Device-to-device (D2D)-assisted sixth-generation$(6 \mathrm{G})$wireless networks necessitate intelligent resource management and power control to improve the overall system performance. Additionally, D2D underlay communication, a key enabler for improving spectral efficiency, is often hindered by interference, which adversely impacts the quality of service (QoS) for cellular users (CUs) and also degrades the performance of D2D users (DUs). Considering this, we propose a deep reinforcement learning-based method for resource management and power allocation in D2D-assisted heterogeneous networks (HetNets). A joint optimization problem integrating power control and channel selection is formulated for maximizing the system's spectral energy efficiency (SEE). Specifically, a multi-agent, double-deep Qnetwork (DDQN) algorithm is proposed in which each user equipment (UE) is trained as an action selection and target estimation agent. These agents dynamically optimize transmit power levels and channel selection to meet the QoS requirements of both CUs and DUs. Simulation results indicate that the proposed approach achieves significant improvement in average SEE by 19.84%, 35.27%, and 69.37% compared to the advantage actor-critic (A2C), deep Q network (DQN), and random allocation (RA) methods, respectively. This highlights the robustness of the proposed method in enabling efficient resource management for D2D communication, contributing to the development of self-sustainable networks.

Device-to-device (D2D)-assisted heterogeneous wireless networks are a key enabler for ultra-massive connectivity in sixth-generation (6G) networks. A critical challenge in realizing self-sustainable networks is achieving high energy efficiency (EE) while ensuring ubiquitous connectivity, meeting quality-of-service requirements, and preserving user privacy. This paper proposes a federated multi-agent double deep Q-network (F-MADDQN) framework for joint mode selection and resource allocation in D2D-assisted heterogeneous cellular networks. The proposed approach aims to maximize system EE under minimum signal-to-interference-plus-noise ratio and throughput constraints for both cellular users (CUs) and D2D users (DUs). In the proposed framework, agents exploit the local observations for channel selection, power allocation, and transmission mode decisions, while federated learning aggregates local DDQN models to preserve privacy and address partial observability. Simulation results demonstrate that the F-MADDQN method significantly enhances EE, achieving 17.55%, 35.24%, 66.72%, and 110.26% higher EE compared to multi-agent advantage actor-critic (MAA2C), multi-agent deep Q-network (MADQN), Hungarian algorithm (HA), and the random selection algorithm (RSA)-based resource allocation schemes, respectively. Moreover, the proposed scheme reduces the outage probabilities of CUs by 21.70%, 39.76%, 51.25%, and 55.43% compared to MAA2C, MADQN, HA, and RSA, respectively. These results show that the proposed framework offers a privacy-preserving solution for improving EE and reliability in D2D-assisted 6G networks.

Dual connectivity (DC) is a feature that allows dual-wireless interface devices to concurrently utilize radio resources from two different wireless network technologies. The aggregate data rate achievable using DC is expected to enhance application performance and user experience if the radio resources of contributing wireless networks are efficiently allocated. Moreover, with the current 5G deployment stage, DC is envisioned as a promising solution to address the 5G coverage holes using the existing 4G long-term evolution (LTE) infrastructure. This article presents an approach to provide statistical delay guarantees for delay-sensitive applications running on devices with DC. We propose a stochastic delay-based DC analytical model using the effective bandwidth concept. The model is applied to two case studies, namely, LTE-WiFi connectivity (licensed with nonlicensed) and 5G-LTE connectivity (intergeneration). The proposed model is used as a tool for effective resource allocation by obtaining the optimal uplink traffic share for each network that minimizes the delay violation probability or data transmission cost. Furthermore, using the analytical model, an algorithm for node admission control (NAC) is developed for DC networks. Our simulation results demonstrate that the proposed model and the NAC algorithm can efficiently allocate resources with stochastic delay guarantees for DC networks.

Smart vehicle parking is a collaborative effort of technology and human innovation where the efforts are to be minimized to save time and efforts. In smart cities it is one of the common challenges to introduce smart parking to increase parking efficiency and combat numerous issues like identification of free parking slot and real-time dynamic updation on traffic to save fuel and energy. In this work, a new cloud-based smart parking architecture is proposed that can help in predicting the available free parking slots in smart cities. Initially, the methodology collects the car count at any near by parking using Internet of Things (IoT) and Cloud-based approach. Later, the approach uses the Kernel Least Mean Square algorithm to make heuristic predictions about future vacancy using auto-regression. The proposed approach thus utilizes the online learning or model training. To validate the efficacy of the proposed work, the testing is done on the real-time dataset. The extensive numerical investigation is performed on parking lots of four international airports of a smart city in actual deployment scenarios. The experimentation has revealed superior performance of the method in terms of vacancy prediction.

Teaching some courses offering basic theoretical knowledge to electrical engineering students, such as Fundamentals of Communication Systems, in a face-to-face setting is different from teaching them online (during the COVID19 pandemic). This is mainly due to the reduced student-instructor interaction during online sessions and the difficulty in demonstrating the operation of systems by simulation software packages while interacting with students. This paper quantifies how the delivery mode of an introductory theoretical course about the fundamentals of communication systems impacts the performance of electrical engineering students using outcome-based learning (OBL), where a variety of assessment tools are used to measure the performance of students. The results indicate a decline in the attainment of the majority of the course learning outcomes with a varying degree, which impacts the corresponding program learning outcomes. An exception to this is a course learning outcome related to the practical design of transmitters and receivers.

Many Internet of Things (IoT) devices generate relatively small-sized data and have limited energy supply. These two factors limit their ability to connect directly to cloud servers through a wireless backbone network without imposing a burden on this network in providing efficient data transfer. In this article, we consider an IoT network architecture where a number of different IoT devices send their data wirelessly to an IoT gateway (or a fog node) via a WiFi network. We focus on characterizing incoming traffic patterns to the gateway for three typical IoT applications with real-time and nonreal-time data transfer requirements, such as video surveillance, smart city, and e-healthcare. Our study is based on generating real IoT traffic traces in a lab environment from various sensors and devices for the aforementioned applications and employing these traces to emulate a network of IoT nodes connected to a gateway via WiFi. In the conducted experiments, different homogenous and nonhomogeneous traffic patterns of the selected applications are examined for synchronized and unsynchronized data sources. Based on our empirical data, the experimental results reveal that the packet interarrival time distribution at the gateway is close to generalized Pareto distribution for homogeneous eHealth and smart city traffic, whereas the Weibull distribution is the nearest to model the empirical packet interarrival time for the rest of the examined traffic patterns. Moreover, we show that employing the experimental findings to analyze the delay performance of connecting the gateway to the cloud, given certain backbone network resources, leads to accurate results.

Summary We propose a model‐based test generation methodology to evaluate the impact of the interaction of the wireless network and application configurations on the performance of mobile networked applications. We consider waiting time delay to model wireless network quality. We classify mobile applications into two groups. Group I represents applications where end‐user experience is mainly affected by waiting time delay during service consumption, while group II represents applications where end‐user experience is affected by waiting time delay before service consumption. Test generation is formulated as an inversion problem. However, for group I applications, solving the inversion problem is expensive. Therefore, we utilize metamorphic testing to mitigate the cost of test oracles. We formulate metamorphic test generation as maximization of the distance between seed and follow‐up test cases. Two test coverage criteria are proposed: user experience and user‐experience‐and‐input interaction. Network models are developed for a mobile device that has network access through a WiFi hot spot and uses either transmission control protocol or user datagram protocol. Two mobile applications are used to demonstrate the methodology: multimedia streaming and web browsing. Application of the methodology when user actions are taken into consideration is also addressed. The effectiveness of the methodology is evaluated using two metrics: the incurred time cost and redundancy in the generated test suite. The obtained results show the advantage of casting test generation as an inversion problem, compared with random testing. For apps with intensive performance models, combining metamorphic testing with the methodology has tremendously reduced the cost of test oracles.

Cooperative intelligent transport system (C-ITS) is an emerging paradigm. It aims at wirelessly increasing the awareness of vehicles of their neighborhood traffic via cooperative awareness messages for safety and non-safety purposes. Toward an efficient C-ITS realization, this paper presents a simple, yet effective, broadcast scheme for I2V/V2I communications. In this scheme, road information or safety related messages are reported from the C-ITS to vehicles via road side units (RSUs) using acknowledged radio broadcasts. Vehicles report any newly observed road status and/or safety related information piggybacked in their acknowledgments. The RSUs report these messages to the rest of the vehicles under its coverage via repeated broadcasts using adaptive inter-broadcast interval based on vehicle density and estimated average channel quality. The scheme aims at decreasing unnecessary vehicle-to-vehicle (V2V) broadcasts, which leads to potentially decreasing the risk of broadcast storms. The paper introduces a comprehensive analysis of the delay performance of the proposed scheme considering practical physical channel conditions. The analysis, supported by validating simulation results, indicates the applicability of the proposed scheme as it can achieve low information transfer latency for high density roads.

Multipath TCP (MPTCP) enables a single TCP connection to use multiple paths for data delivery. This is expected to increase the overall data transfer throughput by utilizing the multihoming capabilities of communicating nodes. This paper introduces an experimental study of MPTCP connections of multihomed wireless nodes. The nodes' transceivers use two wireless interfaces. One is connected to a WiFi network, whereas the other emulates a dedicated link (such as a 3G or 4G). The performance of MPTCP is investigated in a practical setting, where the WiFi network suffers from interference of other nearby WiFi networks. The effect of connection parameters (such as maximum segment size and receiver buffer size) is examined. For such multihomed connections, our findings demonstrate that the MPTCP coupled congestion control algorithm can transfer more traffic over the WiFi link than the dedicated link only if the WiFi channel rate far exceeds the dedicated link rate.

In this paper, we study distributed generation (DG) integration in smart grid, with a focus on the voltage regulation in smart distribution systems. To ensure the operation of a smart distribution system at an acceptable voltage level, voltage regulators are deployed at some strategic locations for voltage control. The two-way communication functionality of the smart distribution system is leveraged such that the voltage regulators are coordinated by a distribution substation. Based on the measurement reports from remote terminal units (RTUs) deployed at DG unit and load connection points, stochastic information management is performed by the distribution substation to address the randomness in renewable power generation and load demand. In this paper, we formulate a voltage regulation problem in the smart distribution system based on power flow analysis, while taking into account communication delays. We show that the problem can be represented as a partially observed Markov decision process (POMDP). Since voltage regulation is performed at a relatively low frequency to avoid excessive wear and tear on the voltage regulators, a large amount of measurements should be reported by the RTUs and processed by the distribution substation for optimal voltage regulation. In order to reduce the communication and computational overhead, we further investigate the voltage regulation problem and mathematically prove that a relatively small amount of information is sufficient for the distribution substation to make an optimal decision. The theoretical results are evaluated based on a case study of IEEE 13-bus test system with real DG power generation and demand data.

ABSTRACT Data/content dissemination among the mobile devices is the fundamental building block for all the applications in wireless mobile collaborative computing, known as mobile peer‐to‐peer. Different parameters such as node density, scheduling among neighboring nodes, mobility pattern, and node speed have a tremendous impact on data diffusion in a mobile peer‐to‐peer environment. In this paper, we develop analytical models for object diffusion time/delay in a wireless mobile network to apprehend the complex interrelationship among these different parameters. In the analysis, we calculate the probabilities of transmitting a single object from one node to multiple nodes using the epidemic model of spread of disease. We also incorporate the impact of node mobility, radio range, and node density in the networks into the analysis. Utilizing these transition probabilities, we estimate the expected delay for diffusing an object to the entire network both for single object and multiple object scenarios. We then calculate the transmission probabilities of multiple objects among the nodes in the wireless mobile network considering network dynamics. Through extensive simulations, we demonstrate that the proposed scheme is efficient for data diffusion in the wireless mobile network. Copyright © 2012 John Wiley & Sons, Ltd.

This paper addresses the relation between message delivery delay and reliability for the communication between a vehicle and a road side unit (RSU). We focus on sparse vehicular sensor networks (VSNs), where timely message delivery and reliable transmission are of significant importance. We present a mathematical framework for the message delivery delay distribution for a two-lane road, where vehicles in one direction act as message carriers for the ones in the other direction and have the freedom to leave the road from randomly distributed road junctions with a certain probability. Packet generator vehicles store the original packets till meeting an RSU while sending multiple copies of each packet to packet carrier vehicles. Our analysis offers an analytical tool for an intelligent transportation system (ITS) service provider to determine the minimum RSU density required to cover a road for meeting a probabilistic requirement of the message delay. Extensive computer simulation results show the accuracy of our analysis and clearly indicate the relation of packet delay and the number of packet replicas.

In this paper, radio resource allocation in a heterogeneous wireless access medium is investigated. Mobile terminals (MTs) are equipped with multiple radio interfaces and are assumed to have multi-homing capabilities. A novel algorithm, namely prediction based resource allocation algorithm, is proposed for the resource allocation. Unlike the existing solutions in literature, the proposed algorithm does not require a central resource manager to perform the radio resource allocation. The MT plays an active role in the resource allocation operation by requesting a bandwidth share from each available network based on the available resources at the network, such that the total allocated bandwidth from different networks satisfies the MT service requirement. The proposed algorithm is suitable for implementation in a dynamic environment with call arrivals and departures, and relies on network cooperation to perform the decentralized radio resource allocation in an efficient manner. Simulation results are presented to investigate the performance tradeoffs of the proposed algorithm.

As essential building blocks of the future smart grid, microgrids can efficiently integrate various types of distributed generation (DG) units to supply the electric loads at the minimum cost based on the economic dispatch. In this paper, we introduce a decentralized economic dispatch approach such that the optimal decision on power generation is made by each DG unit locally without a central controller. The prerequisite power generation and load information for decision making is discovered by each DG unit via a multiagent coordination with guaranteed convergence. To avoid a slow convergence speed which potentially increases the generation cost because of the time-varying nature of DG output, we present a heterogeneous wireless network architecture for microgrids. Low-cost short-range wireless communication devices are used to establish an ad hoc network as a basic information exchange infrastructure, while auxiliary dual-mode devices with cellular communication capabilities are optionally activated to improve the convergence speed. Two multiagent coordination schemes are proposed for the single-stage and hierarchical operation modes, respectively. The optimal number of activated cellular communication devices is obtained based on the tradeoff between communication and generation costs. The performance of the proposed schemes is analyzed and evaluated based on real power generation and load data collected from the Waterloo Region in Canada. Numerical results indicate that our proposed schemes can better utilize the cellular communication links and achieve a desired tradeoff between the communication and generation costs as compared with the existing schemes.

This paper introduces a statistical model that allows the users of a random access-based cognitive radio network sharing a single wireless channel to evaluate the quality-of-service (QoS) capability of the network. The model captures the variation of the channel service process taking into account the statistics of the channel availability in addition to the randomness of the data traffic of the cognitive radio network users. The proposed model allows the cognitive radio network users to employ the effective bandwidth theory and its dual, the effective capacity concept, in order to evaluate the maximum packet delay bound that can be satisfied with a certain violation probability. As a result, the users can adapt the performance of their real-time applications to fit the cognitive radio network status. Simulation results validate the model and demonstrate its accuracy.

We propose an analytical model for data diffusion time/delay in a wireless mobile network using a novel peer-to-peer spatial-demand based information dissemination technique. The demand-based technique for data dissemination is beneficial for mobile network since this fully distributed and scalable network system utilizes only local urge for data and provides faster delivery of information. However, due to mobility and chaotic wireless network, it is difficult to predict the object diffusion time/delay among all the interested nodes in a mobile network. Therefore, the development of an analytical model to anticipate the expected time of data distribution among the nodes in a mobile system is an important area of research. In response to this problem, we first find the probabilities of transmitting object from one node to multiple nodes using the epidemic model of disease spreading. Utilizing these transition probabilities, we construct an analytical model based on Markov chain to calculate the expected delay of information diffusion. In addition, we adopt the mobility and scheduling impact on data transition probabilities in our analytical model. Extensive event-based simulations demonstrate that our analytical model provide near perfect estimation of data diffusion time/delay in wireless mobile networks.

This paper studies the multihop packet delivery delay in a low density vehicular ad hoc network (VANET). We address a disrupted vehicle-to-infrastructure communication scenario, where an end-to-end path is unlikely to exist between a vehicle and the nearest road side unit (RSU). We present an analytical framework, which takes into account the randomness of vehicle data traffic and the statistical variation of the disrupted communication channel. Our framework employs the effective bandwidth theory and its dual, the effective capacity concept, in order to obtain the maximum distance between RSUs that stochastically limits the worst case packet delivery delay to a certain bound (i.e., allows only an arbitrarily small fraction of the packets received by the farthest vehicle from the RSU to exceed a required delay bound). Our study also investigates the effect of the vehicle density, transmission range, and speed difference between vehicles on the end-to-end packet delivery delay. Extensive simulation results validate our analytical framework.

In this paper, we address the relation between message delivery delay and reliability for the communication between a vehicle and a road side unit (RSU). We focus on sparse or low density vehicular ad hoc networks (VANETs), where timely message delivery and reliable transmission are of significant importance. We present an exact message delivery delay distribution for a two-lane road, where vehicles in one direction act as message carriers for the ones in the other direction and have the freedom to leave the road from randomly distributed exits with a certain probability. Our analysis offers a tool for an intelligent transportation system (ITS) service provider to determine the minimal separation between two consecutive RSUs for meeting a probabilistic requirement of the message delay. Simulation results show the accuracy of our analysis.

In this paper, we propose a novel end-to-end resource allocation approach for wireless mesh networks with quality-of-service (QoS) assurance. By introducing additional interference tolerability to each multimedia flow, our proposed approach is shown effective in fostering frequency reuse and increasing the number of multimedia flows supported in the system, outperforming its conventional resource allocation counterpart. Further, the proposed approach is of low complexity, leading to a preferred candidate for practical implementation.

This paper studies the multihop packet delivery delay in a disrupted vehicle-to-infrastructure communication scenario, where an end-to-end connected path is not likely to exist between a vehicle and the nearest road side unit (RSU) due to the intermittent connectivity between adjacent vehicles. We present an analytical framework that takes into account the randomness of vehicle traffic and the statistical variation of the disrupted communication channel. Our framework employs the effective bandwidth theory and its dual, the effective capacity concept, in order to obtain the maximum distance between adjacent RSUs that stochastically limits the worst case packet delivery delay to a certain maximum value (i.e., allows only an arbitrarily small fraction of packets received by the RSU from the farthest vehicle to exceed a required delay bound). Simulation results demonstrate that our analytical framework is accurate in determining the separation distance between RSUs that probabilistically limit the worst case delay bound.

In this paper, we propose a model-based quality-of-service (QoS) routing scheme for IEEE 802.11 ad hoc networks. Unlike most of QoS routing schemes in the literature, the proposed scheme provides stochastic end-to-end delay guarantees, instead of average delay guarantees, to delay-sensitive bursty traffic sources. Via a cross-layer design approach, the scheme selects the routes based on a geographical on-demand ad hoc routing protocol and checks the availability of network resources by using traffic source and link-layer channel modeling, taking into consideration the IEEE 802.11 characteristics and node interactions. Our scheme extends the well developed effective bandwidth theory and its dual effective capacity concept to multihop IEEE 802.11 ad hoc networks. Extensive computer simulations demonstrate that the proposed scheme is effective in satisfying the end-to-end delay bound to a probabilistic limit.

In this paper, we present a fully distributed model-based call admission control (CAC) scheme for IEEE 802.11 multihop ad hoc networks. The scheme predicts the resources that a new call will consume by using both source traffic modeling and link-layer channel modeling, and hence guarantees sufficient network resources for the calls in service. Unlike most of the model-based CAC schemes in the literature, our CAC offers stochastic delay guarantees instead of average delay guarantees for multihop multimedia connections, based on the effective bandwidth theory and its dual effective capacity concept. Simulation results show that our scheme combines both efficient resource utilization and delay guarantees to a certain probabilistic limit.

This paper investigates the near-memoryless behavior of the service time for IEEE 802.11 saturated single-hop ad hoc networks. We show that the number of packets successfully transmitted by any node over a time interval follows a general distribution, which is close to a Poisson distribution with an upper bounded distribution distance. The bound on the distribution distance is almost constant and is mainly affected by some system parameters and very slightly by the number of active nodes in the network. We also show that the service time distribution can be approximated by a geometric distribution. We illustrate that the usage of discrete-time queuing analysis (M/Geo/1) near network saturation greatly simplifies the queuing analysis and leads to sufficiently accurate results for both the first order statistics and the probability distribution of the number of packets in the queuing system. Computer simulation results demonstrate that the M/Geo/1 queuing model is very accurate.

This paper presents a new approach to provide stochastic delay guarantees via fully distributed model-based call admission control for IEEE 802.11 single-hop ad hoc networks. We propose a novel stochastic link-layer channel model to characterize the variations of the channel service process in a non-saturated case using a Markov-modulated Poisson process (MMPP) model. We use the model to calculate the effective capacity of the IEEE 802.11 channel. The channel effective capacity concept is the dual of the effective bandwidth theory. Our approach offers a tool for distributed statistical resource allocation in ad hoc networks, which combines both efficient resource utilization and quality-of-service (QoS) provisioning to a certain probabilistic limit. Simulation results demonstrate that the MMPP link-layer model and the calculated effective capacity can be used effectively in allocating resources with stochastic delay guarantees.

This paper presents a novel stochastic link-layer channel model for IEEE 802.11 ad hoc networks. The model characterizes the variations of the channel service process in a non-saturated case using a Markov-modulated Poisson process (MMPP) model. It offers a tool for distributed statistical resource allocation in ad hoc networks, which combines efficient resource utilization and quality-of-service (QoS) provisioning to a certain probabilistic limit. We also propose a distributed call admission control algorithm based on the link-layer model. Simulation results demonstrate that the model can be used efficiently in allocating resources with stochastic QoS guarantees.

This paper investigates the near-memoryless behavior of the service time for IEEE 802.11 saturated single-hop ad hoc networks. We show that the number of packets successfully transmitted by any node over a time interval follows a general distribution, which is close to a Poisson distribution with an upper bounded distribution distance. We also show that the service time distribution can be approximated by a geometric distribution. We illustrate that the usage of discrete-time queuing analysis (M/Geo/1) near network saturation greatly simplifies the queuing analysis and leads to sufficiently accurate results for both the first order statistics and the probability distribution of the number of packets in the queuing system.

Ultra-wideband (UWB) wireless communication is a promising spread spectrum technology that supports very high data rates and provides precise position information of mobile users. In this paper, we present a position-based quality-of-service (QoS) routing scheme for UWB ad-hoc networks. The scheme applies call admission control and temporary bandwidth reservation for discovered routes, taking into consideration the medium access control (MAC) interactions. Via cross-layer design, it exploits UWB advantages by using the position information in routing and bandwidth reservation. Simulation results demonstrate that the proposed routing scheme is effective in end-to-end QoS support.

Ultra-wideband (UWB) wireless communication is a promising spread-spectrum technology that supports very high data rates and provides precise position information of mobile users. In this paper, we present a position-based quality-of-service (QoS) routing scheme for UWB mobile ad hoc networks. The scheme applies call admission control and temporary bandwidth reservation for discovered routes, taking into consideration the medium access control interactions. Via cross-layer design, it exploits UWB advantages at the network layer by using the position information in routing and bandwidth reservation and by supporting the multirate capability. Simulation results demonstrate that the proposed routing scheme is effective in end-to-end QoS support.