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A Multi-Layer Feedback System Approach toResilient Connectivity of Remotely Deployed Mobile Internet of Things ABSTRACT Enabling the Internet of things in remote environments without traditional communication infrastructure requires a multi-layer network architecture. Devices in the overlay network such as unmanned aerial vehicles (UAVs) are required to provide coverage to underlay devices as well as remain connected to other overlay devices to exploit device-to-device (D2D) communication. The coordination, planning, and design of such overlay networks constrained by the underlay devices is a challenging problem. Existing frameworks for placement of UAVs do not consider the lack of backhaul connectivity and the need for D2D communication. Furthermore, they ignore the dynamical aspects of connectivity in such networks which presents additional challenges. For instance, the connectivity of devices can be affected by changes in the network, e.g., the mobility of underlay devices or unavailability of overlay devices due to failure or adversarial attacks. To this end, this work proposes a feedback based adaptive, self-configurable, and resilient framework for the overlay network that cognitively adapts to the changes in the network to provide reliable connectivity between spatially dispersed smart devices. Results show that the proposed framework requires significantly lower number of aerial base stations to provide higher coverage and connectivity to remotely deployed mobile devices as compared to existing approaches. EXISTING SYSTEM : Connectivity between smart devices is vital in enabling the emerging paradigm of the Internet of things (IoT) . The fundamental goal of the IoT is to inter-connect smart objects so that they can exchange data and leverage the capabilities of each other to achieve individual and/or network objectives such as high situational awareness, efficiency, accuracy and revenue, etc. This connectivity relies on wireless communication networks which have their limitations based on the communication technologies involved. Existing IoT devices are connected to an access point using wireless personal area network (WPAN) technologies such as WiFi, Bluetooth, Zigbee, etc. The access points are in-turn connected to the wired or wireless backhaul networks using wide area network (WAN) technologies . The backhaul network enables connectivity and accessibility between things that are geographically separated. However, they may not always be available such as in remote areas , disaster struck areas, and battlefields. Unmanned aerial vehicles (UAVs) and mobile ground stations are the most viable candidates for providing connectivity in such situations. For instance, during the hurricane Harvey, nearly 95% of the cellular sites in Rockport, Texas went out of service resulting in nearly a complete communication blackout in the region . In such emergency scenarios, where the traditional communication infrastructure is completely devastated, UAVs can prove to be a promising solution to help create a temporary network and resume connectivity in a short span of time. Therefore, there is a growing interest towards the use of drones and UAVs as mobile aerial base stations (BSs) to assist existing cellular LTE networks , public safety networks , and intelligent transportation systems . While this is promising in urban areas where there is high availability of cellular networks which can be used to connect the UAVs to the backhaul, it might not be possible in rural and/or remote regions. Existing frameworks for placement of UAVs do not consider the lack of backhaul connectivity and the need for D2D communication. Furthermore, they ignore the dynamical aspects of connectivity in such networks which presents additional challenges. For instance, the connectivity of devices can be affected by changes in the network, e.g., the mobility of underlay devices or unavailability of overlay devices due to failure or adversarial attacks. PROPOSED SYSTEM : The problem in such settings is to efficiently deploy the overlay network that provides coverage to all the MSDs as well as maintaining connectivity between the MAPs. Since the MSDs can be located in spatial clusters that are arbitrarily separated, the MAPs should be deployed in a way that they remain connected, i.e., each MAP is reachable from other MAPs using D2D communications. This requirement makes it a challenging network planning and design problem.It provides a macroscopic view of one such scenario where the MAPs are appropriately deployed enabling a local inter-network of MSDs without any traditional communication infrastructure. It can be easily connected to the Internet to achieve pervasive connectivity and control over the MSDs. Note that with aerial MAPs, there is an added flexibility to position the BSs arbitrarily in space which might not be possible with other traditional types of access points. They ignore the dynamical aspects of connectivity in such networks which presents additional challenges. For instance, the connectivity of devices can be affected by changes in the network, e.g., the ,mobility of underlay devices or unavailability of overlay devices due to failure or adversarial attacks. To this end, this work proposes a feedback based adaptive, self-configurable, and resilient framework for the overlay network that cognitively adapts to the changes in the network to provide reliable connectivity between spatially dispersed smart devices. CONCLUSION: we present a cognitive connectivity framework that is able to reconfigure itself automatically in a distributed manner to interconnect spatially dispersed smart devices thus enabling the Internet of things in remote environments. Resilience of connectivity has been investigated in response to the mobility of the underlay network as well as random device failures in the overlay network. It is shown that if sufficient number of overlay devices are available, then the developed distributed framework leads to high network connectivity which is resilient to mobility and device failures. However, if sufficient overlay devices are not deployed, the framework tends to provide connectivity locally to the devices in each cluster of the underlay network. A comparison of the proposed approach with existing approaches for placement of BSs reveals significant superiority in terms of the number of BSs required to achieve coverage and the overall connectivity of the devices. We believe that this work provides a useful platform for the development of more sophisticated and efficient algorithms to achieve a variety of objectives in aerial communications using UAVs. Future directions in this work can investigate on ways to make the framework completely distributed. The local observations of MAPs can be used to form a consensus about the locations of the MSDs. Another possible direction to this line of research is to allow MAPs to operate in multiple modes to enable connectivity between a diverse pattern of locations of the MSDs.

A Multi-Layer Feedback System Approach toResilient Connectivity of Remotely Deployed Mobile Internet of Things ABSTRACT Enabling the Internet of things in remote environments without traditional communication infrastructure requires a multi-layer network architecture. Devices in the overlay network such as unmanned aerial vehicles (UAVs) are required to provide coverage to underlay devices as well as remain connected to other overlay devices to exploit device-to-device (D2D) communication. The coordination, planning, and design of such overlay networks constrained by the underlay devices is a challenging problem. Existing frameworks for placement of UAVs do not consider the lack of backhaul connectivity and the need for D2D communication. Furthermore, they ignore the dynamical aspects of connectivity in such networks which presents additional challenges. For instance, the connectivity of devices can be affected by changes in the network, e.g., the mobility of underlay devices or unavailability of overlay devices due to failure or adversarial attacks. To this end, this work proposes a feedback based adaptive, self-configurable, and resilient framework for the overlay network that cognitively adapts to the changes in the network to provide reliable connectivity between spatially dispersed smart devices. Results show that the proposed framework requires significantly lower number of aerial base stations to provide higher coverage and connectivity to remotely deployed mobile devices as compared to existing approaches. EXISTING SYSTEM : Connectivity between smart devices is vital in enabling the emerging paradigm of the Internet of things (IoT) . The fundamental goal of the IoT is to inter-connect smart objects so that they can exchange data and leverage the capabilities of each other to achieve individual and/or network objectives such as high situational awareness, efficiency, accuracy and revenue, etc. This connectivity relies on wireless communication networks which have their limitations based on the communication technologies involved. Existing IoT devices are connected to an access point using wireless personal area network (WPAN) technologies such as WiFi, Bluetooth, Zigbee, etc. The access points are in-turn connected to the wired or wireless backhaul networks using wide area network (WAN) technologies . The backhaul network enables connectivity and accessibility between things that are geographically separated. However, they may not always be available such as in remote areas , disaster struck areas, and battlefields. Unmanned aerial vehicles (UAVs) and mobile ground stations are the most viable candidates for providing connectivity in such situations. For instance, during the hurricane Harvey, nearly 95% of the cellular sites in Rockport, Texas went out of service resulting in nearly a complete communication blackout in the region . In such emergency scenarios, where the traditional communication infrastructure is completely devastated, UAVs can prove to be a promising solution to help create a temporary network and resume connectivity in a short span of time. Therefore, there is a growing interest towards the use of drones and UAVs as mobile aerial base stations (BSs) to assist existing cellular LTE networks , public safety networks , and intelligent transportation systems . While this is promising in urban areas where there is high availability of cellular networks which can be used to connect the UAVs to the backhaul, it might not be possible in rural and/or remote regions. Existing frameworks for placement of UAVs do not consider the lack of backhaul connectivity and the need for D2D communication. Furthermore, they ignore the dynamical aspects of connectivity in such networks which presents additional challenges. For instance, the connectivity of devices can be affected by changes in the network, e.g., the mobility of underlay devices or unavailability of overlay devices due to failure or adversarial attacks. PROPOSED SYSTEM : The problem in such settings is to efficiently deploy the overlay network that provides coverage to all the MSDs as well as maintaining connectivity between the MAPs. Since the MSDs can be located in spatial clusters that are arbitrarily separated, the MAPs should be deployed in a way that they remain connected, i.e., each MAP is reachable from other MAPs using D2D communications. This requirement makes it a challenging network planning and design problem.It provides a macroscopic view of one such scenario where the MAPs are appropriately deployed enabling a local inter-network of MSDs without any traditional communication infrastructure. It can be easily connected to the Internet to achieve pervasive connectivity and control over the MSDs. Note that with aerial MAPs, there is an added flexibility to position the BSs arbitrarily in space which might not be possible with other traditional types of access points. They ignore the dynamical aspects of connectivity in such networks which presents additional challenges. For instance, the connectivity of devices can be affected by changes in the network, e.g., the ,mobility of underlay devices or unavailability of overlay devices due to failure or adversarial attacks. To this end, this work proposes a feedback based adaptive, self-configurable, and resilient framework for the overlay network that cognitively adapts to the changes in the network to provide reliable connectivity between spatially dispersed smart devices. CONCLUSION: we present a cognitive connectivity framework that is able to reconfigure itself automatically in a distributed manner to interconnect spatially dispersed smart devices thus enabling the Internet of things in remote environments. Resilience of connectivity has been investigated in response to the mobility of the underlay network as well as random device failures in the overlay network. It is shown that if sufficient number of overlay devices are available, then the developed distributed framework leads to high network connectivity which is resilient to mobility and device failures. However, if sufficient overlay devices are not deployed, the framework tends to provide connectivity locally to the devices in each cluster of the underlay network. A comparison of the proposed approach with existing approaches for placement of BSs reveals significant superiority in terms of the number of BSs required to achieve coverage and the overall connectivity of the devices. We believe that this work provides a useful platform for the development of more sophisticated and efficient algorithms to achieve a variety of objectives in aerial communications using UAVs. Future directions in this work can investigate on ways to make the framework completely distributed. The local observations of MAPs can be used to form a consensus about the locations of the MSDs. Another possible direction to this line of research is to allow MAPs to operate in multiple modes to enable connectivity between a diverse pattern of locations of the MSDs.