Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks

Sixu, Lu; Muqing, Wu; Zhao, Min

doi:10.1007/s11276-022-02925-x

Cited by 32 publications

(15 citation statements)

References 31 publications

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“…This work examined data collection delays due to the move and stop of the mobile BS for collecting and aggregating the received data from the CHs. Balancing energy consumption and prolonging the network lifetime by designing a cluster routing protocol with a mobile BS were studied in [22]. A particle swarm optimization-based cluster routing algorithm is proposed to calculate the CHs and the sojourn locations of a BS.…”

Section: Related Workmentioning

confidence: 99%

“…In general, using multiple mobile sinks can reduce the number of hops. The proposed algorithms in [21], [22], and [23] didn't focus on the travel time of the sensing data to reach the CH, where the sensing data is collected with unbounded hop count between the CH and cluster sensor nodes which is one of the requirements of the proposed algorithm in this paper.…”

Section: Related Workmentioning

confidence: 99%

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Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks

2023

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Reinforcement Learning (RL) has emerged as a promising approach for improving the performance of Wireless Sensor Networks (WSNs). The Q-learning technique is one approach of RL in which the algorithm continuously learns by interacting with the environment, gathering information to take certain actions. It maximizes performance by determining the optimal result from that environment. In this paper, we propose a data gathering algorithm based on a Q-learning approach named Bounded Hop Count -Reinforcement Learning Algorithm (BHC-RLA). The proposed algorithm uses a reward function to select a set of Cluster Heads (CHs) to balance between the energy-saving and data-gathering latency of a mobile Base Station (BS). In particular, the proposed algorithm selects groups of CHs to receive sensing data of cluster nodes within a bounded hop count and forward the data to the mobile BS when it arrives. In addition, the CHs are selected to minimize the BS tour length. Extensive experiments by simulation were conducted to evaluate the performance of the proposed algorithm against another traditional heuristic algorithm. We demonstrate that the proposed algorithm outperforms the existing work in the mean of the length of a mobile BS tour and a network's lifetime.INDEX TERMS Wireless sensor networks, mobile data gathering, delay tolerance, relay hop count, mobile base station tour.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks

2023

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“…In general, values greater than Th should be flagged as abnormal. Using formula (24) can judge the nonnormally distributed data, thereby improving the applicable scope of NNRAF. The detailed steps of the Multihomed Abnormal Behavior Detection Algorithm are as follows:…”

Section: Multi-homed Abnormal Behavior Detection (Mad)mentioning

confidence: 99%

“…Particle swarm optimization. PSO 24 is an evolutionary search technology that finds the global optimal solution through repeated particles jumping. During the execution of PSO, all particles are assigned initial random positions and initial random velocities.…”

mentioning

confidence: 99%

Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics

Cui

Zhang

Chen

et al. 2022

Sci Rep

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User and entity behavior analytics (UEBA) is an anomaly detection technique that identifies potential threat events in the enterprise's internal threat analysis and external intrusion detection. One limitation of existing methods in UEBA is that many algorithms use deterministic algorithms only for one category labeling and only compare with other samples within this category. In order to improve the efficiency of potential threat identification, we propose a model to detect multi-homed abnormal behavior based on fuzzy particle swarm clustering. Using the behavior frequency-inverse entities frequency (BF-IEF) technology, the method of measuring the similarity of entity and user behavior is optimized. To improve the iterative speed of the fuzzy clustering algorithm, the particle swarm is introduced into the search process of the category centroid. The entity's nearest neighbor relative anomaly factor (NNRAF) in multiple fuzzy categories is calculated according to the category membership matrix, and it is combined with boxplot to detect outliers. Our model solves the problem that the sample in UEBA is evaluated only in one certain class, and the characteristics of the particle swarm optimization algorithm can avoid clustering results falling into local optimal. The results show that compared with the traditional UEBA approach, the abnormal behavior detection ability of the new method is significantly improved, which can improve the ability of information systems to resist unknown threats in practical applications. In the experiment, the accuracy rate, accuracy rate, recall rate, and F1 score of the new method reach 0.92, 0.96, 0.90, and 0.93 respectively, which is significantly better than the traditional abnormal detections.

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“…Compared to other existing algorithms, EEMCS improves energy utilization and increases network lifetime. In [50], a PSO-based algorithm for mobile networks' clustering is designed to further reduce energy consumption and enhance network lifetime. PSO considers residual energy, distance to the next hop, and the number of basic sensing nodes in each cluster for the fitness function.…”

Section: Clustering Mobile Wsns and Icwsnsmentioning

confidence: 99%

Cluster-Based Information-Centric Wireless Sensor Networks Management for Enhanced User Security Satisfaction in the Internet of Things

Gharib¹

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Many Internet of Things applications, such as smart cities and intelligent transportation, require accessible data to users (i.e., data consumers). To address users' timely data access needs, Information-Centric Wireless Sensor Networks (ICWSNs) were proposed that allow users to access data directly from cache nodes. Particularly, ICWSNs are clustered, and Cluster Heads (CHs) are selected to collect data from basic sensing nodes and act as cache nodes. Nevertheless, clustering and ensuring data security in ICWSNs is challenging. This is because sensor nodes are often resource-constrained, heterogeneous (i.e., perform different sensing tasks), and/or mobile.Driven by users' security and timely data access needs, in this thesis, cluster-based ICWSNs' management for enhanced user security satisfaction is investigated. First, a security-aware CHs selection algorithm is proposed to optimize network coverage that is subject to security and energy constraints. Then, cluster-based ICWSNs with heterogeneous communities are modeled analytically and compared to conventional cluster-based ICWSNs with heterogeneous sensor nodes. To overcome the identified energy-latency and security trade-off, a Security Level Aware algorithm for Clusterbased ICWSNs with Heterogeneous communities (SLAC-H) is proposed. In SLAC-H, community leaders collect and forward application-domain-specific data to CHs. Next, Node Embedding with Security Resource Allocation (NESRA) clustering algorithm for mobile ICWSNs is proposed. To improve user security satisfaction, NESRA allocates security resources to sensor nodes based on their location, mobility, and energy resources. Still, when security is set as a priority, many times, more energy is spent on security than is actually required. Therefore, along with the mobility-aware NESRA, User-aware clustering with Security Resource Allocation (USRA) algorithm is proposed. In USRA, a sink node determines which security resource each sensor node will be using for the next round to avoid over-utilization of network resources while satisfying user security needs. A summary of the proposed algorithms and some highlights for future work conclude this thesis. List of Publications List of References viii List of Tables 4.1 Security levels mapping based on the security mechanism used [65]. .

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Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks

Cited by 32 publications

References 31 publications

Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks

Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks

Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics

Cluster-Based Information-Centric Wireless Sensor Networks Management for Enhanced User Security Satisfaction in the Internet of Things

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