. The self-organizing algorithm (SOA) with fair scheduling queue outperforms the weighted queue scheduling in the transfer rate with less loss and time lag. The results obtained through from Figure 10 clearly indicates that the fair queue scheduling with SOA have several advantages over weighted fair queue in different parameters.

Bluetooth wireless technology is a low power, low cost and short‐range RF technology that permits communication between bluetooth enabled devices, and focuses on replacement of cables between electronic devices. Communication between Bluetooth devices follows a strict master‐slave scheme. Each master device can have up to 7 active slaves and forms a so called piconet. In Bluetooth employing conventional scheduling policies such as Round Robin (RR), POLL or NULL packet is sent when the Master or Slave node does not have any data to send which causes a significant waste of resources. The DRR (Deficit Round Robin) scheduling algorithm can avoid the waste of time and slot of the RR scheduling at the sacrifice of fairness. In this paper we propose an improved DRR (IDRR) scheduling algorithm which effectively combines the DRR and bin packing algorithm. Computer simulation reveals that slot utilization is increased up to about 60% while the total number of used slots is decreased up to about 100%. The proposed IDRR scheduling is thus effective for not only basic data transmission but also real‐time multimedia data transmission.

With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.

With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.

Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.

The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

Investigates group scheduling heuristics in a flow shop cellular system with two types of order shipment environment: permitted early shipment (PES) and forbidden early shipment (FES). Once an order is completed in the PES environment, it leaves the system without delay. In the FES environment, however, the suppliers must deliver materials to the system on a just‐in‐time basis; and a completed order cannot leave the system earlier than the customer has specified. The FES environment therefore captures many of the features of the currently popular just‐in‐time (JIT) approach to manufacturing. Examines shop factors such as the order shipment environment, demand pattern variability, and order release policy. Shows that the performance of heuristics in the FES environment differs from their performance in the PES environment, except for tardiness‐related measures. Identifies the best performing heuristics, which can provide guidance for schedulers or decision makers in the selection of preferable heuristics.

The performance of cellular manufacturing (CM) systems has been rigorously investigated during the last two decades. The findings from these studies need to be systematically tabulated, given that they span a wide range of systems and experimental conditions. Some of the findings have also not been in agreement with the prescriptive literature on group technology (GT). No such survey of research exists to date. Attempts to fill the void by providing a taxonomy that categorizes these studies into simulation‐based, analytical and empirical studies. Also discusses past work in the context of various experimental factors and conditions, such as system configurations assumed, setup and operation times, scheduling rules, lot sizes and performance measures used. Summarizes major findings from each stream of literature from the point of view of both researchers and practitioners.

This paper discusses various facets of quality of service (QOS), a technology that allows network managers to prioritize data to optimize bandwidth use and maintain data quality. Given the wide variety and high volume of data as well as bandwidth limitations on networks today, QOS has gained great importance in recent years. Successful implementation of QOS requires selecting from the wide array of models and algorithms available and careful consideration of other technical and organizational issues. Different QOS models and algorithms have been discussed to provide the network manager with an insight into these technologies that can aid in the selection of QOS models and algorithms. Various technical and organizational implementation issues, including how QOS is implemented in routers, have been discussed to assist the network manager to be aware of these issues and properly implement QOS.

Introduction Operations research, i.e. the application of scientific methodology to operational problems in the search for improved understanding and control, can be said to have started with the application of mathematical tools to military problems of supply bombing and strategy, during the Second World War. Post‐war these tools were applied to business problems, particularly production scheduling, inventory control and physical distribution because of the acute shortages of goods and the numerical aspects of these problems.

This paper aims to provide an overview of representative multimedia applications in the cultural heritage sector, as well as research results on quality of service (QoS) mechanisms in internet protocol (IP) networks that support such applications.

The paper's approach is a literature review.

Cultural heritage multimedia applications require greater bandwidth capacity, especially where multiple users share the connections. For such applications, scalability and reliability of quality of service depend on packet‐level QoS mechanisms operating in a full end‐to‐end basis.

The paper provides a foundation for future research directions in the professional communication context. For instance, the QoS mechanisms in possible network infrastructures could be used to explore effective multimedia data dissemination across cultural heritage institutions.

The paper provides an overview of which technologies/mechanisms are being used most broadly and which might provide the most potential for cultural heritage institutions managers considering experimenting in the multimedia communications area.

QoS mechanisms that support multimedia applications in the cultural heritage sector need to become well known by cultural heritage institutions managers and professional associations in the fields of libraries, museums and archives. This paper provides a useful overview of the topic.

Optimal crew allocation and scheduling is an important issue in the management of a large fleet of taxis. Presents a modelling study of a taxi service operation in a Singapore public transport company, with the results adaptable for other similar operations. Proposes two quantitative techniques: the queueing analysis approach and the integer programming approach: These can be used to allocate and schedule the taxi fleet to cover the demand for services at various time periods and geographical zones for the entire country. Presents a routeing and scheduling management information system to support the scheduler in making routeing and scheduling decisions.

As the real‐time applications used in today's wireless network grow, some schemes are required to provide better quality of services. From the evaluation of referenced scheduling scheme provided in IEEE 802.11e standard document, we know that it does not perform well on traffic which is not strictly constant‐bit‐rate (CBR). Therefore, it is necessary to design a more flexible scheme to dynamically adjust the estimation of transmission opportunity allocated to QoS‐enhanced stations with different characteristics of applications. This paper aims to present a data rate estimation algorithm for the scheduler of the IEEE 802.11e hybrid coordination function controlled channel access.

The performance of the algorithm is evaluated through Network Simulator ‐2.

With the proposed algorithm, the QoS access point can provide guaranteed parameters such as delay and throughput for both the real‐time variable‐bit‐rate and CBR traffics.

The paper presents a data rate estimation algorithm for IEEE 802‐11e HCCA scheduler.