Patent Publication Number: US-11025501-B1

Title: Method and system for providing seamless connectivity in an internet of things (IoT) network

Description:
TECHNICAL FIELD 
     The present subject matter relates generally to the field of Internet of Things (IoT), and more particularly, but not exclusively to a method and a system for providing seamless connectivity in an IoT network. 
     BACKGROUND 
     Nowadays, with increase in number of IoT devices in the market and their capabilities, the quantum of data getting exchanged has increased multifold. For instance, a typical manufacturing plant comprises thousands of IoT devices. Although, these IoT devices transfer small data intermittently, multiple IoT devices would be trying to access the network at the same time, which results in congestion. Such congestion not only causes loss of data, but also disables the IoT devices from transferring data. Further, the congestion may not allow the routers to process the data which is received from the IoT devices, which leads to failure in generating response signals at the right time, thereby resulting in catastrophes in the networking environment. 
     Moreover, advancements in the field of IoT demand development of new standards such as 5G to enable connectivity and near real-time transactions over the Internet with a stringent constraint on delay. Currently, to meet such demands, the IoT devices tend to access Internet by connecting with another mobile device or a nearby router to transfer the data. The routers generally form a mesh or a grid to enable the IoT devices to access Internet and connect with other devices. As an example, the IoT devices fixed to moving vehicles form a network with the IoT devices fixed to other nearby vehicles as well as fixed routers established along the route of the moving vehicles. However, support provided by mesh network for network access, largely depends on the presence of other nearby vehicles, which makes the support volatile. Also, such mesh network or grid network would possess limited bandwidth capabilities when many moving vehicles are concentrated in a region, resulting in congestion. 
     Another issue faced with the wireless routers in an IoT network is uneven distribution of load. In some instances, some wireless routers may be overloaded while other wireless routers may be underutilized depending on presence or absence of the IoT devices using them for connectivity. The fixed infrastructure i.e. network of fixed routers may not be able to completely account for dynamic concentration of IoT devices that require network access. In addition, the fixed infrastructure could be underutilized or overutilized depending on factors such as the direction of traffic of moving vehicles, that fluctuate during different times of the day. 
     Some of the existing techniques provide a method to deploy drones to ensure good connectivity, when a device is unable to get required link strength. However, ensuring good connectivity for a certain device cannot account for dynamic concentration of IoT devices that require network access, in a volatile network such as Vehicular Ad hoc Network (VANET). Some other existing techniques disclose methods of deploying drones to form an ad-hoc network during occurrence of natural calamities such as earthquake, for providing network access. However, these techniques are conditional only to certain situations such as natural calamities which also fail to account for dynamic concentration of IoT devices that require network access. 
     Currently, there is no mechanism to cater for dynamic variations in concentration of IoT devices that require network access, especially in the volatile network, apart from the fixed infrastructure. 
     The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the disclosure and should not be taken as an acknowledgement or any form of suggestion that this information forms prior art already known to a person skilled in the art. 
     SUMMARY 
     Disclosed herein is a method of providing seamless connectivity in an Internet of Things (IoT) network. The method includes receiving, by a router deployment system input data from a plurality of input sources connected in an IoT network. The plurality of input sources comprises at least one of a plurality of wireless routers and a plurality of IoT devices. Further, the plurality of wireless routers comprises at least one of a plurality of fixed routers and a plurality of portable routers. Upon receiving the input data, the method includes predicting an intensity of data traffic at each wireless router of the plurality of wireless routers based on total data to be directed by each wireless router, holding time of each of the plurality of input sources, a rate of arrival of data packets from each of the plurality of input sources, and unused buffer space of a buffer associated with each of the plurality of wireless routers. Further, the method includes estimating a need for relocating at least one portable router of the plurality of portable routers, based on the intensity of data traffic at each of the plurality of wireless routers. Finally, the method includes relocating the at least one portable router to a target location for providing a seamless connectivity in the IoT network. The target location is determined based on a location of at least one wireless router at which the intensity of data traffic exceeds at least one of a predefined load threshold and a predefined buffer filling rate threshold, and a location of each of the plurality of input sources connected to the at least one wireless router. 
     Further, the present disclosure includes a router deployment system for providing seamless connectivity in an Internet of Things (IoT) network. The router deployment system includes a processor and a memory communicatively coupled to the processor. The memory stores the processor-executable instructions, which, on execution, causes the processor to receive input data from a plurality of input sources connected in an IoT network. The plurality of input sources comprises at least one of a plurality of wireless routers and a plurality of IoT devices. Further, the plurality of wireless routers comprises at least one of a plurality of fixed routers and a plurality of portable routers. Upon receiving the input data, the processor predicts an intensity of data traffic at each wireless router of the plurality of wireless routers based on total data to be directed by each wireless router, holding time of each of the plurality of input sources, a rate of arrival of data packets from each of the plurality of input sources, and unused buffer space of a buffer associated with each of the plurality of wireless routers. Further, the processor estimates a need for relocating at least one portable router of the plurality of portable routers, based on the intensity of data traffic at each of the plurality of wireless routers. Finally, the processor relocates the at least one portable router to a target location for providing a seamless connectivity in the IoT network. The target location is determined based on a location of at least one wireless router at which the intensity of data traffic exceeds at least one of a predefined load threshold and a predefined buffer filling rate threshold, and a location of each of the plurality of input sources connected to the at least one wireless router. 
     Furthermore, the present disclosure includes a non-transitory computer readable medium including instructions stored thereon that when processed by at least one processor causes a router deployment system to perform operations comprising, receiving input data from a plurality of input sources connected in an IoT network. The plurality of input sources comprises at least one of a plurality of wireless routers and a plurality of IoT devices. The plurality of wireless routers comprises at least one of a plurality of fixed routers and a plurality of portable routers. Further, the instructions cause the processor to predict an intensity of data traffic at each wireless router of the plurality of wireless routers based on total data to be directed by each wireless router, holding time of each of the plurality of input sources, a rate of arrival of data packets from each of the plurality of input sources, and unused buffer space of a buffer associated with each of the plurality of wireless routers. Furthermore, the instructions cause the processor to estimate a need for relocating at least one portable router of the plurality of portable routers, based on the intensity of data traffic at each of the plurality of wireless routers. Furthermore, the instructions cause the processor to relocate the at least one portable router to a target location for providing a seamless connectivity in the IoT network, wherein the target location is determined based on a location of at least one wireless router at which the intensity of data traffic exceeds at least one of a predefined load threshold and a predefined buffer filling rate threshold, and a location of each of the plurality of input sources connected to the at least one wireless router. 
     The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description. 
    
    
     
       BRIEF DESCRIPTION OF THE ACCOMPANYING DIAGRAMS 
       The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which: 
         FIG. 1  shows an exemplary architecture for providing seamless connectivity in an Internet of Things (IoT) network in accordance with some embodiments of the present disclosure. 
         FIG. 2A  shows a detailed block diagram of a router deployment system for providing seamless connectivity in an Internet of Things (IoT) network in accordance with some embodiments of the present disclosure. 
         FIG. 2B - FIG. 2E  show exemplary illustrations of router deployment in accordance with some embodiments of the present disclosure; 
         FIG. 3  shows a flowchart illustrating a method of providing seamless connectivity in an Internet of Things (IoT) network in accordance with some embodiments of the present disclosure; and 
         FIG. 4  is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. 
     
    
    
     It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown. 
     DETAILED DESCRIPTION 
     In the present document, the word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment or implementation of the present subject matter described herein as “exemplary” is not necessarily be construed as preferred or advantageous over other embodiments. 
     While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure. 
     The terms “comprises”, “comprising”, “includes” or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that includes a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises . . . a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method. 
     Disclosed herein are a method and a system for providing seamless connectivity in an Internet of Things (IoT) network. In one embodiment, the present disclosure addresses the problem of accounting for dynamic concentration of IoT devices that require network access. For instance, when many IoT devices from a geo-spatial region attempt to access the Internet at a same time, there arises a need to provide additional bandwidth and accessibility for connectivity to that geo spatial region dynamically, such that, each of the IoT devices are given the opportunity to access the Internet for data transfer and to generate/receive real-time signals for critical applications. In another embodiment, the present disclosure provides support for one or more IoT devices which are out of network range, dynamically, which cannot be addressed by the fixed infrastructure in real-time. 
     A router deployment system disclosed in the present disclosure may initially receive input data from a plurality of input sources connected in an IoT network. In some embodiments, the plurality of input sources may include at least one of a plurality of wireless routers and a plurality of IoT devices. Further, in some embodiments, the plurality of wireless routers may include at least one of a plurality of fixed routers and a plurality of portable routers. Upon receiving the input data, the router deployment system may predict an intensity of data traffic at each wireless router of the plurality of wireless routers based on total data to be directed by each wireless router, holding time of each of the plurality of input sources, a rate of arrival of data packets from each of the plurality of input sources, and unused buffer space of a buffer associated with each of the plurality of wireless routers. Further, the router deployment system may estimate a need for relocating at least one portable router of the plurality of portable routers, based on the intensity of data traffic at each of the plurality of wireless routers. Based on the estimation, the router deployment system relocates the at least one portable router to a target location for providing a seamless connectivity in the IoT network. In some embodiments, the target location is determined based on a location of at least one wireless router at which the intensity of data traffic exceeds a predefined load threshold, a predefined buffer filling rate threshold, and a location of each of the plurality of input sources connected to the at least one wireless router. 
     The present disclosure ensures balanced utilization of the network resources by dynamically deploying portable routers, as per requirement, which in turn eliminates the over utilization or under utilization of the fixed or other portable routers. Further, the dynamic deployment of the portable routers is based on the intensity of data traffic at each of the wireless routers, which in turn helps in accounting for dynamic concentration of IoT devices in a certain location, requiring network access. Therefore, the present disclosure caters to the real-time networking requirements of the IoT devices. Additionally, the present disclosure predicts the intensity of data traffic at each of the wireless routers, based on current input data. This type of prediction enables futuristic decision making related to dynamic deployment of portable routers, to adaptively manage the load on the wireless routers, in real-time. 
     A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the disclosure. 
     In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense. 
       FIG. 1  shows an exemplary architecture for providing seamless connectivity in an Internet of Things (IoT) network in accordance with some embodiments of the present disclosure. 
     The architecture  100  includes a plurality of input sources  101  and a router deployment system  103 . The plurality of input sources  101  may include any source which contributes to data traffic or directs data in an IoT network. In some embodiments, the plurality of input sources  101  may include, but not limited to, wireless router  105   1  to wireless router  105   n  (collectively referred as a plurality of wireless routers  105 ) and IoT device  107   1  to IoT device  107   n  (collectively referred as a plurality of IoT devices  107 ). In some embodiments, the plurality of wireless routers  105  may further include, but not limited to, fixed router  105 -A 1  to fixed router  105 -A n  (collectively referred as a plurality of fixed routers  105 -A) and portable router  105 -B 1  to portable router  105 -B n  (collectively referred as a plurality of portable routers  105 -B). In some embodiments, the plurality of IoT devices  107  and the plurality of wireless routers  105  may be connected to form an IoT network. As an example, the IoT network may be implemented in the form of a wireless mesh network or a grid network. 
     The plurality of IoT devices  107  may transfer data via the plurality of wireless routers  105  based on their requirement and accessibility. As an example, the plurality of IoT devices  107  may include, but not limited to, sensors, mobile phones, smart wearable devices such as smart watches, smart appliances such as television, speakers, refrigerators and the like, monitoring devices, tracking devices, surveillance devices, and the like, which are capable of forming an IoT network. In some embodiments, the plurality of fixed routers  105 -A may be initially deployed at certain locations based on pattern of traffic of moving vehicles. As an example, a location “ABC” may have high amount of traffic between 10 AM-3 PM in a day. Therefore, based on this pattern of traffic, a fixed router  105 -A may be established in the location “ABC” to cater network requirement of the high amount of traffic in the location “ABC”. In another example, a manufacturing plant or a technology park configured with numerous IoT devices  107  is situated in a location. Therefore, high amount of data traffic can be expected in such location due to transmission of data by each of the numerous IoT devices  107 . Hence, fixed routers  105 -A may be established in such location to cater network requirement of the numerous IoT devices  107 . Further, in some embodiments, the plurality of portable routers  105 -B may be deployed at different locations proximal to the deployment location of the plurality of fixed routers  105 -A. The plurality of portable routers  105 -B may be dynamically relocated to a target location based on intensity of data traffic at each of the plurality of wireless routers  105 . In some embodiments, the plurality of portable routers  105 -B may be detachably attached to an unmanned aerial vehicle i.e. the unmanned aerial vehicle may fetch the plurality of portable routers  105 -B from one location and deploy in another location. In some other embodiments, the plurality of portable routers  105 -B may be configured in the unmanned aerial vehicle. For instance, the plurality of portable routers  105 -B may be fixed to the unmanned aerial vehicle. As an example, the unmanned aerial vehicle may include, but is not limited to, a drone. In some embodiments, the plurality of inputs sources  101  i.e. the plurality of wireless routers  105  and the plurality of IoT devices  107  may be associated with the router deployment system  103  via a communication network (not shown in the  FIG. 1 ). In some embodiments, the communication network may be a wireless communication network. 
     The router deployment system  103  may include, a processor  109 , an Input/Output (I/O) interface  111  and a memory  113 . The I/O interface  111  may be configured to receive input data from the plurality of input sources  101 . As an example, the input data may include, but not limited to, status of a buffer associated with each of the plurality of wireless routers  105  at a given time instance, rate of filling of the buffer associated with each of the plurality of wireless routers  105 , number of the plurality of input sources  101  directing data through each of the plurality of wireless routers  105 , holding time of each of the plurality of input sources  101 , data rate corresponding to each of the plurality of input sources  101  and size of data transmitted by each of the plurality of input sources  101 . Upon receiving the input data, the processor  109  may predict an intensity of data traffic at each wireless router  105  of the plurality of wireless routers  105 . In some embodiments, the processor  109  may predict the intensity of data traffic based on total data to be directed by each wireless router  105 , the holding time of each of the plurality of input sources  101 , a rate of arrival of data packets (also referred as data rate in this disclosure) from each of the plurality of input sources  101 , and unused buffer space of the buffer associated with each of the plurality of wireless routers  105 . 
     Further, the processor  109  may estimate a need for relocating at least one portable router  105 -B of the plurality of portable routers  105 -B, based on the intensity of data traffic at each of the plurality of wireless routers  105 . Based on the estimation, the processor  109  may relocate the at least one portable router  105 -B to a target location for providing a seamless connectivity in the IoT network. In some embodiments, the processor  109  may determine the target location based on a location of at least one wireless router  105  at which the intensity of data traffic exceeds a predefined load threshold and a predefined buffer filling rate threshold, and a location of each of the plurality of input sources  101  connected to the at least one wireless router  105 . In some embodiments, the processor  109  may determine an optimal relocation path for relocating the at least one portable router  105 -B to the target location. 
       FIG. 2A  shows a detailed block diagram of a router deployment system for providing seamless connectivity in an Internet of Things (IoT) network in accordance with some embodiments of the present disclosure. 
     In some implementations, the router deployment system  103  may include data  203  and modules  205 . As an example, the data  203  is stored in a memory  113  configured in the router deployment system  103  as shown in the  FIG. 2A . In one embodiment, the data  203  may include input data  207 , predicted data  209 , relocation data  211  and other data  213 . In the illustrated  FIG. 2A , modules  205  are described herein in detail. 
     In some embodiments, the data  203  may be stored in the memory  113  in form of various data structures. Additionally, the data  203  can be organized using data models, such as relational or hierarchical data models. The other data  213  may store data, including temporary data and temporary files, generated by the modules  205  for performing the various functions of the router deployment system  103 . 
     In some embodiments, the data  203  stored in the memory  113  may be processed by the modules  205  of the router deployment system  103 . The modules  205  may be stored within the memory  113 . In an example, the modules  205  communicatively coupled to the processor  109  configured in the router deployment system  103 , may also be present outside the memory  113  as shown in  FIG. 2A  and implemented as hardware. As used herein, the term modules  205  may refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. 
     In some embodiments, the modules  205  may include, for example, a receiving module  221 , an intensity predicting module  223 , an estimating module  225 , a relocating module  227 , a router movement module  229 , a learning module  231  and other modules  233 . The other modules  233  may be used to perform various miscellaneous functionalities of the router deployment system  103 . It will be appreciated that such aforementioned modules  205  may be represented as a single module or a combination of different modules. 
     In some embodiments, the receiving module  221  may receive the input data  207  from a plurality of input sources  101  connected in an IoT network. As an example, the plurality of input sources  101  may include, but not limited to, a plurality of wireless routers  105  and a plurality of IoT devices  107 . In some embodiments, the plurality of wireless routers  105  may include, but not limited to, a plurality of fixed routers  105 -A and a plurality of portable routers  105 -B. As an example, the input data  207  may include, but not limited to, status of a buffer associated with each of the plurality of wireless routers  105  at a given time instance, rate of filling of the buffer associated with each of the plurality of wireless routers  105 , number of the plurality of input sources  101  directing data through each of the plurality of wireless routers  105 , holding time of each of the plurality of input sources  101 , data rate corresponding to each of the plurality of input sources  101  and size of data transmitted by each of the plurality of input sources  101 . 
     Further, in some embodiments, the intensity predicting module  223  may predict an intensity of data traffic at each wireless router  105  of the plurality of wireless routers  105 . Intensity of data traffic at each wireless router  105  indicates a probability of packet drop when the data packets are routed through the corresponding wireless router  105 . In some embodiments, the intensity of data traffic is predicted based on total data to be directed by each wireless router  105 , holding time of each of the plurality of input sources  101 , a rate of arrival of data packets from each of the plurality of input sources  101 , and unused buffer space of a buffer associated with each of the plurality of wireless routers  105 . In some embodiments, the intensity predicting module  223  may determine unused buffer space of the buffer associated with each of the plurality of wireless routers  105  based on a relevance factor of each of the plurality of input sources  101  associated with the corresponding wireless router  105 . The relevance factor of each input source  101  may indicate total buffer space required by the input source  101  to transmit the total data through the corresponding wireless router  105 . 
     In some embodiments, the intensity predicting module  223  may determine the relevance factor of each input source  101  associated with the corresponding wireless router  105  using the below Equation 1.
 
RF( S   n )=[ TD ( S   n )/ TD ( B )]  Equation 1
 
     In the above Equation 1, 
     RF(S n ) indicates relevance factor of an input source S n , where n=0, 1, 2 - - - n; 
     TD(S n ) indicates total size of data to be transmitted by an input source S n  through the wireless router  105 ; and 
     TD(B) indicates total buffer size of the buffer associated with the wireless router  105 . 
     Thereafter, the intensity predicting module  223  may determine the unused buffer space of the buffer associated with each of the plurality of wireless routers  105  using the below Equation 2.
 
Unused buffer space of the buffer=RF( S   1 )+RF( S   2 )+ - - - RF( S   n )  Equation 2
 
In the above Equation 2,
 
“RF(S 1 )+RF(S 2 )+ - - - RF(S n )” indicates sum of relevance factor of each input source  101  associated with the corresponding wireless router  105 .
 
     In some embodiments, the sum of relevance factor of each of the plurality of input sources  101  associated with the wireless router  105  should be less than 1. When the sum of relevance factors is equal to 1, the intensity predicting module  223  may infer that the buffer space of the buffer associated with the wireless router  105  is completely occupied. When the sum of relevance factors is less than 1 but close to 1, for instance, when relevance factor is greater than 0.6, the intensity predicting module  223  may infer that the buffer space of the buffer associated with the wireless router  105  is fast filling i.e. the corresponding wireless router may be considered to be approaching a state of being overloaded. When the sum of the relevance factor is less than 1, for instance, less than or equal to 0.6, the intensity predicting module  223  may infer that the buffer space of the buffer associated with the wireless router  105  is less occupied. 
     Therefore, based on the total data to be directed by each wireless router  105 , holding time of each of the plurality of input sources  101 , a rate of arrival of data packets from each of the plurality of input sources  101 , and unused buffer space of the buffer associated with each of the plurality of wireless routers  105 , the intensity predicting module  223  may predict the intensity of data traffic at each of the plurality of wireless routers  105 . The intensity of the data traffic predicted for each of the plurality of wireless routers  105  may be stored as the predicted data  209 . In some embodiments, the intensity predicting module  223  may predict the intensity of data traffic using one or more machine learning techniques. As an example, the one or more machines learning techniques may include, but not limited to, Long Short Term Memory (LSTM) techniques and Multilayer perceptron techniques. Further, in some embodiments, the prediction of the intensity of data traffic is at least one of a location-wise prediction and a time-wise prediction. 
     Further, in some embodiments, the estimating module  225  may estimate a need for relocating at least one portable router  105 -B of the plurality of portable routers  105 -B, based on the intensity of data traffic at each of the plurality of wireless routers  105 . The processor  109  may estimate the need for relocating the at least one portable router  105 -B, when the intensity of data traffic at the plurality of wireless routers  105  exceeds either a predefined load threshold or a predefined buffer filling rate threshold associated with the corresponding wireless router  105 . 
     In some embodiments, when the intensity of data traffic at the plurality of wireless routers  105  exceeds the predefined load threshold, the estimating module  225  may infer that the corresponding plurality of wireless routers  105  are overloaded and are incapable of handling additional load. In such scenarios, the corresponding plurality of wireless routers  105  may fail to function properly, for instance, may not be able to process data packets, may not be able to generate real-time signals at the right time, may delay transmission of the data packets and the like. To handle such scenarios, the estimating module  225  may estimate the need to relocate at least one portable router  105 -B to share load of the plurality of wireless routers  105  at which the intensity of data traffic has exceeded the predefined load threshold. 
     In some embodiments, when the intensity of data traffic at the plurality of wireless routers  105  exceeds the predefined buffer filling rate threshold, the estimating module  225  may infer that the corresponding plurality of wireless routers  105  are potential overloaded routers. In such scenarios, the estimating module  225  may estimate the need to relocate at least one portable router  105 -B to share load of the plurality of wireless routers  105  at which the intensity of data traffic has exceeded the predefined buffer filling rate threshold, before the potential overloaded routers reach the state of being overloaded. 
     Further, in some embodiments, the relocating module  227  may initially determine the at least one portable router  105 -B among the plurality of portable routers  105 -B to be relocated. In one embodiment, the relocating module  227  may determine the at least one portable router  105 -B based on specification (capability) of the at least one portable router  105 -B and a current location of the at least one portable router  105 -B. In some embodiments, the specification of the plurality of portable routers  105 -B may be different. The at least one portable router  105 -B may be selected by verifying whether the specification of the at least one portable router  105 -B is capable of handling the network requirement of the at least one wireless router  105 . For instance, if the network requirement demands a data rate of 10 Mbps, then the relocating module  227  may determine at least one portable router  105 -B among the plurality of portable routers  105 -B which is capable of handling the data rate of 10 Mbps. Upon determining the at least one portable router  105 -B to be relocated, the relocating module  227  may determine a target location for relocating the at least one portable router  105 -B, when the need for relocating at least one portable router  105 -B is estimated. In some embodiments, the relocating module  227  may determine the target location based on a location of the at least one wireless router  105  at which the intensity of data traffic exceeds at least the predefined load threshold and the predefined buffer filling rate threshold, and a location of each of the plurality of input sources  101  connected to the at least one wireless router  105 . Generally, when the plurality of input sources  101  are re-routed from one wireless router  105  to another portable router  105 -B, the re-routed plurality of input sources  101  may face reduction in signal strength, due to distance factor between the portable router  105 -B and the re-routed plurality of input sources  101 . Therefore, the relocating module  227  determines an optimal target location which ensures maintenance of signal strength even when the plurality of input sources  101  are re-routed from one wireless router  105  to another portable router  105 -B. In some embodiments, the target location of the at least one portable router  105 -B may be determined in a manner that, the at least one portable router  105 -B may share the load of more than one wireless router  105 . In some embodiments, when more than one portable router  105 -B exist proximal to the at least one fixed router  105 -A, then the relocating module  227  may determine a corresponding target location for each of these portable routers  105 -B to ensure extraction of maximum network support from each of these portable routers  105 -B. The relocating module  227  may determine the target location using one or more optimization techniques such as simulated annealing technique. In some embodiments, the target location may be in the form of latitude and longitude co-ordinates. 
     Upon determining the optimal target location, the relocating module  227  may determine an optimal relocation path for relocating the at least one portable router  105 -B to the target location from a current location of the at least one portable router  105 -B. In some embodiments, the relocating module  227  may employ one or more shortest path techniques to determine the optimal path to the target location. Thereafter, the relocating module  227  may transmit the relocation data  211  including the target location and the optimal path to the target location, to the at least one portable router  105 -B, for relocating the at least one portable router  105 -B from the current location to the target location. 
     Further, in some embodiments, the router movement module  229  may generate control signals in real-time to physically route the at least one portable router  105 -B based on the relocation data  211 . In some embodiments, the at least one portable router  105 -B may be detachably attached to an unmanned aerial vehicle i.e. the unmanned aerial vehicle may fetch the at least one portable router  105 -B from the current location and deploy in the target location. In some other embodiments, the at least one portable router  105 -B may be configured in the unmanned aerial vehicle. For instance, the at least one portable router  105 -B may be fixed to the unmanned aerial vehicle. As an example, the unmanned aerial vehicle may include, but not limited to, a drone. In some embodiments, the unmanned aerial vehicle relocating the at least one portable router  105 -B may hover at the target location to provide network assistance to the at least one wireless router  105  at which the intensity of data traffic has exceeded a predefined load threshold or a predefined buffer filling rate threshold. In some other embodiments, the unmanned aerial vehicle may temporarily fix the portable router  105 -B at a parking point proximal to the target location. As an example, the parking point may be a point on a structure such as a building, vehicle stranded in traffic, light post and the like, which can temporarily accommodate the at least one portable router  105 -B. 
     Further, in some embodiments, the learning module  231  may initiate a self-learning process based on each relocation. In some embodiments, the learning module  231  may learn traffic patterns such as traffic volume of moving vehicles in different regions at different instances of time in a day, regions congested with high traffic volume, low traffic volume, moderate traffic volume of moving vehicles at different instances of time in a day and the like. In other embodiments, the self-learning process may be related to deployment of the plurality of portable routers  105 -B. As an example, the learning module  231  may learn deployment positions such as parking points in various target locations, based on each relocation. In yet other embodiments, the self-learning process may be related to learning different locations where the transmission of data through the IoT devices  107  is high, such as manufacturing plants, tech parks and the like. 
     In some embodiments, the plurality of portable routers  105 -B may be relocated to support plurality of fixed routers  105 -A that are overloaded. In some other embodiments, the plurality of portable routers  105 -B may be relocated to support the at least one relocated portable router  105 -B that is overloaded. In yet other embodiments, the plurality of portable routers  105 -B may be relocated to support the potential overloaded routers among the plurality of fixed routers  105 -A and the plurality of portable routers  105 -B. 
     Henceforth, the process of providing seamless connectivity in the IoT network is explained with the help of one or more examples for better understanding of the present disclosure. However, the one or more examples should not be considered as limitation of the present disclosure. 
     Consider an exemplary illustration as shown in the  FIG. 2B , where a fixed router  105 -A 1  is providing network access to vehicles stranded in a traffic signal. Consider the fixed router  105 -A 1  has a bandwidth of 150 Mbps to 900 Mbps, and is capable of supporting upto an Internet Service Provider (ISP) connection speed of 75 Mbps. Further, in this scenario, the IoT devices present in each vehicle act as an input source  101 . Therefore, for the sake of illustration, each vehicle is indicated as the input source  1011 ,  101   2 ,  101   3 , - - -  101   25  in the  FIG. 2B . 
     Further, consider  25  vehicles (vehicle  101   1  to vehicle  101   25 ) are stranded in the traffic signal and the IoT devices present in each vehicle consume 3 Mbps to watch a video. In this scenario, the number of vehicles stranded in the traffic signal is high, and there exists only one fixed router  105 -A 1  at a distance, that is capable of serving the network requirement of the IoT devices associated with the vehicles. Therefore, the vehicles may not get adequate bandwidth or signal strength or network connectivity which is required for watching the video at a data rate of 3 Mbps. Further, since there is only one fixed router  105 -A 1  to serve the network requirement of each vehicle stranded in the signal, the sum of relevance factors of each input source  101  for the fixed router  105 -A would be high. As an example, in this scenario, consider the sum of relevance factors of each input source is 0.9, which is close to 1. Further, when the router deployment system  103  predicts the intensity of the data traffic at the fixed router  105 -A 1 , consider the intensity of data traffic at the fixed router  105 -A 1  exceeds the predefined load threshold. The router deployment system  103  therefore estimates that the fixed router  105 -A 1  is overloaded and there is a need for relocating at least one portable router to reduce the network load on the fixed router  105 -A 1 . As shown in the  FIG. 2B , there are two portable routers  105 -B 1  and  105 -B 2  located at two different points. As shown in the  FIG. 2B , portable router  105 -B 1  is proximal to the fixed router  105 -A 1  when compared to the portable router  105 -B 2 . However, the specification of the portable router  105 -B 1  is such that, it may not be able to handle the network requirement of the input sources  101  associated with the fixed router  105 -A 1 , but specification of the portable router  105 -B 2  is capable of handling the network requirement of the input sources  101 . Therefore, though the portable router  105 -B 1  is closer to the fixed router  105 -A 1  than the portable router  105 -B 2 , based on the specification, the router deployment system  103  selects the portable router  105 -B 2  for the purpose of relocating. 
     Upon selecting the portable router  105 -B 2  for the purpose of relocating, the router deployment system  103  determines the target location  1  i.e. the location at which the portable router  105 -B 2  is to be deployed after relocation and an optimal relocation path to reach the target location  1 . Based on the previous learning, the router deployment system  103  detects a parking point on a building at the target location  1 , where the portable router  105 -B 2  can be deployed. Accordingly, the router deployment system  103  routes the portable router  105 -B 2  to the target location  1  via the optimal relocation path and deploys the portable router  105 -B 2  at the detected parking point, to share the network load of the fixed router  105 -A 1  as shown in the  FIG. 2C . 
     Further, consider a scenario, that after relocation as mentioned in above paragraph, the portable router  105 -B 2  is proximal to another fixed router  105 -A 2  which is overloaded, as shown in the  FIG. 2D . Consider, the portable router  105 -B 2  is under utilized and is capable of handling additional network load of the fixed router  105 -A 2 . In such scenarios, instead of relocating another portable router, the portable router  105 -B 2  can be re-positioned in a manner that, the portable router  105 -B 2  is able to share network load of both the fixed routers  105 -A 1  and  105 -A 2 . Therefore, as shown in the  FIG. 2E , the portable router  105 -B 2  is repositioned from target location  1  to target location  2 . Since, there is no parking point detected at the target location  2 , the unmanned aerial vehicle comprising the portable router  105 -B 2  hovers at the target location  2  to share the network load of both the fixed routers  105 -A 1  and  105 -A 2 . 
       FIG. 3  shows a flowchart illustrating a providing seamless connectivity in an Internet of Things (IoT) network in accordance with some embodiments of the present disclosure. 
     As illustrated in  FIG. 3 , the method  300  includes one or more blocks illustrating a method of providing seamless connectivity in an Internet of Things (IoT) network. The method  300  may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform functions or implement abstract data types. 
     The order in which the method  300  is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method  300 . Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described herein. Furthermore, the method  300  can be implemented in any suitable hardware, software, firmware, or combination thereof. 
     At block  301 , the method  300  may include receiving, by a processor  109  of the router deployment system  103 , input data  207  from a plurality of input sources  101  connected in an IoT network. In some embodiments, the plurality of input sources  101  may include, but not limited to, a plurality of wireless routers  105  and a plurality of IoT devices  107 . The plurality of wireless routers  105  may include, but not limited to, a plurality of fixed routers  105 -A and a plurality of portable routers  105 -B. 
     At block  303 , the method  300  may include predicting, by the processor  109 , an intensity of data traffic at each wireless router  105  of the plurality of wireless routers  105  based on total data to be directed by each wireless router  105 , holding time of each of the plurality of input sources  101 , a rate of arrival of data packets from each of the plurality of input sources  101 , and unused buffer space of a buffer associated with each of the plurality of wireless routers  105 . In some embodiments, the intensity of the data traffic is determined using one or more machine learning techniques. As an example, the one or more machines learning techniques may include, but not limited to, Long Short Term Memory (LSTM) techniques and Multilayer perceptron techniques. 
     At block  305 , the method  300  may include, estimating, by the processor  109 , a need for relocating at least one portable router  105 -B of the plurality of portable routers  105 -B, based on the intensity of data traffic at each of the plurality of wireless routers  105 . In some embodiments, the processor  109  may estimate the need for relocating by estimating whether the intensity of data traffic at each of the plurality of wireless routers  105  exceeds at least one of a predefined load threshold and a predefined buffer filling rate threshold associated with each wireless router  105 . 
     At block  307 , the method  300  may include, relocating, by the processor  109 , at least one portable router  105 -B to a target location for providing a seamless connectivity in the IoT network. In some embodiments, the target location is determined based on a location of at least one wireless router  105  at which the intensity of data traffic exceeds at least the predefined load threshold and the predefined buffer filling rate threshold, and a location of each of the plurality of input sources  101  connected to the at least one wireless router  105 . In some embodiments, the processor  109  may determine the at least one portable router  105 -B to be relocated based on specification of the at least one portable router  105 -B and a current location of the at least one portable router  105 -B. Further, the processor  109  may determine an optimal relocation path for relocating the at least one portable router  105 -B to the target location. 
       FIG. 4  is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. 
     In some embodiments,  FIG. 4  illustrates a block diagram of an exemplary computer system  400  for implementing embodiments consistent with the present invention. In some embodiments, the computer system  400  can be router deployment system  103  that is used for providing seamless connectivity in an Internet of Things (IoT) network. The computer system  400  may include a central processing unit (“CPU” or “processor”)  402 . The processor  402  may include at least one data processor for executing program components for executing user or system-generated business processes. A user may include a person, a person using a device such as those included in this invention, or such a device itself. The processor  402  may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. 
     The processor  402  may be disposed in communication with input devices  411  and output devices  412  via I/O interface  401 . The I/O interface  401  may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial bus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE), WiMax, or the like), etc. 
     Using the I/O interface  401 , computer system  400  may communicate with input devices  411  and output devices  412 . 
     In some embodiments, the processor  402  may be disposed in communication with a communication network  409  via a network interface  403 . The network interface  403  may communicate with the communication network  409 . The network interface  403  may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. Using the network interface  403  and the communication network  409 , the computer system  400  may communicate with a wireless router  105   1  to a wireless router  105   n  (also referred as a plurality of wireless routers  105 ) and an IoT device  107   1  to an IoT device  107   n  (also referred as a plurality of IoT devices  107 ). Further, the communication network  409  can be implemented as one of the different types of networks, such as intranet or Local Area Network (LAN), Closed Area Network (CAN) and such within the autonomous vehicle. The communication network  409  may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), CAN Protocol, Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network  409  may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc. In some embodiments, the processor  402  may be disposed in communication with a memory  405  (e.g., RAM, ROM, etc. not shown in  FIG. 4 ) via a storage interface  404 . The storage interface  404  may connect to memory  405  including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fibre channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc. 
     The memory  405  may store a collection of program or database components, including, without limitation, a user interface  406 , an operating system  407 , a web browser  408  etc. In some embodiments, the computer system  400  may store user/application data, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase. 
     The operating system  407  may facilitate resource management and operation of the computer system  400 . Examples of operating systems include, without limitation, APPLE® MACINTOSH® OS X®, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTION® (BSD), FREEBSD®, NETBSD®, OPENBSD, etc.), LINUX® DISTRIBUTIONS (E.G., RED HAT®, UBUNTU®, KUBUNTU®, etc.), IBM® OS/2®, MICROSOFT® WINDOWS® (XP®, VISTA/7/8, 10 etc.), APPLE® IOS®, GOOGLE™ ANDROID™, BLACKBERRY® OS, or the like. The User interface  406  may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to the computer system  400 , such as cursors, icons, checkboxes, menus, scrollers, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed, including, without limitation, Apple® Macintosh® operating systems&#39; Aqua®, IBM® OS/2®, Microsoft® Windows® (e.g., Aero, Metro, etc.), web interface libraries (e.g., ActiveX®, Java®, Javascript®, AJAX, HTML, Adobe® Flash®, etc.), or the like. 
     In some embodiments, the computer system  400  may implement the web browser  408  stored program components. The web browser  408  may be a hypertext viewing application, such as MICROSOFT® INTERNET EXPLORER®, GOOGLE™ CHROME™, MOZILLA® FIREFOX®, APPLE® SAFARI®, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browsers  408  may utilize facilities such as AJAX, DHTML, ADOBE® FLASH®, JAVASCRIPT®, JAVA®, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system  400  may implement a mail server stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as Active Server Pages (ASP), ACTIVEX®, ANSI® C++/C #, MICROSOFT®, .NET, CGI SCRIPTS, JAVA®, JAVASCRIPT®, PERL PHP, PYTHON®, WEBOBJECTS®, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT® exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system  400  may implement a mail client stored program component. The mail client may be a mail viewing application, such as APPLE® MAIL, MICROSOFT® ENTOURAGE®, MICROSOFT® OUTLOOK®, MOZILLA® THUNDERBIRD®, etc. 
     Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media. 
     The present disclosure provides a method and a system for providing seamless connectivity in an Internet of Things (IoT) network. 
     The present disclosure ensures balanced utilization of the network resources by dynamically deploying portable routers, as per requirement, which in turn eliminates the over utilization or under utilization of the fixed or other portable routers. 
     The dynamic deployment of the portable routers in the present disclosure is based on the intensity of data traffic at each of the wireless routers, which in turn helps in accounting for dynamic concentration of IoT devices in a certain location, requiring network access. 
     The present disclosure caters to the real-time networking requirements of the IoT devices. 
     Additionally, the present disclosure predicts the intensity of data traffic at each of the wireless routers, based on current input data. This type of prediction enables futuristic decision making related to dynamic deployment of portable routers, to adaptively manage the load on the wireless routers, in real-time. 
     A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention. When a single device or article is described herein, it will be apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be apparent that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself. 
     The specification has described a method and a system for providing seamless connectivity in an IoT network. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that on-going technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. 
     Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims. 
     
       
         
           
               
            
               
                   
               
               
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                 Reference 
                   
               
               
                 Number 
                 Description 
               
               
                   
               
               
                 100 
                 Architecture 
               
               
                 101 
                 Plurality of input sources 
               
               
                 103 
                 Router deployment system 
               
               
                 105 
                 Plurality of wireless routers 
               
               
                 107 
                 Plurality of IoT devices 
               
               
                 109 
                 Processor 
               
               
                 111 
                 I/O interface 
               
               
                 113 
                 Memory 
               
               
                 203 
                 Data 
               
               
                 205 
                 Modules 
               
               
                 207 
                 Input data 
               
               
                 209 
                 Predicted data 
               
               
                 211 
                 Relocation data 
               
               
                 213 
                 Other data 
               
               
                 221 
                 Receiving module 
               
               
                 223 
                 Intensity predicting module 
               
               
                 225 
                 Estimating module 
               
               
                 227 
                 Relocating module 
               
               
                 229 
                 Router movement module 
               
               
                 231 
                 Learning module 
               
               
                 233 
                 Other modules 
               
               
                 400 
                 Exemplary computer system 
               
               
                 401 
                 I/O Interface of the exemplary computer system 
               
               
                 402 
                 Processor of the exemplary computer system 
               
               
                 403 
                 Network interface 
               
               
                 404 
                 Storage interface 
               
               
                 405 
                 Memory of the exemplary computer system 
               
               
                 406 
                 User interface 
               
               
                 407 
                 Operating system 
               
               
                 408 
                 Web browser 
               
               
                 409 
                 Communication network 
               
               
                 411 
                 Input devices 
               
               
                 412 
                 Output devices