Restricted Target Wake Time (R-TWT) is designed to prioritize latency-sensitive traffic within a Basic Service Set (BSS). However, when transmissions from Overlapping BSSs (OBSSs) overlap with the R-TWT Service Period (SP), the resulting interference may still disrupt latency sensitive traffic. The IEEE 802.11bn amendment addresses this issue by introducing Coordinated R-TWT (CR-TWT), which mandates neighboring BSSs using the same primary channel to remain silent, thereby extending SP protection. While non-overlapping R-TWT schedules guarantee protection, they may not satisfy the stringent timing requirements of delay-sensitive traffic. Conversely, allowing concurrent transmissions among Multi-Access Points (Multi-APs) in the same SP through spatial reuse can be beneficial. This poses challenges since spatial reuse typically requires a centralized coordinator to collect Received Signal Strength Indicator (RSSI) information and compute the optimal transmit power of involved APs, incurring significant signaling overhead. Meanwhile, the Traffic Identifier (TID) is critical for negotiating R-TWT SPs, as Multi-APs can utilize this information to prioritize high-priority latency-sensitive traffic. These considerations motivate our proposal of Distributed CR-TWT (DCR-TWT), which incorporates TID prioritization and formulates weighted data rate maximization as a convex optimization problem. In DCR-TWT, each AP independently solves its local optimization problem to determine whether to transmit within the SP and its corresponding optimal transmit power, greatly reducing signaling overhead. We evaluate the performance of CR-TWT and DCR-TWT using a full physical layer model coupled with a simplified Medium Access Control (MAC) layer model. An adaptive Modulation and Coding Scheme (MCS) selection mechanism is devised to enhance simulation realism. Results show that DCR-TWT outperforms CR-TWT by achieving more than two times the weighted data rate, a significant reduction in the packet error rate and the packet transmission latency. Furthermore, we identify a limitation in DCR-TWT: excessive scheduling for concurrent transmissions. This overscheduling results in up to 30% of cases failing to meet the minimum Signal-to-Interference-plus-Noise Ratio (SINR) threshold. To overcome this, we propose Enhanced DCR-TWT (EDCR-TWT), which excludes BSSs causing difficulties in the convergence of the convex optimization problem. Experimental results demonstrate a significant improvement: the proportion of cases below the minimum SINR threshold decreases from 30% to below 10% under EDCR-TWT. A comparative analysis reveals that EDCR-TWT outperforms prior work in both weighted data rate and packet error rate.

Due to the rapid development of deep neural networks (DNNs), ensuring their quality has become increasingly important.However, the test oracle problem poses an obstacle to DNN testing because of the massive unlabeled data.Metamorphic Testing (MT) has proven effective in alleviating the test oracle problem, and many efforts have been made to improve the cost-effectiveness of MT for DNNs.Some approaches focus on selecting good metamorphic relations (MRs), while others target the selection of suspicious source test cases.Since follow-up test cases are generated by combining source test cases with MRs, selecting effective pairs of source test cases and MRs is also quite essential and beneficial for MT.In this paper, we propose CMPS, a multi-objective black-box approach for metamorphic test case pair selection.Considering both uncertainty and diversity, CMPS aims to select pairs that can detect more unique faults in the model.It evaluates uncertainty based on model outputs and assesses diversity through clustering source test cases.Furthermore, CMPS can adaptively optimize the selection process based on feedback from the execution results of the selected pairs.We conduct extensive experiments on three datasets and five DNN models to evaluate CMPS's performance.The experimental results demonstrate that CMPS significantly outperforms baseline approaches in both failure triggering and fault detection.

UAV-aided WPSNs (Unmanned Aerial Vehicle aided Wireless Powered Sensor Networks) are promising for the emerging IoT (Internet of Things) applications. In the literature, one UAV is commonly adopted for one WPSN and it wirelessly charges the sensor nodes and collects data from them. In this paper, we address two new issues in UAV-aided WPSN. First, we adopt multiple UAVs for a WPSN in order to support many sensor nodes over a large service area. With multiple UAVs, it is necessary to optimize the locations of the UAVs and the UAV-to-node associations. Second, we utilize the UAVs for three joint functions: (i) wirelessly charging the sensor nodes, (ii) data collection from the sensor nodes, and (iii) data dissemination to the sensor nodes. We formulate an optimization problem to address the above two new issues, where the objective is to maximize the throughput. We prove that this problem is NP-hard and design a heuristic algorithm to tackle it. This algorithm iteratively executes two inter-dependent operations. The first operation optimizes the UAV-to-node associations via load balancing among the UAVs, reducing the data transmission time. The second operation optimizes the UAV locations via Particle Swarm Optimization, reducing the charging time. The simulation results show that: (i) when more UAVs are used, the throughput is significantly increased, and (ii) the proposed algorithm efficiently determines the locations of the UAVs and the UAV-to-node associations for joint charging, data collection and data dissemination.

A University-Enterprise Deep Integration-Oriented Talent Training Approach (UED-IOTTA) was designed to improve the post-competency and employment prospects of software engineering graduates from colleges and universities. This approach is tightly aligned with the demand for talent in enterprises and the development of talent in academic in-stitutions on a variety of dimensions, including employment requirements, professional ethics, and occupational skills. Building on this paradigm, a specific implementation strategy has been developed. This implementation strategy's key components include establishing industry-specific training classes known as UED-IOTTA classes, providing software engineering vocational training, putting industry-academic collaboration supervision techniques into practice, developing progressive pedagogy, and defining particular university-enterprise partnership strategies. Empirical results indicate the noteworthy influence of this cus-tomized talent training approach on improving the caliber of talent advancement in the field of software engineering.

A dense WiFi uses numerous access points (APs) to provide Internet access to many users in an indoor site (such as concert hall or stadium). To deploy dense WiFi, the existing approach adopts two separate QoS requirements: (i) ensuring a minimum throughput for each station, and (ii) ensuring fault tolerance by withstanding the failure of at most$N$APs where$N$is a given value. We observe that these separate requirements lead to costly dense WiFi because the number of required APs increases by about$N$times. To address this issue, we propose a joint throughput and fault tolerance requirement (or joint requirement) to construct cost-effective dense WiFi. This joint requirement ensures that the throughput of each station is at least$\rho_{i}$when any$i$APs fail, where$\rho_{i}$is a given value and$i=0,1,2,\ldots$. For example, when no AP fails, the per-station throughput is at least$\rho_0 = 5$Mbps; when anyone AP fails, the per-station throughput is at least$l$Mbps; when any two APs fail, the per-station throughput is at least$\rho_{2}=1$Mbps. To realize this joint requirement, we formulate and solve an optimization problem for AP placement and resource allocation. The objective is to minimize the number of APs required while fulfilling the joint requirement. Simulation results demonstrate that the joint requirement offers desirable tradeoff between cost and performance, making dense WiFi more cost-effective.

In this paper, we design an evaluation-driven online learning mode. The implementation process of the proposed mode is illustrated. In addition, the evaluation scheme of the online course is explained. Practice results show that the designed online learning mode is effective.

IEEE 802.11ax is the standard for the new generation WiFi networks. In this paper, we formulate the problem of joint access point (AP) placement and power-channel-resource unit assignment for 802.11ax-based dense WiFi. The objective is to minimize the number of APs. Two quality-of-service (QoS) requirements are to be fulfilled: (1) a two-tier throughput requirement which ensures that the throughput of each station is good enough, and (2) a fault tolerance requirement which ensures that the stations could still use WiFi even when some APs fail. We prove that this problem is NP-hard. To tackle this problem, we first develop an analytic model to derive the throughput of each station under the OFDMA mechanism and a widely used interference model. We then design a heuristic algorithm to find high-quality solutions with polynomial time complexity. Simulation results under both fixed-user and mobile-user cases show that: (1) when the area is small (50 × 50$\rm m^2$), our algorithm gives the optimal solutions; when the area is larger (80 × 60$\rm m^2$), our algorithm can reduce the number of APs by 34.9-87.7% as compared to the Random and Greedy algorithms. (2) Our algorithm can always get feasible solutions that fulfill the QoS requirements.

In this paper, we aim to solve inverse kinematics of the integrated robotic arm-hand systems to achieve precision grasping, provided the desired grasp configuration (contact points + contact normals). The key insights of our approach are three-fold. First, we propose a human-inspired thumb-first strategy and consider one finger of the robotic hand as the "thumb" to narrow down the search space and increase the success rate of the algorithm. Second, we formulate the arm-thumb serial chain as a closed chain while other fingers are still as serial chains such that the entire arm-hand system is controlled as a hybrid parallel-serial system. The closed-chain formulation truncates and simplifies the task hierarchy of the entire arm-hand system. Third, we attach a virtual revolute joint to the thumb’s tip with its rotation axis aligning with the thumb’s contact normal to allow this virtual joint to act as the embodiment of the thumb’s functional redundancy. By selecting the thumb’s joints including the virtual revolute joint as the active joints of the arm-thumb closed chain, the arm-thumb system’s self-motion (i.e., the palm pose) and the thumb’s functional redundancy can be directly controlled without using the null space projection. This provides a new possibility to control the self-motion of robot manipulators. Simulation results will demonstrate the advantages and superb performance of the proposed approach for solving the problem of inverse kinematics of achieving precision grasps compared to other classical approaches based on the Damped Least-Squares method [1] in terms of the average success rate (96% v.s. 12%).

IEEE 802.11ax is a promising standard for the next-generation WiFi network, which uses orthogonal frequency division multiple access (OFDMA) to segregate the wireless spectrum into time-frequency resource units (RUs). In this paper, we aim at designing an 802.11ax-based dense WiFi network to provide WiFi services to a large number of users within a given area with the following objectives: (1) to minimize the number of access points (APs); (2) to fulfil the users' throughput requirement; and (3) to be resistant to AP failures. We formulate the above into a joint AP placement and power-channel-RU assignment optimization problem, which is NP-hard. To tackle this problem, we first derive an analytical model to estimate each user's throughput under the mechanism of OFDMA and a widely used interference model. We then design a heuristic algorithm to find high-quality solutions with polynomial time complexity. Simulation results show that our algorithm can achieve the optimal performance for a small area of 50×50 m2. For a larger area of 100×80 m2where we cannot find the optimal solution through an exhaustive search, our algorithm can reduce the number of APs by 32 ~ 55% as compared to the random and Greedy solutions.

In the battery‐free wireless sensor network (BF‐WSN) that harvests radio signal energy, data delivery suffers from a longer delay arising from the energy‐harvesting period. It is significant to develop an energy‐efficient, low‐delay, and reliable data gathering scheme for the BF‐WSN. The goodput‐maximised data delivery scheme (GDDS) is proposed to reliably collect time‐constrained data in the IEEE 802.15.4‐based BF‐WSN. Under the GDDS, the sink's operation period consists of multiple data gathering cycles with each incorporating three phases: charging the nodes, assigning channel occupation time for the nodes, and receiving packets from the nodes. The scheme of accumulating correct data blocks (SACDB) is used in the third phase for the sink to gather data from the nodes. The authors develop an analytical model for the SACDB, from which they derive the time and the energy consumed in transmitting a packet. Then, they derive the goodput and the energy efficiency under the proposed GDDS. The GDDS aims at maximising the goodput by optimising the charging period, the number of data blocks, and the maximum number of transmission trials under the constraint on data gathering time. Simulation results show the GDDS outperforms the existing schemes in terms of the goodput and energy efficiency.

Battery-Free Wireless Sensor Networks (BF-WSNs) have become increasingly useful for many applications and how to ensure timely information exchange between nodes in IP networks and those in BF-WSNs is indispensable. The 6LoWPAN protocol is usually used to deliver IPv6 packets over IEEE 802.15.4 based WSNs, and has resolved the size mismatching problem between IPv6 packets and 802.15.4 Medium Access Control (MAC) frames by using packet fragmentation scheme to break an IPv6 packet into multiple small pieces with each fitted into a single 802.15.4 MAC frame. Unfortunately, IPv6 packets in BF-WSNs may suffer from intolerable delay for timely reassembling back to IPv6 packets. In this paper, we present a Latency Aware IPv6 Packet Delivery (LAID) scheme to reduce such IPv6 packet latency while maintaining high packet delivery ratio. Our LAID considers charging time, data rate, and the Maximum Number of Transmission Trials (MNTT) used in the IEEE 802.15.4 MAC layer so that the minimum latency can be achieved by optimizing the pairing of data rate and MNTT. In addition, we apply network coding to improve packet delivery reliability. Our analysis shows that the proposed LAID significantly outperforms existing schemes with fixed data rates in terms of IPv6 packet latency.