Location Information Of Nodes Research Articles

GAUSS-NEWTON MULTILATERATION LOCALIZATION ALGORITHM IN LARGE-SCALE WIRELESS SENSOR NETWORKS FOR IoT APPLICATIONS

The location information of sensor nodes plays an important role in critical applications like health monitoring, fire detection, and intruder detection. Installing global positioning system (GPS) modules with the sensor node hardware is not a cost-effective solution for knowing the location coordinates. This has lead to rigorous research in defining nascent localization techniques for wireless sensor networks. But, the existing localization techniques use more number of anchor nodes to compute the location coordinates of sensor nodes, and the network deployment becomes costly. This article presents a low complex, range-based localization algorithm called gauss-newton multilateration that uses received signal strength indicator (RSSI) values of the anchor nodes' signals received at the target nodes. The proposed algorithm uses only four static anchor nodes, which are deployed at the corners of the network terrain to locate the sensor nodes with localization accuracy of 90.21% and increased up to 98.59%. Based on the results obtained, the proposed algorithm provides higher localization accuracy, and it is well suited for locating sensor nodes with high accuracy in large scale wireless sensor networks.

Machine learning based efficient routing protocol in wireless sensor network

Data loss and recovery are important factors that directly affect the efficiency of the wireless sensor network (WSN). The wireless channel characteristics have a significant impact on data transmission and reception. On the receiver side, the most difficult tasks are maximizing packet delivery ratio and recovering lost data. In some cases, cyclic redundancy check (CRC) based algorithms can provide better data recovery. The CRC method can be made adaptive by using channel characteristics to correct the error bits. This paper evaluates the performance of the proposed machine learning-based efficient routing protocol (ML-ERP). For data recovery, the CRC with channel impulse response (CIR) prediction based on sensor node location information was used. The data recovery capability of ML-ERP increased the network efficiency in terms of packet delivery ratio. Also, due to less data loss, the energy efficiency of the network was also improved by almost 6 % ove existing protocols.

PDDQN-HHVBF Routing Protocol Based on Empirical Priority DDQN to Improve HHVBF

Reinforcement learning (RL) has been successfully applied to underwater routing protocols due to its powerful ability of distributed decision making. However, the traditional RL has slow convergence speed and low learning efficiency in underwater. Meanwhile, too many studies focus on using RL to find low hop paths rather than short distance paths in underwater routing, while the long distance of ocean communication is the significant reason for the packets collision and energy loss in underwater. Based on the above problems, this paper proposes the PDDQN-HHVBF (Empirical Priority DDQN to Improve Hop-by-Hop Vector-Based Forwarding) protocol for M-UWSNs (Mobile source node Underwater Wireless Sensor Networks), in which AUV (Autonomous Underwater Vehicle) is used as source node to collect data and transmit data hop-by-hop to Sink node through underwater nodes. The proposed protocol is adopt to find the optimal relay nodes in pipeline referred HHVBF protocol by requesting the max Q value according to three states of the residual energy of nodes, the number of candidate relay nodes and the geographical location information of all candidate relay nodes in time. This because PDDQN-HHVBF avoids the strong correlation between data samples, and its playback samples will not be too concentrated or lead to over fitting. It can converge rapidly in underwater environment. In addition, the requesting Q value mechanism related to the geographical location information can find the optimal relay node with short distance propagation in large-scale networks, which will reduce the number of packets collision, and then saving energy and improving network lifetime. In addtion, the in-time requesting for Q value can cope with the nodes drift affected by ocean current movement. In addition, the Q value related to the residual energy of nodes and the number of candidate relay nodes will effectively load balancing nodes, prolong network lifetime and avoid routing holes. Finally, the “Store-Carry-Forward” mechanism proposed for AUV, this mechanism store and carry packets when facing routing holes until find the optimal relay node for forwarding, which will improve PDR and save energy of AUV significantly. The simulation results show that, the proposed PDDQN-HHVBF protocol converges about 30% faster than DQELR. Although its delay is higher than DQELR and ROEVA for requesting Q value. It outperforms VBF, HHVBF, DQELR, and ROEVA in terms of energy efficency, PDR, and lifetime, which are analyzed by varying speed of nodes from 0 m/s to 3 m/s with 1000 nodes and varying number of nodes from 500 to 3000 with speed in 1 m/s.

A spectral clustering algorithm for intelligent grouping in Dense Wireless Networks

The density of wireless networks has been increasing with the popularization of mobile devices. Dense wireless networks (DWN) present challenges such as the current spectral scarcity and the growing demand for capacity. The Restricted Access Window (RAW) mechanism was introduced by the IEEE 802.11ah amendment to improve DWN performance. RAW restricts the number of stations that can access the channel by arbitrarily separating them into groups. K-Means clustering has shown potential to find more efficient groups using the geographical coordinates of each station. However, due to the mobile and dynamic nature of such networks, location information is difficult to obtain in practice. In this paper, we consider the use of spectral clustering to increase the performance of DWN with hidden terminals. We discuss how a spectral clustering algorithm that generates RAW groups can be implemented in practice without the geographic location of each node. We also compare the performance of the spectral clustering algorithm with the standard grouping method used in IEEE 802.11ah, with the K-Means clustering (i.e., based on node location information), and with the hidden matrix-based regrouping (HMR) algorithm. Simulation results considering several density levels, different traffic patterns, and different propagation models indicate that spectral clustering significantly outperforms both the standard grouping and HMR in terms of collision rate, throughput, and delay. It also closely approximates – and sometimes surpasses – the performance of the K-Means clustering while being much more practical to implement because it does not require knowledge on nodes’ geographical coordinates.

A Spherical Band-based DV-Hop localization technique for three-dimensional wireless sensor network

Wireless Sensor Networks have been the focal point of research for many years due to their wide range of application areas. Such networks consist of resource-constrained sensor nodes that are generally not equipped with any positioning component due to cost issues. This requires the adoption of suitable methodologies to infer the location of the deployed sensor nodes. Location information of such sensor nodes can be obtained with the help of some location-aware nodes. Numerous localization algorithms exist in the literature. Amongst them, Distance Vector Hop (DV-Hop) is a computationally less expensive algorithm that uses hop count values between sensor nodes and anchor nodes for location estimation of the deployed sensor nodes. However, the traditional DV-Hop algorithm produces a larger positioning deviation for a higher hop count value. Several existing works attempt to address this issue by either modifying the hop size or optimizing the estimated position resulting in comparatively higher localization errors and computationally expensive. This paper aims to solve the issue by modifying the hop size by dividing it into equal-sized spherical bands (SB). Sensor nodes use this SB value for computing their distances from anchor nodes and non-coplanar anchor nodes for location estimation. The simulation results demonstrate that the mean localization error of the proposed approach has reduced approximately by 75%, 66%, and 47% in comparison to traditional DV-Hop, 3D PSODV-Hop, and 3D GAIDV-Hop respectively.

The node deployment of wireless sensor networks based on mobile edge computing

When deploying network nodes, there are many redundant nodes, low network coverage and high energy consumption of network nodes, the node deployment method of wireless sensor networks(WSN) based on mobile edge computing is studied. WSN nodes are divided into anchor nodes and unknown nodes. Taking the location information of anchor nodes as a reference, the specific location of unknown nodes is obtained by trilateral measurement. Minimizing the node distance error is taken as the objective function, and the cuckoo search algorithm is used to solve it to obtain the final location result of the node. The mobile edge computing method is used to design the node deployment method of WSN to complete the node deployment. Simulation results show that the number of redundant nodes in this method is 3, maximum network coverage is 89%, maximum energy consumption of network nodes is 34.3J.

An artificial hummingbird algorithm based localization with reduced number of reference nodes for wireless sensor networks

Wireless Sensor Networks (WSN) are widely used in recent years due to the advancements in wireless and sensor technologies. Many of these applications require to know the location information of nodes. This information is useful to understand the collected data and to act on them. Existing localization algorithms make use of a few reference nodes for estimating the locations of sensor nodes. But, the positioning and utilization of reference nodes increase the cost and complexity of the network. To reduce the dependency on reference nodes, in this paper, we have developed a novel optimization based localization method using only two reference nodes for the localization of the entire network. This is achieved by reference nodes identifying a few more nodes as reference nodes by the analysis of the connectivity information. The sensor nodes then use the reference nodes to identify their locations in a distributive manner using Artificial Hummingbird Algorithm (AHA). We have observed that the localization performance of the reported algorithm at a lower reference node ratio is comparable with other algorithms at higher reference node ratios.

Distributed Computation Offloading and Trajectory Optimization in Multi-UAV-Enabled Edge Computing

The Internet of Things (IoT) technology has expanded network space by interconnected devices, which has been widely used in various fields, such as environmental monitoring, object tracking, risk warning, etc. Due to insufficient computing capacity, limited battery life, and unreliable communication environment in IoT, unmanned aerial vehicle (UAV)-enabled edge computing has been recently utilized to provide enhanced coverage and efficient computational support in the scenarios with sparse or unreliable ground infrastructure, such as disaster rescue, emergency response, military fields, etc. However, UAV-enabled edge computing faces many challenges, such as low offloading efficiency, high energy consumption, high complexity, etc. In this article, a distributed computation offloading scheme is proposed to provide computational support to large-scale IoT nodes and optimize the energy efficiency of multiple UAVs. First, to provide accurate and efficient computational support, a real-time intelligent positioning algorithm is designed to obtain the precise location information of IoT nodes. Then, a distributed computation offloading and path planning algorithm is presented, which jointly optimizes the computation offloading of large-scale IoT nodes and trajectory planning of multiple UAVs to reduce the energy consumption of UAVs. Furthermore, we develop a closed-form theoretical analysis model to demonstrate that the algorithm enables a performance guarantee related to energy efficiency. Finally, extensive simulations have been conducted and show that the proposed scheme can greatly improve the system utility and energy efficiency.

Localization and correction of location information for nodes in UWSN-LCLI

Location is essential to identify the floating senor nodes in underwater. Underwater Sensor Networks (UWSNs) enable various critical applications like monitoring underwater environment, flora and fauna, sea life, exploration such as earthquake, disaster mitigation, motion tracking. In many cases, an underwater sensor node is necessary. Some points make the underwater study different and slightly complex. The first point is that radio signal cannot detect the underwater environment, despite radio waves acoustic signals used, which runs due to water currents. The second point is that earthy networks are deployed in the 2D environment, but 3D node deployment is needed in the Underwater environment. The third important point is locating the sensor node position in 3D deployment because nodes move (Floating Nodes) with water waves. It is a very complex assumption to fix the location of the sensor node.Various locations finding algorithms are proposed for the earthy network. Still, the underwater acoustic communication channel's unique properties, like high and varying transmission delay, required the Algorithm to locate the node's position. Only a few localization algorithms are proposed and presented in recent years to address these location finding requirements. Existing algorithms are distributed, scalable, and iterative in nature, but at the same Time, these algorithms use many acknowledgement messages for location identification and communication. To reduce the number of messages and to overcome the transmission, a new Location Finding and Correcting Algorithm for underwater acoustic sensor networks has been proposed in this paper. Our proposed Algorithm (LCLI- Localization and Correction of Location Information for nodes in UWSNs) find the Location of nodes accurately and efficient by considering localization errors. In this paper, we have also proposed a location correcting Algorithm that may use when the localization errors are more. Our proposed Algorithm (UWSN-LCLI) is compared with existing proposed algorithms, and our Algorithm outperforms and shows significant improvement.

Indoor Positioning Technology and Control of Mobile Home Service Robot

With the rapid development of the market economy, location information services have shifted from outdoor to indoor, requiring higher system accuracy. Indoor positioning and control is an indispensable technical support for mobile home service robots to perform system tasks, and this technology is also an important parameter that marks the level of intelligence of a home service robot. However, in indoor places, how to obtain the target node location information timely and accurately and how to control and path planning should be the first issues to be considered in the design of home robots at this stage. This article gave a brief overview of the current indoor positioning technology and control, analyzed the more widely and frequently used indoor positioning methods, and deeply studied the ultrasonic positioning technology. On this basis, a mobile home service robot system was designed, and simulation experiments were carried out, respectively, to test the receiving time and arrival time of the node signal, the node positioning accuracy, the optimal degree of route planning, and the absolute and relative errors of navigation and path estimation. The experimental results showed that the accuracy of the robot system positioning proposed in this article reached more than 80%, and the relative error was only 4.21%, which verified its feasibility and effectiveness in practical applications. Popularizing it in the market can effectively improve the system performance and intelligence of home service robots.

AIGCrank: A new adaptive algorithm for identifying a set of influential spreaders in complex networks based on gravity centrality

The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process. This problem finds many practical applications in numerous areas such as information dissemination, epidemic immunity, and viral marketing. However, most existing influence maximization algorithms are limited by the “rich-club” phenomenon and are thus unable to avoid the influence overlap of seed spreaders. This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy, named AIGCrank, to identify a set of influential seeds. Specifically, the gravity centrality jointly employs the neighborhood, network location and topological structure information of nodes to evaluate each node’s potential of being selected as a seed. We also present a recursive ranking strategy for identifying seed nodes one-by-one. Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.

AUV-Aided Data Importance Based Scheme for Protecting Location Privacy in Smart Ocean

Data collection in underwater acoustic sensor networks (UASNs) and the exposure of node location information pose a threat to the security of the entire network. Therefore, the main challenge for underwater acoustic sensor network security is to protect the security and privacy of the node locations. Compared to active attacks, the characteristics of passive attacks are indistinguishable. Therefore, this research focuses on passive attacks in underwater acoustic sensor networks, and an autonomous underwater vehicle (AUV)-aided data-importance-based scheme for protecting location privacy (DIS-PLP) is proposed. The DIS-PLP comprises three main parts. First, the anchor node calculates the importance of the current data based on the historical dataset and decides whether to send data packets to the base station according to the importance result. Subsequently, an AUV privacy violation is defined, and an AUV path planning method is proposed to prevent attackers from tracking the AUV to obtain the location of the source node. Finally, to prevent nodes from waiting too long for AUVs, a secure multi-hop transmission method is proposed that uses fake source nodes to send fake source data packets to hide the real traffic. The simulation results show that the DIS-PLP offers well performance in terms of safety time and delay, which has strong practical significance for Smart Ocean to improve the level of network security.

An Underwater Acoustic Network Positioning Method Based on Spatial-Temporal Self-Calibration.

The emergence of underwater acoustic networks has greatly improved the potential capabilities of marine environment detection. In underwater acoustic network applications, node location is a basic and important task, and node location information is the guarantee for the completion of various underwater tasks. Most of the current underwater positioning models do not consider the influence of the uneven underwater medium or the uncertainty of the position of the network beacon modem, which will reduce the accuracy of the positioning results. This paper proposes an underwater acoustic network positioning method based on spatial-temporal self-calibration. This method can automatically calibrate the space position of the beacon modem using only the GPS position and depth sensor information obtained in real-time. Under the asynchronous system, the influence of the inhomogeneity of the underwater medium is analyzed, and the unscented Kalman algorithm is used to estimate the position of underwater mobile nodes. Finally, the effectiveness of this method is verified by simulation and sea trials.

Identification of critical nodes for the Integrated Energy Systems based on complex networks and unified energy flows

As the scale of the Integrated Energy System network continues to expand and the coupling and conversion of multiple energy flows deepens, the system becomes more complex and vulnerable, which poses new challenges to the stability of the system structure and the safety of its operation. Identifying the key nodes of the Integrated Energy System is the key to improve the stability and security of the system. In order to comprehensively analyze the node attributes in the integrated energy network, complex network theory and unified energy flow theory are applied to the static topology and dynamic operation state analysis of the Integrated Energy System, and the key node identification method of the Integrated Energy System is proposed from the attributes of node location information, global information and the impact on nodes during operation. Finally, simulation experiments are conducted with a typical Integrated Energy System to verify the effectiveness and superiority of the identification method proposed in this paper.

ESDU: An elastic stripe-based delta update method for erasure-coded cross-data center storage systems

The erasure-coded cross-data center storage system can achieve high disaster tolerance and low redundancy. But as it has large cross-data center update traffic, its data update time is long. In erasure-coded storage systems, each data object is sequentially divided into several stripes, and each stripe consists of several data packets. When erasure-coded stripes do not undergo insertion or deletion, existing work can effectively reduce cross-data center update traffic by performing delta updates—delta update methods can update the old stripe without transferring matched packets that are new and old stripes' duplicate packets with the same offset-within-stripe. However, because existing delta update methods' stripe size is fixed, when a stripe undergoes insertion or deletion, its subsequent stripes' duplicate packets' offset-within-stripe will change. In this scenery, the matched packet number is small, resulting in large cross-data center update traffic. This paper proposes an elastic stripe-based delta update method for erasure-coded cross-data center storage systems (ESDU). Under insertion or deletion, ESDS tries to avoid duplicate packets' offset-within-stripe changing (i.e., maximizing matched packets) by adjusting the stripes' size flexibly according to the duplicate packet locating result. So, it can reduce cross-data center traffic. Moreover, ESDU can optimize stripes' update topology based on the location information of storage nodes to reduce cross-data center update traffic further. In addition, we implement an erasure-coded cross-data center storage system adopting ESDU, called ECESD. Experiments with the workloads derived from EduCoder's real-world trace show that compared with the existing erasure-coded cross-data center storage system adopting the fixed stripe-based delta update method, ECESD reduces average update time by 89.6%. Moreover, compared with a replication-based storage system with a delta update method (HadoopRsync), ECESD achieves an 8.3% shorter update time and much smaller redundancy.

A Novel Anchorless Node Positioning Method for Wireless Sensor Network

Wireless sensor network sensing data is meaningless without any location information, and a large number of anchor node-based localization methods have been proposed to obtain the location information of nodes. However, this kind of method requires a certain number of anchor nodes deployed in the network and complex information interaction between nodes. Therefore, it is not ideal in positioning cost and effect. A WSN positioning method based on the steering antenna of the base station is proposed to address this problem. By rotating the orientation information of the antenna, accurate positioning can be achieved without the assistance of anchor nodes, and no signal interaction between nodes is required. Theoretically, the method is easier to be deployed in the actual scene to realize the positioning of sensor nodes. Experimental results show that the proposed method is better than most positioning methods assisted by anchor node in positioning accuracy, positioning time, and energy consumption.

An adaptive message return strategy for same direction multilane in vehicular networks

Due to the roadside unit can not fully achieve seamless coverage and the dynamic change of network topology in vehicular networks, problems such as the interruption of vehicle service, poor stability of communication link, and high network delay will be caused. To solve these problems, this paper proposes a codirectional multilane adaptive message return (CMAMR) strategy based on edge computing. Firstly, we establish a CMAMR communication model. Then, according to the movement trajectory of the receiving node, we propose a single-hop and a multi-hop message return link strategy. The former combines the single-hop link quality and node location information to construct a node selection function to assist the head node in selecting an optimal forwarding node to form a single-hop message return link. The latter assists the RSU to select the optimal multi-hop message return link in the same lane by establishing a link adaptive quality evaluation function, thus improving the user’s quality of server. The simulation results show that the link network delay of the CMAMR strategy is 14.6%, 42.86%, and 38.47% lower than those of the random with forwarding progress (RFP), most forward with fixed radius (MFR), and traditional vehicle-to-infrastructure and vehicle-to-vehicle cooperative communication (TVCC) message return strategies, respectively; correspondingly, the link reliability is at least 9.13% higher for the CMAMR strategy.

Minimizing Routing Overheads in Zone Routing Protocol (ZRP); Replacing Zone Radius with Node Location Information

There are many routing protocols suggested for use in Mobile Ad Hoc Networks (MANETs). Among them (the routing protocols) is the Zone Routing Protocol (ZRP). ZRP uses the concept of zones to ensure that proactive activities are limited to a defined area around a node. The zone is determined by the radius (hops). By limiting a proactive zone on the radius, the transmission of data packets is made effective. However, any node that resides beyond the zone, is reachable reactively. The literature review indicates that one of the shortcomings in ZRP is the overlapping of zones. Furthermore, the exercise of determining zone radius is complicated. The overlapping of zones occurs because individual nodes and their zones are determined by a hop-count radius. Consequently, this overlapping of zones leads to increased routing overhead in ZRP. In an effort to resolve zone overlapping and thus reduce routing overheads, this study suggests the application of the Location-Aided Routing (LAR) Scheme I in the Interzone Routing Protocol (IARP) scheme of ZRP. By applying LAR Scheme 1 algorithm, the need for a hop-count-based radius is eliminated. Location information of nodes in IARP ensures that zones do not overlap, resulting in reduced routing overheads. This study simulates elementary parameters of delay, drop rates, deliver ratio, and the general throughput of ZRP based on the number of nodes, the area, and the size of the data packets. The conventional algorithm of ZRP was rendered on NS-2 and while the nodal-location enhanced ZRP algorithm on OMNET++. Simulation results suggest that the positionally enhanced ZRP algorithm minimizes zonal overlapping hence better data packets throughput, minimized delay and drop rates, and augmented data packets delivery ratio.

Related Biological Factors of Mobile Sensor Network Technology on Sprint Performance

Node localization and temporal synchronization, as two key parts of each self-organized and localization-aware wireless sensor network (WSN), have been a key topic for research and applications. The initial prototype of the sensor network is the same as that of the local area network. All nodes are connected by wires, and there is a central control node. All nodes transmit data to the central node point-to-point. With the development and progress of wireless communication technology, the current sensor network has developed into a WSN. Without a central node, all nodes can communicate with each other; so, it is natural to develop positioning technology in WSN. Node positioning in wireless sensor networks refers to the process in which sensor nodes determine the location information of other nodes in the network through a certain positioning technology based on the location information of a few known nodes in the network. The principle of positioning is purely geometric in mathematics. With the in-depth promotion of WSN in the application field, there are more and more requirements for high precision positioning, the higher the positioning accuracy, the higher the requirements for network time synchronization, and the problem of node clock synchronization and high precision positioning of the node can be studied together. Solving the problem of node clock synchronization can further provide support for node positioning in a variety of different environments. As a new ultrabroadband (UWB) carrier-free communication technology with nanoscale temporal resolution, it has been widely used in high-precision node positioning systems in recent years, UWB technology is the most advanced noncarrier wireless communication technology that uses bandwidths above 1 GHz and uses nonsine wave narrow pulses from nanoseconds to picoseconds to transmit data. Therefore, it occupies a very wide spectrum. UWB technology has the advantages of low system complexity, low transmit signal power spectrum density, insensitive to channel fading, and high positioning accuracy. It is especially suitable for high-speed wireless access in dense multipath places such as indoors, providing a technical basis for the engineering implementation of high-precision positioning algorithms. However, the current situation of Chinese track and field events has not kept pace with the development of Chinese competitive sports, and even the level of individual events has a gradual decline. Therefore, it is very meaningful to study the relevant biological factors that affect sprint performance. This article analyzes the related biological factors that affect the performance of the sprint, combines the knowledge of physiology to analyze the training methods that appear in the sprint from the physiological perspective, and analyzes the related biological factors that affect the performance of the sprint. This article chooses to divide them into men’s group (3 groups) and women’s group (3 groups), with 4 people in each group. Experiment proved that after the experiment, these are the following factors: the fatigue of the nervous system, the technical difference of sprinting, the change of muscle fiber enzyme activity, the order of muscle fiber cross-sectional area and muscle activation, and the recruitment of muscle fiber types. Impact P value less than 0.05, which shows that the factors that affect sprint performance are complex, and the biological factors that affect step length can be studied through anthropometry. The impact of step frequency on sports performance is very important.

3D Localization for Mobile Node in Wireless Sensor Network

Wireless sensor network (WSN) is an emerging technology that can detect, collect, and transmit information in a specific unknown area in an unknown environment. It is currently playing an increasingly important role in the fields of national defense, medical and health, and daily life. WSN node location information is extremely important in many WSN applications. The data information collected by WSN is developed based on known node location information. The node location is one of the important issues in WSNs. Location information is very important for wireless sensors. A WSN without sensor node location information is meaningless because almost all WSN applications need to know node location information, such as animal populations, tracking research, early warning of building fires, management of goods in warehouses, and traffic monitoring systems. Several research works are underway to expand the 2D positioning algorithm in WSN to 3D regardless of the deployment structure of sensor nodes. This paper proposes an improved Savarese algorithm to the problem of singularity in WSN node localization. The proposed algorithm is a modified version of the conventional Savarese algorithm, and it solves the singularity problem and improved the positioning accuracy. Simulation results show that the proposed algorithm effectively improved system performance, and the accuracy is improved over 2.83% and 2.96% than the existing algorithms. The proposed scheme is effective for indoor environments while it can be deployed outdoor for small-scale.