Long-range (LoRa) wireless networks have been widely proposed as efficient wireless access networks for battery-constrained Internet of Things (IoT) devices. However, applying the LoRa-based IoT network in search-and-rescue (SAR) operations will have limited coverage caused by high signal attenuation due to terrestrial blockages, especially in highly remote areas. To overcome this challenge, using unmanned aerial vehicles (UAVs) as a flying LoRa gateway to transfer messages from ground LoRa nodes to the ground rescue station can be a promising solution. In this paper, an artificial intelligence-empowered SAR operation framework using a UAV-assisted LoRa network in different unknown search environments is designed and implemented. The problem of the flying LoRa (FL) gateway control policy is modeled as a partially observable Markov decision process to move the UAV towards the LoRa transmitter carried by a lost person in the known remote search area. A deep reinforcement learning (RL)-based policy is designed to determine the adaptive FL gateway trajectory in a given search environment. Then, as a general solution, a deep meta-RL framework is used for SAR in any new and unknown environments. The proposed deep meta-RL framework integrates the information of the prior FL gateway experience in the previous SAR environments to the new environment and then rapidly adapts the UAV control policy model for SAR operation in a new and unknown environment. To analyze the performance of the proposed framework in real-world scenarios, the proposed SAR system is experimentally tested in three environments: a university campus, a wide plain, and a slotted canyon at Mongasht mountain ranges, Iran. Experimental results show that if the deep meta-RL-based control policy is applied instead of the deep RL-based one, the number of SAR time slots decreases from 141 to 50. Moreover, in the slotted canyon environment, the UAV energy consumption under the deep meta-RL policy is respectively 57% and 23% less than the deep RL and Actor-Critic RL policies.

Today, we are witnessing an ever increasing demand for ubiquitous user connectivity, and at the same time, the surging of advanced handheld devices, with enhanced storage and computing capabilities. These advanced devices can not only satisfy the needs of their owners but also act as a host to provide related services to users nearby. In this regard, the concept of IoT federated infrastructure, where heterogeneous devices pool their resources to match wideranging user requirements, has been attracting increasing attention. To address the heterogeneity of these devices and achieve efficient system resource utilization, in this paper, we introduce a novel IoT Federated Infrastructure-as-a-Service(FDIaaS) framework to support the opportunistic cooperation among IoT devices. In this context, IoT devices are transformed to a Micro-Provider (MP) as a host who can offer their spare resources to the neighboring users. The interaction between the Edge Provider (EP) as a trusted third agent and the MP is modeled as a two-stage Stackelberg game interleaved with a dynamic coalition formation game. Particularly, an incentive mechanism to form FDIaaS using the IoT devices is designed as a centralized optimization problem. Next, to find this incentive policy in a distributed manner, a coalition formation-based solution is proposed. Simulation results show that On average, the successfully executed task rate under the proposed mechanism is only 7% less than the optimal solution. Moreover, when the number of IoT devices increases more federation with higher size formed under our proposed incentive mechanism.

Smart cities use internet to make the day-to-day living of city inhabitants more comfortable and secure. In this regard, long-range wide area networks (LoRaWAN) are ideally suited to provide IoT wireless access networks in smart city applications. LoRaWAN uses the long-range (LoRa) technology at the physical layer to provide long-range wireless connectivity. Considering the shadow fading of the large and closely located buildings in the cities, one of the key challenges in the LoRaWAN deployment is finding the optimal location of gateways while guaranteeing network coverage. In particular, the LoRa gateway deployment becomes more complicated due to the stochastic shadowing of the city buildings due to the random locations of IoT devices. In this paper, a novel analytical framework is proposed that enables the joint gateway placement and spread factor (SF) assignment in LoRaWAN while being cognizant of random building shadow fading. Inspired by the chance-constrained method, a joint stochastic gateway placement and SF assignment problem subject to network coverage constraint is formulated for LoRaWANs. Then, a new unsupervised learning-aware greedy algorithm based on the ““size-constrained weighted set cover” concept is proposed to find an approximate solution to the formulated LoRa gateway planning problem in the city environments. The proposed algorithm is simulated over the campus of Shahid Chamran University of Ahvaz at IRAN. Simulation results demonstrate the effectiveness of the proposed approach compared to the benchmarks. For example, on average, the network coverage probability under our proposed unsupervised learning-aware greedy algorithm is $15 \%$ and $10 \%$ more than the greedy and clustering ones.

The smart village is a promising approach for achieving socio-economic sustainability in rural areas. This paper utilizes Social Internet of Things (SIoT) methodologies to realize the smart village concept through efficient and cost-effective IoT technology. Each physical sensor and IoT device has a virtual counterpart Digital Twin (DT) at the edge for effective data analysis and optimization. For emerging public health services, monitoring indoor air quality (IAQ) in critical rural buildings is crucial. This paper proposes a probabilistic graphical model to capture IAQ changes using low-cost LoRa end nodes (EN) and gateway devices. These devices measure light intensity, temper- ature, and polluting gas concentration levels. The unsupervised k-means algorithm clusters the real-time IAQ data. At the same time, a Markov-based model visualizes and predicts IAQ changes. The model parameters are updated in real-time using data from a deployed LoRa wireless network. The framework was evaluated in rural areas near Ghaletol, Khuzestan province, Iran, with deployments in schools, agri- cultural warehouses, medical centers, and supermarkets. The best IAQ Markov states were 3 for schools, 3 for agricultural warehouses, 4 for medical centers, and 5 for supermarkets. For instance, the supermarket's IAQ model showed a polluting gas concentration of 862.6 ppm, an indoor temperature of 28.66°C, and a light intensity of 70.05 Lux.

Wireless virtual reality (VR), a key 3GPP use case of emerging cellular systems, imposes new visual and haptic requirements directly linked to the Quality of Experience (QoE) of VR users. These QoE requirements can only be met by wireless connectivity that offers high-rate and high-reliability low-latency communications (HR2LLC), unlike the low rates commonly associated with ultrareliable low-latency communication. The high rates for VR over short distances can only be supported by an enormous bandwidth, available in the terahertz (THz)-frequency bands. To explore the potential of THz for meeting HR2LLC requirements, a quantification of the risk for an unreliable VR performance is conducted through a novel and rigorous characterization of the tail of the end-to-end (E2E) delay. Then, a thorough analysis of the Tail-Value-at-Risk (TVaR) is performed to concretely characterize the behavior of extreme wireless events crucial to the real-time VR experience. In particular, the probability distribution function of the THz transmission delay is derived and then used to infer the system reliability scenarios with guaranteed Line of Sight (LoS) as a function of THz network parameters. Numerical results show that abundant bandwidth and low molecular absorption are necessary to improve the reliability. However, their effect remains secondary compared to the availability of LoS, which significantly affects the THz HR2LLC performance. In particular, for scenarios with guaranteed LoS, a reliability of 99.999% (with an E2E delay threshold of 20 ms) for a bandwidth of 15 GHz along with data rates of 18.3 Gbps can be achieved by the THz network, compared to a reliability of 96% for twice the bandwidth, when blockages are considered.

In this paper, a new framework for optimizing the resource allocation in a millimeter-wave-non-orthogonal multiple access (mmWave-NOMA) communication for crowded venues is proposed. MmWave communications suffer from severe blockage caused by obstacles such as the human body, especially in a dense region. Thus, a detailed method for modeling the blockage events in the in-venue scenarios is introduced. Also, several mmWave access points are considered in different locations. To maximize the network sum rate, the resource allocation problem is formulated as a mixed-integer non-linear programming (MINLP) problem, which is NP-hard in general. Hence, a three-stage low-complex solution is proposed to solve the problem. At first, a user scheduling algorithm, i.e., modified worst connection swapping (MWCS), is proposed. Secondly, the antenna allocation problem is solved using the simulated annealing algorithm. Afterward, to maximize the network sum rate and guarantee the quality of service constraints, a non-convex power allocation optimization problem is solved by adopting the difference of convex programming approach. The simulation results show that, under the blockage effect, the proposed mmWave-NOMA scheme performs on average 23% better than the conventional mmWave-orthogonal multiple access scheme. Moreover, the proposed solution's performance is close to the optimal value while reducing complexity by 96%.

In this paper, a novel framework for guaranteeing ultra-reliable millimeter wave (mmW) communications using multiple artificial intelligence (AI)-enabled reconfigurable intelligent surfaces (RISs) is proposed. The use of multiple AI-powered RISs allows changing the propagation direction of the signals transmitted from a mmW access point (AP) thereby improving coverage particularly for non-line-of-sight (NLoS) areas. However, due to the possibility of highly stochastic blockage over mmW links, designing an intelligent controller to jointly optimize the mmW AP beam and RIS phase shifts is a daunting task. In this regard, first, a parametric risk-sensitive episodic return is proposed to maximize the expected bitrate and mitigate the risk of mmW link blockage. Then, a closed-form approximation of the policy gradient of the risk-sensitive episodic return is analytically derived. Next, the problem of joint beamforming for mmW AP and phase shift control for mmW RISs is modeled as an identical payoff stochastic game within a cooperative multi-agent environment, in which the agents are the mmW AP and the RISs. Two centralized and distributed controllers are proposed to control the policies of the mmW AP and RISs. Todirectlyfind a near optimal solution, the parametric functional-form policies for the controllers are modeled using deep recurrent neural networks (RNNs). The deep RNN-based controllers are then trained based on the derived closed-form gradient of the risk-sensitive episodic return. It is proved that the gradient update algorithm converges to the same locally optimal parameters as the deep RNN-based centralized and distributed controllers. Simulation results show that the error between the policies of the optimal and the RNN-based controllers is less than 1.5%. Moreover, the variance of the achievable rates resulting from the deep RNN-based controllers is 60% less than the variance of the risk-averse baseline.

In this paper, the problem of associating reconfigurable intelligent surfaces (RISs) to virtual reality (VR) users is studied for a wireless VR network. In particular, this problem is considered within a cellular network that employs terahertz (THz) operated RISs acting as base stations. To provide a seamless VR experience, high data rates and reliable low latency need to be continuously guaranteed. To address these challenges, a novel risk-based framework based on the entropic value-at-risk is proposed for rate optimization and reliability performance. Furthermore, a Lyapunov optimization technique is used to reformulate the problem as a linear weighted function, while ensuring that higher order statistics of the queue length are maintained under a threshold. To address this problem, given the stochastic nature of the channel, a policy-based reinforcement learning (RL) algorithm is proposed. Since the state space is extremely large, the policy is learned through a deep-RL algorithm. In particular, a recurrent neural network (RNN) RL framework is proposed to capture the dynamic channel behavior and improve the speed of conventional RL policy-search algorithms. Simulation results demonstrate that the maximal queue length resulting from the proposed approach is only within 1% of the optimal solution. The results show a high accuracy and fast convergence for the RNN with a validation accuracy of 91.92%.

In this paper, a novel framework for guaranteeing ultra-reliable millimeter-wave (mmW) communications using a smart, artificial intelligence (AI)-powered mmW reflector is proposed. The use of an AI-powered reflector allows changing the propagation direction of mmW signals and, thus, improving coverage particularly for non-line-of-sight (LoS) areas. However, due to the possibility of stochastic blockage over mmW links, designing an intelligent phase shift-control policy for the mmW reflector to guarantee ultra-reliable mmW communications becomes very challenging. In this regard, first, based on the framework of risk-sensitive reinforcement learning, a parametric risk-sensitive episodic return is proposed to maximize the expected bit rate while mitigating the risk of non-LoS mmW link in the presence of future stochastic blockage over the mmW links. Then, a closed-form approximation for the gradient of the risk- sensitive episodic return is analytically derived. To \emph{directly} find the optimal policy for the proposed phase-shift controller, a parametric functional-form policy is implemented using a deep recurrent neural network (RNN). Then, based on the derived closed-form gradient of risk-sensitive episodic return, the deep RNN-based parametric functional-form policy is trained. The efficiency of the proposed AI-powered reflector is evaluated in an office environment. Simulation results show that the root-mean- square errors between the optimal and approximate phase shift-control policies of the proposed deep RNN is 1.35% in the worst case. Moreover, on average, the mean value and variance of the achievable rates resulting from the deep RNN-based policy are only 1% and 2% less than the optimal solution for different unknown mobile users' trajectories, respectively.

In this paper, the problem of self-organizing, correlation-aware clustering is studied for a dense network of machine-type devices (MTDs) deployed over a cellular network. In dense machine-to-machine networks, MTDs are typically located within close proximity and gather correlated data, and, thus, clustering MTDs based on data correlation leads to a decrease in the number of redundant bits transmitted to the base station. The clustering problem is formulated as an evolutionary game, which models the interactions among a massive number of MTDs, in order to decrease MTD transmission power. A novel utility function that captures the tradeoff between minimizing the average MTD transmission power per cluster and maximizing cluster size (or minimizing signaling overhead) is proposed. To solve this game, a distributed algorithm is proposed to allow a massive number of MTDs to autonomously form clusters. It is shown that the proposed distributed algorithm converges to an evolutionary stable strategy (ESS) that is robust to a small portion of MTDs deviating, e.g., due to some stochastic changes in the M2M environment from the stable cluster formation at convergence. The maximum fraction of MTDs that can deviate from the ESS, while still maintaining a stable cluster formation, is derived. Simulation results show the efficiency of the proposed algorithm in clustering MTDs with highly correlated data: on average, the proposed approach yields reductions of up to 44.1% and 15.25% in terms of the transmit power per cluster, compared to forming clusters with the maximum possible size and uniformly selecting a cluster size, respectively.

In this paper, a novel framework for optimizing the caching of popular user content at the level of wireless user equipments (UEs) is proposed. The goal is to improve content offloading over wireless device-to-device (D2D) communication links. In the considered network, users belong to different social communities while their UEs form a single multi-hop D2D network. The proposed framework allows us to exploit the multi-community social context of users for improving the local offloading of cached content in a multi-hop D2D network. To model the collaborative effect of a set of UEs on content offloading, a cooperative game between the UEs is formulated. For this game, it is shown that the Shapley value (SV) of each UE effectively captures the impact of this UE on the overall content offloading process. To capture the presence of multiple social communities that connect the UEs, a hypergraph model is proposed. Two line graphs, an influence-weighted graph, and a connectivity-weighted graph, are developed for analyzing the proposed hypergaph model. Using the developed line graphs along with the SV of the cooperative game, a precise offloading power metric is derived for each UE within a multi-community, multi-hop D2D network. Then, UEs with high offloading power are chosen as the optimal locations for caching the popular content. Simulation results show that, on the average, the proposed cache placement framework achieves 12, 19, and 21 percent improvements in terms of the number of UEs that received offloaded popular content compared to the schemes based on betweenness, degree, and closeness centrality, respectively.

Millimeter wave (mmW) communication is a promising solution for providing high-capacity wireless network access. However, the benefits of mmW are limited by the fact that the channel between a mmW access point and the user equipment can stochastically change due to severe blockage of mmW links by obstacles such as the human body. Thus, one main challenge of mmW network coverage is to enable directional line-of-sight links between access points and mobile devices. In this paper, a novel framework is proposed for optimizing mmW network coverage within hotspots and in-venue regions, while being cognizant of the body blockage of the network's users. In the studied model, the locations of potential access points and users are assumed as predefined parameters while the orientation of the users is assumed to be stochastic. Hence, a joint stochastic access point placement and beam steering problem subjected to stochastic users' body blockage is formulated, under desired network coverage constraints. Then, a greedy algorithm is introduced to find an approximation solution for the joint deployment and assignment problem using a new “size constrained weighted set cover” approach. A closed-form expression for the ratio between the optimal solution and approximate one (resulting from the greedy algorithm) is analytically derived. The proposed algorithm is simulated for three in-venue regions: the meeting room in the Alumni Assembly Hall of Virginia Tech, an airport gate, and one side of a stadium football. The simulation results show that, in order to guarantee network coverage for different in-venue regions, the greedy algorithm uses at most three more access points (APs) compared to the optimal solution. The results also show that, due to the use of the additional APs, the greedy algorithm will yield a network coverage up to 11.7% better than the optimal, AP-minimizing solution.

In this paper, a novel protocol is proposed in which mobile terminals (MT) form a virtual Multiple-input Multiple-output (MIMO) uplink by means of device relaying on Device to Device (D2D) tier in 5G Cellular Network. The competitive scenario is considered in which each of the selfish MTs tries to transmit its own data and not relay others' data in the formed virtual MIMO. The main focus is to design an incentive for MTs to form the virtual MIMO and cooperate in relaying others data. A direct revelation on-line mechanism for the BS is designed, in order to assist forming a stable virtual MIMO. A self-punishment mechanism is also proposed in which MTs autonomously punish malicious MTs that do not cooperate in relaying. We prove that our designed direct revelation on-line mechanism and proposed self-punishment mechanism enforce all-cooperation (all-C) profile as a Nash equilibrium (NE), under uncertainty in the presence of MTs in the formed virtual MIMO. Our simulation results confirm that the proposed protocol, even in the competitive scenario, increases the bit rate and decreases power consumption at the same time. The proposed protocol can improve the energy efficiency up to 35 percent compared to a non-cooperative case, i.e., Single-Input Multiple-Output (SIMO) uplink. Moreover, if the multi-user MIMO transmission is used for the uplink medium access layer, the proposed protocol can improve the energy efficiency up to 42 percent compared to SIMO uplink with multi-user MIMO transmission. Under the proposed OCVM protocol with Shapley value fairness, the price of anarchy reaches to 0.78 in the competitive scenario. In addition, the energy efficiency improvement of our proposed protocol is almost robust to the preferences of MTs. Simulation results show that if BS employs our on-line mechanism and MTs autonomously punish malicious MTs, the malicious MTs cannot gain by defecting from relaying other MTs' data.

In this paper, the problem of correlation-aware clustering is studied for a dense network of machine-type devices (MTDs) deployed over a cellular network. In such dense networks, MTDs sense an environment and transmit their data to the base station (BS) via a cellular uplink. However, since MTDs are typically closely located to each other they will gather correlated data, and, thus, large amounts of redundant bits can be transmitted to the BS. To address this problem, an evolutionary coalitional (EC) game is proposed to cluster MTDs into coalitions in a fully distributed and autonomous manner, based on the correlation of their data. The proposed EC game allows a reduction in the number of redundant bits being sent to the BS, while also reducing the energy used for transmission by each MTD. To solve the EC game, a distributed coalition formation algorithm is proposed and shown to reach an evolutionary stable coalition structure, which is robust to a small portion of MTDs changing their strategy at the stable outcome. For this game, the maximum portion of MTDs that can deviate from the stable coalitional structure is derived. Simulation results show that the proposed approach can effectively cluster MTDs with highly correlated data which, in turn, enables those MTDs to eliminate a large number of redundant bits. Moreover, the results show that, for a given maximum correlation factor and network density, the transmission energy per MTD can be decreased by 19%, compared to a baseline merge-and-split algorithm. In addition, when a maximum correlation factor is considered, the number of redundant bits that can be eliminated per coalition is increased by 50%, compared to the merge-and-split algorithm.

In this paper, a novel self-punishment based scheduling algorithm for a cooperative real- time content download application is designed. In the proposed protocol, selfish mobile devices autonomously form cooperative groups. For each formed group, the base station transmits the content to a selected mobile device designated as seed. Then, the seed shares the content with other mobile devices called sinks over device-to-device links. After analyzing the proposed protocol using a repeated game, new self-punishment mechanisms by revocation or by decreasing the bit rate, are proposed. Such self-punishment mechanisms enable the mobile devices in each cooperative group to autonomously punish selfish seeds without requiring any help from other mobile devices outside cooperative group. ‌Based on the proposed self-punishment mechanisms, a fair algorithm is designed to schedule the seeds in each cooperative group. Then, the designed scheduling algorithm is implemented using an Android application that is developed using Java in the Android Development Tool Bundle. The developed Android application does not depend on the operation system of mobile devices. Simulation results demonstrate that, on the average, the proposed protocol improves the energy efficiency of mobile devices to download real-time content of around 42 % compared to a traditional multicast scenario. Moreover, the proposed protocol does not let the energy efficiency of mobile devices degrade more than 11 %, on average, from the optimal solution even when all the mobile devises are selfish. The practical results show that the maximum difference in the run time of a real- time video over real-world smartphone screens is less than 500 milliseconds when the smartphones form a cooperative group using the developed Android application.

Millimeter wave (mmW) communication is a promising solution for providing high-capacity wireless network access. However, the benefits of mmW are limited by the fact that the channel between a mmW access point and the user equipment can stochastically change due to severe blockage of mmW links by obstacles such as the human body. Thus, one main challenge of mmW network coverage is to enable directional line-of-sight links between access points and mobile devices. In this paper, a novel framework is proposed for optimizing mmW network coverage within hotspots and in-venue regions, while being cognizant of the users' orientation. In the studied model, the locations of potential access points and users are assumed as predefined parameters while the orientation of the users is assumed to be stochastic. Hence, a joint stochastic access point placement and beam steering problem is formulated, under desired network coverage constraints. Then, a greedy algorithm is introduced to solve the joint deployment and assignment problem using a “size constrained weighted set cover” approach. A closed-form approximation ratio between the optimal and approximate solutions is analytically derived. Simulation results show that, in order to guarantee coverage constraint for the Alumni Assembly Hall of Virginia Tech, the greedy algorithm uses at most one more AP compared to the optimal solution. The results also show that, due to the use of an additional AP, the greedy algorithm will yield a network coverage that is about 8% better than the optimal, AP-minimizing solution.

Millimeter wave (mmWave) communication is a promising solution for providing high capacity wireless access to regions with high traffic demands. The main challenge of mmWave communications is the availability of directional line of sight links between access points and mobile devices which stochastically change due to high attenuation in mmWave propagation and severe blockage of mmWave links with obstacles such as human bodies. In this paper, a novel framework for optimizing the deployment of mmW access points, while being cognizant of the mobile devices orientation, is proposed. In the studied model, the locations of potential access points and users are assumed as predefined parameters while the orientation of the users is changing stochastically. To minimize the number of access points while satisfying the line of sight coverage of mobile devices, first, a joint access point placement and mobile device assignment problem is proposed, assuming that the orientation of each user is deterministically known. This formulation is then extended to the case in which the orientation of the user is stochastic. Finally, the proposed deterministic and stochastic joint access point placement and mobile device assignment schemes are evaluated under various system parameters. Simulation results demonstrate the advantage of the proposed stochastic scheme to the deterministic scheme, in terms of reducing the load on access points. Moreover, on average, the proposed stochastic scheme can increase the probability of user satisfaction up to 24% for 0.95 requested coverage probability compared to the deterministic case.

In this paper, the problem of random access contention between machine type devices (MTDs) in the uplink of a wireless cellular network is studied. In particular, the possibility of forming cooperative groups to coordinate the MTDs' requests for the random access channel (RACH) is analyzed. The problem is formulated as a stochastic coalition formation game in which the MTDs are the players that seek to form cooperative coalitions to optimize a utility function that captures each MTD's energy consumption and time-varying queue length. Within each coalition, an MTD acts as a coalition head that sends the access requests of the coalition members over the RACH. One key feature of this game is its ability to cope with stochastic environments in which the arrival requests of MTDs and the packet success rate over RACH are dynamically time-varying. The proposed stochastic coalitional game is composed of multiple stages, each of which corresponds to a coalitional game in stochastic characteristic form that is played by the MTDs at each time step. To solve this game, a novel distributed coalition formation algorithm is proposed and shown to converge to a stable MTD partition. Simulation results show that, on the average, the proposed stochastic coalition formation algorithm can reduce the average fail ratio and energy consumption of up to 36% and 31% for a cluster-based distribution of MTDs, respectively, compared with a noncooperative case. Moreover, when the MTDs are more sensitive to the energy consumption (queue length), the coalitions' size will increase (decrease).