Interference analysis in cognitive wireless sensor network based smart factory

KỶ YẾU HỘI THẢO KHOA HỌC QUỐC GIA CITA 2020 “CNTT VÀ ỨNG DỤNG TRONG CÁC LĨNH VỰC” Interference Analysis in Cognitive Wireless Sensor Network Based Smart Factory V.A.Q. Nguyen1 and T.T.V Tran1, 1 Dept. of Computer and Electronics Engineering, Vietnam-Korea University of Information and Communication Technology, Vietnam ce@vku.udn.vn Abstract. Currently, cognitive wireless sensor network based smart factory has been studied and results have confirmed that it is a promising system. In this study, we clarify fundamental features of the framework and clear out the relations between them. Then based primarily on the power allocation factors, we examine interference between cognitive sensors and primary sensors even as enhancing throughput of the network. Eventually, Matlab/Simulink based simulation is conducted to verify the analysis. Keywords: IoT, cognitive radio, wireless sensor network, interference, smart factory 1 Introduction Recent advances in sensor technology have allowed for wireless communication technology to be intro- duced not only in homes, but also in industrial control network. Especially, there are significant changes in process control and automation under utilizing Internet of Things in smart factory, where various types of communication techniques are deployed. The manufacturing environment introduces emerging technologies, wireless sensor networks, big data , cloud computing, embedded system and mobile Internet [1]. In particular, the rapid growth of wireless sensor network tech-nologies including machine-to - machine and device-to - device communications in- dicates the emergence of smart factories capable of providing industrial Things Internet (IoT) service [2]. In this service, a computing system gathers different types of data from machines and sensors in this process, and mines a vast volume of data collected to acquire useful information for factory operation. Machines are automatically controlled using the information obtained to generate a control command that is successful. Industrial Internet of Things or Smart Factory thus makes it possible to optimize the operation of the factory without human resources. In addition, smart factories have an architecture that is highly complicated and needs a holistic approach to control and management [3]. Machinery and equipment in smart factories will have the ability to improve processes through self-optimization and autonomous making, rather than continuing to run fixed program operations. This could also predict and avoid future failure. In the IoT-based smart factory, however, deal- ing with a real-time process in the control system is always an advanced research content. Smart machines need to use the data from their own modules and other devices in real-time to achieve self-optimization and autonomy. Wireless sensor technology eventually plays a critical role in IoT-based smart factory design perfor- mance. A wireless sensor provides impressive benefits such as ease of deployment, improved versatility and cost savings. In addition, it may be used in extreme conditions where the equipment can operate in the atmosphere of chemicals or vibrations [4]. Even then, smart factory deployment is subject to barriers re- sulting from their wireless medium such as communication signal fading, multi-path propagation, shadow effect and interference issues [5]. WSNs also undergo cross-technology interference with coexisting wire- less devices such as smartphones and laptops equipped with 802.11 radios that are operating in the unli- censed ISM band. Understanding interference is thus crucial to support mission-critical applications in the smart factory. Otherwise, physical deterioration may result in inadequate operation. As a result, the opti- mum operation of smart factory depends on developing a method that can manage, predict and mitigate interference caused to packet loss. 60 V.A.Q Nguyen and T.T.V Tran Recently, the cognitive wireless sensor network (CWSN) is the architecture that provides effective spec- trum use that requires opportunistic access to the cognitive radio spectrum and overcomes shortage of the spectrum. Additionally, sharing spectrum may also help eliminate collision and reduce the delay incurred by dense sensor nodes deployment [1].This motivates the concept of CWSN that allows secondary user or cognitive sensor nodes (CSNs) access primary users’ licensed spectrum while their communication does not conflict with primary user. Based on the available networks and their limitations, CSNs are exploring to underlay, overlay, or interweave with the spectrum of those primary users without significantly affecting their communication [2]. Due to its outstanding advantages such as utilization and efficiency [3], this paper mentions the underlying spectra. In the sharing of underlay spectrum, CSNs could even operate if the interference caused to the primary user is still below threshold point. Power allocation needs to be taken into account in underlay spectrum sharing as it can have a direct impact on channel interference in CWSN and temperature interference in primary receiver. Hence, in developing and implementing cognitive wireless sensor-based smart factory, how to distribute the transmitting power of the sensor to reach the interference threshold, and optimize CWSN utility is a critical task. In this paper clears out interference analysis based on power allocation and in cognitive wireless sensor based smart factory. The rest of the paper is organized as follow: Session 2 overviews the CWSN based smart factory. An interference awareness in IoT based smart factory is analyzed in section 3. Some analysis is given along with simulation in section 4. Finally, we concluded the paper and identify the open issues for future research. 2 Fundamental of CWSN smart factory 2.1 Framework of control system based on IoT Fig. 1. A smart factory based on IoT [3] Figure 1 illustrates an IoT-based control system architecture composed of four sub-divisions, namely phys- ical assets, industrial network, server, and terminal control. The physical resources are implemented as intelligent things that can communicate with each other via the industrial network. In the cloud, there seem to be various information systems that can gather data from the physical resource and communicate with humans via the terminals [4]. The physical resources are physical items such as intelligent machines, intelligent products, etc. These intelligent things can interact with each other through and outside the industrial network; they can work together to achieve a system-wide purpose. The physical artifacts create a self-contained and self-organized production system based on an industrial network and smart negotiation mechanism through the real-time control system. Industrial network (IN) forms a type of critical infrastructure for communication and for connecting the physical and cloud layer. It is superior to the cable network. In addition to wired system, IN systems offer superior advantages. IN can also be installed in harsh environments in which equipment can be installed in a chemical or vibration setting. This makes the IN compulsory for an intelligent factory. Cloud is also another element of the infrastructure. The cloud provides for big data applications a highly flexible solution in such a way that storage as well as computing capacities can be increased on demand. The smart devices generate large amounts of data that can be transferred to the cloud via IN to process information systems. Big data Analytics will then help to control and supervise system management and optimization. Control and Supervision Terminal connect people to the intelligent plant. Intelligent devices Physical Resources Industrial Network Cloud Supervision control 61 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC GIA CITA 2020 “CNTT VÀ ỨNG DỤNG TRONG CÁC LĨNH VỰC” such as mobile phones, tablets or PCs enable people to access cloud data, use a various set-up, or operating and diagnosing management, even remotely via the Internet. 2.2 System model of CWSN based industrial network In this paper, cognitive wireless sensor network is modeled as directed graph (, ), where = {1, 2, . . . , } is a set of Primary Users (PUs), = {1, 2, . . . , , . . . , } is a set of CSN. Let con- sider a cognitive wireless sensor network with coexistence PUs and CWSN as depicted in Figure 1. Fig. 2. Architecture of cognitive wireless sensor network based smart factory The CSNs could share spectrum band with the PUs under underlay technique. In this paper we assume that the CWSN has information about the locations of stationary PUs. 3 Interference Modeling Spectrum sensing technique is the most significant parameters in the Cognitive Radio technology. Since the spectrum scarcity plays a vital role in wireless communications we are in the situation to use it in an efficient methodology. To identify the holes in the spectrum and allocate to the unlicensed users (SU) is one of the predominant ways to use the spectrum efficiently but finding the holes is again a major issue. Therefore to find the spectrum holes when the licensed users (PU) are not used the spectrum we are in need of finding the best optimization technique to find the spectrum holes. For spectrum assignment, based on a basic model presented in [5]; it comprises five phases: spectrum sensing, interference modeling in a PUs view point, Interference modeling in a SUs view point, Network capacity optimization and Channel assign- ment. In order to protect licensed PUs’ communication the cumulative interference caused by CSNs must be kept below the interference maximum threshold at the primary receiver. We mainly focus on the rule which allows the CSN to share the frequency band with the PUs as long as the impact on the PUs transmis- sion due to the CSNs interference received by PUs is acceptable. In the CWSN, each sensor with cognition function is able to sense and access ISM bands, achieving the following two goals: 1) To avoid interference from CWSN to the licensed primary receivers. 2) To ensure CWSNs transmit successfully. The interference constraint can be written as [3]: = ∑ ≤ , (1) where is the interference channel gain between the i-th cognitive wireless sensor and the j-th primary user, is the transmitter power of the CSN and is the maximum interference level at j-th primary re- ceiver. 62 V.A.Q Nguyen and T.T.V Tran In consideration of communication link between every pair of CSN against the intranet interference from other CSN transmitters. Then, for each CSN ∈ Ν, the received constraint on the signal to interference plus noise ratio is defined:[4] = ∑ , (2) where ℎ is the channel gain between i-th CSN and its receiver, ℎ is the interference caused by other CSNs, ℎ is the aggregate interference of PUs to the CSN and the channel noise power, respectively. To meet the demand of CSN, the SINR at each CSN’ receiver should be larger than a threshold [5] ≥ , ∀, (3) where is the SINR requirement at the CSN receiver of link . In accordance with the Shannon formula for link capacity, the highest data rate on a given link is a function of and the bandwidth of the channel that is used for that link: = log 1 + () ∑ () ,∈ , (4) − () ) represents the distance between transmitters i and its receiver. denotes the channel bandwidth and is the noise spectral density. 4 Simulation setup and result Our objective is to optimize sensors’ transmission power under the interference constraints with the goal on maximizing the aggregate system throughput of secondary network. 1. The system throughput of CWSN is maximize while each CSN’s required SINR is maintained above a given value . 2. Interference to the j-th PUs is sustained below a given threshold . In this part, we just only analysis the performance of a CWSN with respect of interference based on power allocation and compare the performance with a difference parameter. In our simulation, PU and CSN are built based on Matlab/Simulink as you can see in Figure 3 and Figure 4, respectively. In each model, the Simulink’s parameters are set following [5]. In a scenario in which 3 PUs, 10 SUs and 4PUs, 10SUs have been randomly distributed in space of 20 20. Addition, three capacity based on Greedy, Radix tree and Genetic algorithm are applied in our model to examine the optimal for our study. In Greedy method, is a recursive algorithm in which each iteration simply selects the link with the maximum capacity and removes its interfering links in the inter- ference graph; this procedure continues until no other link remains in the interference graph. Radix tree is a kind of retrieval tree, in which an edge is associated with a bit and a node denotes a string (bitmap) that represents the bits of the path from the root to that node [6]. Genetic Algorithm is a heuristic algorithm with lower complexity to approximate Greedy and Radix tree algorithms [7]. 63 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC GIA CITA 2020 “CNTT VÀ ỨNG DỤNG TRONG CÁC LĨNH VỰC” Fig. 3. Primary User block Fig. 4. Cognitive sensor node block Space distribution of CUs and SUs in both scenario 3CUs, 10SUs and 4PUs, 10SUs is depicted in Figure 5. The effect of different algorithms are illustrated in Figure 6. It can be seen that the CWSN’s throughput increases gradually with increasing number of iterations. The Greedy algorithm seems to gets the best result compared to other algorithms with a certain iteration. 64 V.A.Q Nguyen and T.T.V Tran Fig. 5. Randomly location of PUs and SUs Fig. 6. Throughput of CWSN with three algorithm 5 Conclusion Cognitive Wireless Sensor Network based Smart Factory concept has been revolutionized and develpoped into a new age with recent innovations that intergrate wireless sensor networks and computer networks. 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