Autonomous exploration in unknown environments is a fundamental capability for intelligent mobile IoT systems, especially in scenarios where prior environmental information is unavailable. In such settings, mobile IoT devices are required to achieve safe and efficient full-area coverage based solely on onboard sensing and limited computational resources. However, the unpredictable and continuously changing nature of unknown environments poses significant challenges to adaptive and collision-free exploration, particularly for resource-constrained mobile IoT devices. To address these challenges, we propose an autonomous full-area exploration algorithm for mobile IoT devices based on reward strategy cloning. Specifically, a state representation method using color mapping is designed to improve the information intensity of input state in full-area coverage exploration missions. Simultaneously, to address the issue of sparse rewards in full-area exploration missions, we construct an intensive reward shaping function that integrates exploration rewards, collision penalties, and incentives for exploring frontier trends. Furthermore, a lightweight exploration model that maps state to action reward is designed for mobile IoT devices with limited computing power and storage resources. Moreover, we propose a reward-sensitive dynamic ϵ-greedy strategy that adaptively balances exploration and exploitation based on real-time performance trends. Finally, empirical results demonstrate the robustness of the proposed algorithm in exploring various complexities and dynamic environments. In particular, the computational complexity of the proposed exploration model is significantly reduced compared to other models.

This paper focuses on the robust beamforming for space-air-ground integrated networks (SAGIN) under uncertain channel state information (CSI). In SAGIN, uncertain CSI undermines the precise adjustment of beamforming, posing a major challenge to meeting users’ quality of service (QoS) requirements. To address this challenge, we first formulate the optimization problem as a quadratic program, which ensures a specified outage probability to maintain QoS. We then propose a multiple kernel-based machine learning method to model the uncertain CSI as an asymmetric convex polyhedron set. Leveraging semidefinite relaxation, the quadratic objective function is transformed into a linear function composed of beamforming matrix traces. Under the constructed CSI uncertainty set, a general robust transformation method is developed to linearly approximate the original probability constraints. Finally, we reformulate the joint optimization problem as a standard semidefinite program (SDP) and design an adaptive robust strategy to find its optimal solution. Simulation results show that our proposed method outperforms traditional robust and non-robust methods, effectively addressing limitations in existing SAGIN-related research.

The advent of the 5G RedCap, the upcoming 6G and the proliferation of the Internet of Things (IoT) have catalyzed the rapid advancement of unmanned aerial vehicle (UAV) technology while also promoting UAVs' widespread application. In IoT-enabled environments where the global positioning system (GPS) signals are compromised, visual simultaneous localization and mapping (V-SLAM) technology has emerged as an effective positioning solution, valued for its reliability. However, the presence of dynamic elements in complex environments, such as pedestrians and vehicles, poses challenges to the positioning accuracy of UAVs employing V-SLAM for navigation. This paper proposes a dynamic feature filtering-based SLAM (DFF-SLAM) approach to eliminate the impact of dynamic factors in dynamic environments, thereby enhancing the positioning accuracy of UAVs in IoT-enabled complex environments. Firstly, a semantic detection thread is designed to identify semantic information in the scene and acquire prior dynamic targets, facilitating the filtering of prior dynamic feature points. Secondly, optical flow tracking conducted at each level of the image pyramid facilitates feature point matching across consecutive images. Finally, the epipolar geometry constraint is utilized to determine the motion status of remaining feature points, further filtering out dynamic feature points. Simulation results demonstrate that compared to traditional visual SLAM systems, the UAV equipped with the DFF-SLAM system achieves more accurate positioning and meets real-time positioning requirements when navigating through IoT enabled complex environments

The utilization of machine learning models has become prevalent in various wireless devices to deduce the network status from various tracing data, e.g., link capacity, channel fading, etc. To cope with the increasing complexity of the network environment, deep neural models are leveraged to mine the high-dimensional network tracing data for a variety of intelligent applications. However, due to the limited resource that allocated to the network stack process, it is infeasible to train deep neural models due to the constrained computing power and absence of large-scale labeled data. Besides, the network device can barely support quick inference of large model, thus cannot support promptly response to the network conditions. In this paper, we propose a practical fast model inference system that can run high accuracy model over tiny wireless devices that are constrained in both memory and CPU power. Specifically, we design a knowledge-distillation based training method for a light-weight model that deployed at device side that can migrate the knowledge from a well-trained deep model. It is shown that our system can support fast model inference over tiny devices, which can greatly improve the network throughput in a multi-user access system by inferring the transmission collision from channel error, and thus can improve the accuracy of the link adaptation. We have conducted practical experiments to verify our system and discuss the possible extensions.

Caching popular contents at the base station (BS) in maritime Internet of Things (IoT) networks makes sensor nodes be free from frequently responding to user requests, which can remarkably save the energy consumption of sensor nodes. However, to ensure the freshness of contents, cached contents need to be updated periodically. Frequent content updating can minimize the age of information (AoI) of contents while increase the energy consumption of sensor nodes. To make a better tradeoff between the AoI and energy consumption, in this paper, both the cache placement and content updating interval are jointly optimized to minimize the weighted sum of AoI of contents and energy consumption of sensor nodes. As the formulated problem is a mixed integer nonlinear programming problem, the cache placement and the content updating interval are alternatively optimized. For the cache placement problem, a local optimal solution is achieved via the binary constraint reformulation and successive convex approximation. For the content updating problem, the optimal solution with semi-closed form is derived. Simulation results show that our proposed algorithm outperforms other benchmarks in terms of the weighted sum of AoI and energy consumption.

In the era of Internet of Things (IoT), various computation-intensive applications emerge and bring great challenges to IoT devices with limited computation capability. Mobile edge computing (MEC) provides rich computing resources for IoT devices and improves applications’ execution efficiency. In this paper, we model applications as directed acyclic graphs (DAG) and target to minimize applications’ execution latency in heterogeneous MEC networks. To solve this problem, a Deep Q-Network (DQN)-based task scheduling (DQNTS) algorithm is proposed by utilizing deep reinforcement learning (DRL). By modeling the task scheduling process as a Markov decision process (MDP) and designing its critical elements, satisfying scheduling decisions are obtained. Simulation results show that the proposed algorithm achieves lower execution latency than the compared algorithms and it is adaptable to different MEC network environments.

Cooperative task offloading has emerged as a compelling computing paradigm for balancing spatially uneven task workloads and computational resources in distributed mobile edge computing (MEC) systems. However, enabling cooperation among multiple MEC nodes inevitably requires extra communication and computational energy overheads which might counteract the cooperation gain without energy-efficient offloading mechanisms. This paper presents an adaptive cooperative task offloading algorithm aiming at maximizing the time-averaged energy efficiency for small cell MEC networks enabled by millimeter-wave backhauls. With the considered network dynamics, the proposed algorithm makes a good tradeoff between the harvested cooperation utility and the total energy consumption in the long term. In addition, our algorithm ensures the network stability and fulfills the task admission rate requirement of each individual user equipment, by making slot-based decisions over time without requiring a-priori knowledge of the network dynamics. Simulation results verify the outstanding performance of the proposed algorithm by comparing with the static cooperative and adaptive non-cooperative schemes.

Wireless networks in next generation will provide users ubiquitous computing services with low delay by devices at the network edge, namely mobile edge computing (MEC). The intensive computation tasks can be partially offloaded to the MEC server via the wireless link and then processed through the MEC computation resources to cater for the delay demand. A parallel computation process is formed in the MEC network consists of local computation at MEC users (MUs) and MEC computation at MEC servers. However, the fluctuating wireless channel environment, changeable spatial distribution of MUs and the randomness of MEC servers’ locations make it hard to characterize and guarantee the end-to-end quality of service requirements. In this work, we are devoted to analyze and optimize the overall delay bound for MEC networks under two orthogonal frequency division multiple access (OFDMA) strategies via stochastic network calculus (SNC). Specifically, Poisson cluster process is utilized to capture the randomness of MEC servers’ and users’ spatial locations and to derive the Laplace transform of interference suffered by an MU of interest. The upper bounds for the delay violation probability of two OFDMA strategies are established by exploiting SNC with the Mellin transform of signal-to-interference ratio. Furthermore, we propose an optimal task offloading scheme by minimizing the overall delay, which balances the local computation delay and MEC delay.

Unmanned aerial vehicles (UAV) wireless communications have attracted great interests in 5G networks due to its high mobility, on-demand deployment and low cost. However, it arises new serious concerns about the malicious eavesdropping attacks against UAV communications. In this paper, we study the UAV transmission with a wiretap Rayleigh fading channel, over which UAVs transmit data to a target receiver in an unsafe environment with multiple eavesdroppers. A secure transmission scheme is proposed for satisfying various performance requirements including secrecy and transmission latency, considering the unavailability of wiretappers' instantaneous channel state information (CSI). In particular, secrecy performance is measured by the derivation of secure transmission probability (STP) by physical layer security (PLS) technique. A novel stochastic-network-calculus (SNC) approach is proposed to analyze the service capability of the wiretap channel and as well calculate the latency bounds, whilst obtaining the internal relationship between secrecy and latency. Simulation results verify the theoretical performance bounds, which provide a guidance for designing secure transmission strategies with various performance requirements.

Abstract Energy harvesting (EH) aided Internet of Things (IoT) network is a promising paradigm to librate IoT network from energy deficiency. Dynamic energy and traffic scheduling in such a scenario is challenging due to temporal correlation of energy constraints and delay requirements of IoT applications. In this paper, joint energy management and sampling rate control to explore the tradeoff between network utility and delay performance are studied while maintaining the energy causality constraint. Taking into account the dynamic characteristics of EH process, channel fading and traffic arrivals, a stochastic optimisation problem is formulated to maximise the network utility. Leveraging the Lyapunov optimisation approach, combined with the idea of weight perturbation, a framework is proposed to decompose the stochastic problem into several deterministic sub‐problems that can be solved separately. Based on the framework, an online resource allocation algorithm is developed to achieve two major goals: first, balancing energy consumption and energy harvesting to stabilise their data and energy queues; second, deriving the utility‐delay tradeoff by adjusting the control parameter. The stability of data buffer and energy buffer in the proposed network is theoretical verified with performance analysis.

The development of the 5G network is envisioned to offer various types of services like virtual reality/augmented reality and autonomous vehicles applications with low-latency requirements in Internet-of-Things (IoT) networks. Mobile-edge computing (MEC) has become a promising solution for enhancing the computation capacity of mobile devices at the edge of the network in a 5G wireless network. Additionally, multiple radio access technologies (multi-RATs) have been verified with the potential in lowering the transmission latency and energy consumption, while improving the Quality of Services (QoS). Benefiting from the cooperation of multi-RATs, large latency-sensitive computing service tasks (L2SC) can be offloaded by different RATs simultaneously, which has great practical significance for data partitioned oriented applications with large task sizes. In this article, to enhance the L2SC offloading services for satisfying low-latency requirements with low energy consumption, we investigate the energy-latency tradeoff problem for partial task offloading in the MEC-enhanced multi-RAT network, considering the limitation of energy and computing in capability-constrained end devices in IoT networks. Specifically, we formulated the L2SC task computation offloading problem to minimize the weighted sum of the latency cost and the energy consumption by jointly optimizing the local computing frequency, task splitting, and transmit power, while guaranteeing the stringent latency requirement and the residual energy constraint. Due to the nonsmoothness and nonconvexity of the formulated problem with high complexity, we convert the tradeoff problem into a smooth biconvex problem and propose an alternate convex search-based algorithm, which can greatly reduce the computational complexity. Numerical simulation results show the effectiveness of the proposed algorithm with various performance parameters.

To capture the ubiquitous randomness such as time-varying wireless channel and unpredictable task arrivals in the non-orthogonal multiple access aided multi-access edge computing networks, we formulate a stochastic optimization problem aiming to minimize the time-average cost for Internet of Things devices in this paper. Due to the absence of distribution of random network information, we develop a stochastic gradient descent (SGD) based method to learn the randomness online and minimize the cost asymptotically. The proposed SGD method makes decisions only depending on the observed network information in each time-slot and achieves an [O(ε),O(1/ε)]-tradeoff between the cost-optimality and task queue backlog. To polish this tradeoff, we further propose a momentum-based SGD method by amending SGD iterations with momentum terms, which can efficiently accelerate algorithm convergence while reducing the task queue backlog without loss of cost-optimality. Finally, simulation results confirm the outstanding performance of the proposed methods.

With the ubiquitous deployment of mobile sensors and smart devices, the scope of Internet of things (IoT) has extended to the space of mobile networks, where IoT terminals are moving around instead of being fixed in buildings, ground infrastructures, etc. In this paper, we consider such mobile Internet of things (MIoT), and propose an autonomous rate control (RC) scheme for the uplink transmission from MIoT terminals to access stations. A deep reinforcement learning (DRL) based approach is designed to capture the channel variations of the link and to improve the effectiveness of the rate selection for each egress frame. Extensive simulations are conducted for MIoT terminals including vehicles and UAVs and show significant throughput performance improvement comparing with traditional methods, as well as the robustness and scalability of the DRL-RC algorithm. The proposed DRL-RC can provide inspirations for efficient and scalable link adaptation schemes for MIoT terminals.

Non-orthogonal multiple access (NOMA) is considered as a potential technique to relieve the congestion due to concurrent access from massive devices in machine-to-machine (M2M) communication system. However, the cochannel interference caused by NOMA, and the energy budget of machine-type devices (MTDs), become the bottleneck to further improve the system performance. Given above issues, we formulate the joint user scheduling and energy management problem as a stochastic optimization problem. Specifically, the goal of the problem is to maximize the long-term average sum rate under the constraint of all MTDs’ quality-of-service (QoS) requirements. For tractability, the stochastic problem is firstly transformed into two static subproblems based on Lyapunov optimization. Then, using successive convex approximation (SCA) method, we design an effective algorithm to deal with the joint user scheduling and power allocation subproblems, which is a mixed integer and non-convex programming (MINCP). Simulation results demonstrate that our proposed algorithm has a good performance in convergence and outperforms other schemes in terms of user satisfaction.

The future 5G wireless network is largely driven by the increasing heavy traffic and spectrum scarcity. Cognitive Radio (CR) techniques provide a potential solution for improving the spectrum efficiency. In this paper, we study the stochastic framework for the CR networks, considering different quality of service (QoS) requirements. To analyze the performance of the CR network, we adopt a poisson point process (PPP) to capture the mobility and randomness of user location. A stochastic-network-calculus (SNC) based approach is proposed to model the wireless transmission and evaluate the network performance. In order to achieve the performance metrics of end-to-end (E2E) delay and backlog in the entire network, we propose a new conception named as effective service process (ESP) which is able to capture the QoS requirements of users. Furthermore, we evaluate the performance in the exponential domain, which can present the E2E analysis more directly. The simulation results verify the theoretical analysis and show that the performance in the CR networks can be derived perfectly with the proposed approach, considering the stochastic traffic arrival and designed service model in our schedule.

The ever-increasing growth in maritime activities with large amounts of Maritime Internet-of-Things (M-IoT) devices and the exploration of ocean network leads to a great challenge for dealing with a massive amount of maritime data in a cost-effective and energy-efficient way. However, the resources-constrained maritime users cannot meet the high requirements of transmission delay and energy consumption, due to the excessive traffic and limited resources in maritime networks. To solve this problem, mobile edge computing is taken as a promising paradigm to help mobile devices from edge servers via computation offloading considering the different quality of service (QoS) with the complex ocean environments, resulting in energy saving and increased transmission latency. To investigate the tradeoff between latency and energy consumption in low-cost large-scale maritime communication, we formulate the offloading optimization problem and propose a two-stage joint optimal offloading algorithm, optimizing computation and communication resource allocation under limited energy and sensitive latency. At the first stage, the maritime users make the decision on whether to offload a computation considering their demands and environments. Then, the channel allocation and power allocation problems were proposed to optimize the offloading policy which coordinates with the center cloud servers at the second stage, considering the dynamic tradeoff of latency and energy consumption. Finally, numerical simulation results show the effectiveness of the proposed algorithm.

Energy harvesting with combination of multiple cooperating radio access technologies (multi-RAT) is regarded as a promising network paradigm to improve the energy efficiency of 5G networks. In this paper, we propose a resource-on-demand energy scheduling strategy for multi-RAT wireless networks, where the varying energy demand of the network can be satisfied by both grid power and harvested energy. Due to the high sensitivity to uncertainties of energy harvesting, a dynamic network energy queue model is designed first considering the inherently stochastic and intermittent nature of the harvested energy. Then, to minimize time-averaged grid power consumption and make effective utilization of harvested energy, the energy scheduling is formulated as a stochastic optimization problem subject to data queue stability and harvested energy availability, considering the high dynamics of wireless channel states and renewable energy sources. Following the Lyapunov optimization framework, the stochastic grid power minimization problem is decomposed into a network flow control subproblem, a network energy management subproblem, and a network resource allocation subproblem, respectively. In order to solve these subproblems, we develop a dynamic adaptive resource-on-demand (DAROD) algorithm to effectively reduce the grid power consumption cost by allocating the resource efficiently based on the dynamic demands of multi-RAT networks. Finally, the tradeoff between grid power consumption cost and network delay is achieved, in which the increase of network delay is approximately linear with the network control parameter V and the decrease of grid power consumption cost is at the speed of 1/V. Extensive simulations are conducted to verify the theoretical analysis and show the effectiveness of our proposed algorithm.

To satisfy the high requirements on operation efficiency in the 5G network, self-organizing network (SON) is envisioned to reduce the network operating complexity and costs by providing SON functions, which can optimize the network autonomously. However, different SON functions have different time scales and inconsistent objectives, which leads to conflicting operations and network performance degradation, raising the needs for SON coordination solutions. In this paper, we devise a multiple time-scale coordination management scheme (MTCS) for densely deployed SONs, considering the specific time scales of different SON functions. Specifically, we propose a novel analytical model named M time-scale Markov decision process, where SON decisions made in each time-scale consider the impacts of SON decisions in other M - 1 time scales on the network. Furthermore, in order to manage the network more autonomously and efficiently, a Q-learning algorithm for SON functions in the proposed MTCS scheme is proposed to achieve a stable control policy by learning from history experience. To improve energy efficiency, we then evaluate the proposed MTCS scheme with two functions of mobility load balancing and energy saving management with designed network utility. The simulation results show that the proposed SON coordination scheme significantly improves the network utility with different quality of experience requirements while guaranteeing stable operations in wireless networks.

In 5G networks, self-organizing network (SON) is envisioned to reduce the network operating complexity and costs by providing SON functions, especially in ultra- dense small cell networks. However, diverse SON functions have different time scales and inconsistent targets, which leads to the operation conflicts and network performance degradation. In this paper, we devise a multiple time-scale coordination management scheme (MTCS) to guarantee efficient and stable network operations for densely deployed SONs, where different SON functions have their own specific time scales. Specifically, we propose a novel analysis model , named M time-scale Markov decision process (MMDP), where SON decisions made in each time-scale considers the impacts of SON decisions in other M-1 time scales on the network. Then the proposed scheme with two functions of mobility load balancing (MLB) and energy saving management (ESM) is evaluated in terms of the designed network utility. Simulation results demonstrate that the proposed SON function coordination scheme significantly improves the network utility, while guaranteeing the stability of cooperative operations in wireless networks.

Small cell networks (SCNs) are envisioned as a promising solution to increase the network capacity and coverage. The densely deployments of SCNs in 5G networks pose new challenges for energy-efficient network management. Energy harvesting technique is put forward as a relatively new energy saving concept. However, due to the opportunistic nature of energy harvesting, the uncertainty and complexity will be introduced in energy harvesting SCNs (EH-SCNs) network management. In this paper, we study the self- organized cell operation management problem with different quality of service (QoS) requirements of users, in which the EH-SCNs needs to perform cell activation operation in a distributed manner with the uncertainty of harvested energy. With the assumption of Markovian energy harvesting process, multi-armed bandit game (MAB) based Thompson Sampling algorithm is developed to solve the small cell activation problem with a self-organized manner in EH-SCNs. Simulation results show that our proposed approach is particularly suitable to manage the large-scale EH-SCNs more efficiently under uncertain environment with incomplete information.

Recently, millimetre‐wave (mmWave) wireless fronthauls have been regarded as an effective solution to deploy remote radio heads with higher flexibility and efficiency in cloud radio access networks (C‐RANs). Different from the traditional fibre fronthauls, in order to maximise the utilisation of the time‐frequency resource, the mmWave wireless fronthauls are more expected to operate in a dynamic allocation manner. In this study, the energy efficient mmWave fronthaul and OFDMA resource optimisation in C‐RANs is investigated. The TDMA‐based fronthaul allocation mechanism is first presented and then the joint resource optimisation is formulated as an energy efficiency (EE) maximisation problem which is in the form of a mixed‐integer non‐linear fractional programming (MINLFP) problem. By taking advantage of the Dinkelbach method, the MINLFP problem is transformed into a subtractive optimisation problem and solved by using the Lagrange dual decomposition theory. Moreover, a maximal weighted bipartite graph matching approach is proposed to determine the optimal resource block allocation. Finally, extensive simulation results are provided to evaluate the EE performance of the proposed algorithm by comparing with several benchmark schemes, and it shows that the proposed algorithm can achieve great EE performance gain over the benchmark schemes.

To enable sustainable wireless networks, though new technologies have been proposed to improve the system spectrum efficiency, the energy efficiency (EE) is also of vital importance due to the increasing users and devices. Active array system (AAS) and heterogeneous networks (HetNets) have been reckoned as an enormous enhancement in spectrum efficiency and EE. In this study, the energy efficient user association and resource allocation problem for preference‐aware multicast service in AAS aided HetNets is investigated, formulated as a mixed‐integer non‐linear fractional programming. By generalised fractional programming theory and Lagrangian dual decomposition, an iterative algorithm is devised to determine user association and resource allocation. Further, an efficient solution is proposed to perform quality of service‐guaranteed user association and resource allocation to maximise the EE. Simulation results demonstrate the convergence performance and potential gain of the proposed algorithms in terms of EE.

In this study, the authors investigate the resource allocation for orthogonal frequency‐division multiple access (OFDMA) wireless networks, where the base station is powered by renewable energy and electric grid. To fully exploit the renewable energy, the authors propose an energy‐aware resource allocation (EARA) algorithm to maximise the network utility, which captures the tradeoff between the system throughput and the grid energy consumption. Specifically, the EARA algorithm only has to track the current system states (e.g. channel and queueing conditions) without requiring a relevant priori distribution knowledge, making it applicable for practical OFDMA wireless networks with unpredictable channel dynamics, renewable energy arrivals and stochastic traffics. Moreover, the performance achieved by the EARA algorithm is theoretically characterised. Most importantly, the authors develop an implementation architecture to take the EARA algorithm into practice, and also analyse the low implementation costs (e.g. low computational complexity, trivial signalling overhead etc.). Finally, simulation results verify the theoretical analysis and also demonstrate the advantages of the EARA algorithm.