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In loss-and-delay sensitive wireless sensor networks (WSNs), especially when the duty cycles of nodes are extremely low, it is a challenge to ensure that data can be transmitted to sink nodes with high reliability and low delay. To address this problem, in this paper, we propose a data collection scheme named Adaptive Virtual Relaying Set (AVRS) where a set of relay nodes with more reliable connections to the sender node is selected to form its Virtual Relaying Set (VRS) to help transmit packets. In ring-based WSNs, each node in a VRS helps send packets in turn to the upper ring before the transmission is successful, or the packet is dropped if they all fail. Therefore, the larger the VRS (implying more retransmission chances), the higher the packet transmission reliability and the lower the delay will be. On the other hand, as the sender node has to stay active during the transmission stage, having a large VRS will cause huge energy consumption. Combined with the fact that the energy consumption of different parts of WSNs is unbalanced, nodes in the near-sink area (i.e., hotspots) have extremely high energy cost while nodes in the far-sink area (i.e., non-hotspots) still have ample energy remaining when the network dies. The main idea of the AVRS is the following rule. The size of the VRS of nodes is determined adaptively according to its energy usage pattern, making the size small in hotspots and relatively large in non-hotspots. The AVRS takes advantage of the residual energy of nodes in far-sink areas to achieve improved network data collection performance. Meanwhile, as nodes in near-sink areas have small VRS, the network can maintain a long lifespan without any reductions. Both theoretical analyses and simulative results demonstrate that the AVRS can improve data transmission reliability by more than 50% and reduce network delay by at least 33%. [ABSTRACT FROM AUTHOR]

As one of promising distributed multi-robot system, Unmanned Aerial Vehicles (UAVs) can collaborate to offload complex tasks in edge networks. A Smart and Trust Multi-UAV Task Offloading (STMTO) system is established to offload tasks from Internet of Thing (IoT) devices to edge severs through UAVs with a trust style. In STMTO system, first, a group of UAVs is dispatched to relay tasks from devices to edge servers with rich computing resource. A collaborative task collection scheme is proposed to minimize energy consumption and the task processing delay by dividing working area for each UAV and designing the flight trajectory. Secondly, a many-to-many task double auction model is established for devices and edge servers to maximize the offloading utility, where devices act as buyers, edge servers as sellers, and UAVs as auctioneers. Last, to resist attack of malicious edge servers and ensure the task security, a novel trust evaluation method based on the comparison of true utility and expected utility is integrated in auction mechanism. The theoretical analysis and implementation results show that the proposed STMTO system not only achieve the best utility for devices and edge servers simultaneously, but also identify the malicious edge servers and protect task from attacks. [ABSTRACT FROM AUTHOR]

Neighbor discovery is basic for Wireless Sensor Networks (WSNs) communication, which is the key to network performance. Since sensor nodes were powered by battery, sleep/active rotation was widely adopted to save energy, which led to the neighbor discovery problem. Many timely neighbor discovery protocols are proposed for wake-up scheduling to expedite the discovery and data routing while saving energy. In this paper, an asymmetric quorum-based neighbor discovery (AQND) protocol is proposed to reduce the delay of neighbor discovery and data routing while improving the energy utilization and maintaining a high lifetime. According to our theoretical analysis, the proposed AQND protocol outperforms previous strategies in main performance indicators such as reducing the average relative error of duty cycle, the maximum neighbor discovery delay and the end-to-end discovery delay while improving the energy utilization ratio. In addition, using the proposed AQND protocol to improve the above network performances doesn't reduce lifetime. Maintaining the end-to-end delay consistent with the previous strategies, the lifetime of network can be improved. [ABSTRACT FROM AUTHOR]