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An essential application of wireless sensor networks is to successfully respond to user queries. Query packet losses occur in the query dissemination due to wireless communication problems such as interference, multipath fading, packet collisions, etc. The losses of query messages at sensor nodes result in the failure of sensor nodes reporting the requested data. Hence, the reliable and successful dissemination of query messages to sensor nodes is a non-trivial problem. The target of this paper is to enable highly successful query delivery to sensor nodes by localized and energy-efficient discovery, and recovery of query losses. We adopt local and collective cooperation among sensor nodes to increase the success rate of distributed discoveries and recoveries. To enable the scalability in the operations of discoveries and recoveries, we employ a distributed name resolution mechanism at each sensor node to allow sensor nodes to self-detect the correlated queries and query losses, and then efficiently locally respond to the query losses. We prove that the collective discovery of query losses has a high impact on the success of query dissemination and reveal that scalability can be achieved by using the proposed approach. We further study the novel features of the cooperation and competition in the collective recovery at PHY and MAC layers, and show that the appropriate number of detectors can achieve optimal successful recovery rate. We evaluate the proposed approach with both mathematical analyses and computer simulations. The proposed approach enables a high rate of successful delivery of query messages and it results in short route lengths to recover from query losses. The proposed approach is scalable and operates in a fully distributed manner.
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http://dx.doi.org/10.3390/s130607472 | DOI Listing |
Chaos
September 2025
Centre for Audio, Acoustics and Vibration (CAAV), School of Mechanical and Mechatronic Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Measurements acquired from distributed physical systems are often sparse and noisy. Therefore, signal processing and system identification tools are required to mitigate noise effects and reconstruct unobserved dynamics from limited sensor data. However, this process is particularly challenging because the fundamental equations governing the dynamics are largely unavailable in practice.
View Article and Find Full Text PDFJ Civ Struct Health Monit
May 2025
Vibration Engineering Section, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QF UK.
This paper presents the development of a time-synchronised wireless vision sensor network using the global navigation satellite system (GNSS). The sensor network, named the flexible vision network (FVN), offers significant advantages over existing wired or wireless time-synchronised vision sensor networks. These advantages include: 1) spatial flexibility, with no distance limitations between sensor nodes imposed by Ethernet cables or WiFi communication, 2) scalability in the number of nodes due to its independent time-sync operation based on satellites without any time-sync interaction with other nodes, and 3) straightforward time synchronisation with other heterogeneous sensor systems, such as accelerometers or dynamic strain systems, due to its independent time-sync operation providing an absolute time reference.
View Article and Find Full Text PDFDistrib Comput
June 2025
Computer Science, Durham University, Durham, DH1 3LE United Kingdom.
Beeping models are models for networks of weak devices, such as sensor networks or biological networks. In these networks, nodes are allowed to communicate only via emitting beeps: unary pulses of energy. Listening nodes have only the capability of : they can only distinguish between the presence or absence of a beep, but receive no other information.
View Article and Find Full Text PDFWater Res
August 2025
Centre for Water Systems, University of Exeter, Exeter EX4 4QF, United Kingdom; KWR Water Research Institute, Nieuwegein 3430 BB, The Netherlands. Electronic address:
Water distribution networks (WDNs) constitute essential urban infrastructure, yet their monitoring is hindered by limited monitoring conditions. Soft sensing methods have been applied to estimate pressure at unmonitored nodes using the latest deep learning models, however, they rely on large datasets from the same WDNs for training. There is a critical gap in pressure estimation of WDNs under realistic monitoring limitations.
View Article and Find Full Text PDFSensors (Basel)
August 2025
WiLab, CNIT/DEI, University of Bologna, 40136 Bologna, Italy.
Vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications are two key components of intelligent transport systems (ITSs) that can share spectrum resources through in-band overlay. V2V communication primarily supports traffic safety, whereas V2N primarily focuses on infotainment and information exchange. Achieving reliable V2V transmission alongside high-rate V2N services in resource-constrained, dynamically changing traffic environments poses a significant challenge for resource allocation.
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