Wenbo Zhang

A Disaster Management-Oriented Path Planning for Mobile Anchor Node-Based Localization in Wireless Sensor Networks

The localization of sensor nodes is a significant issue in wireless sensor networks (WSNs) because many applications cannot provide services without geolocation data, especially during disaster management. In recent years, a promising unknown-nodes positioning method has been developed that localizes unknown nodes, employing a GPS-enabled mobile anchor node moving in the network, and broadcasting its location information periodically to assist localization. In contrast to most studies on path planning that assume infinite energy of the mobile anchor node, the anchor node in this study, consumes different amounts of energy during phases of startup, turning, and uniform motion considering the aftermath of disasters. To enable a trade-off between location accuracy and energy consumption, a path-planning algorithm combining a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) and SCAN algorithm (SLMAT) is proposed. SLMAT ensures that each unknown node is covered by a regular triangle formed by beacons. Furthermore, the number of corners along the planned path is reduced to save the energy of the mobile anchor node. In addition, a series of experiments have been conducted to evaluate the performance of the SLMAT algorithm. Simulation results indicate that SLMAT outperforms SCAN, LMAT, HILBERT, and Z-curve in terms of localization accuracy and energy consumption.

A Joint Energy Replenishment and Data Collection Algorithm in Wireless Rechargeable Sensor Networks

Energy constraint is a critical issue in the development of wireless sensor networks (WSNs) because sensor nodes are generally powered by batteries. Recently, wireless rechargeable sensor networks (WRSNs), which introduce wireless mobile chargers (MCs) to replenish energy for nodes, have been proposed to resolve the root cause of energy limitations in WSNs. However, existing wireless charging algorithms cannot fully leverage the mobility of MCs because unity between the energy replenishment process and mobile data collection has yet to be realized. Thus, in this paper, a joint energy replenishment and data collection algorithm for WRSNs is proposed. In this algorithm, the network is divided into multiple clusters based on a

Resource-utilization-aware energy efficient server consolidation algorithm for green computing in IIOT

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E2HRC: An Energy-Efficient Heterogeneous Ring Clustering Routing Protocol for Wireless Sensor Networks

-means algorithm. Two MCs visit the anchor point in each cluster by moving along the shortest Hamiltonian cycle in opposite directions. The positions of anchor points are calculated by the base station (BS) based on the energy distribution in each cluster. A spare MC is assigned to the network in case either of the two MCs depletes its energy before reaching the BS. After the two MCs’ current tours are over, a semi-Markov model is proposed for energy prediction so anchor points can be updated in the next round. Simulation results demonstrate the semi-Markov-based energy prediction model is highly precise, and the proposed algorithm can replenish energy for network energy effectively.

AREP: An asymmetric link-based reverse routing protocol for underwater acoustic sensor networks

Cloud computing has become an indispensable infrastructure that provides multi-granularity services to support large applications in the Industrial Internet of Things (IIOT), Cloud data centers have been built or extensively enlarged to cope with the growing computation and storage requirements of IIOT. The energy consumption of cloud data centers is dramatically increasing, which has created a lot of problems with greenhouse gas emissions and service costs. Server consolidation is a popular approach to reduce cloud data centers energy consumption by minimizing the number of active physical machines. Most of the extant research has focused on server reduction in the consolidation process, but unbalanced resource utilization among different physical machines can cause the waste of physical resources. This paper proposes a resource-utilization-aware energy efficient server consolidation algorithm (RUAEE) that can be used to improve resource utilization while reducing the number of virtual machine live migrations. Experimental results show that RUAEE can reduce the energy consumption and service-level agreement (SLA) violation in cloud data center. The proposedRUAEE in this paper entails four parts: disposition of overloaded hosts, adjustment of unbalance hosts, selection of underloaded hosts and VM placement to improve hostsresource utilization and reduce the number of SLA violations.At the beginning of server consolidation, overloaded hosts are disposed for improving the Quality of Service.The resource utilization description model is proposed for identifying unbalance hosts and unbalance hosts are selected for workload adjustment.And then, the underloaded host selection module detects low-utilized hosts by Select Factor (SF) value and migrates all of their running VMs out for switching them off to achieve energy saving.At last, migrating VMs are elaborately placed on suitable PMs to improve destination hostsresource utilization.

Probabilistic Neighborhood-Based Data Collection Algorithms for 3D Underwater Acoustic Sensor Networks

A heterogeneous ring domain communication topology with equal area in each ring is presented in this paper in an effort to solve the energy balance problem in original IPv6 routing protocol for low power and lossy networks (RPL). A new clustering algorithm and event-driven cluster head rotation mechanism are also proposed based on this topology. The clustering information announcement message and clustering acknowledgment message were designed according to RFC and original RPL message structure. An energy-efficient heterogeneous ring clustering (E2HRC) routing protocol for wireless sensor networks is then proposed and the corresponding routing algorithms and maintenance methods are established. Related messages are analyzed in detail. Experimental results show that in comparison against the original RPL, the E2HRC routing protocol more effectively balances wireless sensor network energy consumption, thus decreasing both node energy consumption and the number of control messages.

MCRA: A Multi-Charger Cooperation Recharging Algorithm Based on Area Division for WSNs

Abstract Routing protocol design is one of the fundamental requirements of Underwater Acoustic Sensor Networks (UASNs). However, traditional underwater routing protocols focus on the particularities of underwater environment, while ignoring the features of underwater nodes that influence route establishment. In this study, the impact of the directional beam width of underwater nodes on communication links was fully explored. The presented case studies indicate that for fixed beam width of directional antennas, the relative position change of two geographically adjacent nodes is prone to generate asymmetric links. To ensure bidirectional data communication between source nodes and destination nodes, an asymmetric link-based reverse routing (AREP) is proposed. In AREP, each node maintains a neighbor table in which items are used to analyze the link state. Routing paths are established by prioritizing the utilization of symmetric links. When an asymmetric link is required to be introduced into a routing path, a three-hop-constrained circuitous route will be established for ease of reverse routing search. The routing void problem is addressed in AREP through eliminating void nodes. A routing update is periodically conducted to dynamically adjust previous routing paths to the change of network topology caused by node mobility. Simulation results demonstrated that AREP improves network performance in terms of transmission delay, packet delivery ratio, as well as energy consumption.

Probabilistic Neighborhood Location-Point Covering Set-Based Data Collection Algorithm With Obstacle Avoidance for Three-Dimensional Underwater Acoustic Sensor Networks

Marine environmental monitoring provides crucial information and support for the exploitation, utilization, and protection of marine resources. With the rapid development of information technology, the development of three-dimensional underwater acoustic sensor networks (3D UASNs) provides a novel strategy to acquire marine environment information conveniently, efficiently and accurately. However, the specific propagation effects of acoustic communication channel lead to decreased successful information delivery probability with increased distance. Therefore, we investigate two probabilistic neighborhood-based data collection algorithms for 3D UASNs which are based on a probabilistic acoustic communication model instead of the traditional deterministic acoustic communication model. An autonomous underwater vehicle (AUV) is employed to traverse along the designed path to collect data from neighborhoods. For 3D UASNs without prior deployment knowledge, partitioning the network into grids can allow the AUV to visit the central location of each grid for data collection. For 3D UASNs in which the deployment knowledge is known in advance, the AUV only needs to visit several selected locations by constructing a minimum probabilistic neighborhood covering set to reduce data latency. Otherwise, by increasing the transmission rounds, our proposed algorithms can provide a tradeoff between data collection latency and information gain. These algorithms are compared with basic Nearest-neighbor Heuristic algorithm via simulations. Simulation analyses show that our proposed algorithms can efficiently reduce the average data collection completion time, corresponding to a decrease of data latency.

IGRC: An improved grid-based joint routing and charging algorithm for wireless rechargeable sensor networks

Recent breakthroughs in wireless charging technologies have greatly promoted the development of rechargeable wireless sensor networks (WSNs). To improve the lifetime of WSNs in many applications, the charging efficiency of mobile chargers (MCs) and the energy supplement of MCs should be improved. Although optimized charging path schemes in WSNs have been studied extensively, little attention has been paid to determine the energy consumption of MCs while charging and their movement during the charging tasks. In this paper, we analyze the relationship of the movement energy consumption of MCs and their energy transfer to the nodes and put forward our algorithm for improving the charging efficiency of the MCs. We divide the entire network into different charging regions and propose three charging schemes based on different situations in each region. The idea of cooperation among the MCs to charge MCs further enhances the charging efficiency of the MCs. A simulation demonstrates the advantages of our algorithm for improving the lifetime and charging efficiency of the MCs. This paper aims to improve the lifetime of WSNs and to decrease the cost for charging nodes and results in a longer lifetime for WSNs in applications with limited energy.

A Classification Detection Algorithm Based on Joint Entropy Vector against Application-Layer DDoS Attack

Data collection is the core function of underwater acoustic sensor networks (UASNs). Lately, ambulatory data gathering methods are being popularized in real applications. However, due to present mobile underwater data collection investigations that are on the basis of 2-D scenarios, the associated approaches are not suitable for 3-D UASNs. Additionally, mobile-element-assisted data collection usually brings special issues on obstacle avoidance. Accordingly, we propose a probabilistic neighborhood location-point covering set-based data collection algorithm with obstacle avoidance for 3-D UASNs. The proposed algorithm initially generates a space lattice set to establish the probabilistic neighborhood location-point covering set for data collection, so as to optimize the data collection latency. Then, an autonomous underwater vehicle traverses only location points in the constructed covering set with a hierarchical grid-based obstacle avoidance strategy. The simulation experiments are performed to verify the proposed algorithm compared with other existing underwater data collection algorithms. Simulations show that our proposed algorithm achieves better performance in terms of data collection latency, data collection efficiency, and obstacle avoidance.