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1. DRE ² : Achieving Data Resilience in Wireless Sensor Networks: A Quadratic Programming Approach

   https://doi.org/10.1109/MASS50613.2020.00019  

   Hsu, Shanglin; Yu, Yuning; Tang, Bin (December 2020, 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS))

   null (Ed.)

   We focus on sensor networks that are deployed in challenging environments, wherein sensors do not always have connected paths to a base station, and propose a new data resilience problem. We refer to it as DRE2: data resiliency in extreme environments. As there are no connected paths between sensors and the base station, the goal of DRE2 is to maximize data resilience by preserving the overflow data inside the network for maximum amount of time, considering that sensor nodes have limited storage capacity and unreplenishable battery power. We propose a quadratic programming-based algorithm to solve DRE2 optimally. As quadratic programming is NP-hard thus not scalable, we design two time efficient heuristics based on different network metrics. We show via extensive experiments that all algorithms can achieve high data resilience, while a minimum cost flow-based is most energy-efficient. Our algorithms tolerate node failures and network partitions caused by energy depletion of sensor nodes. Underlying our algorithms are flow networks that generalize the edge capacity constraint well-accepted in traditional network flow theory. 

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2. DRE2: Achieving Data Resilience in Wireless Sensor Networks: A Quadratic Programming

   Hsu, Shanglin; Yu, Yuning; Tang, Bin (December 2020, IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS 2020))

   null (Ed.)

   We focus on sensor networks that are deployed in challenging environments, wherein sensors do not always have connected paths to a base station, and propose a new data resilience problem. We refer to it as DRE2: data resiliency in extreme environments. As there are no connected paths between sensors and the base station, the goal of DRE2 is to maximize data resilience by preserving the overflow data inside the network for maximum amount of time, considering that sensor nodes have limited storage capacity and unreplenishable battery power. We propose a quadratic programming-based algorithm to solve DRE2 optimally. As quadratic programming is NP-hard thus not scalable, we design two time efficient heuristics based on different network metrics. We show via extensive experiments that all algorithms can achieve high data resilience, while a minimum cost flow-based is most energy-efficient. Our algorithms tolerate node failures and network partitions caused by energy depletion of sensor nodes. Underlying our algorithms are flow networks that generalize the edge capacity constraint well-accepted in traditional network flow theory. 

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3. Energy-Efficient Node Deployment in Wireless Ad-hoc Sensor Networks

   https://doi.org/10.1109/ICC40277.2020.9149186  

   Guo, Jun; Karimi-Bidhendi, Saeed; Jafarkhani, Hamid (June 2020, IEEE International Conference on Communications (ICC-20))

   We study a wireless ad-hoc sensor network (WASN) where N sensors gather data from the surrounding environment and transmit their sensed information to M fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs that minimizes a Lagrangian combination of sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd-like algorithm to optimize node deployment. Simulation results show that our proposed algorithm outperforms the existing deployment algorithms, on average. 

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4. Optimal Cache Allocation under Network-Wide Capacity Constraint

   https://doi.org/10.1109/ICCNC.2019.8685587  

   Mai, Van Sy; Ioannidis, Stratis; Pesavento, Davide; Benmohamed, Lotfi (April 2019, 2019 International Conference on Computing, Networking and Communications (ICNC))

   Network cache allocation and management are important aspects of an Information-Centric Network (ICN) design, such as one based on Named Data Networking (NDN). We address the problem of optimal cache size allocation and content placement in an ICN in order to maximize the caching gain resulting from routing cost savings. While prior art assumes a given cache size at each network node and focuses on content placement, we study the problem when a global, network-wide cache storage budget is given and we solve for the optimal per-node cache allocation. This problem arises in cloud-based network settings where each network node is virtualized and housed within a cloud data center node with associated dynamic storage resources acquired from the cloud node as needed. As the offline centralized version of the optimal cache allocation problem is NP-hard, we develop a distributed adaptive algorithm that provides an approximate solution within a constant factor from the optimal. Performance evaluation of the algorithm is carried out through extensive simulations over multiple network topologies, demonstrating that our proposal significantly outperforms existing cache allocation algorithms. 

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5. Fine-Grained Trajectory Optimization of Multiple UAVs for Efficient Data Gathering from WSNs

   https://doi.org/10.1109/TNET.2020.3027555  

   Luo, Chuanwen; Satpute, Meghana N.; Li, Deying; Wang, Yongcai; Chen, Wenping; Wu, Weili (February 2021, IEEE/ACM Transactions on Networking)

   null (Ed.)

   The increasing availability of autonomous smallsize Unmanned Aerial Vehicles (UAVs) has provided a promising way for data gathering from Wireless Sensor Networks (WSNs) with the advantages of high mobility, flexibility, and good speed. However, few works considered the situations that multiple UAVs are collaboratively used and the fine-grained trajectory plans of multiple UAVs are devised for collecting data from network including detailed traveling and hovering plans of them in the continuous space. In this paper, we investigate the problem of the Fine-grained Trajectory Plan for multi-UAVs (FTP), in which m UAVs are used to collect data from a given WSN, where m ≥ 1. The problem entails not only to find the flight paths of multiple UAVs but also to design the detailed hovering and traveling plans on their paths for efficient data gathering from WSN. The objective of the problem is to minimize the maximum flight time of UAVs such that all sensory data of WSN is collected by the UAVs and transported to the base station. We first propose a mathematical model of the FTP problem and prove that the problem is NP-hard. To solve the FTP problem, we first study a special case of the FTP problem when m = 1, called FTP with Single UAV (FTPS) problem. Then we propose a constantfactor approximation algorithm for the FTPS problem. Based on the FTPS problem, an approximation algorithm for the general version of the FTP problem when m > 1 is further proposed, which can guarantee a constant factor of the optimal solution. Afterwards, the proposed algorithms are verified by extensive simulations. 

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