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As an emerging Low-Power Wide Area Networks (LPWAN) technology, LoRa is dedicated to providing long-range connections for pervasive Internet-of-Things devices. As LoRa operates in the unlicensed spectrum with an ALOHA-based MAC-layer protocol stack, transmissions from multiple LoRa end-devices inevitably collide with each other, leading to packet losses and increased transmission delay. Targeting at collisions caused by interferences under the same spreading factor (SF) settings, researchers introduce multiple lines of techniques. Despite their efforts, these techniques commonly neglect the potential collisions caused by interferences under different SF settings, resulting in imperfect orthogonality. Given the disparate transmission power configurations and diverse deployed locations, the collisions under different SFs commonly exist in practical networks and significantly limit the LoRa reliability. This paper presents X-MAC, the first scheduler aware of imperfect orthogonality. Technically, X-MAC detects the collisions under different SFs via tracking historical transmissions, and performs dynamic channel scheduling to avoid collisions caused by interferences under the same and different SFs. Extensive evaluations on testbed devices show that, compared with the state-of-the-art methods, X-MAC boosts the network scalability (number of concurrent end-devices) by 1.26× to 2.41× with packet reception rate requirement of $> $> 95%.

Metaverse has been viewed as the next generation of human-computer interaction, which requires collecting information from both the physical and virtual world. One potential way is to employ virtual service providers (VSPs) to finish collection tasks by designing posted-pricing mechanisms via the crowdsensing platform. As VSPs’ costs and values are usually unknown, learning the optimal posted-pricing policy under uncertainty is undoubtedly critical to utilize the budget efficiently. However, existing posted-pricing learning algorithms assume that agents provide services without blocking and agents’ attributes follow an independent identical distribution, both of which are unrealistic in Metaverse, e.g., VSPs should continuously sense the physical world to make provided services realistic, which makes the long working VSP unavailable/blocked for a certain period of time. In this paper, we address the budgeted pricing problem under uncertainty by considering blocking constraints and unknown non-identical VSPs’ attributes. The problem is modeled as a Budgeted-pricing Blocking Bandit (B2-bandit) problem, which remains unaddressed even for the oracle case with known VSPs’ information. We thus first propose a pricing policy for the oracle case with an instance-dependent approximation ratio to the global optimum. For the general B2-bandit problem with unknown information, we propose an online learning algorithm satisfying blocking constraints and incurring an accumulated regret up to $O(MK\log B)$ as compared to the oracle approximation algorithm, where $M,K,B$ are the number of VSPs, candidate prices and the budget, respectively. Experiments on real datasets validate that the proposed algorithm improves more than 172% accumulated value compared to baseline pricing algorithms.

In a crowdsensing platform, users are invited to provide data services, and multiple requesters compete for desired services. Due to users’ costs of providing services, it is critical to design incentive mechanisms to incentivize users with (monetary) rewards. Meanwhile, requesters may have individual budgets and compete for services with different procurement abilities. Such a setting falls into the budget-feasible mechanism design. However, most of the existing budget-feasible mechanisms focus on one-sided markets with a single requester rather than the two-sided markets with multiple requesters having different procurement abilities. Moreover, requesters and users can be selfish and strategic with their private information, which requires preventing information manipulation on both requesters’ and users’ sides. In this paper, we investigate budget-feasible mechanisms in two-sided crowdsensing markets where multiple strategic requesters come with private budgets to obtain services from the strategic users. We also consider the fairness on the requesters’ side, i.e., a requester with more budget should obtain more service. We propose budget-feasible mechanisms for two models by distinguishing the types of services, i.e., the homogeneous or heterogeneous services. All proposed mechanisms satisfy fairness, budget feasibility, truthfulness on both users’ and requesters’ sides, and the constant approximation ratio. Numerical experiment results further demonstrate the efficiency of our proposed mechanisms.

Unmanned aerial vehicles (UAVs) are being widely exploited for various applications, e.g., traversing to collect data from ground sensors, patrolling to monitor key facilities, moving to aid mobile edge computing. We summarize these UAV applications and formulate a problem, namely the general waypoint-based PoI-visiting problem. Since energy is critical due to the limited onboard storage capacity, we aim at minimizing flight energy consumption. In our problem, we pay special attention to the energy consumption for turning and switching operations on flight planning, which are usually ignored in the literature but play an important role in practical UAV flights according to our real-world measurement experiments. We propose specially designed graph parts to model the turning and switching cost and thus transfer the problem into a classic graph problem, i.e., general traveling salesman problem, which can be efficiently solved. Theoretical analysis shows that such problem transformation has the graph redefinition approximation ratio upper bound, $max\lbrace \Theta /\delta,2\rbrace$max{Θ/δ,2}, where $\Theta$Θ is related to the designed graph parts and $\delta$δ is a constant. Finally, we evaluate our proposed algorithm by simulations. The results show that it costs less than 107% of the optimal minimum energy consumption for small scale problems and costs only 50% as much energy as a naive algorithm for large scale problems.