Multi-Object Tracking (MOT) of polyps in colonoscopy videos serves as a foundational component for intelligent medical workflows. In tracking-by-detection algorithms, managing the equilibrium among competing optimization objectives during data association presents a complex issue. Current approaches, including threshold-based weighted linear aggregation methodologies and hierarchical cascade matching schemes, demonstrate persistent limitations when deployed in endoscopic polyp monitoring scenarios characterized by vigorous polyp movements and complex morphological deformations. Thus, this study proposes a novel polyp tracking framework named MO2Tracker, which can simultaneously optimize multiple objectives during the data association phase. Specifically, MO2Tracker integrates three discriminative similarity cues in parallel: polyp coordinates, detection confidence scores, and appearance consistency, to establish associations between new detections and existing trajectories. To resolve this optimization challenge, we implement Pareto Front analysis via a multi-objective optimization algorithm, deriving Pareto Front solutions. Subsequently, we design two novel selection paradigms, knee-oriented selection and human-inspired selection, to filter optimal detector-trajectory pairings from the Pareto Front. Extensive experiments conducted on two public datasets and one private dataset validate the superior performance of MO2Tracker. On the public dataset SUN-SEG, MO2Tracker achieves improvements of +4.1% in Multi-object Tracking Accuracy (MOTA), +7.2% in Higher Order Tracking Accuracy(HOTA), and +5.8% in IDF1 Score.

Unmanned Aerial Vehicles (UAVs) have emerged as relay nodes to alleviate multi-user traffic congestion in wireless communication networks due to their flexibility, fast deployment and line-of-sight links. However, the selfish attribute of both users and UAVs presents significant challenges to the efficient participation of UAVs in multiple relay tasks, particularly with conflicting profit objectives. In this article, we propose a double auction-based incentive mechanism that incentivizes UAVs to engage in relay tasks while achieving a strategic trade-off between network throughput and market fairness. Specifically, UAVs (sellers) provide relay services to ground users (buyers), with base station acting as an auctioneer responsible for matching user and UAV, charging fees from users, and giving payments to UAVs. The presence of heterogeneous users and UAVs complicates the relay tasks processing. Additionally, designing an effective payment strategy that meets economic properties further exacerbates the difficulty. To address these challenges, we propose a throughput maximization algorithm to enhance network communication efficiency and design a winner and pricing determination algorithm to establish a reasonable market. The simulation results demonstrate that our mechanism achieves higher transaction quantity than benchmark schemes (McAfee, Candidate critical pair, and Vickrey-Clarke-Groves (VCG)), motivating UAVs to participate in relay tasks for users.

Summary In postdisaster rescue scenario, unmanned aerial vehicles (UAVs) are effective tools to help ground users in disaster areas to transmit rescue‐critical data to the relief center in time due to their flexible mobilities and fast deployment. However, how UAVs choose disaster areas to take part in traffic offloading with limited bandwidth resources is challenging. This article investigates UAVs' access selection and disaster areas' bandwidth allocation scheme by jointly optimizing network throughput, bandwidth and UAVs' energy cost. The UAVs form coalitions to participate in traffic offloading cooperatively, where each UAV selects a disaster area independently. Specifically, the bandwidth resources are dynamically allocated to all disaster areas when a new coalition partition forms. The UAVs adjust their access choices for disaster areas when the bandwidth allocation changes. When no UAVs migrate to other disaster areas, the network achieves a stable state. The UAVs fly to the final selected disaster area and provide traffic offloading services. To resolve traffic offloading competitions yet enable cooperations among UAVs, we address the UAVs' access selection issue by coalition formation game. A gradient projection method is then proposed to allocate bandwidth resources which maximizes the benefit of the network. We demonstrate that the UAVs' access selection and disaster areas' bandwidth allocation algorithms are convergent. The simulation results demonstrate that our proposed cooperation order is better than the pareto and selfish orders, thereby increasing the benefit of the network.

The energy harvesting of unmanned aerial vehicle (UAV) has been researched extensively in recent years. However, the existing energy harvesting between the base station and UAVs does not consider the information asymmetry factor, which means the base station provides the radio frequency (RF) energy for UAVs in the context of UAVs’ partial private information. In order to maximize the base station’s utility or payoff, it is crucial for the base station to motivate more UAVs to harvest RF energy. In the paper, we propose an effective incentive energy harvesting mechanism in UAV communication network, which is a challenging problem since there exist interest conflicts that the base station and UAVs are rational individuals who maximize their utilities. Our objective is to make the base station’s utility maximum via balancing the tradeoff between transmit power cost and charged price benefit, while incentivizing UAVs to purchase transmit power. We design a series of optimal energy harvesting contract with different price discounts targeting different types of UAVs by contract theory. Owing to information asymmetry, we analyze two different information scenarios: complete and incomplete information. We suppose the base station knows each UAV’s type in complete information, then we analyze the practical case that the base station is aware of incomplete information of UAV’s private information. The base station aims to maximize its utility by providing contract. The UAVs choose the contract meeting the individual rationality (IR) and incentive compatibility (IC) rules while maximizing their utilities. Our simulation shows that the energy harvesting mechanism maximizes the base station’s utility and stimulates UAVs to purchase RF energy transmit power in different scenarios. Compared with other methods, our proposed optimal contract can improve the utility of the base station while maximizing the utility of UAVs.

Traditional health insurance schemes make contracts with periodic premiums according to the health conditions of customers. Insurance companies are thus seeking an incentive for a healthy lifestyle of customers. Nowadays, more and more people want to gather metrics associated with physical activity. High quality and quantity information introduced by wearable devices may indicate the potential risks of acute conditions. Therefore, wearable technology brings new opportunities for insurance companies to change the interaction way with customers and to improve risk management for users. In this article, we investigate the problem of incentive mechanism design for the insurance market boosted by wearable technology. Designing an effective incentive mechanism is challenging since both the insurer and the users are selfish and rational parties who try to maximize their own utility. There exists an information asymmetry, i.e., the insurer is unaware of the intrinsic physical fitness of each user (type of users). Inspired by the contract theory, we design a series of optimal insurance contracts with different premium discounts and recommended exercise levels targeting different types of users. We first analyze the scenario where the insurer knows the type of each individual, then move to the more practical case where the insurer has incomplete information of user types. We theoretically prove that the proposed contracts satisfy individual rationality and incentive compatibility, which enables the insurer to achieve utility maximization and each type of user to choose the most appropriate contract. We have conducted extensive simulations to provide insight into the performance under different scenarios. The numerical results verify that our contract-based insurance incentive mechanism maximizes insurer's utility and motivates users to exercise more.

Cellular vehicle‐to‐everything‐ (C‐V2X‐) based communications can support various content‐oriented applications and have gained significant progress in recent years. However, the limited backhaul bandwidth and dynamic topology make it difficult to obtain the multimedia service with high‐reliability and low‐latency communication in C‐V2X networks, which may degrade the quality of experience (QoE). In this paper, we propose a novel cluster‐based cooperative cache deployment and coded delivery strategy for C‐V2X networks to improve the cache hit ratio and response time, reduce the request‐response delay, and improve the bandwidth efficiency. To begin with, we design an effective vehicle cluster method. Based on the constructed cluster, we propose a two‐level cooperative cache deployment approach to cache the frequently requested files on the edge nodes, LTE evolved NodeB (eNodeB) and cluster head (CH), to maximize the overall cache hit ratio. Furthermore, we propose an effective coded delivery strategy to minimize the network load and the ratio of redundant files. Simulation results demonstrate that our proposed method can effectively reduce the average response delay and network load and improve both the hit ratio and the ratio of redundant files.