It is crucial that communication between an unmanned aerial vehicle (UAV) and the ground station (GS) be secure, and both devices should mutually authenticate each other to ensure that an adversary cannot obtain communicated information. Moreover, dynamically adapting the session time of an authenticated session can decrease the idle time of a session and consequently reduce the window of opportunity for an adversary to interfere with the communication link. In light of these considerations, we design aphysically unclonable function (PUF)andfuzzy extractor-based UAV-GS authentication mechanism calledUAV Authentication with Adaptive Session (UAAS). In UAAS, both UAV-GS and UAV-UAV authentication are two-way. We use aThompson Sampling (TS)-based approach to intelligently adapt the duration of a session. Both formal and informal security proofs are presented along with a computation and communication cost analysis to analyze the performance of UAAS. It is noted that UAAS is secure against various well-known attacks and has a lower communication cost than several baseline mechanisms. Due to the noise reduction that occurs in PUFs, the computational cost of UAAS is higher than that of baselines that ignore noise. Also, our simulation shows that UAAS significantly outperforms baselines when it comes to network performance.

Unmanned aerial vehicles (UAVs)-aided mobile-edge computing (MEC) systems face several challenges that hinder their practical implementation. First, the broadcast nature of wireless communications can cause security issues. Second, UAVs have constrained onboard power. Finally, the UAV should be able to serve a maximum number of ground users (GUs). It is also crucial to maintain fairness such that all GUs get equal opportunities to securely offload tasks to UAVs. We seek to address the aforementioned challenges by designing an intelligent mechanism,FairLearn, which maximizes the fairness in secure MEC services by controlling the UAV 3D trajectory, transmission power, and scheduling time for task offloading by mobile GUs. To this end, we formulate a maximization problem and solve it using adeep neural network (DNN)-based model, where the UAVs collaboratively learn the model by utilizing afederated learning (FL)approach. Each UAV uses areinforcement learning (RL)-based approach to individually generate the training dataset, making the training data span different network scenarios. Our model is based on UAV pairs, where one UAV executes the GUs' offloaded tasks, while the other is a jammer that suppresses eavesdroppers. The simulation evaluation of FairLearn shows that it significantly improves the performance of UAV-enabled MEC systems.

Unmanned aerial vehicles (UAVs) are operated remotely without the presence of a unified system of identity authentication, and wireless communications in untrusted environments can cause the loss of valuable data carried by UAVs. Traditional UAV authentication mechanisms are centralized approaches, which suffer from a single point of failure problem and may incur high complexity computations. Therefore, it is crucial to establish a distributed authentication mechanism between the ground station controller (GSC) and a UAV. Moreover, in case of UAV swarms, the high mobility of the UAVs affects the stability of UAV communications, which leads to the degradation of the UAV authentication performance. Addressing these challenges, we design a blockchain-based distributed authentication mechanism, known asSwarmAuth, for UAV swarms, where the GSC and UAVs follow a mutual authentication approach using physical unclonable functions (PUFs), and the K-means clustering-based intelligent approach is used to dynamically create location-based clusters. The blockchain helps store UAVs’ authentication information in an immutable storage and the associated smart contracts provide a convenient access control model. The security analysis of SwarmAuth is carried out through both formal and informal proofs considering general attacks. Experimental evaluation shows that SwarmAuth can assure trustworthy communications and improve the network performance.

IEEE 802.11ax uses the concept of primary and secondary channels, leading to theDynamic Bandwidth Channel Access (DBCA)mechanism. By applying DBCA, a wireless station can select a wider channel bandwidth, such as$40/80/160$MHz, by applying the channel bonding feature. However, during channel bonding, inappropriate bandwidth selection can cause collisions. Therefore, to avoid collisions, a well-developed media access control (MAC) protocol is crucial to effectively utilize the channel bonding mechanism. In this paper, we address a collision scenario, calledOutside Warning Range Problem (OWRP), that may occur during DBCA when a wireless station interferes with another wireless station after channel bonding is performed. Therefore, we propose a MAC layer mechanism,Intelligent Bonding Avoiding Collision (IBAC), that adapts the channel bonding level in DBCA in order to avoid the OWRP. We first design a theoretical model based on Markov chains for DBCA while avoiding the OWRP. Based on this model, we design aThompson samplingbased Bayesian approach to select the best possible channel bonding level intelligently. We analyze the performance of the IBAC through simulations where it is observed that, comparing to other competing mechanisms, the proposed approach can enhance the network performance significantly while avoiding the OWRP.

The 5th Generation (5G) New Radio (NR) and beyond technologies will support enhanced mobile broadband, very low latency communications, and huge numbers of mobile devices. Therefore, for very high speed users, seamless mobility needs to be maintained during the migration from one cell to another in the handover. Due to the presence of a massive number of mobile devices, the management of the high mobility of a dense network becomes crucial. Moreover, a dynamic adaptation is required for the Time-to-Trigger (TTT) and hysteresis margin, which significantly impact the handover latency and overall throughput. Therefore, in this paper, we propose an online learning-based mechanism, known asLearning-basedIntelligentMobilityManagement (LIM2), for mobility management in 5G and beyond, with an intelligent adaptation of the TTT and hysteresis values. LIM2 uses a Kalman filter to predict the future signal quality of the serving and neighbor cells, selects the target cell for the handover usingstate-action-reward-state-action (SARSA)-based reinforcement learning, and adapts the TTT and hysteresis using the$\epsilon$-greedypolicy. We implement a prototype of the LIM2 in NS-3 and extensively analyze its performance, where it is observed that the LIM2 algorithm can significantly improve the handover operation in very high speed mobility scenarios.

For the rapid increase in resource requirements in large scale Data Centers (DCs), enterprises have brought hyperconverged architecture where the storage pool is built up by the individual storage components associated with different servers, and it is shared among all the Virtual Machines (VMs) or containers through a common network infrastructure. Due to the sharing of network bandwidth among the application generated network traffic and the storage traffic from shared storage infrastructure, quality of service (QoS) performances of networking and storage intensive applications are affected, which further impacts VM or container migrations with dynamic workload scenarios. In this article, we propose NetStor to ensure QoS for various collocated network and storage intensive workloads over a hyperconverged architecture and ensure QoS during VM or container migration. NetStor uses a workload estimation strategy for VMs and containers, and applies the strategic decisions for resource allocation and migration based on environmental learning and workload characterization. NetStor supports a dynamic QoS provisioning that is workload-agnostic catering to both VMs and containers, and is unique to the best of our knowledge. We have implemented NetStor over a hyperconverged DC architecture on a testbed and found that NetStor can enhance network performance significantly compared to other related mechanisms discussed in the literature.

Dynamic bandwidth operation in IEEE 802.11ac helps wireless access points to tune channel widths based on carrier sensing and bandwidth requirements of associated wireless stations. However, wide channels result in a reduction in the carrier sensing range, which leads to the problem of channel sensing asymmetry. As a consequence, access points face hidden channel interference that may lead to as high as 60% reduction in the throughput under certain scenarios of dense deployments of access points. Existing approaches handle this problem by detecting the hidden channels once they occur and affect the channel access performance. In a different direction, in this paper, we develop a method for avoiding hidden channels by meticulously predicting the channel width that can reduce interference as well as can improve the average communication capacity. The core of our approach is a deep probabilistic machinery based on point process modeling over the evolution of channel width selection process. The proposed approach, SmartBond, has been implemented and deployed over a testbed with 8 commercial wireless access points. The experiments show that the proposed model can significantly improve the channel access performance although it is lightweight and does not incur much overhead during the decision making process.

IEEE 802.11ac high throughput extension for wireless local area network comes with a large number of link layer configuration parameters, such as 4 different channel bonding levels, 10 different modulation and coding schemes, frame aggregation setup etc. However, the optimal combination of link configuration parameters, which maximizes the link layer performance, depends on the perceived channel quality based on the signal strength, channel noise and external interference. Considering the highly dynamic, nonlinear and time-varying nature of wireless channel quality, a dynamic adaptation of link configuration parameters gives a stable and optimized link layer performance. Nevertheless, the existing literature fails to design a robust mechanism for handling all the parameters simultaneously. In this article, we develop a control theoretic approach governed by a deep probabilistic machinery to design a robust and scalable dynamic link parameter adaptation mechanism. We apply deep neural network based Gaussian process regression to predict the link layer throughput and model predictive control based approach to find out the link configuration parameter that optimizes the overall link layer performance. The proposed mechanism is implemented and tested over a testbed setup, and we observe that it can significantly boost up the link layer performance compared to various baseline mechanisms.

In the presence of the Internet of Things (IoT) devices, the end-users require a response within a short amount of time which the cloud computing alone cannot provide. Fog computing plays an important role in the presence of IoT devices in order to meet such delay requirements. Though beneficial in these latency-sensitive scenarios, the fog has several implementation challenges. In order to solve the problem of micro-service placement in the fog devices, we propose a framework with the objective of achieving low response time. This problem has been formulated as an optimization problem to improve the response time by considering the time-varying resource availability of the fog devices as constraints. We propose an orchestration framework named Pick-Test-Choose (PTC) to solve the problem. PTC uses Bayesian Optimization based iterative reinforcement learning algorithm to find out a micro-service allocation based on the current workload of the fog devices. PTC employs containers for service isolation and migration of the micro-services. The proposed architecture is implemented over an in-house testbed as well as in iFogSim simulator. The experimental results show that the proposed framework performs better in terms of response time compared to various other baselines.

IEEE 802.11ax high-throughput wireless access networks support multi-user multiple-input multiple-output (MU-MIMO)-based communication, where a set of spatially apart wireless stations forms a user group and uses different spatial streams for simultaneous transmission and reception. In this architecture, dynamic user group selection is an important aspect for maintaining high-throughput fair channel access. In addition, the physical and media access control parameters, like channel bonding levels, modulation, and coding schemes need to be tuned based on the selected user group to utilize the maximum available capacity. In this paper, we design an online learning-based approach over a centralized logical control architecture, called intelligent MU-MIMO user selection with link adaptation (IMMULA), where a central controller collects the performance statistics under various configuration space and applies a reinforcement learning strategy to select the best-suited configurations dynamically at periodic intervals. The performance of IMMULA is analyzed over a testbed consisting of 6 IEEE 802.11ac access points and 20 wireless stations. The results show that IMMULA improves network performances significantly compared to other baseline mechanisms.

IEEE 802.11ac supports Dynamic Bandwidth Channel Access (DBCA), where a wireless station dynamically selects the channel bandwidth based on the availability of the secondary channels. Although DBCA reduces the possibility of starvation due to non-availability of secondary channels, however, to the best of our knowledge, no existing works look into the performance benefits of IEEE 802.11ac DBCA based on theoretical modeling. In this paper, we develop a two dimensional Markov chain approach to model the performance of DBCA under various channel bonding conditions. We validate the proposed model based on a real testbed implementation. From the thorough analysis of the numerical results obtained from the model, we show that although DBCA improves channel utilization for secondary channels, it requires proper channel allocations and bonding level distributions across the wireless channels for reducing unfairness in the network. We observe that under certain circumstances, the secondary channel users can affect the throughput of primary channel users, which may introduce a short-term unfairness and a significant performance drop in the network.

Wi-Fi enabled hand-held devices have quickly occupied the consumer market as a result of the remarkable customer acceptance of IEEE 802.11 standard. In this regard, the demand of high throughput introduces high throughput standards such as IEEE 802.11ac. It supports Dynamic Bandwidth Channel Access (DBCA), where a wireless station selects channel bandwidth dynamically based on the availability of the secondary channels. But the widely-used contention based medium access mechanism provides an opportunistic access of secondary channels and affects the performance of DBCA. Consequently, unfairness in channel access is increased in DBCA, which further reduces average throughput of stations. In this paper, we develop a hybrid adaptive resource reservation mechanism, Hybrid Adaptive DBCA (HA-DBCA), for supporting fair channel access in DBCA. In HA-DBCA, a polling based online learning mechanism is designed to avoid starvation of primary channel users. Through IEEE 802.11ac testbed implementation, we show that HA-DBCA improves throughput fairness in DBCA significantly along with other performance parameters.

High fluctuation of signal strength is evident in wireless channel under mobile environment. IEEE 802.11n and IEEE 802.11ac based wireless technologies experience a challenge for selecting link configuration parameters, like number of spatial streams, channel bonding, advanced modulation and coding schemes, frame aggregation etc., dynamically under mobility. Selection of the best possible data rate by tuning link parameters is a challenging issue due to the channel asymmetry in mobile environment. In this paper, we propose an adaptive learning mechanism, HT-MobiRate, for high throughput dynamic link adaptation under mobile scenario. HT-MobiRate is based on Thompson sampling and inspired from multi-armed bandit approach. To the best of our knowledge, this invention is first in the direction of link adaptation for IEEE 802.11ac under mobile environment. We analyze the performance of HT-MobiRate with a practical high throughput wireless testbed built over 6 IEEE 802.11ac supported access points and 20 IEEE 802.11ac clients (both client boards as well as smart-phones). We recognize that it performs considerably better than other competing schemes proposed in the literature for link adaptation in static environment.

Internet access via wireless hotspots is an ever increasing demand with the inception of smart cities, where most of the users connect the Internet with their WiFi enabled devices. A set of wireless devices forms a basic service set (BSS) connected to an access point (AP). However, large number of APs are deployed in the form of extended service set (ESS) to balance the traffic load and to provide seamless data connectivity. Recent studies show that in a public WiFi hotspot, a mobile device remains in the overlapping region of multiple APs. Due to geographically sparse distributions of mobile devices, an AP may need to keep its interfaces on to serve only a few devices which otherwise can be shifted to another active AP. In this paper, we develop CrowdAP, an energy balancing AP coordination mechanism; where the minimum number of APs are computed such that the underlying mobile devices can be served without any degradation in performance, while the rest of the APs can go to the sleep state to save power. We analyze the performance of CrowdAP through simulation as well as from testbed, and show that it is able to save significant energy in the network.

High throughput wireless access networks based on IEEE 802.11ac show a significant challenge in dynamically selecting the link configuration parameters based on channel conditions due to large pool of design set, like number of spatial streams, channel bonding, guard intervals, frame aggregation and different modulation and coding schemes. In this paper, we develop a learning based approach for link adaptation motivated by the multi-armed bandit based distributed learning algorithm. The proposed link adaptation algorithm, BanditLink, explores different possible configuration options based on observing their impact over the network performance at various channel conditions. We analyze the performance of BanditLink from simulation results, and observe that it performs significantly better compared to other competing mechanisms proposed in the literature.