For a seamless deployment of the Internet of Things (IoT), there is a need for self-organizing solutions to overcome key IoT challenges that include data processing, resource management, coexistence with existing wireless networks, and improved IoT-wide event detection. One of the most promising solutions to address these challenges is via the use of innovative learning frameworks that will enable the IoT devices to operate autonomously in a dynamic environment. However, developing learning mechanisms for the IoT requires coping with unique IoT properties in terms of resource constraints, heterogeneity, and strict quality-of-service requirements. In this paper, a number of emerging learning frameworks suitable for IoT applications are presented. In particular, the advantages, limitations, IoT applications, and key results pertaining to machine learning, sequential learning, and reinforcement learning are studied. For each type of learning, the computational complexity, required information, and learning performance are discussed. Then, to handle the heterogeneity of the IoT, a new framework based on the powerful tools of cognitive hierarchy theory is introduced. This framework is shown to efficiently capture the different IoT device types and varying levels of available resources among the IoT devices. In particular, the different resource capabilities of IoT devices are mapped to different levels of rationality in cognitive hierarchy theory, thus enabling the IoT devices to use different learning frameworks depending on their available resources. Finally, key results on the use of cognitive hierarchy theory in the IoT are presented.key property of low-cost, low-capability MTDs is low computational capability as discussed in [1] and [18]. However, existing learning frameworks, such as decision trees [18] or reinforcement learning [13] and [28], can be

In this paper, the problem of distributed resource allocation is studied for an Internet of Things (IoT) system, composed of a heterogeneous group of nodes compromising both machine-type devices (MTDs) and human-type devices (HTDs). The problem is formulated as a noncooperative game between the heterogeneous IoT devices that seek to find the optimal time allocation so as to meet their qualityof-service (QoS) requirements in terms of energy, rate and latency. Since the strategy space of each device is dependent on the actions of the other devices, the generalized Nash equilibrium (GNE) solution is first characterized, and the conditions for uniqueness of the GNE are derived. Then, to explicitly capture the heterogeneity of the devices, in terms of resource constraints and QoS needs, a novel and more realistic game-theoretic approach, based on the behavioral framework of cognitive hierarchy (CH) theory, is proposed. This approach is then shown to enable the IoT devices to reach a CH equilibrium (CHE) concept that takes into account the various levels of rationality corresponding to the heterogeneous computational capabilities and the information accessible for each one of the MTDs and HTDs. Simulation results show that the CHE solution maintains stable performance. In particular, the proposed CHE solution keeps the percentage of devices with satisfied QoS constraints above 96%for IoT networks containing up to 10,000 devices without considerably degrading the overall system performance in terms of the total utility. Simulation results also show that the proposed CHE solution brings a two-fold increase in the total rate of HTDs and deceases the total energy consumed by MTDs by 78% compared to the equal time policy. N. Abuzainab and W. Saad are with the department For example, the performance of MTDs that require ultra low latency or HTDs that require high data rates can can be severely affected by collisions. Thus, there is a need to design a new, distributed IoT multiple access scheme that can satisfy the requirements of devices with strict QoS constraints. A. Related WorksThere has been significant recent interest in developing resource allocation mechanisms suitable for the IoT such as in [8]-[14], [16], [17]. Centralized scheduling schemes for IoT LTE networks are proposed in [8]-[11]. In [8], a resource management scheme that dynamically allocates time resources between MTDs and HTDs based on current traffic conditions and QoS requirements. The works in [9] and [10] propose schemes that allocate the LTE resources to MTDs and HTDs based on a bipartite graph. In [11], the authors propose two seperate uplink scheduling schemes for HTDs and MTDs in an LTE system based on channel conditions and delay requirements while taking fairness into account. Other works such as in [12] and [13]adopted game-theoretic approaches for distributed resource allocation problems in the IoT. The authors in [12] study the problem of throughput maximization of MTDs under random access. However, in [12], devices are considered of equal ca...

In this paper, the problem of network connectivity is studied for an adversarial Internet of Battlefield Things (IoBT) system in which an attacker aims at disrupting the connectivity of the network by choosing to compromise one of the IoBT nodes at each time epoch. To counter such attacks, an IoBT defender attempts to reestablish the IoBT connectivity by either deploying new IoBT nodes or by changing the roles of existing nodes. This problem is formulated as a dynamic multistage Stackelberg connectivity game that extends classical connectivity games and that explicitly takes into account the characteristics and requirements of the IoBT network. In particular, the defender's payoff captures the IoBT latency as well as the sum of weights of disconnected nodes at each stage of the game. Due to the dependence of the attacker's and defender's actions at each stage of the game on the network state, the feedback Stackelberg solution (FSE) is used to solve the IoBT connectivity game. Then, sufficient conditions under which the IoBT system will remain connected, when the FSE solution is used, are determined analytically. Numerical results show that the expected number of disconnected sensors, when the FSE solution is used, decreases up to 46% compared to a baseline scenario in which a Stackelberg game with no feedback is used, and up to 43% compared to a baseline equal probability policy. Ca,t(at, bt) otherwise.The expression of the defender's utility is given by U d,t (at, bt) =    u i , if bt = b d,ih , u L , if bt = b L,h , 0, otherwise.

The problem of quality of service (QoS) and jammingaware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks. To ensure robust communication against jamming, an interferenceaware routing protocol is developed that allows nodes to avoid communication holes created by jamming attacks. Then, a distributed cooperation framework, based on deep reinforcement learning, is proposed that allows nodes to assess network conditions and make deep learning-driven, distributed, and real-time decisions on whether to participate in data communications, defend the network against jamming and eavesdropping attacks, or jam other transmissions. The objective is to maximize the network performance that incorporates throughput, energy efficiency, delay, and security metrics. Simulation results show that the proposed jamming-aware routing approach is robust against jamming and when throughput is prioritized, the proposed deep reinforcement learning approach can achieve significant (measured as three-fold) increase in throughput, compared to a benchmark policy with fixed roles assigned to nodes.

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