Today, Internet of Things (IoT) is a technology paradigm which convinces many researchers for the purpose of achieving high performance of packets delivery in IoT applications such as smart cities. Interconnecting various physical devices such as sensors or actuators with the Internet may causes different constraints on the network resources such as packets delivery ratio, energy efficiency, end-to-end delays etc. However, traditional scheduling methodologies in large-scale environments such as big data smart cities cannot meet the requirements for high performance network metrics. In big data smart cities applications which need fast packets transmission ratio such as sending priority packets to hospitals for an emergency case, an efficient scheduling mechanism is mandatory which is the main concern of this paper. In this paper, we overcome the shortcoming issues of the traditional scheduling algorithms that are utilized in big data smart cities emergency applications. Transmission information about the priority packets between the source nodes (i.e., people with emergency cases) and the destination nodes (i.e., hospitals) is performed before sending the packets in order to reserve transmission channels and prepare the sequence of transmission of theses priority packets between the two parties. In our proposed mechanism, Software Defined Networking (SDN) with centralized communication controller will be responsible for determining the scheduling and processing sequences for priority packets in big data smart cities environments. In this paper, we compare between our proposed Priority Packets Deadline First scheduling scheme (PPDF) with existing and traditional scheduling algorithms that can be used in urgent smart cities applications in order to illustrate the outstanding network performance parameters of our scheme such as the average waiting time, packets loss rates, priority packets end-to-end delay, and efficient energy consumption.
The technology scene has been dominated for years by the term “Smart Cities”, which seek to provide a digital environment that stimulates learning and creativity that promotes a sense of happiness and health. There is more than one definition of this term, and sometimes more than one designation, such as “Digital Cities” and “Ecological Cities”, and they disagree according to the goals set by those responsible for developing them [
The term smart cities and big data have long been of particular interest in the world of technology, and the use of these technologies is no longer a matter of technical luxury in many fields, but rather a necessity that is required by the need to control devices and equipment remotely based on the data that been recorded immediately on the one hand, and the need to process huge data that helps to make decisions automatically and instantly on the other hand. The merging of these two terms has led to the creation of new applications in promising smart environments, which are based on solving problems, facilitating matters, and enhancing the decision-making process, so that smart cities collect information between information technology and operational infrastructure, and big data plays its role in managing and analyzing large data with high-speed via sensors, applications, and devices to collect, share and transfer data in real time [
In order to manage this data, there is a need to distinguish three levels: collection level, management and processing level, and application level, which this paper [
The IoT and big data technologies play an important role in the development of smart cities. The Internet of Things allows to connect disparate devices and communicate over networks and data that is potentially meant to be shared. IoT and Big Data technologies help smart cities to manage large amounts of data generated using technologies like Hadoop, Spark, Hbase and big data analytics tools like machine learning, data mining, data analytics, etc. The information collected has a great impact in various areas of the smart city such as security to ensure the safety of people; Transportation to reduce traffic jams; Urban planning identifies areas in need of improvement and modernization; Sustainability to provide sustainable development needs to be renewed [
The integration of the Internet of Things and Big Data has become the backbone of smart city initiatives to connect human resources, social capital, and ICT infrastructure to respond to public challenges, achieve sustainable development, and raise the standard of living of individuals [
In general, “Smart Cities” work on developing the economic and social levels and their purpose is to provide an environmentally friendly digital environment that stimulates learning and creativity so that if you are able to carry out your business and carry out your activities from your current place based on technology, which in turn helps improve the efficiency of resource consumption and improve the standard of living, the availability of good and fast services, easy mobility, and a safe and less polluted environment. All these are indicators that you live in a city that has the main characteristics of smart cities. New technological developments have helped governments realize the dream of smart cities, as developments in the Internet of things based on connecting devices, exchanging data and launching central control systems have contributed to saving energy consumption and improving the traffic management system in addition to artificial intelligence applications that analyze a large database of data to enrich the process of making the decision, and has already been relied upon in many areas, including the environment, agriculture, financial institutions, health and educational facilities [
‘Smart city’ has various definitions. Some are tech savvy; others emphasize the ideological aspect. However, at the heart of the concept is the desire to use technology to better serve cities and urban communities [
Smart cities is a new concept for inserting the technology in environment using different electronic devices, sensors and interaction methods between terminals for the purpose of collecting data. These data are used to manage resources and provide services to end users [
Originally, the main idea of the IoT is to interconnect multiple analog and digital electronic devices so that information can be communicated efficiently. IoT is a growing field, and it is something that is growing exponentially over the next few years [
So, in one of IoT applications, if a person is watching TV, and a sensor installed in the refrigerator finds that the water bottle in the refrigerator needs to be refilled because the water level has get down, the refrigerator will turn on all the sensors that installed in the rooms of the house to detects that if there is someone in the house who can go to the refrigerator and refill the water bottle, conveying the information by broadcasting the message “Search” to all of these sensors that have close to a transmission range of 8–10 meter, which overlaps each other, allowing them to freely communicate and share messages with each other. Also, The IoT can be very useful in solving the problems of people with physical or special disabilities in their daily lives [
The IoT field is a comprehensive concept that includes integrating several devices, software and networks together to obtain the desired results and achieve the optimal meaning of IoT. Which an integrated IoT system includes four main components which are sensors (or any other type of physical device), Internet connection, data processing software and a user interface.
One of the features of IoT is that it has the potential to accelerate the ‘sharing economy’. So, by introducing new technologies for managing and tracking small objects, new auxiliary and economical items can be shared outside of society, airplanes, cars and motorcycles. As the trend continues, it exclusively introduces new applications that drive new business models and revenue prospects. It takes devices and sensors to a more precise level, enabling the creation of new uses, new applications, new services, and new business models that were previously uneconomical. It is also dangerous for many established industries. According to Gartner's chart today, it is one of the top 5 IoT technologies in the world. This means that it is widely used in a variety of roles in various fields such as smart home, vehicle tracking, monitoring of children and the elderly, and daily work. However, the reality is that the sector currently employs many IoT-enabled devices [
As for the mechanism of IoT, it starts with sensors that start collecting data from their environment in which they are located, and then this data is sent to the Cloud, which is a huge network of super servers that provide different services to individuals and companies, where the sensors are connected to the servers in different ways that may constitute: Smartphones, satellites, Wi-Fi, Bluetooth, etc. Once the data reaches the cloud, it is processed using a data analysis software. The process of processing this data may be simple or complex, depending on the amount and type of data obtained. Finally, the results are sent to the end user in the form of a specific alert, so that the user changes or adjusts the sensor settings, and sometimes the sensors are modified automatically without the need for human intervention [
Big data is a collection of large and complex data sets that are difficult to process using data management tools. Assignments include capturing, curating, storing, retrieving, sharing, sending, analyzing, and visualizing. Big data is moved more frequently and at larger sizes across networks across the four dimensions of volume, speed, diversity, and reliability [
Big data provides a deeper understanding of customer requirements, so if it can be processed profitably, it provides the enterprise with a highly competitive advantage and makes the right and appropriate decisions within the enterprise. Based on the information extracted from the customer database, it will improve efficiency and profit in a more effective way and reduce losses. Therefore, scientists regularly face limitations due to large datasets in many areas, including genetics, phylogeny, meteorology, complex physics simulations, biology, and environmental studies. All of this is huge and fast data. Some workflows require the ability to transfer a single 100TB dataset within hours. In today's packet networks, data is transmitted over the network as a set of packets that are transmitted one at a time without looking at the entire data with associated Quality of Service (QoS) requirements. Therefore, it is difficult to provide QoS for bidirectional big data applications, the usefulness of the network is low, and the enhancement of the forwarding protocol is very urgent. Whether optical networks operating in circuit-switched patterns have a new role for this [
Big data is receiving widespread attention in developing business applications such as IoT, and one of these service-based services is the efficient delivery of entity data that is currently designed based on end-to-end communication on the Internet [
The processing of big data currently requires massively parallel processing on thousands of servers, all of which must be directly connected to each other. With the advent of large data sets, there is an ongoing demand for additional data exchange capacity in data centers. Ultra-wideband data center network operators face the daunting challenge of scaling their networks (bandwidth) to a hitherto unimaginable size, maintaining a seamless connection between any two points in their network.
In current data networks, data is a packet from an application before it enters the network and passes on the network packet by packet until all the pieces reach their destination. This simplifies the interaction between applications and the network, as packet processing and transport are the only functions that the network must perform, but it also comes with the disadvantage of increasing the amount of traffic and data [
The rest of this paper is organized as follows. In Section 2, we discuss some related works regarding the scheduling algorithms in big data smart cities. In Section 3, we give our problem statement. Section 4 details our proposed Priority Packets Deadline First (PPDF) scheme with Software Defined Networks (SDN) technique, and explains all related algorithms (i.e., connecting the source nodes with the SDN controllers algorithm, filtering data packets algorithm, and forwarding priority packets algorithm). Section 5 illustrates the simulation results and evaluates the performance of the proposed PPDF scheme with SDN technique and compare PPDF with existing well known scheduling algorithms utilized in big data smart cities. Finally, we conclude the paper in Section 6.
The term IoT refers to the network of devices capable of collecting and sharing data with other devices on the same network. This allows objects to be sensed and controlled remotely through the existing network infrastructure. When the devices are able to communicate with each other, this leads to Creating a platform where automation can be programmed, for example a smart house is one of the best examples of how the Internet of Things can be used well, where every piece of office equipment can be monitored, whether it is smart locks installed on every door, a smart coffee machine or a unit Air conditioning or smart refrigerator, controlled remotely. All of these services need to be scheduled in several ways that will be based on reducing costs, improving safety and automation, improving workers’ efficiency, and also reducing the level of electricity consumption and improving the mechanism of communication of devices with each other.
IoT is about designing optimal scheduling algorithms, which should maximize CPU usage and throughput and minimize latency and power consumption [
Authors in [
The authors presented an IoT network device that contains an interface with one specific computational resource available. Efficient use of available IoT resources improves the quality of service (QoS) of IoT networks that serve smart cities [
At the same time, they provided a method of mathematical optimization to minimize the total cost of allocating all demand in the scheduling window, taking into account the tolerance level of each service. It also proves that their problem is computationally difficult and provides numerical results to gain insight into the impact of the various price weighting features of the allocation distribution within the scheduling window.
Several sets of simulations were run to evaluate how using a different set of price weighting functions affects the distribution of allocations over time. If the mixture consists of tolerant services over generous services, the former takes precedence and thus at least most of the former are provided as early as possible. Conversely, if the mixture consists of tolerant services rather than intolerant services, and if the needs of the latter are fully met at the beginning of the reservation window, then, the latter will be fully served as soon as possible. Overall, the numerical results demonstrate the ability of the formulation to model acceptance levels of smart city services.
This system proposed a mechanism to schedule updates via the IoT network to minimize energy consumption while meeting the constraints of the deadline for updating all devices. It mathematically formulates the energy efficiency update scheduling problem as an optimization problem using a new energy model of the update process and propose an algorithm to estimate the optimal schedule for updating all devices in the network. And by examining the proposed algorithm on three network instances, including the tree-part-mesh-full-mesh topology. Simulation results show that this algorithm can achieve near-optimal values. This is a difference of only 3.2% from the minimum in the best case [
It focuses on the IoT network model, which includes many connected IoT devices and gateways. All gateway devices are considered “nodes” in the chart. The device receives the component at both the gateway and other devices. You can download from multiple nodes at the same time or send to multiple nodes, but you can only download up to one component on one node at a time. They proposed an algorithm called ESUS, which uses step P
Based on [
The preprocessing of individual tasks based on user queries is executed by the first module Task PreProcessor that works within the IoT gateway. The main role of an IoT gateway is to create a workgroup and identify the virtual object that leads the workgroup. Creating a task group requires a task preprocessing that the task preprocessor does. The TPP is responsible for splitting user queries into multiple tasks and specifying the appropriate sensor to perform the task [
The virtual leader node is responsible to lead the workgroup. Therefore, it keeps track of all the nodes that have joined the workgroup. Therefore, it is the virtual reader's responsibility to choose the route that forwards the task's packets between task groups to complete the task. The virtual reader makes a work path selection to reach the sensor node determined using the ACO algorithm. Issues that cannot be negotiated are directly assigned to the TPS, issues that can be negotiated are divided into groups, and only the issues of the leader of each group are assigned to the TPS. TPS schedules tasks based on delay parameters. Jobs with the lowest time delay are reserved first from the run queue, followed by scheduled tasks with the highest delay [
For the purpose of fast scheduling of joint detection traffic in IoT, a joint detection scheduling model on the bottom of the IoT is designed in this model [
Many cities are now seeking to make use of Big Data, and emerging technologies such as: Artificial Intelligence, Machine Learning, IoT etc., to facilitate the daily lives of residents. So that there is a relationship between LoT in smart cities and big data. LoT can tackle most of the challenges related to big data. Since the technologies of LoT will spread within most sectors, this will lead to the flow of very large volumes of data and new methods will be established to collect this data, analyze it and benefit from its information.
According to the paper [
According to the authors [
They proposed new centralized routing and scheduling algorithms and evaluated the new approach using extensive simulations with other centralized routing and scheduling algorithms, resulting in better quality of service (QoS) measurements than traditional methods. It shows excellent performance. The results also show that the performance of the entire network, which uses the previous network environment to determine the routing path of packets, can be significantly improved. Large-scale applications, such as IoT heterogeneous networks, high-load networks, network environment approaches can use network resources to improve network performance and have a significant impact on end-user experience.
According to the vast number of these devices used in smart cities, the efficient management of IoT resources and the data generated by these resources is one that has brought about unprecedented growth in big data. Causes previously related capacity issues on the device. Existing networks are not sufficient to support this huge amount of data transfer, so the authors [
Software-Defined Networking (SDN) is a new concept that transforms the way networking hardware works with software and transforms networks toward virtualized services. Technological advances in virtualization services, distributed systems architecture, big data, and cloud computing require fast and resilient networks that adapt quickly when using centralized smart technologies and SDNs are changing both the role of networks and where the control is located. SDN technologies can be deployed on virtual machines that can be installed on servers, such as VMware NSX, or run-in new switches that accept advanced network operating systems such as Big Switch or Cumulus.
Because IoT devices are internet-based and contain sensitive information, security concerns are being raised and it needs to find ways to improve security between these types of devices (SDN) is a promising computer network technology that introduces a central program named ‘SDN Controller’ that enables network-wide control. Therefore, using SDN is a solid solution for improving IoT networking performance and overcoming the shortcomings that currently exist [
According to [
In IoT systems, these low-power devices are a protection challenge for IoT systems because they are highly vulnerable to cyberattacks, which reduces the stability of the system. Software-Defined Networks (SDN) aims to greatly facilitate policy enforcement and dynamic network reconfiguration. This white paper describes several architectures for improving network and system security of IoT integration via SDN [
The authors provide a network management method applicable to the configuration of traffic routes and the functioning of nodes included in software-defined network routes. In other words, the advanced functions of each switch node can be utilized as network resources and as a result, users can provide a traffic path that efficiently supports customized services [
Which, If the user can select the data path to the IoT service and configure the configuration (encryption, security, QoS, etc.), it will be convenient for the IoT service to provide efficient service and management. When a user using an IoT terminal requests the setting of a path for a communication service, SDN's central control system provides the user with a list of configurable paths. And because of that proposed method from a list of user-configurable routes, the user can choose the most appropriate route for the service as he requires. In addition, the central control plane can provide users with information about the node functions supported by each switch node composing the path in the form of a selectable menu, and as a result, which of each switch node composing the path can provide the user. At least one node function can be selected for at least one service the user wants. Therefore, the quality of service can be improved because the route can be easily configured to suit the user's requirements [
Based on [
Packet Forwarding Priority (PFP) has been available in off-the-shelf routers for a long time, and support is provided by various models from popular brands such as Cisco and Juniper Networks. Network operators have come to rely on these mechanisms to manage their networks. For example, as a way to rate-limit certain classes of applications (peer-to-peer), PFPs can have a significant impact on the performance of applications beyond what administrators target. PFP can also seriously affect the accuracy of the output of measurement tools and the effectiveness of network troubleshooting procedures [
The authors demonstrate a simple method for enhancing multimedia real-time performance in 802.11 WLANs [
Authors id this paper [
In IoT applications for smart cities, a large number of shot messages need to be sent from a source node to a destination node with high level of network performance metrics such as the power efficiency, the transmission time, the delay etc. One of the main challenges of IoT in smart cities is how to schedule the access to the communication channel while the network nodes become inactive most of the time and regularly send their packets when needed without human interaction. However, some urgent packets in smart city applications such as informing a hospital about emergency cases occurred in a specific location needs fast packets delivery to the destination node, and giving this prioritize packet all permission in order to ensure high transmission ratio with low latency. To address this issue in big data smart cities, many scheduling algorithms have been proposed recently for big data smart cities. All of these algorithms focus on how to ensure high scheduling network efficiency and appropriate allocation of network resources. For example, scheduling algorithms such as Earliest deadline First (EDF) [ ● The proposed scheduling algorithm is class-based algorithm which enable the destination node to explore information about the source node data packets. The priority packets will be transmitted first based on a threshold factor for the packet deadline. We call our proposed mechanism as Priority Packet Deadline First (PPDF). Packets that their deadline are expired will be dropped from the network channels in order to achieve high efficiency of network resources and low transmission overhead. In smart cities, depending on a packet deadline threshold instead of the threshold of packet transition time will lead to better utilization on the network bandwidth. ● We take the advantages of using centralized communication scenario based on SDN controller to handle all the required transmission for priority packets to the final destination node. The main idea behind the SDN controller is SDN to enhance the delivery of priority packets and ignoring the nonpriority packets (reduce the number of packets transmitted to the destination node). In addition, SDN controller can reduce the power consumption for IoT nodes and hence enhancing the smart cities network lifetime. ● In the network module for the proposed PPDF as well as SDN techniques, we proposed three algorithms, that are the broadcasting data packets algorithm at source nodes, determining priority packets algorithm at SDN controllers, and scheduling/forwarding priority packets algorithm at SDN controllers. ● We compare our proposed PPDF scheduling algorithm with existing scheduling schemes used in big data smart cities in order to demonstrate how the proposed PPDF algorithm achieve high performance metrics in terms of average waiting time, priority packets loss rate, priority packets end-to-end delay and the average energy consumption.
In the proposed PPDF algorithm, static nodes are distributed randomly in the sensing area. We have source nodes, intermediate nodes and master nodes (the SDN controllers). Three different types of data packets are generated by source nodes. First, high priority data packets, which are the packets need to be sent correctly (quickly) to the SDN controllers. These data packets preempt both medium priority data packets and nonpriority data packets. High priority data packets (level 1) such as packets that contains data about fire or emergency situations in a specific location. Second, medium priority data packets (level 2), which are the packets that can preempt the nonpriority data packets. These data packets need to be sent to the SDN controller in order to carry information about the network capabilities in the presence of the fire or the emergency location. Third, nonpriority data packets (level 3) which are the packets that have very long deadline, and they can be delivered to the SDN controller or not depending on the heavy traffics of the network.
Named Data Networking (NDN) in IoT can use the main feature of broadcast scenario of data packets to the SDN controllers where the source nodes broadcast data packets to all their neighbors. These neighbor nodes or intermediate nodes forward data packets to the SDN controllers or provide them from their cash memories. Thus, the probability of correctly delivering the data packets based on broadcast scenario to the SDN controllers increased dramatically. Redundant data packets with different priority levels (i.e., level 1, 2 and 3) can be delivered to the SDN controllers, which take the responsibilities for data packets scheduling, and the access the Medium Access Control (MAC) channel in order to forward the data packets to the final destination nodes.
The network model of the proposed PPDF algorithm with SDN controller in shown in
All nodes in the proposed network model have specific tables, and data packets are distributed into slots as shown in
Data packets will be broadcasted from the source nodes to the SDN controllers through the intermediate nodes in the proposed PPDF algorithm. The PPDF algorithm has four main architectures, which are explained in the following subsections. These four architectures include connecting source nodes with SDN controllers, access control mechanism, filtering data packets and forwarding data packets (see in
The SDN controller sends its location information via broadcast messages (Discover_Packet) to all Source Nodes (SN) in the network. At SN, when the broadcast messages arrive correctly, the SN checks if the locations information of SDN controller didn't store before in the Neighborhood Table (NhT), then the SN will update its own NhT and connect to the registered SDN (i.e., the SDN controller ID number). The information that is updated in NhT include node ID number, SDN controller ID number, neighbor node ID number, content name, and the deadline timer. After that, the SC sends broadcast message (Registered_Packet) toward registered SDN controller. Therefore, intended SDN controller will pick up the Registered_Packet for further processing. Once the exact SDN controller receives the Registered_Packet, the Network Table (NT) which includes information about the whole network nodes will be updated at the SDN controller (i.e., see in Algorithm 1).
Now, the connection between the SDN controllers and the SN is discovered by Algorithm 1. The SDN controller will take the responsibility to manage data packet transmission to the final destinations. Once the data packets (i.e., packets indicate emergency situations that are occurred in specific locations) arrive to the SDN controllers, they will be processed by entering three main stages, which are the filtering data packets access control, and forwarding data packets.
In the access control mechanism, the source node distinguishes between data packets based on their types. As mentioned earlier, there are three different types of data packets with various priorities (i.e., level 1, 2 and 3 priority data packets). Once the source node has data packet needs to be sent to the SDN controller and the conditional access control will check the frames of the data packet. The deadline timer in the packet frame will be checked in order to compare the priorities of various data packets of the source node or the neighbor nodes which are located in its range. Every source node can send data packets and access to the control channel through three different types of access channels. Therefore, queue 1 (
Thus, data packets will be stored in the control channel based on the deadline timer (i.e., data packets with shortest deadline first access to the control channel). In
In filtering data packets architecture, data packets need to be sent to the final destination must be compared based on their deadline which is the threshold that determine which packets can access to the communication channel. In this proposed PPDF algorithm, there are two scenarios for the data packets that compete to access two the communication channel (see in Algorithm 2).
First scenario, multiple source nodes have multiple data packets compete to access to the communication channel which have the same priority. In filtering data packets architecture, suppose the packets priorities are denoted as
Second scenario: multiple source nodes have multiple data packets compete to access to the communication channel which have different priorities. Suppose that as
Now, either scenario filters the data packet need to be entered the communication channel. Time Division Multiple Access (TDMA) MAC protocol are utilized to push the filtered data packets into the communication channel in order to deliver these priority packets to the SDN controller for further processing. Once the SDN controllers receive the data packets, they will follow the shortest path algorithm in order to deliver the data packets to the final destinations as we will explained in the forwarding subsection (i.e., Subsection 4.2.4).
After completing the connection between the source nodes and the SDN controller, and perpetrating the access control stage with distinguishing between priority and nonpriority data packets, data packets will follow TDMA MAC protocol in order to be delivered to the SDN controllers. Once the data packets arrived to the corresponding SDN controller, the SDN controller will check the NT table for matching with the received data packets. If the match is applicable, then the SDN controller will follow the Dijkstra shortest path algorithm in order to deliver the data packets to the final destination. If the match is inapplicable, then the data packets will be dropped. Hence, the redundancy of data packets will be reduced as well as saved the network resources. In addition, in our PPDF algorithm, we also use the energy threshold level (
In this section, we conduct the simulation results to evaluate our proposed PPDF algorithm in comparison with similar algorithms utilized in big data smart cities of IoT. The proposed PPDF algorithm is compared with Dynamic Multilevel Priority packet scheduling algorithm (DMP) [
Parameter | Value | Parameter | Value |
---|---|---|---|
Simulation time (s) |
5000 s |
Transmission speed (kb/s) |
300 kb/s |
Number of source nodes | 1000 | Energy threshold | 20% |
Number of SDN controllers | 50 | Transmission power (dbm) | 20 dbm |
Transmission rage (m) | 30 m | Wireless interface | IEEE 802.15.4 |
Packet size (byte) | 200, 400, 600, 800 and 1000 | Propagation | Two ray ground model |
For the first performance metric (i.e., the average waiting time), the simulation results show that when generating various data packets from source nodes with different transmission rate, the network conditions can ensure different data packets transmission to the SDN controllers and then to the final destination node without collisions that consume network resources. In other words, the simulation network model can be tuned from sending high priority data packets to low priority data packets easily.
For the second performance metric (i.e., packets loss rate),
For the third performance metric (i.e., end-to-end delay),
For the fourth performance metric (i.e., energy consumption),
In this paper, the researchers proposed PPDF scheduling algorithm for emergency applications of big data smart cities. The proposed PPDF preserves network resources via transferring the priority information between source nodes and destinations. Source nodes with high priority data packets, that represent emergency situations indications, will be delivered through high priority communication channels, while packets with low priority information will be delivered through low priority data channels. Nodes compete to access to the communication channel by using TDMA MAC protocol in the transmission path between the source nodes and the SDN controllers. However, packets follow the earliest deadline first in the transmission path between the SDN controllers and the destination nodes. In our paper, the authors proposed four architectures for the PPDF algorithm which are connecting the source nodes with SDN controllers, the access control mechanism, determining priority packets stage and forwarding process. The proposed PPDF algorithm is compared with existing well known algorithms used in big data smart cities, and the simulation results shows how our proposed algorithm has high performance efficiency in terms of the average waiting time, the packet loss rate, the end-to-end delay and the energy consumption. In the future work, we will increase the flexibility of the PPDF algorithm via increasing the number of static nodes and adding mobile nodes in the network model. More simulation performance metrics can be measured with mobile nodes which are moving from one location to another to be closed to the SDN controllers.
We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project Number (TURSP-2020/150), Taif University, Taif, Saudi Arabia.