Article Link | Author's Personal Website

Abstract: Multi-access Edge Computing (MEC) is an emerging computing architecture to release the resource burden of the centralized cloud and reduce the mobile application latency. Services management and MEC requests routing is a major problem in MEC systems. Existing works mainly focus on the one-hop centralized request routing strategies. However, the centralized one-hop routing method is not suitable enough since the MEC network is a distributed system, and the number of MEC requests increases dramatically. In this paper, we have proposed an online problem. In such problem, we jointly consider the mobile edge service management and the distributed multi-hop requests routing in an MEC network in which the MEC requests randomly generate. We prove that such problem is NP-Hard even in the off-line scenario. Furthermore, we propose an approximation algorithm to manage the MEC services and two distributed online algorithms to route MEC requests. The approximation ratio and competitive ratio of these algorithms have been analyzed. Experiments are carried out to evaluate the performance of the algorithms and simulation results imply that these algorithms are effective and efficient.

⭐️ Note: All episodes are automatically generated by NotebookLM, and there may be some discrepancies between the content and the original texts.

Article Link | Author's Personal Website

Abstract: The deployment of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BSs) has emerged as a promising solution to enhance communication services provided to ground users. However, deploying UAV-BSs faces challenges including the cooperation of multiple UAVs, dynamic user distribution, low-reliability issues of UAVs, and efficient redeployment in large environments. Existing literature addresses some of these challenges but lacks a comprehensive approach. In this paper, we investigate all these challenges and propose a novel deployment framework with the objective of maximizing the quality of communication service by dynamically deploying UAV-BSs. The proposed framework employs a decentralized approach, allowing UAV-BSs to locally adjust their locations and rapidly respond to changes, thus ensuring stable and efficient service supply to ground users. In addition, we implement multiple simulation experiments to evaluate the performance of the proposed framework in solving the UAV-BSs redeployment problem in large and dynamic environments. The results demonstrate its ability to effectively improve the quality of communication services.

⭐️ Note: All episodes are automatically generated by NotebookLM, and there may be some discrepancies between the content and the original texts.

Article Link | Author's Personal Website

Abstract: Using Unmanned aerial vehicles (UAV) as base stations for providing communication services to urban residents has emerged as a novel trend in the era of the Internet of Things (IoT). However, UAV base stations (UAV-BSs) applications have faced some challenges, such as the finite energy capacity, the limited coverage range, and the complex flying environment. Consequently, addressing these challenges and maximizing communication service quality remains a significant problem. In order to achieve higher service quality, it is necessary for UAV-BSs to collaborate with each other, optimize their serving positions, and conserve energy. Although existing works have investigated some of these challenges, there has been a lack of work that considered all of them jointly. In this work, we propose a Local-based scheduling algorithm which aims to maximize the service quality by achieving a good balance between flying status and serving status of UAV-BSs. The proposed method has been evaluated by theoretical analysis and simulation experiments. The results demonstrate its effectiveness in significantly improving the communication service quality.

⭐️ Note: All episodes are automatically generated by NotebookLM, and there may be some discrepancies between the content and the original texts.

Article Link | Author's Personal Website

Abstract: Due to the large volume of IoT data, conventional sensor network-based and cloud-based IoT systems cannot handle latency-sensitive and resource-consuming IoT applications. Sensor networks do not have enough computation resources and also suffer from a limited network lifetime. On the other hand, the cloud-based IoT system is far away from the users and the physical world, and cannot satisfy the real-time requirements of IoT applications. We adopt the IoT edge network to address these challenges and process IoT applications in modern IoT systems. The IoT edge network is an emerging computing architecture in the IoT. Compared to the sensor nodes in conventional sensor networks, the edge servers have more computation resources. Compared to the remote cloud, the edge servers are closer to the users and the physical world. However, processing IoT applications in the edge network still remains challenging. First, how to process concurrent IoT applications has not been fully investigated. Second, the inner relationship between the network resource and the application latency has not been deeply analyzed. Third, the function conflict problem in edge servers has not been taken seriously. To solve the above challenges, we propose the Energy and Latency Efficient Processing Plan for Concurrent IoT Applications Problem which aims to construct an application processing plan by jointly considering the concurrency, the energy-latency relationship, and the function conflict problems. We prove that such a problem is NP-Hard, and algorithms are proposed accordingly. Furthermore, we also estimate the performance of the proposed algorithms by numerical results.

⭐️ Note: All episodes are automatically generated by NotebookLM, and there may be some discrepancies between the content and the original texts.

Article Link | Author's Personal Website

Abstract: Duty-cycle scheduling is an effective way to balance energy consumption and prolong network lifetime of wireless sensor networks (WSNs), which usually requires a connected dominating set (CDS) to guarantee network connectivity and coverage. Therefore, the problem of finding the largest number of CDSs is important for WSNs. The previous works always assume all the nodes are non-rechargeable. However, WSNs are now taking advantage of rechargeable nodes to become energy harvest networks (EHNs). To find the largest number of CDSs then becomes completely different. This is the first work to investigate, how to identify the largest number of CDSs in EHNs to prolong network lifetime. The investigated novel problems are proved to be NP-Complete and we propose four approximate algorithms, accordingly. Both the solid theoretical analysis and the extensive simulations are performed to evaluate our algorithms.

⭐️ Note: All episodes are automatically generated by NotebookLM, and there may be some discrepancies between the content and the original texts.

Article Link | Author's Personal Website

Abstract: Battery-free wireless sensor network (BF-WSN) is a newly proposed network architecture to address the limitations of traditional wireless sensor networks (WSNs). The special features of BF-WSNs make the coverage problem quite different and even more challenging from and than that in traditional WSNs. This paper defines a new coverage problem in BF-WNs which aims at maximizing coverage quality rather than prolonging network lifetime. The newly defined coverage problem is proved to be at least NP-Hard. Two sufficient conditions, under which the optimal solution of the problem can be derived in polynomial time, are given in this paper. Furthermore, three approximate algorithms are proposed to derive nearly optimal coverage when the sufficient conditions are unsatisfied. The time complexity and approximate ratio of the three algorithms are analyzed. Extensive simulations are carried out to examine the performance of the proposed algorithms. The simulation results show that these algorithms are efficient and effective.

⭐️ Note: All episodes are automatically generated by NotebookLM, and there may be some discrepancies between the content and the original texts.

Article Link⁠ | ⁠Author's Personal Website

Abstract: Despite considerable advancements in specialized hardware, the majority of IoT edge devices still rely on CPUs. The burgeoning number of IoT users amplifies the challenges associated with performing multiple Deep Neural Network inferences on these resource-limited, CPU-only edge devices. Existing strategies, including model compression, hardware acceleration, and model partitioning, often involve a trade-off in inference accuracy, are unsuitable due to hardware specificity, or lead to inefficient resource utilization. In response to these challenges, this paper introduces L-PIC (Latency Minimized Parallel Inference on CPU)—a framework expressly devised to optimize resource allocation, decrease inference latency, and maintain result accuracy on CPU-only edge devices. A series of comprehensive experiments have verified the superior efficiency and effectiveness of the L-PIC framework in comparison to the state-of-the-art method. Remarkably, compared to the state-of-the-art method, L-PIC can reduce the inference latency of multi-DNN by an average of approximately 30% across all tested scenarios.

⭐️ Note: All episodes are automatically generated by NotebookLM, and there may be some discrepancies between the content and the original texts.

Article Link | Author's Personal Website

Abstract: Unmanned Aerial Vehicles (UAVs) can be an important component in the Internet of Things (IoT) ecosystem due to their ability to collect and transmit data from remote and hard-to-reach areas. Ensuring collision-free navigation for these UAVs is crucial in achieving this goal. However, existing UAV collision-avoidance methods face two challenges: conventional path-planning methods are energy-intensive and computationally demanding, while deep reinforcement learning (DRL)-based motion-planning methods are prone to make UAVs trapped in complex environments— especially for long trajectories with unpredictable obstacles— due to UAVs' limited sensing ability. To address these challenges, we propose a hybrid collision-avoidance method for the real-time navigation of UAVs in complex environments with unpredictable obstacles. We firstly develop a Human-in-the-Loop DRL (HL-DRL) training module for mapless obstacle avoidance and secondly establish a global-planning module that generates a few points as waypoint guidance. Moreover, a novel goal-updating algorithm is proposed to integrate the HL-DRL training module with the global-planning module by adaptively determining the to-be-reached waypoint. The proposed method is evaluated in different simulated environments. Results demonstrate that our approach can rapidly adapt to changes in environments with short replanning time and prevent the UAV from getting stuck in maze-like environments.

⭐️ Note: All episodes are automatically generated by NotebookLM, and there may be some discrepancies between the content and the original texts.

Article Link | Author's Personal Website

Abstract: Path planning is one of the most essential parts of autonomous navigation. Most existing works suppose that the environment is static and fixed. However, path planning is widely used in random and dynamic environments (such as search and rescue, surveillance, and other scenarios). In this paper, we propose a Deep Reinforcement Learning (DRL)-based method that enables unmanned aerial vehicles (UAVs) to execute navigation tasks in multi-obstacle environments with randomness and dynamics. The method is based on the Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithm. In order to predict the impact of the environment on UAV, the change of environment observations is added to the Actor-Critic network input, and the two-stream Actor-Critic network structure is proposed to extract features of environment observations. Simulations are carried out to evaluate the performance of the algorithm and experiment results show that our method can enable the UAV to complete autonomous navigation tasks safely in multi-obstacle environments, which reflects the efficiency of our method. Moreover, compared to DDPG and the conventional TD3, our method has better generalization ability.

⭐️ Note: All episodes are automatically generated by NotebookLM, and there may be some discrepancies between the content and the original texts.