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1. “Multi-Modulation Scheme for RFID-based Sensor Networks"

   https://doi.org/10.1007/978-3-030-67720-6_2  

   Z. Tian and Z.J. Haas ( January 2021 , Lecture notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering)

   Gao, H. ; Fan, P. ; Wun, J. ; Xiaoping, X. ; Yu, J. ; Wang, Y. (Ed.)

   RFID technology is playing an increasingly more important role in the Internet of Things, especially in the dense deployment model. In such networks, in addition to communication, nodes may also need to harvest energy from the environment to operate. In particular, we assume that our network model relies on RFID sensor network consisting of Wireless Identification and Sensing Platform (WISP) devices and RFID exciters. In WISP, the sensors harvest ambient energy from the RFID exciters and use this energy for communication back to the exciter. However, as the number of exciters is typically small, sensors further away from an exciter will need longer charging time to be able to transmit the same amount of information than a closer by sensor. Thus, further away sensors limit the overall throughput of the network. In this paper, we propose to use a multi-modulation scheme, which trades off power for transmission duration. More specifically, in this scheme, sensors closer to the exciter use a higher-order modulation, which requires more power than a lower-order modulation assigned to further away sensors, for the same bit error rate of all the sensors’ transmissions. This reduces the transmission time of the closer sensors, while also reducing the charging time of the further away sensors, overall increasing the total net-work throughput. The evaluation results show that the RFID sensor network with our multi-modulation scheme has significantly higher throughput as compared with the traditional single-modulation scheme. 

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2. Adaptive Sampling and Quick Anomaly Detection in Large Networks

   Xian, X. ; Semenov, A. ; Hu, Y. ; Wang, A. ; Jin, Y. ( October 2022 , IEEE transactions on automation science and engineering)

   The monitoring of data streams with a network structure have drawn increasing attention due to its wide applications in modern process control. In these applications, high-dimensional sensor nodes are interconnected with an underlying network topology. In such a case, abnormalities occurring to any node may propagate dynamically across the network and cause changes of other nodes over time. Furthermore, high dimensionality of such data significantly increased the cost of resources for data transmission and computation, such that only partial observations can be transmitted or processed in practice. Overall, how to quickly detect abnormalities in such large networks with resource constraints remains a challenge, especially due to the sampling uncertainty under the dynamic anomaly occurrences and network-based patterns. In this paper, we incorporate network structure information into the monitoring and adaptive sampling methodologies for quick anomaly detection in large networks where only partial observations are available. We develop a general monitoring and adaptive sampling method and further extend it to the case with memory constraints, both of which exploit network distance and centrality information for better process monitoring and identification of abnormalities. Theoretical investigations of the proposed methods demonstrate their sampling efficiency on balancing between exploration and exploitation, as well as the detection performance guarantee. Numerical simulations and a case study on power network have demonstrated the superiority of the proposed methods in detecting various types of shifts. Note to Practitioners —Continuous monitoring of networks for anomalous events is critical for a large number of applications involving power networks, computer networks, epidemiological surveillance, social networks, etc. This paper aims at addressing the challenges in monitoring large networks in cases where monitoring resources are limited such that only a subset of nodes in the network is observable. Specifically, we integrate network structure information of nodes for constructing sequential detection methods via effective data augmentation, and for designing adaptive sampling algorithms to observe suspicious nodes that are likely to be abnormal. Then, the method is further generalized to the case that the memory of the computation is also constrained due to the network size. The developed method is greatly beneficial and effective for various anomaly patterns, especially when the initial anomaly randomly occurs to nodes in the network. The proposed methods are demonstrated to be capable of quickly detecting changes in the network and dynamically changes the sampling priority based on online observations in various cases, as shown in the theoretical investigation, simulations and case studies. 

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3. Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

   https://doi.org/10.1145/3606263  

   Yu, Yuning ; Hsu, Shanglin ; Chen, Andre ; Chen, Yutian ; Tang, Bin ( October 2023 , ACM Transactions on Sensor Networks)

   We aim to preserve a large amount of data generated insidebase station-less sensor networks(BSNs) while considering that sensor nodes are selfish. BSNs refer to emerging sensing applications deployed in challenging and inhospitable environments (e.g., underwater exploration); as such, there do not exist data-collecting base stations in the BSN to collect the data. Consequently, the generated data has to be stored inside the BSN before uploading opportunities become available. Our goal is to preserve the data inside the BSN with minimum energy cost by incentivizing the storage- and energy-constrained sensor nodes to participate in the data preservation process. We refer to the problem as DPP:datapreservationproblem in the BSN. Previous research assumes that all the sensor nodes are cooperative and that sensors have infinite battery power and design a minimum-cost flow-based data preservation solution. However, in a distributed setting and under different control, the resource-constrained sensor nodes could behave selfishly only to conserve their resources and maximize their benefit.

   In this article, we first solve DPP by designing an integer linear programming (ILP)-based optimal solution without considering selfishness. We then establish a game-theoretical framework that achieves provably truthful and optimal data preservation in BSNs. For a special case of DPP wherein nodes are not energy-constrained, referred to as DPP-W, we design a data preservation game DPG-1 that integrates algorithmic mechanism design (AMD) and a more efficient minimum cost flow-based data preservation solution. We show that DPG-1 yields dominant strategies for sensor nodes and delivers truthful and optimal data preservation. For the general case of DPP (wherein nodes are energy-constrained), however, DPG-1 fails to achieve truthful and optimal data preservation. Utilizing packet-level flow observation of sensor node behaviors computed by minimum cost flow and ILP, we uncover the cause of the failure of the DPG-1. It is due to the packet dropping by the selfish nodes that manipulate the AMD technique. We then design a data preservation game DPG-2 for DPP that traces and punishes manipulative nodes in the BSN. We show that DPG-2 delivers dominant strategies for truth-telling nodes and achieves provably optimal data preservation with cheat-proof guarantees. Via extensive simulations under different network parameters and dynamics, we show that our games achieve system-wide data preservation solutions with optimal energy cost while enforcing truth-telling of sensor nodes about their private cost types. One salient feature of our work is its integrated game theory and network flows approach. With the observation of flow level sensor node behaviors provided by the network flows, our proposed games can synthesize “microscopic” (i.e., selfish and local) behaviors of sensor nodes and yield targeted “macroscopic” (i.e., optimal and global) network performance of data preservation in the BSN.

    

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4. Achieving energy‐neutral data transmission by adjusting transmission power for energy‐harvesting wireless sensor networks

   https://doi.org/10.1002/wcm.2669  

   Tan, Qian ; An, Wei ; Han, Yanni ; Luo, Haiyan ; Liu, Yanwei ; Ci, Song ; Tang, Hui ( February 2016 , Wireless Communications and Mobile Computing)

   Recently, benefiting from rapid development of energy harvesting technologies, the research trend of wireless sensor networks has shifted from the battery‐powered network to the one that can harvest energy from ambient environments. In such networks, a proper use of harvested energy poses plenty of challenges caused by numerous influence factors and complex application environments. Although numerous works have been based on the energy status of sensor nodes, no work refers to the issue of minimizing the overall data transmission cost by adjusting transmission power of nodes in energy‐harvesting wireless sensor networks. In this paper, we consider the optimization problem of deriving the energy‐neutral minimum cost paths between the source nodes and the sink node. By introducing the concept of energy‐neutral operation, we first propose a polynomial‐time optimal algorithm for finding the optimal path from a single source to the sink by adjusting the transmission powers. Based on the work earlier, another polynomial‐time algorithm is further proposed for finding the approximated optimal paths from multiple sources to the sink node. Also, we analyze the network capacity and present a near‐optimal algorithm based on the Ford–Fulkerson algorithm for approaching the maximum flow in the given network. We have validated our algorithms by various numerical results in terms of path capacity, least energy of nodes, energy ratio, and path cost. Simulation results show that the proposed algorithms achieve significant performance enhancements over existing schemes. Copyright © 2016 John Wiley & Sons, Ltd.

    

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5. Optimizing resource constrained distributed collaborative sensing

   G. de Veciana M. Stecklein, H. Beytur ( June 2021 , IEEE ICC Workshop on Spectrum Sharing Technology for Next Generation Communications)

   null (Ed.)

   Collaborative sensing of spatio-temporal events/processes is at the basis of many applications including e.g., spectrum and environmental monitoring, and self-driving cars. A system leveraging spatially distributed possibly airborn sensing nodes can in principle deliver better coverage as well as possibly redundant views of the observed processes. This paper focuses on modeling, characterising and quantifying the benefits of optimal sensor activation/scanning policies in resource constrained settings, e.g., constraints tied to energy expenditures or the scanning capabilities of nodes. Under a natural model for the process being observed we show that a periodic sensor activation policy is optimal, and characterize the relative phases of such policies via an optimization problem capturing knowledge of the sensor geometry, sensor coverage sets, and spatio-temporal intensity and event durations. Numerical and simulation results for simple different sensor geometries exhibit how performance depends on the underlying processes. We also study the gap between optimal and randomized policies and how it scales with the density of sensors and resource constraints. 

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